E. coli cell-free protein expression system. Our results expand the utility of T7 promoters in cell based as well as cell-free synthetic biology applications.Engineered gene circuits offer an opportunity to harness biological systems for biotechnological and biomedical applications. However, reliance on native host promoters for the construction of circuit elements, such as logic gates, can make the implementation of predictable, independently functioning circuits difficult. In contrast, T7 promoters offer a simple orthogonal expression system for use in a variety of cellular backgrounds and even in cell-free systems. Here we develop a T7 promoter system that can be regulated by two different transcriptional repressors for the construction of a logic gate that functions in cells and in cell-free systems. We first present LacI repressible T7lacO promoters that are regulated from a distal lac operator site for repression. We next explore the positioning of a tet operator site within the T7lacO framework to create T7 promoters that respond to tet and lac repressors and realize an IMPLIES gate. Finally, we demonstrate that these dual input sensitive promoters function in an  Engineering synthetic gene circuits entails the redesign of existing gene networks or the creation of novel genetic functions to perform a predetermined task. Construction of these circuits has been valuable in attaining a bottom up understanding of biological systems and offeOne such orthogonal expression system exploits the mono-subunit T7 RNA polymerase, which is commonly utilized because of its simplicity, stability, processivity, and specificity. In contrast to multi-subunit bacterial RNA polymerases, T7 polymerase recognizes a specific 17 base pair promoter sequence and does not require co-factors to activate transcription . T7 RNA However, a significant challenge to implementing complex gene regulatory circuits with T7 promoters is that, unlike bacterial promoters, the commonly used viral promoters have few natural mechanisms for activating or repressing gene expression. Synthetic T7 promoter variants can be repressed by transcription factors such as LacI and TetR only when bound to a relatively short regulatory region positioned proximal to and downstream from the transcription start site ,23-27. AE. coli lac promoters contain auxiliary Lac operators (LacO) upstream and downstream to the E. coli lac promoter. At low LacI concentrations, presence of additional Lac operators induces DNA loop formation in the intervening DNA, thereby increasing the probability of LacI occupancy of the E. coli lac promoter lac[araBAAD[gal[cI has beenAD[gal[cI. Additiontrations. DNA looent sites.  Here, we implemented an IMPLIES gate using a strategy that harnesses DNA looping. While the IMPLIES gate is not commonly used in electrical engineering, the value of constructing an IMPLIES gate described here, lies in the fact that, IMPLIES gates together with NOT gates can be utilized to realize any logic function, as described in E. coli lac promoters[First, we assessed the potential advantages of DNA looping for control of the T7 promoter system. Without looping, very weak repression is observed, as shown by pREPT7 01 in promoters and demoE. coli lac promoters, a substantial decrease in repression levels accompanied an increase in operator distance [in vivo data for LacI mediated transcriptional control with E. coli promoters. The dual binding site design leads to a significant increase in repression levels when compared to a single LacI binding site. In our T7 system, as observed with E. coli promoters, controlling the distance between protein binding sites may enable further tuning of T7lacO promoters Click here for additional data file.Figure S1Effect of additional source of LacI.Gene expression results from pREGT7 15b and pREPT7 11 encoded on pET3a backbones in response to addition of IPTG were carried out in order to examine the effect of the presence of an additional source of LacI in the cell. The use of pET15b backbone results in a higher concentration of LacI protein inside the cell as opposed to pREPT7 01 (pET3A backbone) which does not (manuscript lacI. The GFP concentration units are expressed as µM/OD600.(TIFF)Click here for additional data file.Figure S2Effect of arabinose concentration on repression from pREPT7 11.The effect of adding different concentrations of arabinose to induce T7 RNA polymerase concentrations on repression levels from T7lacO promoters with auxiliary operators was examined. pREPT7 11 was co- transformed with pTetRLacI into BL21-AI cells and T7 RNA polymerase expression was induced by 0.02%, 0.1% and 0.2% L-arabinose. The protein expression response to 30 µM IPTG was determined using a plate reader. An increase in repression levels results from reduction in arabinose concentrations. A 1.4 fold induction is observed in the presence of IPTG when the arabinose concentration is 0.2%. A 5-fold induction is observed when the IPTG concentration is reduced to 0.02%. The data shown are fluorescence responses from pREPT7 11 plasmids to three different arabinose concentrations after 200 minutes. GFP concentration units are expressed µM/OD600.(TIFF)Click here for additional data file.Figure S3Effect of placing tet operator at positions overlapping the promoter.The effect of placing TetR binding sites at positions overlapping the T7 promoter was examined. The upper panel (A) illustrates the constructs pDRT7 21, pDRT7 23, pDRT7 25 and pDRT7 27. The underlined text refers to the tetO operator sequence whereas T7 promoter sequence is depicted by bold text. The graphs in the lower panel (B) indicate the expression response of these plasmids in the presence/absence of 30 µM IPTG and 200ng/ml aTc. The normalized GFP concentration units are expressed units of µM/OD600.(TIFF)Click here for additional data file.Information S1Effect of placing tet operator at positions overlapping the promoter.(DOCX)Click here for additional data file.Figure S4Time course of expression from truncated promoters.Protein expression time courses for the constructs pDRT7 21, pDRT7 23, pDRT7 25 and pDRT7 27 in the absence and presence of 30 μM IPTG are described. These truncated promoters had different effects on LacI mediated repression when considered in isolation from TetR repression system. In the absence of IPTG, while the expression from pDRT7 21, pDRT7 23 and pDRT7 25 remains low at time points after 200 minutes, pDRT7 27 appears strong at later time points due to leaky repression from T7lacO. Moreover, the highly processive T7 RNA polymerase can generate large enough amounts of RNA transcripts to saturate the translational machinery[machinery and ther(TIFF)Click here for additional data file.Figure S5Effect of distance between lac operators on LacI mediated repression.The effect of changing lacO inter-operator distance on LacI and Tet mediated repression was examined. The distance between the lac operators was shortened to 70 bases while retaining the tetO at the -27 position to yield pDRT7 70. Under the conditions tested, LacI mediated repression from this construct was not significantly different from pDRT7 27. However, as with pDRT7 27, TetR negatively regulated LacI mediated repression. Both the distance and the relative phasing between the lac operators have an effect on the efficiency of LacI mediated repression and that an inter-operator distance of 70 bp achieves the strongest repression of E. coli Lac promoters[600.promoters,30. The (TIFF)Click here for additional data file.Figure S6Potential logic gates that can be constructed from IMPLIES gate.The design of NOT, AND, and OR gates from our experimentally characterized gates are shown. The described dual input T7 regulation approach lends itself to the simple construction of IMPLIES gates. IMPLIES gates in conjunction with NOT gates can together be used to implement any logic function. Shown are how AND and OR gates can be constructed from IMPLIES and NOT gates. Letting I represent the IMPLIES function of a and b, where I=a’+b, an AND gate can be constructed as ab=I’ and an OR gate can be constructed as I. Genetic implementations are shown in the figure, where a, b, c, and d are different repressor proteins, and o(a), o(b), o(c), and o(d) represent the respective operator sites for each of these repressor proteins.(TIFF)Click here for additional data file.Table S2Absorbance values for experiments described in Fitness effects must be carefully considered in live cell experiments, particularly with the use of the highly processive T7 RNA polymerase, which can potentially overwhelm the host’s expression machinery. In the described live E. coli experiments, inducing T7 RNA polymerase at low levels averts this toxicity issue. Specifically, T7 RNA polymerase was induced with 0.02% arabinose, which is approximately 1/10th the concentration required for maximal induction. (DOCX)Click here for additional data file.Table S3Absorbance values for experiments described in (DOCX)Click here for additional data file.Information S2Sequences of regulatory region. In the described sequences, the blue colored bases indicate the Lac operator, the brown indicates the tet operator, the black underlined text corresponds to the T7 promoter, the red sequences are the ribosome binding site and the red underlined text corresponds to the T7 terminator.(DOCX)Click here for additional data file.


