Vibrio parahaemolyticus (Vp) is a marine halophilic bacterium that is commonly associated with oysters and shrimp. Human consumption of contaminated shellfish can result in Vp mediated gastroenteritis and severe diarrheal disease. Vp encodes two type 3 secretion systems (T3SS-1 and T3SS2) that have been functionally implicated in cytotoxicity and enterotoxicity respectively. In this study, we profiled protein secretion and temporal promoter activities associated with exsA and exsB gene expression. exsA is an AraC-like transcriptional activator that is critical for activating multiple operons that encode T3SS-1 genes, whereas exsB is thought to encode an outer membrane pilotin component for T3SS-1. The exsBA genetic locus has two predicted promoter elements. The predicted exsB and exsA promoters were individually cloned upstream of luxCDABE genes in reporter plasmid constructs allowing for in situ, real-time quantitative light emission measurements under many growth conditions. Low calcium growth conditions supported maximal exsB and exsA promoter activation. exsB promoter activity exhibited high basal activity and resulted in an exsBA co-transcript. Furthermore, a separate proximal exsA promoter showed initial low basal activity yet eventually exceeded that of exsB and reached maximal levels after 2.5 h corresponding to an entry into early log phase. exsA promoter activity was significantly higher at 30°C than 37°C, which also coincided with increased secretion levels of specific T3SS-1 effector proteins. Lastly, bioinformatic analyses identified a putative expanded ExsA binding motif for multiple transcriptional operons. These findings suggest a two wave model of Vp T3SS-I induction that integrates two distinct promoter elements and environmental signals into a complex ExsA activation framework. Vibrio parahaemolyticus (Vp) is a significant food borne human pathogen associated with severe diarrhea arising from enteric infections  or 50 μg for Escherichia coli DH5α. Sterile media supplements  were added to basal LB media from concentrated stock solutions followed by a brief 10 min equilibration prior to use in assays.Bacterial strains generated and used in this study are listed in Table luxCDABE cassette  was amplified from chromosomal DNA by PCR using primers NT387 and NT388 followed by cloning into pFLAG-CTC. A PCR using primers NT139 and NT140 and exsA/pFLAG-CTC as DNA template yielded a product containing a 5′ tac promoter and a 3′ FLAG tag fused to exsA. The product was finally blunt end cloned into pVSV105 and the resulting plasmid was introduced into Vp by conjugal mating. All plasmid constructs were subjected to DNA sequence analyses to verify cloned DNA.All PCR, cloning, and recombinant DNA techniques were performed using standard protocols. Routine DNA cloning steps were performed in DH5α, followed by introducing relevant recombinant plasmids into Vp using electroporation. Restriction endonucleases and DNA polymerases were purchased from New England Biolabs (NEB). Oligonucleotide primers were obtained from Integrated DNA Technologies (IDT) Table . pJW15 cexsB to within exsA, including the intergenic region.Total RNA was isolated from Vp using a Qiagen RNeasy kit. RNA preparations were treated with DNase and then column purified. Reverse transcription reactions using primer AL400 were performed with Omniscript reverse transcriptase (Qiagen) using the protocol described by the manufacturer. A PCR reaction using cDNA template generated from the reverse step was performed using primers AL399 and AL400. A separate PCR using Vp genomic DNA was used to generate the predicted amplicon control of 1.5 kb. The amplicon spans the partial open reading frame of 595 0.025) were grown for 2.5 h in LB supplemented with magnesium and ethylene glycol tetracetic acid (Mg+EGTA) followed by a OD595 measurement by a Biophotometer Plus (Eppendorf) at a path length of 10 mm. The samples were then aliquoted into clear bottom white walled multiwell plates (Costar #3610) and measured for light emission by counts per second (c.p.s.) using a Victor X5 instrument (Perkin Elmer). For multiple time point assays, the relevant samples were initially prepared in the same way as for the end-point assays, followed by immediate OD595 and cps reading at each time point. The data were subjected to a Two Way ANOVA with a standard Tukey test. Plotted data is the average from three experiments. Data was plotted using GraphPad Prism5 software.Luciferase reporter assays were performed similar to a previous promoter study  were harvested after 4 h of growth. Three milliliters of culture was centrifuged to remove whole cells, followed by filtering the clarified supernatant through a 0.22 μm PVDF syringe-end filter (Millipore). The resulting culture supernatant was subjected to trichloracetic acid precipitation [10% (vol/vol)] on ice. The sample was centrifuged at 16,000 × g followed by resuspending the pellet in 10 μl