Deciphering the most common modes by which chromatin regulates transcription, and how this is related to cellular status and processes is an important task for improving our understanding of human cellular biology. The FANTOM5 and ENCODE projects represent two independent large scale efforts to map regulatory and transcriptional features to the human genome. Here we investigate chromatin features around a comprehensive set of transcription start sites in four cell lines by integrating data from these two projects.Transcription start sites can be distinguished by chromatin states defined by specific combinations of both chromatin mark enrichment and the profile shapes of these chromatin marks. The observed patterns can be associated with cellular functions and processes, and they also show association with expression level, location relative to nearby genes, and CpG content. In particular we find a substantial number of repressed inter- and intra-genic transcription start sites enriched for active chromatin marks and Pol II, and these sites are strongly associated with immediate-early response processes and cell signaling. Associations between start sites with similar chromatin patterns are validated by significant correlations in their global expression profiles.The results confirm the link between chromatin state and cellular function for expressed transcripts, and also indicate that active chromatin states at repressed transcripts may poise transcripts for rapid activation during immune response. The transcriptional landscape of human cells is tightly linked to chromatin structure. By modulating chromatin, transcription factors (TFs) and chromatin modifying enzymes decides which transcripts, and the amount of each that are produced by a cell ,2. DecipOne of the important findings in the ENCODE project was the impact on gene expression by different combinations of chromatin modifications at regulatory elements throughout the genome. Chromatin modifications are post-translational chemical modifications, most commonly methylations and acetylations, on the N-terminal tails of the eight histone proteins constituting the nucleosome core. These modifications affect the interaction between the core and the DNA wrapped around it, as well as interactions with chromatin-binding proteins, resulting in configurations of open and closed chromatin . In addiFor studies of chromatin profile shapes, a crucial step is the definition of anchor points throughout the genome, which are used as reference positions for the study of shapes in the neighborhood of the anchor points. TSSs of annotated genes are examples of such anchor points. However, these generally represent a too limited selection of genomic sites for general analysis, considering that a large amount of regulation takes place distal from any annotated gene TSS. Other strategies for anchor point definitions have thus included binding sites for clusters of transcription factors  or for sIt has now become well established that the regulatory landscape of the human genome includes much more than the genomic regions surrounding the approximately 22 000 currently well annotated genes. The 184 827 globally defined transcripts from FANTOM5, as well as the mapping of chromatin states and transcription factors in ENCODE are both attempts to map the characteristics and diversity of these transcriptional events, and the mechanism that regulate them. In contrast to most previously known genes, the function of these novel transcripts is mostly unknown. However, several have been shown to correlate with transcriptional outputs of nearby genes ,21-30. WThe four cell lines K562, GM12878, HeLa-S3 and HepG2 common to ENCODE and FANTOM5 all include the complete set of 12 chromatin marks mapped by ChIP-Seq in ENCODE. In addition, the 184 827 RTSSs from FANTOM5 defined globally over 975 human tissues, cell lines and primary cells represent an opportunity to investigate chromatin marks at RTSSs repressed in the respective cell-lines, as well as the expressed ones. In this study we have used globally defined RTSSs from FANTOM5 as anchor points, and investigated combinations of enrichment and shape profiles for chromatin marks around these anchor points. Most RTSSs are intra- or inter-genic, rather than being located at or close to currently annotated TSSs. These RTSSs are mostly repressed in the four cell lines studied, however, we also discovered a substantial number of such repressed inter- and intra-genic RTSSs harboring activating chromatin marks and Pol II, indicative of regulatory elements poised for transcription. Using a tool for ontology analysis in genomic regions, we found that these RTSSs were strongly associated with immediate-early responses and cell signaling. Shape profiles for chromatin marks around expressed RTSSs were subjected to a two-level clustering procedure, identifying metaclusters with combinatorial characteristics of enrichment and shape. These metaclusters differed substantially in functional ontology annotations, average RTSS expression, location of RTSSs with respect to nearby genes, and CpG content, indicating that the clusters are biologically relevant. Finally we validated the associations between RTSSs within metaclusters, showing that the global expression levels of corresponding RTSSs are correlated.http://fantom.gsc.riken.jp/5/.This work is part of the FANTOM5 project. Data downloads, genomic tools and co-published manuscripts are summarized at annotated RTSSs overlapping exactly with RefSeq TSS annotations, ii) intragenic RTSSs overlapping with full gene annotations, iii) intergenic RTSSs having no overlap with annotated genes, and iv) proximal RTSSs located at most 150 bp up- or downstream for annotated gene TSSs . We then mapped the expression profile for these 179 369 RTSSs in each of the four cell lines K562, GM12878, HeLa-S3 and HepG2. To get an overview of the genomic locations of the globally defined RTSSs in each cell line, we divided the 179 369 RTSSs into expressed and repressed RTSSs (Methods), and then further into the following categories: i) The four cell lines used in this study were specifically mapped by CAGE in FANTOM5 for comparison with ENCODE. We could therefore use the 179 369 globally defined RTSSs as anchor points for studying enrichment and profile shapes for the 12 chromatin marks downloaded from ENCODE for each of the four cell lines (Methods). We divided the RTSSs into expressed and repressed, and calculated the number of overlaps for each chromatin mark in a 500 bp extension around each RTSS. Figure The enrichment of active chromatin marks on a subset of repressed RTSSs led us to further investigate the shapes of chromatin marks around these RTSSs. We therefore collected and analyzed profiles for all chromatin marks in expressed and repressed RTSSs for the 179 369 globally defined RTSSs in each cell line (Methods). Average profiles . The meta-clustering was applied independently in each cell line. For each chromatin mark, the Pearson correlation coefficient was calculated between chromatin mark profiles for each expressed individual RTSS and the 5 subprofiles for the respective chromatin marks, resulting in 60 correlation coefficients  calculated for each RTSS. The matrix of all expressed RTSSs and corresponding correlation coefficients was then subjected to k-means clustering with the prior number of clusters set to 10. A heatmap of the cluster results together with subprofiles for all clusters for all chromatin marks are shown in Figure The efforts made by FANTOM5, ENCODE and others have confirmed the huge landscape of transcriptional events existing in addition to the well-known catalogue of protein coding genes. Most of this landscape consists of non-coding transcripts, whose exact functions have yet to be determined. One commonly described property of these non-coding transcripts is their tendency to affect the regulation of nearby genes. To determine the biological relevance of the defined metaclusters, we linked the metaclusters to functional annotations using the publicly available Genomic Regions Enrichment of Annotations Tool (GREAT) . GREAT iWe first observed that the metaclusters in each cell line differed in the number of associated RTSSs, and average RTSS expression level. The number of RTSSs associated with each cluster varied from >11 000 for the largest clusters to 2–3000 for the smallest, while expression levels could be separated into high, intermediate and low [Additional file To determine functional associations of the different metaclusters, we analyzed functional terms extracted from GREAT for each metacluster. Due to possible confounding of RTSSs located close to each other in the genome, we used two strategies referred to as permissive and conservative for analysis in GREAT (Methods). For both strategies we used the total set of RTSSs in all metaclusters as background data. By doing this, we identify terms significantly over-represented in one metacluster compared to other metaclusters, rather than compared to a general genomic background. A total of 5229 and 3671 genes significantly related to 2114 and 1293 terms were retrieved from GREAT for all metaclusters by this approach, for the permissive and conservative strategy respectively, while no significant genes or terms were retrieved for random selections of RTSSs. Using the permissive strategy, all metaclusters were associated with many, often related, significant terms, while this was only true for a subset of metaclusters in the conservative strategy. Terms associated with individual metaclusters were considerably more different between metaclusters in the same cell line than between metaclusters in different cell lines [Additional file To investigate metaclusters with corresponding functional associations between cell-lines, we performed PCA on all clusters from each cell-line (40 clusters) using genes from the most significant terms from GREAT for each cluster as model variables. After performing PCA, a plot of the first two principal components revealed several interesting relationships between the metaclusters, and these relations between clusters were mostly conserved between a permissive and conservative strategy employed for functional associations  and gamma (IFNγ). We observed similar levels of Pol II for our selected RTSSs relative to Pol II levels for expressed RTSSs before and after stimulation of both IFNα and IFNγ  genes affected in all cell lines, and 705 (61%) affected in only one cell line. Similar to ubiquitously expressed genes in general, the CpG content in promoters of the 46 genes affected in all cell lines was significantly higher  than for promoters in the other 1102 genes. Affected genes are both repressed and  expressed in their respective cell-lines, in proportions similar to expressed and repressed genes in general. When considering only the repressed genes in the gene list, they showed higher signals for active chromatin marks compared to generally repressed genes, indicating that they may exist in a somewhat poised state as well [Additional file To investigate possible functions of the selected poised RTSSs, we again used GREAT for GO annotation (Methods). We found that our selected RTSSs were highly enriched for terms related to Immune Response and Signaling in all four cell lines, compared to randomly selected sets of RTSSs Figure . So even in mice , we founSeveral studies have now shown that the transcriptional landscape of human cells is far more diverse than previously anticipated ,45,46. IIn this study we have assumed that an important function of regulatory elements, including RTSSs producing non-coding transcripts, is to affect the transcription of nearby genes. The RTSSs is a subset of the general collection of regulatory elements available to a cell, which also includes enhancers and insulators, many of which do not produce their own transcripts. There are individual examples of situations where regulatory elements affect flanking genes -50, and de novo production of transcription factors [FOS