electrophoretic sample loading buffer (2XESB) as previously described .The Vp exsB and exsA promoters, the relevant chromosomal DNA fragments were fused to a promoterless luxCDABE gene cassette within pJW15 . This indicated that the cloned exsB and exsA promoters were functional and supported transcriptional activity when Vp was grown on LB.To investigate individual exsA-lux, we set out to evaluate exsA gene expression under a variety of media conditions at 30°C. This growth temperature was previously shown to support high levels of translocon and effector protein secretion via T3SS-I  assay experiments, real-time in situ exsA promoter activation is directly proportional to light emitted from the bacterial culture . Notably, the addition of calcium to LB media containing magnesium and EGTA lowered exsA and exsB promoter activation  implicating calcium sensing in T3SS-1 gene expression (see Discussion).ExsA is known to act at many Vp T3SS-1 operons, including those involved in forming secretion apparatus components  than that of exsA (5000–7000 units) at early time points (0–30 min). Maximal exsA and exsB promoter activation occurred at 2.5 h in growth media containing Mg+EGTA. This time point also corresponded to when the bacteria were entering into early log phase growth . The ExsA-FLAG expression plasmid or empty control plasmid was transferred to Vp harboring pexsA-lux via conjugation. The resulting bacterial strains were then subjected to lux assays. ExsA overexpression did not impact exsA promoter activity when bacteria were grown in LB supplemented with magnesium +EGTA (data not shown). When bacteria overexpressing ExsA were grown in LB only, exsA promoter activity increased after 2.5 h compared to bacteria expressing native levels of ExsA. The increase reached statistical significance after 3.5 h (p = 0.015) and continued to 4.5 h , ExsA overexpression was observed to upregulate exsA promoter activity. This result is in complete agreement with a previous observation showing increased T3SS-1 associated gene expression upon ExsA overexpression (see Discussion).A common feature of AraC family regulators is the ability to auto-activate gene expression. To investigate this possibility for Vp ExsA, we generated a plasmid encoded ExsA-FLAG expression construct, with its transcription regulated by a h Figure . These dP. aeruginosa, where exsACDE are co-transcribed as a single operon. Hence, in P. aeruginosa the transcriptional activator ExsA is co-regulated with its own inhibitor. In Vp, the gene organization is different from P. aeruginosa. Specifically, exsD has its own promoter   and vpa0451 (a chaperone gene co-transcribed with vpa0450), revealed DNA sequences consistent with our prediction. Specifically, considerable identity to the binding motif (TTTAGN4TT), along with an A-box and a G/C box  and we provide evidence in this study that exsA (vp1699) and exsD (vp1698) are regulated by ExsA. Putative expanded ExsA binding sites are located upstream of chaperone and effector gene pairs .Next, we considered that the observed increase in T3SS-1 associated proteins and effectors under LB+Mg+EGTA growth conditions Figure  likely rexsA gene expression. Specific growth conditions produced differential exsA promoter activity. Maximal exsA gene expression correlated with elevated T3SS-1 protein secretion levels when Vp was grown in media supplemented with Mg+EGTA at 30°C. In contrast, exsA gene expression and T3SS-1 activity was minimal (baseline) when Vp was cultured in the presence of calcium. We identify that ExsA serves as an auto-activator, while at the same time positively regulates gene expression of exsD, an inhibitor of ExsA activity. Lastly, combining known features of DNA binding motifs from Vp ExsA, P. aeruginosa ExsA, and Yersinia VirF, fresh bioinformatic analyses identified a putative expanded Vp ExsA DNA binding motif linked to multiple T3SS-1 genetic operons. The findings indicate that Vp ExsA is a key entry level regulator of T3SS-1 gene expression and that multiple genetic and environmental factors contribute to exsA gene expression levels.In this study, we profiled two distinct promoter elements that control exsA  was at low baseline levels. In sharp contrast, Vp growth in Mg+EGTA significantly upregulated exsA promoter activity. Hence, growth conditions can impact exsA gene expression levels with consequences for T3SS-1 activity.A clear finding from this study is that high T3SS-1 secreted protein levels correlate with an increase in  Figures , 7. We h Figures , althougexsA gene expression is elevated at 30°C compared to 37°C  located upstream of these genes , Pseudomonas (ExsA) and Vp (ExsA) (Wattiau and Cornelis, exsA can complement a P. aeruginosa exsA mutant (King et al., The 11 identified genetic loci in Figure exsA expression. Growth media and temperature were found to impact on T3SS-1 activity, which was linked to exsA expression levels. ExsA auto-activates is