and JUN were associated with poised RTSSs in all cell lines, and we observed a higher CpG content in genes associated with all cell types than genes associated with three or less cell types, in accordance with other data on subsets of immediate-early response genes [The investigation of average chromatin profiles around repressed and expressed RTSSs has revealed a subtle relationship between open and closed chromatin, and between transcript repression and expression. Especially our analyses of a selected subset of poised RTSSs that are substantially enriched for active chromatin marks, but with zero expression levels, shows that chromatin state is not always directly correlated with active transcription. In addition we also observe slight enrichment of repressive marks, especially H3K9me3, at expressed RTSSs. Possible reasons for H3K9me3 enrichment in gene bodies have been described previously , but the factors , and thi factors ,63. In ose genes .In our study we set the predefined number of clusters for the k-means clustering to 5 for clustering of individual chromatin marks, and 10 for the combination of marks. Other studies have identified higher number of profiles, both for each chromatin mark and for combinations of marks , where tThe regulatory landscape governing transcription in different cell types is highly complex. However, it is also predictable, in that the same cell type responds similarly every time it is subjected to the same environmental cue, and coordinated, in that several transcriptional elements respond in the same manner to stimulation. Genome wide mapping of various features, whether it is expression level measurements, TSS activity level, chromatin configuration, DNase HS or transcription factor binding and activity, all leave traces of this coordinated action. Thus, it is an important challenge to integrate such data and determine at what level it is meaningful to look for general patterns that are robust and predictable on a global scale, to investigate what these patterns mean in terms of function and phenotype, and what the main components that govern these patterns are. In this study we have used combinations of chromatin marks around a global set of experimentally defined TSSs, and identified subsets of TSSs with similar chromatin configuration, several of which have functional associations. Hopefully this and related strategies, together with integration of even more genome wide features, will continue to reveal patterns of ubiquitous and cell type specific gene regulation, expression and function.We have integrated chromatin data from the ENCODE consortium with the robust set of globally defined TSSs from FANTOM5 to investigate how chromatin features can be used to distinguish TSSs with different properties in four cell lines analyzed by both consortia. We find that most TSSs are repressed in the cell lines studied here, however, a substantial number of the repressed TSSs are enriched with active chromatin marks. These TSSs are strongly associated with immediate-early response processes and cell signaling. Expressed TSSs can be clustered into subsets based on combinations of both enrichment and profile shape of individual chromatin marks. We identified three main groups of clusters which differ in average TSS expression, CpG island enrichment, TSS location with respect to nearby genes and functional GO terms. Interestingly, groups with clusters enriched for TSSs distal to nearby genes show the strongest functional associations. Finally we show that nearby TSSs with similar chromatin configuration show better correlation in global expression profiles than nearby TSSs in general, thus validating the link between chromatin states and cellular function.bigWig files from ENCODE . For RTSS from FANTOM5, we started with a preliminary global CAGE RTSS dataset of 180 338 robust RTSS , DNase hypersensitivity  and Pol II  were downloaded from ENCODE for the four cell lines K562, GM12878, HeLa-S3 and HepG2 . In addiin paper  (mapabil.broadPeak for histone modifications and histone variant H2A.Z and .narrowPeak for DNase HS). An overlap between an enrichment peak and a RTSS was identified if the enrichment profile overlapped the RTSS plus a 500 bp extension from each end of the RTSS region. The extension was used because some chromatin marks associate with RTSSs up- or downstream, rather than at the exact position of the RTSS. Isolated RTSSs were defined as RTSSs with a genomic distance of at least 2kbp from any other RTSSs. The p-value for each overlap was calculated by the Genomic Hyperbrowser [To define expressed RTSS, we used a mapped tag threshold of 5 for the cell lines GM12878, HeLaS3 and HepG2 and 3 for K562. The reason for the lower threshold in K562 was that the CAGE tag library for K562 contained a lower total number of tags (10.7 m) than the other three cell lines . Only RTSSs regions with zero tag count were defined as repressed. RefSeq genes (UCSC Genome Browser 18.10.2011) ,66 were rbrowser  using a rbrowser .bigWig files from ENCODE, and profiles were calculated by summing values in 100 bp intervals ±3kbp around RTSSs as described for the chromatin marks.Profiles around each of the 179 369 globally defined RTSSs in each cell line for all chromatin marks were calculated from ChIP-Seq mapped sequence read libraries downloaded from ENCODE. Replicates for each chromatin mark were pooled. RTSS center positions were used as genomic anchor points for profile regions spanning 3 kbp in both directions from the anchor point. Because the average sequence read length was estimated to be around 200 bp , start positions for the mapped reads were shifted by +100 bp for reads mapped to the positive strand, and -100 bp for reads mapped to the negative strand. Each profile was then calculated by summing all start positions in 100 bp intervals up and downstream of the anchor point, extending 3kbp in each direction. To limit the impact of noise, only RTSSs overlapping with ENCODE-defined significantly enriched regions for each chromatin mark were used to calculate the average profiles. Nucleosome data for K562 and GM12878 were downloaded as kcluster function in the Python Bio.Cluster package. We used Pearson Correlation as distance measure rather than Euclidian Distance to emphasize profile shape rather than intensity differences, and also to reduce the effect of normalization. We did repeated analyses with number of passes, npass, set to 10, and found that this number made each clustering fairly reproducible as evaluated by visual inspection of the resulting profiles. We thus chose 200 passes in the final clustering of each chromatin mark, which should be sufficient to produce robust cluster profiles. All other parameters were set to default. To investigate the effect of confounding, we also selected profiles from isolated expressed RTSSs and clustered them separately. Clustered profiles using only the isolated clusters were generally comparable to cluster profiles from the full sets of expressed RTSSs. We thus used results from the full set clustering for further analysis.Clustering was performed for each chromatin mark in each cell line individually using k-means clustering with number of clusters set to 5. We chose this number both because it generally produced subprofiles that were clearly distinct in shape, and to avoid too much combinatorial variation for the subsequent meta-clustering (see below). For clustering we used profiles for all expressed RTSSs in each cell line. The profiles were calculated using the RTSS center position as anchor point, and averaging sequence read intensities in 100 bp windows extending 3kbp both up and downstream. RTSSs with profiles containing less than 100 reads for a chromatin mark were filtered out for the clustering of this mark. Applying this filter resulted in between 15 000 and 50 000 profiles clustered for each active mark, and between 1000 and 15 000 profiles for each repressive mark. All profiles selected for clustering were smoothed prior to clustering using Gaussian convolution with window size of 7 bins. Removing edge effects caused by the smoothing reduced the number of measuring points in each profile from 60 to 48. Clustering was performed using the The initial clustering produced 5 average subprofiles for each of the 12 chromatin marks in each cell-line. For each expressed RTSS, a Pearson correlation coefficient was calculated between each of the RTSS chromatin profiles and the five subprofiles for this chromatin profile. This resulted in 60 correlation coefficients for each of the expressed RTSSs . Correlation coefficients where the total chromatin signal was below the predefined threshold of 100 where set to zero. The matrix of expressed RTSSs and chromatin profiles was then subjected to k-means clustering, with number of clusters set to 10 and number of passes to 1000. Other cluster parameters were the same as previously defined. The clustering was performed independently in each cell line.In each cell line profiles for all active marks around each repressed RTSS were pooled to create a general active profile for each RTSS. A RTSS was selected for the robust subset if it i) overlapped with a peak-region of significant enrichment for any chromatin mark, ii) the total signal for the pooled profile was above a threshold set to 1000 reads, iii) the correlation of the RTSS profile to the average pooled repressed profile was above 0.5, and iv) the correlation of the RTSS profile to the average repressed profile was significantly better  than the correlation to the average expressed profile. For the last criteria we implemented a statistical test for comparing dependent correlations . A totalMolecular Function, Biological Process, PANTHER Pathway, Pathway Commons, BioCyc Pathway and MSigDB Pathway. Only terms displayed by GREAT were included in the analysis. In default mode, GREAT only displays the top 20 terms for each category which pass two statistical tests : A binomial test which accounts for over-representation in genomic regions, and a hypergeometric test which accounts for over-representation in functionally associated gene sets. In addition, the region fold enrichment must be larger than 2 for a term to be reported. For each cluster, localization enrichment with respect to associated genes was retrieved from Region-Gene Association Graphs (Binned by orientation and distance to TSS) displayed by GREAT. An issue with the initial analysis using RTSS locations in each cluster was the possibility of confounding of nearby RTSSs. RTSSs located less than a few hundred bp apart may have a confounded chromatin signature, which may bias the significance of some terms in GREAT. To deal with this issue, we applied two strategies for CAGE analysis. In the permissive strategy we used all RTSSs in each metacluster as input, thus allowing more weight to be put on regions where many RTSSs are located close together, while in the conservative strategy we merged all RTSSs within a 100 bp window surrounding anchor RTSSs. The RTSSs used as anchors for merging were the ones having the highest proximity to other RTSSs. The matrix used for PCA on GREAT terms was constructed by first listing all genes associated with significant terms for all metaclusters. Then, for each gene and each cluster, a value of 1 was assigned if significant terms for this cluster contained the gene, and 0 if the gene was not contained in the significant terms. This procedure resulted in a matrix where each of the 40 clusters is a sample, each gene is a variable, and each elements in the matrix have the value 0 or 1. For the poised RTSSs we collected terms from the same GREAT categories as for the metaclusters, but now we used the general human genomic background provided by GREAT rather than a customized background for the analysis. To evaluate the GREAT terms for the selected repressed RTSSs we compared them to terms generated using random sets of RTSSs with set sizes equal to the selected RTSS sets, and drawn randomly from i) the total set of all repressed RTSSs, and