own expression, whereas its negative regulator ExsD is expressed under similar conditions. ExsA is therefore a central transcriptional activator that responds to environmental stimuli to regulate T3SS-1 associated genes. The results suggest that temporal ExsA cellular levels exhibit fine tuning regulatory roles resulting in differential T3SS-1 activity.In summary, Vp was found to have two distinct mechanisms to initiate The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Germ, a computational method that estimates the likelihood that any two narrowly defined genomic locations are jointly occupied by RNA Polymerase II. Germ takes a blind deconvolution approach to simultaneously estimate the likelihood of RNA Polymerase II occupation as well as a model of the arrangement of read alignments relative to locations occupied by RNA Polymerase II. Both types of information are utilized to estimate the likelihood that RNA Polymerase II jointly occupies any two genomic locations. We apply Germ to RNA Polymerase II ChIA-PET data from embryonic stem cells to identify the genomic locations that are jointly occupied along with transcription start sites. We show that these genomic locations align more closely with features of active enhancers measured by ChIP-Seq than the locations identified using the ChIA-PET Tool. We also apply Germ to RNA Polymerase II ChIA-PET data from motor neuron progenitors. Based on the Germ results, we observe that a combination of cell type specific and cell type independent regulatory interactions are utilized by cells to regulate gene expression.RNA Polymerase II ChIA-PET data has revealed enhancers that are active in a profiled cell type and the genes that the enhancers regulate through chromatin interactions. The most commonly used computational method for analyzing ChIA-PET data, the ChIA-PET Tool, discovers interaction anchors at a spatial resolution that is insufficient to accurately identify individual enhancers. We introduce  Regulatory regions that are scattered throughout the genome control the differential expression of genes in different cell types. One of the most well characterized types of regulatory regions is the enhancer . TranscrA single gene may be regulated by multiple enhancers in the same cell type, and such regulatory relationships have been shown to span large genomic distances . MethodsGerm. This method utilizes a blind deconvolution step to model the positional noise in read pair alignments relative to locations of protein occupancy directly from the data. Another benefit of the blind deconvolution step is that a detailed model of the distribution of PolII occupancy is obtained simultaneously with the model of positional noise. Germ utilizes both models obtained through blind deconvolution to inform a model of joint protein occupancy which reflects the likelihood that any two genomic locations are simultaneously occupied by a single PolII instance. Such joint occupancy events reflect underlying chromatin interactions that involve PolII.To identify active enhancers and assign them to the genes that they regulate, we analyzed ChIA-PET  data forGerm, information about the occupancy of the protein is not used to refine the locations and sizes of the regions identified to be involved in chromatin interactions. Also, the ChIA-PET tool does not explicitly model the positional noise of read pair alignments relative to locations of protein occupancy other than by extending aligned locations by a heuristically determined number of base pairs.The most common approach to analyzing ChIA-PET data is implemented by the ChIA-PET Tool . This apSprout [Sprout assumes that proteins occupy point locations and that ChIA-PET data reflect interactions only between such point locations. This assumption works well for factors such as CTCF that bind to the genome in a punctate fashion. PolII, however, is observed to occupy regions of variable width which are not accurately modeled by point locations. The assumption made by Sprout allows statistical power to be gained when modeling punctate binding factors while causing information to be lost when modeling PolII data. Germ preserves more detailed models of protein occupancy resulting in less loss of information. A benefit of this approach is that the density of protein occupancy can be queried for any location, not just the set of point locations that Sprout would identify as occupied.We previously developed a method for analyzing ChIA-PET data called Sprout . Sprout Germ to interact with TSSs exhibit stronger enrichment for properties of active enhancers than corresponding locations discovered by the ChIA-PET Tool. Furthermore, the distal locations discovered by Germ to interact with TSSs align with locations enriched for active enhancer properties with very high spatial resolution. These findings support the analysis of PolII ChIA-PET data with Germ as a useful approach for identifying the locations of active