ii) the set of repressed RTSSs with not overlapping significantly enriched chromatin regions from ENCODE. To enumerate the terms related to immune response, we counted terms containing variants of the words immune, interferon, interleukin, cytokine, inflammation, TNF, NFkB and TCF. For terms related to signaling we only counted variants of the word signaling. Associated genes were also extracted from the GREAT reports.RTSS regions for each of the 40 metaclusters (10 in each cell-line) were individually submitted to the Genomic Regions Enrichment of Annotations Tool (GREAT)  using deCpG island coverage and enrichment in RTSS promoters (defined as the region 200 bp upstream of a RTSS) in 40 metaclusters, as well as promoters for genes affected by our selection of poised RTSSs, was computed using the Genomic HyperBrowser . A trackThe correlation between two RTSSs was calculated as Pearson correlation between expression levels over all 975 cell types and tissues analyzed in FANTOM5. For each cluster and the set of poised RTSSs in each cell-line, we used annotated TSSs for genes associated with each cluster from GREAT, and calculated intra-correlations between all RTSSs in increasing distances of 0.05, 0.2, 0.5, 1, 5, 10, 50 and 150 kbp upstream and downstream from the annotated gene TSS. Correlations were calculated for all globally defined RTSSs within the region, and RTSSs contained in each cluster only for the same region. Random correlations were calculated as all intra-correlations between 100 randomly selected RTSSs from the global set.RTSS(s): Robust clusters of transcription start site(s); TSS(s): Transcription start site(s); CAGE: Cap analysis of gene expression; TF: Transcription factor; PCA: Principal component analysis.RIKEN Omics Science Center ceased to exist as of April 1st, 2013, due to RIKEN reorganization.The authors declare that they have no competing interests.MR and FD conceived of the project. MR did most of the data analysis and drafted the manuscript. GKS did statistical analysis using the Genomic HyperBrowser. FD supervised the project and contributed to the final manuscript. HK managed the data handling, COD, PC and ARRF were responsible for FANTOM5 management and concept. All authors read and approved the final manuscript.The file contains supplementary Figure S1 to S9, S11, S12 and S14 to S16, supplementary Table S10, S17 and S18, and supplementary text S13.Click here for file


Plasmodium falciparum shows a remarkably complex pattern of gene expression during the erythrocytic cycle, paradoxically contrasting with the relatively low number of putative transcription factors encoded by its genome. The spatial organization of nuclear subcompartments has been correlated with the regulation of virulence genes. Here, we investigate the nuclear architecture of transcription during the asexual cycle of malaria parasites. As in mammals, transcription is organized into discrete nucleoplasmic sites in P. falciparum, but in a strikingly lower number of foci. An automated analysis of 3D images shows that the number and intensity of transcription sites vary significantly between rings and trophozoites, although the nuclear volume remains constant. Transcription sites are spatially reorganized during the asexual cycle, with a higher proportion of foci located in the outermost nuclear region in rings, whereas in trophozoites, foci are evenly distributed throughout the nucleoplasm. As in higher eukaryotes, transcription sites are predominantly found in areas of low chromatin density. Immunofluorescence analysis shows that transcription sites form an exclusive nuclear compartment, different from the compartments defined by the silenced or active chromatin markers. In conclusion, these data suggest that transcription is spatially contained in discrete foci that are developmentally regulated during the asexual cycle of malaria parasites and located in areas of low chromatin density.Increasing evidence shows that the spatial organization of transcription is an important epigenetic factor in eukaryotic gene regulation. The malaria parasite  Plasmodium falciparum, the parasite that causes severe malaria, has a complex life cycle with several distinct stages of development, each characterized by unique morphological, physiological and molecular features.Almost half of the world population lives in areas of endemic malaria, a tropical disease that causes approximately 800,000 deaths every year. Despite progress in sequencing the genome of the parasite P. falciparum genome has frustrated the discovery of regulatory sequences by both bioinformatics methods and classical phylogeny, with a few exceptions P. falciparum genome encodes RNA polymerase II (RNA pol Il) basal transcription machinery and associated transcription factors  demonstrated for the first time that transcription is organized into discrete foci throughout the nucleus P. falciparum. Here, we report that transcription is organized in foci developmentally regulated in terms of both number and location during the asexual cycle. Transcription mostly occurs in areas of low chromatin density, in a novel compartment, distinct from some of the subcompartments previously described in the P. falciparum nucleus.Given the importance of spatial organization of chromatin and nuclear compartmentalization for malaria parasite gene regulation, we sought to quantitatively and qualitatively characterize the spatial organization of global transcription in P. falciparum 3D7 strain was maintained in RPMI 1640 medium supplemented with sodium bicarbonate (Welgene #LM011-05) and 3% hematocrit  cultured at 37°C in an atmosphere of 1% O2, 5% CO2 and 94% N2. For routine maintenance of cultures, parasitemia was kept between 0.5 and 5%. Prior to BrUTP incorporation experiments (described below), parasites were subjected to gelatin purification of mature stages  in RPMI and incubated at 37°C for 1 h. The supernatant containing only mature parasites ) was collected in a fresh tube, washed once in RPMI, and placed in culture. Fresh red blood cells were added to a final hematocrit of 3%, as described above. The cultures were gassed and incubated at 37°C for 4 hours to allow the invasion to proceed (the erythrocytic cycle of the 3D7 strain lasts approximately 40 to 44 hours), and after this period, the culture was treated with 5% sorbitol, in a proportion of 4 volumes of 5% sorbitol to 1 volume of pelleted red blood cells (RBCs), for 5 min at 37°C. This treatment is intended to lyse mature forms, which did not complete their developmental cycle. Young stages  are not affected by this treatment. The pellet containing only ring-infected and non-infected RBCs was washed in culture media and cultured for an additional period as needed. For the BrUTP labeling experiments, cultures were used at 4 points in their erythrocytic cycle: early rings from 0 to 4 hpi , rings from 8 to 12 hpi (referred to as at 10 hpi), trophozoites from 20 to 24 hpi (referred to as at 22 hpi) and schizonts from 32 to 36 hpi (referred to as 34 hpi). Typically, two rounds of gelatin/sorbitol synchronization (treatments in two consecutive erythrocytic cycles) are needed to achieve a high degree of synchronization in the population (approximately more than 90% of parasites at the same phase of the cycle).The P. falciparum was adapted from a previously published report g for 2 min at room temperature, and resuspended at 1×109 infected red blood cells (iRBC)/ml in ice-cold wash buffer . Lysolecithin  was added to a final concentration of 0.01%, and after homogenization, cells and parasites were permeabilized for 5 min in an ice water bath, then centrifuged at 2500×g for 10 min at 4°C and washed 3 times in cold wash buffer. Cells were resuspended at 1×109 iRBCs/ml in cold wash buffer either with or without the addition of 200 µg/ml of α-amanitin and incubated for 5 min at 37°C. Subsequently, 2 mM ATP, 1 mM GTP, 1 mM CTP and/or 1 mM BrUTP  were added to both preparations, and nucleotide incorporation was allowed to proceed for 10 min at 37°C. The parasites were then immediately fixed in 4% paraformaldehyde in PBS at pH 7.4 (4% PFA) and at a density of 1.25×108 iRBCs/ml for 20 min at room temperature. After fixation, the parasites were centrifuged at 2500×g for 10 min at 4°C, washed once in PBS and placed onto a microscope slide in a humid chamber at room temperature for 30 min to allow parasites to adhere to the glass. Fluorescent microspheres  were included in the sample preparation to be used as reference for scale and Z image alignment during automated image analysis (see below), in case of confocal Z series.The incorporation of 5-bromouridine 5′-triphosphate (BrUTP) in Parasites on glass slides (prepared as described above) were permeabilized in 0.1% Triton X-100 in PBS for 2 min at room temperature, washed 3 times in PBS and blocked for 30 min in 4% BSA in PBS pH 7.4 (4% BSA), followed by primary antibody incubation. An anti-deoxyuridine (anti-BrdU) mouse monoclonal antibody  was used for the detection of BrUTP-labeled nascent RNA (BrRNA); rabbit polyclonal serum was used for the detection of PfSir2A airy, and both channels were acquired simultaneously (for imaging of DAPI combined with Alexa Fluor 555 staining) or sequentially (for imaging of DAPI combined with Alexa Fluor 488 staining) using two independent photon multipliers (PMTs). A 405 nm diode laser, a 488 nm argon laser, and a 561 nm DPSS green laser were used for the excitation of DAPI, Alexa Fluor 488 and 555, respectively, and emission was acquired at 415–485 nm for DAPI: 515–570 nm for Alexa Fluor 488 and 570–645 nm for Alexa Fluor 555. Each Z optical slice was composed of an average of 16 frames at 512×512 pixels, 8 bits, with the size of the voxel  corresponding to 58×58×122 nm. After acquisition, 2D and 3D images were assembled using the ImageJ software Two-dimensional images were obtained using a Nikon Eclipse 90i microscope equipped with an oil immersion 100×/1.4 PlanApoVC objective and the quantitative Nikon DS-QiMc camera controlled by NIS-Elements AR software. Images were acquired in gray scale in the quantitative range of intensity. Three-dimensional images (Z series) were acquired on a confocal microscope equipped with a 63×/HCX 1.4 PL Apo lbdBL oil immersion objective. The diaphragm was set to 1.00 P. falciparum. The SNR was optimized by increasing the number of images acquired and averaging them to 16 frames per Z optical slice. To ensure that at the end of long acquisition periods the Z slices were correctly assembled in 3D, fluorescent microspheres were used as references by a customized algorithm that removes intrinsic microscope stage motion and chromatic aberrations, which are negligible in most applications but became important in the case of imaging the miniscule P. falciparum nucleus A significant level of precision is required for detection and localization of sites of RNA synthesis inside the nucleus of the parasite. To increase the relevance of image processing, images were acquired with a signal-to-noise ratio (SNR) as high as possible, mainly due to the low level of fluorescence of microscopic subnuclear structures of To quantitatively compare transcription foci positions across different nuclei and different experiments, each focus radial position was normalized to its respective nucleus radius. Briefly, the nucleus ellipsoid was projected onto a sphere of radius = 1, and the positions of the sites were determined and relativized in relation to this radius. Then, three concentric nuclear zones of equal volume were defined.t-tests were performed using the software GraphPad Prism version 4.03 (www.graphpad.com). p values less than 0.05 were considered significant. For the analysis of transcription sites radial distribution, we applied the method described by Unpaired in situ by the incorporation of modified nucleotides, such as 5-bromouridine 5′-triphosphate (BrUTP) in permeabilized cells. Given our interest in studying the spatial organization of transcription in P. falciparum, we standardized the technique of incorporation of BrUTP for this parasite. This technique was difficult to perform because, among other factors, the parasite is extremely sensitive to permeabilization. It is also noteworthy that we attempted to standardize the incorporation of bromouridine (BrU) in cultures , but P. falciparum in culture did not incorporate BrU in nascent RNA, at least at levels detectable by immunofluorescence (data not shown). As shown in P. falciparum. In the earliest stages of the asexual cycle (2 hpi), nascent transcription is visualized mostly as a few, low-intensity spots in the periphery of the nucleus; as the parasite progresses in the asexual cycle, in rings at 10 hpi and trophozoites at 22 hpi, a higher number of transcription foci is seen. It is also possible to visualize transcription foci in several nuclei of segmented schizonts, thus demonstrating that this technique enables the sensitive and specific detection of transcription throughout the asexual cycle.Transcription can be visualized The incorporation and detection of BrUTP into nascent RNA (BrRNA) can be inhibited by the treatment of permeabilized cells with the RNA polymerase II inhibitor α-amanitin, leaving only the RNA polymerase I transcription foci to be visualized . As expeP. falciparum genes expressed during the asexual cycle are developmentally regulated The majority of P. falciparum. The use of automated analysis of subnuclear structures enables the objective and unbiased detection of the object studied A customized image analysis algorithm was developed to quantitatively characterize the number and distribution of transcription sites in the nuclei of different asexual forms of Using synchronized populations, we acquired confocal Z series of parasites labeled by BrUTP in two distinct stages of the erythrocytic cycle: ring (10 hpi) and trophozoite 22 hpi – , and thep<0.0001; see The appearance and average number of transcription sites were found to be markedly different in the two phases: at 10 hpi, transcription sites appeared more as individualized foci, while at 22 hpi, sites appeared more juxtaposed and less individualized, with the BrRNA signal radiating from the center of the sites to the nucleoplasm . The numP. falciparum detected at 10 and 22 hpi would correlate with changes in nuclear volume to maintain a constant density of transcription sites per unit of nuclear volume. However, the average nuclear volume between 10 and 22 hpi remains virtually unchanged (approximately 5 µm3), and the difference in the average volume between the two populations is not statistically significant , contrasting with the distribution of sites at 22 hpi, when they were almost evenly dispersed among the three nuclear concentric zones of equal volume (p = 0.332). Altogether, the data suggest that transcription sites are dynamic structures that are spatially and developmentally regulated during the asexual cycle of P. falciparum.Unexpectedly, analysis of the nuclear distribution demonstrated that there is spatial reorganization of transcription sites during the erythrocytic cycle : whereasP. falciparum correspond to regions of low chromatin density. The data also suggest that although the number and distribution of transcription sites in the malaria parasite nucleus differ from those found in higher eukaryotes, the compartmentalization of transcription in discrete sites that differ from chromosomal regions is conserved in P. falciparum.We also observed that the most intense transcription sites were often located in areas of low DAPI labeling. Analysis of the relationship between the BrRNA and DAPI signals reveals that these two markers are inversely related and that BrRNA signal peaks, which correspond to transcription sites, are located in areas of low DAPI signals, for both 10 hpi  and 22 ηP. falciparum is highly subcompartmentalized. Given this scenario, we probed the relationship between the transcription sites and some of the characterized subcompartments of P. falciparum. We analyzed the distribution of the nucleolar and telomeric cluster marker PfNop1 var and rifP. falciparum. We also consistently observed one transcription site that is peripheric and seemed to be excluded from the H4ac domain, further reinforcing the concept that general acetylation is not a marker of ongoing transcription. Likewise, the marker H3K79me3 showed little to no colocalization with transcription sites, suggesting that this specific histone H3 modification is not associated with active transcription.The nucleus of The silencing factor PfSir2A is found in foci at the nuclear periphery, telomeric clusters and nucleolus P. falciparum correspond to a marker of ongoing transcription.The compartment defined by PfSir2A appears to be formed by two distinct components, which showed an interesting pattern in relation to transcription sites: one PfSir2A area, which was strongly labeled and clearly localized to one pole of the nuclear periphery did not colocalize with transcription sites, which are located on the opposite pole of the nucleus; the other areas, which were faintly labeled and formed foci suggestive of telomeric clusters, exhibited a partial overlap with transcription sites . The nucPlasmodium falciparum is the product of several layers of control P. falciparum gene regulation. In this work, we quantitatively studied the overall spatial organization of nascent transcription in malaria parasites. Using a customized computer algorithm, the number and distribution of transcription sites in the nucleus of P. falciparum were analyzed. This automated image analysis method has the advantage of allowing for unbiased transcription site determination and counting. Surprisingly, the average number of transcription sites, as well as their nuclear distribution, change during the asexual cycle, suggesting that the transcription compartment is a dynamic, developmentally regulated structure. Rings at 10 hpi have a higher number of transcription sites than trophozoites at 22 hpi, and this change in site number does not correlate with changes in nuclear volume between the two stages. The higher number of sites found in rings compared to trophozoites contrasts with the higher measured intensity of transcription sites in trophozoites than in rings, suggesting that transcription sites in trophozoites have more mRNA than those in rings. This interpretation is in agreement with the fact that trophozoites have a higher amount of mRNA than rings Gene expression in var genes occurs at the periphery of the nucleus var in the ring stage. The periphery is not, however, the region of preferential transcription during the whole asexual cycle, and in trophozoites, the central, intermediate and peripheral regions have the same probability of containing transcription sites. However, as the experimental procedures for BrUTP labeling of nascent transcripts require cell permeabilization, we cannot rule out unknown effects of permeabilization on the observed spatial distribution of transcriptional foci.Transcription of var promoter leads to downregulation of endogenous rif, stevor and Pfmc-2TM, implying that these gene families share the same regulators/activators/transcription factors and that these are limited in number The fact that there are far fewer transcription sites than the estimated number of genes transcribed in each stage Based on the hypothesis that genes must share a transcription site to be transcribed, several possibilities can be entertained including whether gene clustering at transcription sites are stochastic or deterministic events and whether this potential organization is linked to the periodic regulation of genes during the asexual cycle. In this sense, it would be of fundamental importance to evaluate how transcription sites relate to the recently described putative plant-like transcription regulators apetala 2 (ApiAP2) P. falciparum undergo spatial redistribution between 10 and 22 hpi is quite surprising and reinforces the notion that transcription sites are plastic structures, which are regulated differently during the process of development/differentiation that occurs in the asexual cycle.We also observed a redistribution of transcription sites in different nuclear areas from rings to trophozoites: in rings, transcription occurs mostly in the outermost third of the nucleus, whereas in trophozoites, transcription sites are also distributed in the central, intermediate and peripheral nuclear areas. This is also the first report to our knowledge to quantitatively demonstrate that transcription sites may have a preferential localization in a nuclear zone, as mammalian transcription sites are distributed throughout the nucleus P. falciparum.We observed that transcription occurs in nuclear areas with a low DAPI labeling and assumed that these areas were poor in chromatin. The same is true for mammalian cells, in which transcription has been shown to be located at the surface of chromosomes, in the so-called interchromatin space P. falciparum spatially defines a nuclear compartment different from the compartment defined by PfSir2A, PfNop1 and active chromatin histone mark H4ac. Apparently, H4ac is distributed throughout the nucleus and appears to mark all chromatin visualized by DAPI, suggesting that the state of chromatin acetylation is insufficient to determine the transcription of a gene in P. falciparum. These observations are in accordance with what has been described in Our data also demonstrate that transcription in P. falciparum, and further studies are necessary to characterize whether any of these are transcription sites markers as well. A more straightforward approach would be a functional characterization of P. falciparum RNA polymerases and their subunits, a fundamental undertaking that has still surprisingly not been achieved despite the relatively large number of studies on gene regulation and expression in Plasmodium, including some initial characterization work on RNA polymerases Several nuclear markers have been characterized for P. falciparum is organized in discrete foci, which resemble the transcription factories of higher eukaryotes. The transcription foci are dynamic structures that vary in number and distribution during the asexual cycle. Contrary to higher eukaryotes, the number of sites per P. falciparum nucleus does not appear to be related to the size of the nucleus (nuclear volume), and the intensity of transcription sites is higher in trophozoites than in rings. Given that evidence points to more active genes than sites observed in a given moment of the asexual cycle, it can be concluded that active genes would have to share sites while being transcribed. The nuclear subcompartment defined by transcription sites is different from some other compartments described previously for P. falciparum, and it exhibits low chromatin density.In conclusion, the results presented here suggest that transcription in P. falciparum and to demonstrate that this nuclear subcompartment is dynamic and developmentally regulated. As the unique mechanisms of gene expression and regulation in malaria parasites raise the interesting possibility of targeting these pathways for drug discovery and ultimately malaria control, further studies are needed to address the issue of whether transcription sites are assembled upon the need for gene transcription or if they are pre-assembled structures, to which genes are targeted, stochastically or deterministically, to be transcribed.This is the first study to address the spatial organization of global transcription in Figure S1Nascent RNAs can be specifically labeled by BrUTP incorporation in P. falciparum. Incubation of permeabilized P. falciparum cells with ATP, GTP, CTP and BrUTP allows for the detection of BrRNA by immunofluorescence . The presence of the RNA polymerase II inhibitor α-amanitin blocks BrUTP incorporation into mRNA, and only nascent rRNA can be visualized in 1 or 2 spots per nucleus. When cells are incubated with BrUTP in the absence of ATP, GTP and CTP, no BrRNA can be visualized. Bars, 1 µm.(TIF)Click here for additional data file.