enhancers at high resolution as well as pairing the identified enhancers with their regulatory targets.We examined ChIP-Seq data for several enhancer-related factors to demonstrate that locations that are distal to annotated transcription start sites (TSSs) and are determined by By measuring transcription levels using RNA-Seq, we show that the number of enhancers that a gene interacts with is correlated with greater levels of transcription. We provide evidence that genes switch the enhancers that they interact with and that enhancers that are actively utilized in both cell types may in some cases switch the genes that they regulate. Finally, we compare the enhancers used by genes in embryonic stem cells (ESCs) and motor neuron progenitors (pMNs) and observe that cell type specific enhancers are enriched for cell type appropriate transcription factor motifs.Germ is a novel method for analyzing ChIA-PET data that presents a detailed view of the occupancy of the genome by a protein of interest. Germ accomplishes this by modeling the distribution of self-ligation read pairs as a convolution of a model of the fragmentation process and an estimate of the marginal distribution of protein occupancy. The estimated marginal distribution is then used to inform the estimation of the joint distribution of protein occupancy. The estimated joint distribution reflects a detailed view of the likelihood that pairs of genomic locations are simultaneously occupied by a protein of interest.Germ first estimates a two dimensional distribution over genomic coordinates that models the alignment of self-ligation read pairs  be the distance between the aligned locations of the ends of -+ read pair ri. The likelihood that -+ read pair ri was produced by self-ligation according to d(ri) can be expressed in terms of quantities that can be estimated from the dataLet d(ri)) for all -+ read pairs can be estimated by applying an unweighted kernel approachPr of The ad pairs ∑i∑j12h-d(ri)|zi = self) directly for the same reason that we cannot estimate the self-ligation read pair distribution directly. We can estimate Pr(d(ri)|zi = inter) directly because all non -+ read pairs are produced by inter-ligation. We also apply an unweighted kernel approach to estimate this distributionWe cannot estimate Pr(hnon−+ by approximately minimizing the We choose an appropriate d(ri)) and Pr(d(ri)|zi = inter), we can estimate Pr(d(ri)|zi = self) by assuming that Pr(d(ri)) is a mixture of the distributions Pr(d(ri)|zi = self) and Pr(d(ri)|zi = inter)Given estimates for Pr(By rearranging the terms in this equation we can obtainzi = self) = 1 − Pr(zi = inter). We assume that the average number of read pairs with each of the three strand orientations other than -+ is a good estimator for the number of -+ read pairs that were produced by inter-ligation. We use this information to estimate Pr(zi = inter)The final missing component is Pr(z = self|d(ri))K2 is a bivariate standard Gaussian distribution with no correlation between the dimensions. To choose an appropriate bandwidth hself we approximately minimize This allows us to estimate the self-ligation read pair distribution using a weighted kernel approach weighted by Pr(RSF). If we let q be the genomic location occupied by the protein,We assume that the self-ligation read pair distribution is the result of the convolution of the marginal distribution of protein occupancy and a distribution that models DNA fragmentation which we will refer to as the read spread function  that is induced by the flaws in the lens. The PSF specifically describes the effect that the lens flaws will have on a theoretical point source of light. In our case, the RSF describes the manner in which self-ligation read pairs are likely to be distributed given the theoretical occupancy of the protein at a genomic location.Simultaneously deconvolving the marginal distribution of protein occupancy and the RSF is known, the marginal distribution of protein occupancy can be approximately recovered using a standard approach known as Richardson-Lucy (RL) deconvolution [If we assume at first that the volution , 24. Theq = u) and preserves the non-negativity and sum of the initial guess Pr0(q = u). To extend RL deconvolution to the blind case, we take an approach similar to that proposed in [RL deconvolution has been shown empirically to converge to a maximum-likelihood estimate for Pr with the lower coordinate end of the read pair r(1) and the higher coordinate protein location q(2) with the higher coordinate end of the read pair r(2).Chromatin looping allows proteins to simultaneously occupy two genomic locations . Inter-lThe last equality reflects an assumption that we make that the location occupied by the protein is independent of the read pair end that it is not associated with. We will demonstrate that these terms are non-zero in only a relatively small window around their associated read pair end and that the non-associated