The emphasis of the discussion is on prokaryotic gene regulation, but our analysis can be extended to eukaryotic cells as well.According to recent experimental evidence, promoter architecture, defined by the number, strength and regulatory role of the operators that control transcription, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect variability in gene expression in a systematic rather than case-by-case fashion. In this article we make such a systematic investigation, based on a microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters  and how each of these affects the level of variability in transcriptional output from cell-to-cell. We propose that direct comparison between  in vivo, and as the basis of a more systematic approach to designing new promoter architectures.Stochastic chemical kinetics provides a framework for modeling gene regulation at the single-cell level. Using this framework, we systematically investigate the effect of promoter architecture, that is, the number, quality and position of transcription factor binding sites, on cell-to-cell variability in transcription levels. We compare architectures resulting in transcriptional activation with those resulting in transcriptional repression. We start from simple activation and repression motifs with a single operator sequence, and explore the parameter regime for which the cell-to-cell variability is maximal. Using the same formalism, we then turn to more complicated architectures with more than one operator. We examine the effect of independent and cooperative binding, as well as the role of DNA mechanics for those architectures where DNA looping is relevant. We examine the interplay between operator strength and operator number, and we make specific predictions for single-cell mRNA-counting experiments with well characterized promoters. This theoretical approach makes it possible to find the statistical response of a population of cells to perturbations in the architecture of the promoter; it can be used to quantitatively test physical models of gene regulation  A fundamental property of all living organisms is their ability to gather information about their environment and adjust their internal physiological state in response to environmental conditions. This property, shared by all organisms, includes the ability of single-cells to respond to changes in their environment by regulating their patterns of gene expression. By regulating the genes they express, cells are able to survive, for example, changes in the extracellular pH or osmotic pressure, switch the mode of sugar utilization when the sugar content in their medium changes, or respond to shortages in key metabolites by adapting their metabolic pathways. Perhaps more interesting is the organization of patterns of gene expression in space and time resulting in the differentiation of cells into different types, which is one of the defining features of multicellular organisms. Much of this regulation occurs at the level of transcription initiation, and is mediated by simple physical interactions between transcription factor proteins and DNA, leading to genes being turned on or off. Understanding how genes are turned on or off (as well as the more nuanced expression patterns in which the level of expression takes intermediate levels) at a mechanistic level has been one of the great challenges of molecular biology and has attracted intense attention over the past 50 years.Drosophila melanogasterB. subtilisE. coliThe current view of transcription and transcriptional regulation has been strongly influenced by recent experiments with single-cell and single-molecule resolution A number of theoretical and experimental studies have revealed multiple ways in which the architecture of the gene regulatory network affects cell-to-cell variability in gene expression. Examples of mechanisms for the control of stochasticity have been proposed and tested, including the regulation of translational efficiency Our goal is to examine all of these different promoter architectures from a unifying perspective provided by stochastic models of transcription leading to mRNA production. The logic here is the same as in earlier work where we examined a host of different promoter architectures using thermodynamic models of transcriptional regulation First, we investigate in general theoretical terms how the architecture of a promoter affects the level of cell-to-cell variability. The architecture of a promoter is defined by the collection of transcription factor binding sites , their number, position within the promoter, their strength, as well as what kind of transcription factors bind them , and how those transcription factors bind to the operators . We apply the master-equation model of stochastic gene expression in vivo tests of the molecular mechanisms of gene regulation by transcription factors that have been proposed as a result of in vitro biochemical experiments. The idea of using spontaneous fluctuations in gene expression to infer properties of gene regulatory circuits is an area of growing interest, given its non-invasive nature and its potential to reveal regulatory mechanisms in vivo. Different theoretical methods have recently been proposed, which could be employed to distinguish between different modes (e.g. AND/OR) of combinatorial gene regulation, and to rule out candidate regulatory circuits The second point of this paper is to make use of stochastic kinetic models of gene regulation to put forth in vivo. In particular, we focus on how varying the different parameters  should affect the level of variability. This way, cell-to-cell variability in gene expression is used as a tool for testing kinetic models of transcription factor mediated regulation of gene expression in vivo.Here, we make experimentally testable predictions about the level of cell-to-cell variability in gene expression expected for different bacterial promoters, based on the physical kinetic models of gene regulation that are believed to describe these promoters The remainder of the paper is organized as follows: First we describe the theoretical formalism we use to determine analytic expressions for the moments of the probability distribution for both mRNA and protein abundances per cell. Next, we examine how the architecture of the promoter affects cell-to-cell variability in gene expression. We focus on simple and cooperative repression, simple and cooperative activation, and transcriptional regulation by distal operators mediated by DNA looping. We investigate how noise in gene expression caused by promoter activation differs from repression, how operator multiplicity affects noise in gene expression, the effect of cooperative binding of transcription factors, as well as DNA looping. For each one of these architectures we present a prediction of cell-to-cell variability in gene expression for a bacterial promoter that has been well characterized experimentally in terms of their mean expression values. These predictions suggest a new round of experiments to test the current mechanistic models of gene regulation at these promoters.In order to investigate how promoter architecture affects cell-to-cell variability in gene expression, we use a model based on classical chemical kinetics  in matrix form.This has several advantages, but the most important one is that the matrix approach reduces the task of obtaining analytical expressions for the moments of the steady state mRNA distribution for an arbitrarily complex promoter to solving two simple linear matrix equations (more details are given in the The matrices appearing in equation (4) all have simple and intuitive interpretations. The matrix An example of matrices An important limit of the master equation, which is often attained experimentally, is the steady state limit, where the probability distribution for mRNA number per cell does not change with time. Although the time dependence of the moments of the mRNA distribution can be easily computed from our model, for the sake of simplicity and because most experimental studies have been performed on cells in steady state, we focus on this limit. As shown in The vector Make a list of all possible promoter states and their kinetic transitions Construct the matrices vector , .Solve equations (7–8) to obtain Plug solutions of (7–8) into equations (5–6) to obtain the moments.The normalized variance of the mRNA distribution in steady state is then computed from the equation:Equation (9) reveals that, regardless of the specific details characterizing promoter architecture, the intrinsic noise is always the sum of two components, and it can be written asThe first component is due to spontaneous stochastic production and degradation of single mRNA molecules, it is always equal to the Poissonian expectation of In order to quantify the effect of the promoter architecture in the level of cell-to-cell variability in mRNA expression, we define the deviation in the normalized variance caused by gene regulation relative to the baseline Poisson noise for the same mean see :(12)Therefore, the deviation in the normalized variance caused by gene regulation is equal to the ratio between the variance and the mean. This parameter is also known as the Fano factor. Thus, for any given promoter architecture, the Fano factor quantitatively characterizes how large the mRNA noise is relative to that of a Poisson distribution of the same mean (i.e. how much the noise for the regulated promoter elevates with respect to the Poisson noise). This is the parameter that we will use throughout the paper as the metric of cell-to-cell variability in gene expression.n as a state variable that represents the copy number of proteins per cell):b is equal to the protein burst size (the average number of proteins produced by one mRNA molecule). The mean protein per cell is given by For proteins, the picture is only slightly more complicated. As shown in the b. Therefore, the vectors b multiplying all the different transcription rates. We conclude that the promoter contribution to the noise takes the exact same analytical form both for proteins and for mRNA, with the only other quantitative difference being the different rates of degradation for proteins and mRNA. Therefore, promoter architecture has the same qualitative effect on cell-to-cell variability in mRNA and protein numbers. All the conclusions about the effect of promoter architecture on cell-to-cell variability in mRNA expression are also valid for proteins, even though quantitative differences do generally exist. For the sake of simplicity we focus on mRNA noise for the remainder of the paper.The reader is referred to the in vivo and in vitro, which we summarize in E. coli promoters, like the Plac or the PRM promoter, we extend their reach by using them as “typical” parameters characteristic of bacterial promoters, with the idea being that we are trying to demonstrate the classes of effects that can be expected, rather than dissecting in detail any particular promoter. The rate of association for transcription factors to operators in vivo is assumed to be the same as the recently measured value for the Lac repressor, which is close to the diffusion limited rate In order to evaluate the equations in our model, we use parameters that are consistent with experimental measurements of rates and equilibrium constants offk is the rate  at which a transcription