read pair end has minimal effect on the manner in which we compute these terms. We transform the first term within the sum into quantities that we can compute using Bayes’ TheoremRSF that was estimated during the blind deconvolution step. For read pair ends that align to the—strandWe assume that we can obtain Correspondingly, for read pair ends that align to the + strandq(1) = u) is the distribution of protein marginal occupancy that was estimated in the previous step. The prior read distribution Pr and q(2) are independent. This is clearly not true, since otherwise we would have no need of estimating their joint distribution. But, since v, the purpose of Pr(q(2) = v|q(1) = u) is mainly to fine tune the probability that q(2) = v if q(2) = v|q(1) = u) to roughly agree with peaks of Pr(q(2) = v) if they exist, and so we assume that we can swap one for the other in this case. Finally, we assume that The approximation in  incorporThese transformations allow us to write the estimated joint distribution of protein occupancy asX. For example, when analyzing RNA PolII ChIA-PET data, a common query might be to detect regions that are jointly occupied by RNA PolII along with a location from set of annotated transcription start sites (TSSs). If we define TSS to be a set of annotated TSSs, we refer to GermTSS as the process of estimating Pr only for v ∈ TSS.In many situations we are interested in estimating the joint occupancy of a protein with a set of genomic locations reg of size w base pairs, let Z ∼ Binomial and M is the size of the mappable genome, we then evaluate the significance of the protein occupancy within reg as Pr(Y > Z). In other words, we calculate the probability that more self-ligation read pairs would be associated with reg according to a uniform distribution of protein occupancy than would be associated with reg according to the estimated distribution of protein occupancy.Once we have estimated distributions of marginal and joint protein occupancy from ChIA-PET data we evaluate the significance of the estimated protein occupancy within a given region or the joint occupancy within a given pair of regions. We describe our approach as applied to a marginal distribution of protein occupancy and then extend the approach to joint distributions. Given a genomic region rega and regb, let Z ∼ Binomial and Y ∼ Binomial, we then evaluate the significance of the joint protein occupancy of the regions rega and regb as Pr(Y > Z).We extend this approach to evaluating the significance of pairs of regions according to a joint distribution of protein occupancy. Given a pair of regions v ∈ X that is obtained by applying GermX is void of mass for much of its domain. This is because not enough inter-ligation read pairs can be sequenced to fully explore this space given current technologies. Without considering the mass that is missing from the estimate of vi ∈ X and let τi that is missing from ti, then we can find a setting of the τi such that τi and larger values of the τi will cause portions of the estimated distribution to be evaluated as less significant.The estimate τi we introduce a procedure that allows us to choose τi large enough to avoid overestimating the significance of portions of the estimated distribution. We first choose a set of candidate regions for each vi ∈ X which we will evaluate for significance based on f and adding a region reg to the set for vi if imax such that ∀i, timax ≥ ti. We choose some c > 1 and set τimax = (c − 1)timax. We hold τimax fixed and apply an iterative procedure to find settings for τi (i ≠ imax) such that i ≠ imax and computeTo choose an appropriate setting of the f in the following way. For a region reg in the set for vi we let Z ∼ Binomial and Y ∼ Binomial, the significance of the estimated joint protein occupancy of vi and reg is Pr(Y > Z). We evaluate the significance of the regions in the sets for all v ∈ X and identify the regions that have an associated Pr(Y > Z) less than some threshold such as 0.05. We call these regions significant. For each region, we also note the number of read pairs in Rinter that contributed to p for that region. If the ratio of the number of significant regions supported by only one read pair to the total number of significant regions is greater than some target threshold, such as 0.1, we increase c and begin the process of finding a new set of τi. If there are too few significant regions supported by one read pair with Pr(Y > Z) < 0.05 we reduce c and find new τi. In this manner we search for c that achieves a target fraction of weakly supported jointly occupied regions within the set of all regions that evaluate as significant.Once this converges, we evaluate the significance of the regions defined using the threshold Germ to PolII ChIA-PET data from ESCs [Germ detects as interacting with TSSs we found that these locations align closely with locations that appear to be active enhancers. We incorporated a set of annotated TSSs from the UCSC knownGene database to profile the occupancy of PolII conditioned on the locations of the annotated TSSs. For each TSS, Germ provided a set of regions that are jointly occupied by PolII along with the TSS. The joint occupation of a region with a TSS by PolII indicates that this region is spatially proximal to the TSS and that PolII is also present at the junction between the region and the TSS. PolII tends to occupy relatively broad regions of the genome, but upon examining the distributions of PolII occupancy that we estimate with Germ, we observed that regions of elevated occupancy generally contain locations with locally maximal likelihood of occupancy. We noted the location within each TSS-interacting region that Germ determines to be the most likely anchor point for the interaction. As shown in Germ estimated anchor points are informative in that they align closely with maximal locations of enrichment for active enhancer-related ChIP-Seq data.We applied rom ESCs  to identGerm results. We obtained the set of interactions called by the ChIA-PET Tool from the same ChIA-PET data and filtered out the interactions that do not contain a TSS within either anchor region. Since the ChIA-PET Tool interactions do not include estimates of the most likely locations within the anchor regions that are jointly occupied by RNA PolII, we chose the midpoint of each anchor region as the approximate maximally occupied location. We further filtered the interactions to identify the set of interactions that contain a TSS within one anchor region and for which the midpoint of the other anchor region is at least 2kb away from any TSS. As shown in Germ. To quantify the enhancer properties at the locations identified by Germ and the ChIA-PET Tool we identified 500 bp windows centered on the locations identified by the two methods. We examined the significance of enrichment for each of the ChIP-Seq data within each of the identified windows as shown in Germ identified 2924 and the ChIA-PET Tool identified 3098). The greater percentage of significantly enriched locations within the Germ identified locations for all of the ChIP-Seq data emphasizes the usefulness of analyzing PolII ChIA-PET data with Germ for the purpose of identifying active enhancers.The difficulty in extracting locations that interact with TSSs from results obtained using the ChIA-PET Tool highlights the superior informativeness of Germ identifies TSS-interacting locations that align closely with enhancer related ChIP-Seq data, we decided to investigate whether the interactions detected by Germ appear to influence the expression levels of the genes involved. We performed PolII ChIA-PET with motor neuron progenitors (pMNs) and applied Germ in order to characterize enhancers that are differentially utilized between pMNs and ESCs. We also performed RNA-Seq to profile transcription levels of genes in both cell types. We hypothesized that the interactions that Germ identifies between TSSs and locations that are more than 2 kb away from any TSS reflect functional interactions between enhancers and promoters. We call such interactions TSS-nonTSS interactions. As shown in Since Germ identifies as involved in TSS-nonTSS interactions to the levels of the genes that are involved in TSS-nonTSS interactions in ESCs. As shown in The observed correlation between TSS-nonTSS interactions and transcription levels led us to ask whether the existence of nearby active enhancers is enough to induce a TSS-nonTSS interaction and increase transcription levels or if active enhancers specifically target genes that are not necessarily the closest gene. We compared the transcription levels of the genes closest to the locations that Germ identifies interact with anywhere from one to a hundred or more distinct TSSs. We wondered whether enhancers that target more genes exhibit stronger enhancer characteristics. We collected the locations that interact with TSSs according to Germ in either ESCs or pMNs. We grouped these locations based on the number of TSS-nonTSS interactions in which they are involved in ESCs. As shown in We observed that TSS-interacting locations that Germ identifies as TSS-interacting are active enhancers, we decided to investigate whether the sequence context of Germ identified enhancers reflects their cell type specificity. We grouped the Germ identified enhancers according to their cell type utilization resulting in 2,217 enhancers that are only utilized in ESCs, 950 that are only utilized in pMNs, and 314 that are utilized in both cell types. We tested for the presence of several sequence motifs corresponding to the binding preferences of several transcription factors that are relevant to one or both cell types in 1 kb windows centered on the enhancer locations. We observed interesting patterns of motif presence for many of the factors as shown in Given the evidence that we collected that indicate that the locations