factor dissociates from the promoter, and −1, is written as the product of two quantities: −1s−1. For simplicity, we assume that the binding reaction is diffusion limited, namely, Operator strength reflects how tightly operators bind their transcription factors, and it is quantitatively characterized by the equilibrium dissociation constant Throughout this paper, we also make the assumption that the mean expression level is controlled by varying the intracellular concentration of transcription factors, a scenario that is very common experimentally To generate mRNA time traces, we applied the Gillespie algorithm To generate the probability distributions, it is convenient to reformulate the entire system of mRNA master equations in terms of a single matrix equation. To do this, we first define a vectorWe first investigate a promoter architecture consisting of a single repressor binding site, and examine how operator strength affects intrinsic variability in gene expression. Although this particular mode of gene regulation has been well studied theoretically before We compute the mean and the Fano factor for this architecture following the algorithm described in the Mathematical Using the relationship between Here we have defined the equilibrium dissociation constant between the repressor and the operator as: The Fano factor for the mRNA distribution can be computed from equation (12) and we obtain:Therefore, for any given value of the mean, the Fano factor depends only on two parameters: the maximal mRNA or protein expression per cell, and a parameter that reflects the strength of binding between the repressor and the operator: The effect of operator strength on the output of transcription and translation is illustrated in lacOid, or the cI2-In order to show that the effect of operator strength on the cell-to-cell variability is general and does not depend on the particular set of parameters chosen in the simulation, in The Fano factor has a characteristic shape whereby it takes values approaching 1 at low and high transcription levels with a peak at intermediate values. The reason for this shape is that for very low transcription levels the promoter is nearly always inactive, firing only very rarely. In this limit successive transcription events become uncorrelated and the time in between them is exponentially distributed, leading to a distribution of mRNA per cell that approaches a Poisson distribution characterized by a Fano factor equal to 1. In contrast, for very high transcription levels the promoter is nearly always active, switching off very rarely and staying in the off state for short times. In this limit, transcription events are again uncorrelated and exponentially distributed, leading once again to a Poisson distribution of mRNA number. It is only for intermediate values of the mean that the promoter is switching between a transcriptionally active and an inactive state. This causes transcription to occur in bursts, and the mRNA distribution to deviate from Poisson, leading to a Fano factor that is larger than 1.In In lacUV5 promoter, which consists of a single operator overlapping with the promoter. Based on a simple kinetic model of repression, in which the Lac repressor competes with RNAP for binding at the promoter, we can write down the An example of the single repressor-binding site architecture is a simplified version of the PDual repression occurs when promoters contain two or more repressor binding sites. Here, we consider three different scenarios for architectures with two operators: 1) repressors bind independently to the two operators, 2) repressors bind cooperatively to the two operators and 3) one single repressor may be bound to the two operators simultaneously thereby looping the intervening DNA. At the molecular level, cooperative repression is achieved by two weak operators that form long-lived repressor-bound complexes when both operators are simultaneously occupied. Transcription factors may stabilize each other either through direct protein-protein interactions The kinetic mechanisms of gene repression for both the cooperative and independent repressor architectures are reproduced in The R promoter, which is controlled by the lysogenic repressor cI. The wild-type PR promoter consists of three proximal repressor binding sites, OR1, OR2 and OR3, with different affinities for the repressor (OR2 is ∼25 times weaker than OR1) L1, OL2 and OR3. For simplicity, we consider a simpler version of PR, harboring a deletion of the three distal operators. In the absence of these operators, the OR3 operator plays only a very minor role in the repression of this promoter, and it can be ignored R1 and OR2. The cI repressor binds cooperatively to OR1 and OR2, and that cooperativity is mediated by direct protein-protein interactions between cI bound at each operator As an example of the two repressor-binding sites architecture, we consider a simplified version of the lytic phage-λ Plac promoter is a prominent example of this architecture. The kinetic mechanism of repression characterizing this promoter architecture is presented in c have both been measured in vitro for the particular case of the Lac repressor in vivo data Repression may also be enhanced by the presence of distant operators, which stabilize the repressed state by allowing certain repressors to simultaneously bind to both distant and proximal operators, forming a DNA loop We first examine how the presence of the auxiliary operator affects the level of cell-to-cell variability in mRNA expression. In c = 100, so that c = 1, we find that the presence of the auxiliary operator leads to an increase in noise  is given by D. That is, we keep the number of repressors constant, and instead we alter the distance between the two operators. In particular, as the operator distance is changed, both the mean and the variance will change, and therefore a direct comparison between An example of this type of architecture is a simplified variant of the PTranscriptional activators bind to specific sites at the promoter from which they increase the rate of transcription initiation by either direct contact with one or more RNAP subunits or indirectly by modifying the conformation of DNA around the promoter This simple activation architecture is illustrated in The mean mRNA can be changed by adjusting the intracellular concentration of the activator. The rate at which one of the activators binds to the promoter is proportional to the activator concentration: The Fano factor can be computed using equations (5–12) and it is shown as entry 2 of Table S2 in With these equations in hand, we explore how operator strength affects noise in gene expression in the case of activation. Stronger operators bind to the activator more tightly than weak operators, leading to longer residence times of the promoter in the active state.In To get a sense of the differences between these two standard regulatory mechanisms, we compare simple repression with simple activation. In l to see  in the aAn example of simple activation is the wild-type Dual activation architectures have two operator binding sites. Simultaneous binding of two activators to the two operators may lead to a larger promoter activity in different ways. For instance, in some promoters each of the activators may independently contact the polymerase, recruiting it to the promoter. As a result, the probability to find RNAP bound at the promoter increases and so does the rate of transcription We first investigate the effect of dual activation in the limit where binding of the two transcription factors is not cooperative. Assuming that activators bound at the two operators independently recruit the polymerase, we compare this architecture with the simple activation architecture. The mechanism of activation is depicted in We then ask whether this is also true when the binding of activators is cooperative. We assume a small cooperativity factor et al. Together with the results from the dual repressor mechanism, these results indicate that multiplicity in operator number may introduce significant intrinsic noise in gene expression. Multiple repeats of operators commonly appear in eukaryotic promoters RM promoter R1, OR2 and OR3) for the cI protein, which acts as an activator. When OR2 is occupied, cI activates transcription. OR1 has no direct effect on the transcription rate, but it helps recruit cI to OR2, since cI binds cooperatively to the two operators. Finally, OR3 binds cI very weakly, but when it is occupied, PRM becomes repressed. There are variants of this promoter R3 that make it unable to bind cI. In RMRM.An example of cooperative activation is the lysogenic phage-λ PThe DNA sequence of a promoter encodes the binding sites for transcriptional regulators. In turn, the collection of these regulatory sites, known as the architecture of the promoter, determines the mechanism of gene regulation. The mechanism of gene regulation, determines the transcriptional response of a promoter to a specific input, in the form of the concentration of one or more transcription factors or inducer molecules. In recent years we have witnessed an increasing call for quantitative models of gene regulation that can serve as a conceptual framework for reflecting on the explosion of recent quantitative data, testing hypotheses, and proposing new rounds of experiments A class of stochastic kinetic models have been formulated that make it possible to calculate either the probability distribution of mRNA or proteins per cell or its moments, for simple models of gene regulation involving one active and one inactive promoter state In this paper we have explored the feasibility of this stochastic analog of thermodynamic models as a general framework to understand gene regulation at the single-cell level. Using this approach we have examined a series of common promoter architectures of increasing complexity, and established how they affect the level of cell-to-cell variability of the number of mRNA molecules, and proteins, in steady state. We have found that, given the known kinetic rates of transcription factor association and dissociation from operators, the level of variability in gene expression for many well studied bacterial promoters is expected to be larger than the simple Poissonian expectation, particularly for mRNA and short-lived proteins. We have investigated how the level of variability generated by a simple promoter consisting of one single operator differs from more complex promoters containing more than one operator, and found that the presence of multiple operators increases the level of cell-to-cell variability even in the absence of cooperative binding. Cooperative binding makes the effect of operator multiplicity even larger. We also found that operator strength is one of the major determinants of cell-to-cell variability. Strong operators cause larger levels of cell-to-cell variability than weak operators. We have also examined the case where one single repressor may bind simultaneously to two operators by looping the DNA in between. We have found that the stability of the DNA loop is the key parameter in determining whether DNA looping increases or decreases the level of variability, suggesting a potential role of DNA mechanics in regulating cell-to-cell variability.in vivo with single-cell gene expression data. Direct comparison between the model and experimental data offers an