that Germ algorithm to ChIA-PET data successfully recovers genomic regions that are enriched for enhancer-related ChIP-Seq data. Their identity as enhancers is further supported by the observation that the interactions that we identify between these regions and TSSs are correlated with transcription levels. Technologies for profiling chromatin interactions genome-wide such as ChIA-PET, Hi-C, and 5C have yet to reach maturity and present analytical challenges such as inherently high false negative rates. Our observations suggest that gene regulation by long-range chromatin interactions with enhancers is a highly dynamic process. Genes that are expressed in more than one cell type may utilize different enhancers to maintain or adjust their expression. This hypothesis is supported by the observation that differentially utilized enhancers contain varying sets of motifs that are recognized by cell-type appropriate transcription factors. The observation that the relationships between enhancers and genes may be not fixed between cell types has been previously noted [We have demonstrated that applying the ly noted , althougly noted –37 whichβ-mercaptoethanol and 100 U ml-1 leukemia inhibitory factor. Motor neuron differentiation of ESCs was performed as previously described [μM all-trans retinoic acid (Sigma) and 0.5 μM Smo agonist of hedgehog signaling .Hb9::GFP transgenic mouse-derived (HBG3) ESCs were cultured over a layer of neomycin resistant Mitomycin-C-treated fibroblasts (Millipore) in EmbryoMax D-MEM (Millipore) supplemented with 15% ESC-grade fetal bovine serum (Thermo Fisher), l-glutamine (Gibco), 0.1 mM escribed . Brieflyw represent the width of the region, M represent the size of the mappable genome, p be the enrichment in the region, N be the number of uniquely mapped reads in the dataset, Z ∼ Binomial, and Y > Z).ESC ChIP-Seq sequence data were obtained for H3K27ac, Med1, Med12, Smc1a, and p300 , 8. SequTotal RNA from mouse embryonic stem cells or motor neuron progenitors was isolated using Trizol Reagent (Invitrogen). mRNA was isolated and strand specific RNA-Seq was performed following the Illumina Truseq protocol. Read pairs were aligned to the mouse genome (version mm10) using STAR . FragmenChIA-PET experiments were performed as previously described. Briefly, on the appropriate day of differentiation, embryoid bodies were dissociated in trypsin into single cell suspension. Cells were cross-linked using 1% formaldehyde. Cross-linked chromatin was fragmented by sonication to a size of approximately 300bp. Chromatin complexes were immunoprecipitated with monoclonal anti-RNAPII  coated protein G Dynabeads (Life Technologies). A small portion of ChIP enriched DNA was eluted from beads for quantification. To prepare ChIA-PET libraries DNA was end polished with T4 DNA polymerase (NEB). To assess the degree of intermolecular proximity ligation end polished DNA was divided into 2 aliquots and each ligated to linkers (A or B). The two samples were then joined together for proximity ligation under dilute conditions. Following ligation samples were treated with Mme1 to release paired end tag (PET) constructs. PET constructs were amplified and submitted to sequencing on Illumina Genome Analyzer II.https://github.com/christopherreeder/germ.Complete Java source code is available from


MYC is a basic domain helix-loop-helix leucine zipper (bHLH-LZ) transcriptional regulator that forms a heterodimer with the related but much smaller bHLH-LZ protein Max to bind sequence-specifically to DNA. In the last few years, the understanding of MYC action has undergone a complete transformation. Originally thought of as a conventional transcription factor with a large but limited number of distinct target genes, MYC has emerged as a transcriptional amplifier, whereby global transcription is tuned to specific cellular levels of MYC. Evidence for this universal involvement of MYC in transcription was originally obtained in studies of the protein-coding transcriptome , 2. HoweIt has been known for some time that MYC can affect the transcription of short noncoding RNAs such as microRNAs . HoweverThe reach of MYC to all of the non-coding transcriptome constitutes a huge, new expansion of MYC's sphere of influence. Considering that the vast majority of all transcribed sequences is non-coding, their inclusion among the transcriptional clients of MYC amounts to multiplying the MYC universe.The effect of MYC on non-coding transcripts is correlated with MYC binding to promoter-proximal sites and therefore appears to be a direct function of MYC, not mediated by a different downstream transcriptional regulator. Although MYC binding to transcriptional start sites is identical in different cell types, the resulting expression patterns are cell type-specific. This surprising observation is unexplained at this time and requires further analysis. As is the case with the coding transcriptome, MYC can function as a