opportunity to validate these kinetic mechanisms of gene regulation.We have examined the difference between activators and repressors, and found that repressors tend to generate less cell-to-cell variability than activators at low expression levels, whereas at high expression levels repressors and activators generate similar levels of cell-to-cell variability. We conclude that induction of gene expression by increasing the concentration of an activator leads to a more heterogeneous response at low and moderate expression levels than induction of gene expression by degradation, sequestration or dilution of a repressor. In addition, we have used this model to make quantitative predictions for a few well characterized bacterial promoters, connecting the kinetic mechanism of gene regulation that we believe applies for these promoters There are two different classes of sources of cell-to-cell variability in gene expression. The first class has its origins in the intrinsically stochastic nature of the chemical reactions leading to the production and degradation of mRNAs and proteins, including the binding and unbinding of transcription factors, transcription initiation, mRNA degradation, translation, and protein degradation. The noise coming from these sources is known as intrinsic noise E. coli promoters, PRM and Plac/araB. subtilisE. coli, which drives the entrance of a fraction of cells into a lactose metabolizing phenotype The experimental evidence gathered so far indicates that intrinsic noise is the dominant source of cell-to-cell variability in bacteria of the mRNA copy number. In a recent single-molecule study, transcription was monitored in real time for two different E. coli and found that extrinsic noise was the dominant source of cell-to-cell variability in protein expressed from a variant of the PL promoter in a variety of different strains E. coli promoters involved in the biosynthetic pathway of lysine B. subtilis, and it was reported that the data could be well explained by a model consisting only of intrinsic noise At the protein level, the contribution of extrinsic and intrinsic noise to the total cell-to-cell variability has also been determined experimentally for a variety of promoters and different kinds of bacteria. The first reports examined intrinsic and extrinsic protein noise in In summary, all studies that have measured mRNA noise in bacteria so far report that intrinsic noise contributes substantially to the total cell-to-cell variability. This is further supported by observations that most of the mRNA variability comes from intrinsic sources in yeast The aim of this paper is to formulate a set of predictions that reflect the class of kinetic models of gene regulation in bacteria that one routinely finds in the literature Two recent studies measured intrinsic mRNA noise in yeast Other studies lacUV5 promoter in E. coliFinally, it is worth going back to bacteria, and discussing the only study that has yet examined the effect of a promoter-architecture motif on cell-to-cell variability in gene expression. In this paper, the authors investigated the effect of DNA looping on the total cell-to-cell variability for the PMore recently, several impressive experimental studies have measured the noise in mRNA in bacteria for a host of different promoters  can settle this question. In case the Fano factor for this distribution is not one (as expected for a Poisson distribution) this can signal a possible transcription factor-independent source of variability. The stochastic models studied here can be extended to account for this situation. For example, the promoter can be made to switch between an on and an off state, where the transcription factors are allowed to interact with promoter DNA only while it is in the on state. In this case the mRNA fluctuations produced by an unregulated promoter will not be Poissonian. One can still investigate the affect of transcription factors by measuring how they change the nature of mRNA fluctuations from this new base-line. Comparison of this extended model with single-cell transcription experiments would then have the exciting potential for uncovering novel modes of transctriptional regulation in prokaryotes.For the purpose of isolating the effect of individual promoter architectural elements on cell-to-cell variability in gene expression, we have artificially changed the value of one of those parameters, while keeping the other parameters constant. For instance, we have investigated the effect of altering the strength of an operator on the total cell-to-cell variability. In order to do this, we ask how changes in the dissociation rate of the transcription factor alter the cell-to-cell variability, given that all other rates  remain constant. This assumption is not necessarily always correct, since very often the operator sequence overlaps the promoter, and therefore changes in the sequence that alter operator strength also affect the sequence from which RNAP initiates transcription, which can potentially affect the overall rates of transcription. As is usually the case, biology presents us with a great diversity of forms, shapes and functions, and promoters are no exception. One needs to examine each promoter independently on the basis of the assumptions made in this paper, as many of these assumptions may apply for some promoters, but not for others.For the same reason of isolating the effect of promoter architecture and cis-transcriptional regulation on cell-to-cell variability in gene expression, when we compare different architectures we make the simplifying assumption that they are transcribing the same gene, and therefore that the mRNA transcript has the same degradation rate. Care must be taken to take this into account when promoters transcribing different genes are investigated, since the mRNA degradation rate has a large effect on the level of cell-to-cell variability.in vivo experiments suggest that this scenario may be important in yeast promoters containing arrays of operators We have also assumed that when transcription factors dissociate from the operator, they dissociate into an averaged out, well-mixed, mean-field concentration of transcription factors inside the cell. The possibility of transcription factors being recaptured by the same or another operator in the promoter right after they fall off the operator is not captured by the class of models considered here. Recent In spite of all of the simplifications inherent in the class of models analyzed in this paper, we believe they are an adequate jumping off point for developing an intuition about how promoter architecture contributes to variability in gene expression. Our approach is to take a highly simplified model of stochastic gene expression, based on a kinetic model for the processes of the central dogma of molecular biology, and add promoter dynamics explicitly to see how different architectural features affect variability. This allows us to isolate the effect of promoter dynamics, and develop an intuitive understanding of how they affect the statistics of gene expression.It must be emphasized, however, that the predictions made by the model may be wrong if any of the complications mentioned above are significant. This is not necessarily a bad outcome. If the comparison between experimental data and the predictions made by the theory for any particular system reveals inconsistencies, then the model will need to be refined and new experiments are required to identify which of the sources of variability that are not accounted for by the model are in play. In other words, experiments that test the quantitative predictions outlined stand a chance of gaining new insights about the physical mechanisms that underlie prokaryotic transcriptional regulation.Text S1Mathematical derivations and supplementary information. A derivation of all equations in the text is presented, together with its corresponding tables and figures.(2.04 MB DOC)Click here for additional data file.


The development of complex organisms is believed to involve progressive restrictions in cellular fate. Understanding the scope and features of chromatin dynamics during embryogenesis, and identifying regulatory elements important for directing developmental processes remain key goals of developmental biology.in vivo DNaseI sensitivity to map the locations of regulatory elements, and explore the changing chromatin landscape during the first 11 hours of Drosophila embryonic development. We identified thousands of conserved, developmentally dynamic, distal DNaseI hypersensitive sites associated with spatial and temporal expression patterning of linked genes and with large regions of chromatin plasticity. We observed a nearly uniform balance between developmentally up- and down-regulated DNaseI hypersensitive sites. Analysis of promoter chromatin architecture revealed a novel role for classical core promoter sequence elements in directing temporally regulated chromatin remodeling. Another unexpected feature of the chromatin landscape was the presence of localized accessibility over many protein-coding regions, subsets of which were developmentally regulated or associated with the transcription of genes with prominent maternal RNA contributions in the blastoderm.We used Our results provide a global view of the rich and dynamic chromatin landscape of early animal development, as well as novel insights into the organization of developmentally regulated chromatin features. The progressive restriction of cellular fate is a hallmark of development and is believed to involve the sequential modification and perpetuation of chromatin states . HoweverDrosophila melanogaster embryo is one of the best characterized systems for addressing this challenge. During the first 11 hours of development, a single diploid cell, the fertilized egg (0 hours) undergoes nuclear division to form a blastoderm of approximately 6,000 undifferentiated cells (3 to 4 hours), followed by further division and differentiation into 40,000 cells organized into specific tissues such as nerve, muscle and epithelia (11 hours) .Drosophila Genome Project; bp: base pair; ChIP: chromatin immunoprecipitation; CRM: cis-regulatory module; DDD: developmentally dynamic domain; DDE: developmentally dynamic element; DHS: DNase I hypersensitive site; DPE: downstream promoter element; DRE/DREF: DNA replication-related element; FDR: false discovery rate; INR: initiator; MTE: motif ten; ORC: origin recognition complex; TSS: transcription start site; UTR: untranslated region.BDGP: Berkeley The authors declare that they have no competing interests.ST, XL, JAS and MDB conceived and designed the experiments and analyses and wrote the paper. XL, PJS, TC and EG performed the wet laboratory experiments. ST, RS, RT, JAS and MDB analyzed the data. WF, AH and SC characterized CRM activity. All authors read and approved the final manuscript.Supplementary tables and figures.Click here for fileFDR 1% DHSs in euchromatic DNA for stage 5, 9, 10, 11 and 14 embryos . Genome coordinates of the 1% FDR DHSs at stages 5, 9, 10, 11 and 14.Click here for fileFDR 1% accessible regions in euchromatic DNA for stage 5, 9, 10, 11 and 14 embryos . Genome coordinates of the intersection of 1% FDR accessible regions at stages 5, 9, 10, 11 and 14.Click here for fileDevelopmentally dynamic domains in euchromatic DNA. Genome coordinates of DDDs from Figure Click here for file