positive as well as negative regulator of transcription. The genome-wide effects of MYC on the non-coding transcriptome were discovered by RNAseq analysis. However, for selected genes, these data have been independently validated for specific lncRNAs by determining steady-state RNA levels by qRT-PCR and direct transcriptional effects by nuclear run-on experiments. This data is augmented by visualization with global epigenetic and MYC ChIP data.The emergent importance of the non-coding transcriptome leads to the necessary question of how this class of RNA is transcriptionally regulated. MYC can now be considered to be an important, but not the only player, in this network of key regulators. We are at the cusp of a much more detailed analysis of regulatory mechanisms. MYC is also arguably the "Emperor of Oncogenes", involved as a driving force in most and probably all human cancers. A complete understanding of the mechanisms by which MYC achieves these oncogenic effects remains a daunting challenge, a challenge that has been considerably magnified by the regulatory role of MYC in the noncoding transcriptome. Exciting new genome editing and modulatory technologies will likely be adopted to generate a first compendium of cancer-relevant lncRNAs, while providing hints of their underlying function in the cell. It is a task that promises to provide critical new insights into the molecular biology of cancer.


Tyr, 5S rRNA and 7SL RNA genes. Depletion of EZH2 or inhibition of EZH2 methyltransferase activity led to upregulation of Pol III target gene transcription. EZH2-mediated repression of Pol III transcribed gene expression requires presence of SUZ12. SUZ12 was able to interact with TFIIIC complex and knockdown of SUZ12 decreased occupancy of EZH2 and H3K27me3 at the promoter of Pol III target genes. Our findings pointed out a previously unidentified role of PRC2 complex in suppressing transcription of Pol III transcribed non-translated RNA genes, putting Pol III on a new layer of epigenetic regulation.Polycomb repression complex 2 (PRC2) component EZH2 tri-methylates H3K27 and exerts epigenetic repression on target gene expression. EZH2-mediated epigenetic control of RNA polymerase II (Pol II) transcribed coding gene transcription has been well established. However, little is known about EZH2-mediated epigenetic regulation of RNA polymerase III (Pol III) transcription. Here we present a paradigm that EZH2 is involved in the repression of Pol III transcription via interaction with transcriptional factor complex IIIC (TFIIIC). EZH2 and H3K27me3 co-occupy the promoter of tRNA Polycomb group proteins are important epigenetic regulators that play crucial roles in embryonic development and differentiation ,2. EZH2 Overexpression of EZH2 is associated with progression of various tumors, including prostate and breast cancers ,12. RecePol III is responsible for the transcription of a series of small non-translated RNAs including transfer RNA (tRNA), the smallest subunit of ribosome (5S rRNA) and 7SL RNA. Transcriptional factors of Pol III contain TFIIIA, TFIIIB and TFIIIC. . A varieThe fast development of deep sequencing technology allows large scale detection of modifications for genes coding small RNAs, including those transcribed by Pol III. ChIP-seq data mining in several studies suggests that modulation of Pol III Histone marks that were used to be found at promoters of Pol II transcribed genes are also present at promoters of Pol III target genes –28. Rese2. SUM159 and MDA-MB-231 cells were treated with DZNep (Millipore) at different concentrations from 1 to 10 μM for 24 to 48 h before harvest.The human cervical cancer cells HeLa and breast cancer cells MCF7, MDA-MB-231 and SUM159 were obtained from ATCC (American Type Culture Collection). All cells were grown in RPMI 1640 or Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum (FBS) and cultured at 37°C with 5% COThe pCMV6-Myc-DKK-GTF3C3 expression plasmid was purchased from OriGene . The Flag-EZH2 plasmid was kindly provided by Professor Wei-guo Zhu (Peking University). The Flag-EZH2ΔSET plasmid was obtained by cloning the N-terminal 609 amino acids into the 3× Flag expression vector (Sigma). All constructs were confirmed by DNA sequencing. Transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instruction. For transient gene silencing, small interfering RNAs were transfected into cells using RNAimax transfection reagent (Invitrogen) according to the standard protocol. Target sequences for transient silencing were: 5′-GCUGGGACAUGUACAAUGU-3′ (siRNA 1) and 5′-GGUCGCAGAUGUGUAUAAU-3′ (siRNA 2) for EZH2, 5′-GUCGCAACGGACCAGUUAA-3′ (siRNA 1) and 5′-GACUACAGAUCUACAAACA-3′ (siRNA 2) for SUZ12.Cells were lysed with NP-40 buffer ). Antibodies against actin (Zhongshan Golden Bridge Biotechnology), EZH2 , TFIIIC102 (Santa Cruz sc-101176), TFIIIC110 (Santa Cruz sc-81406), TFIIIC63 (Santa Cruz sc-134082), SUZ12 (CST #3737), H3 (CST #9715), H3K27me3 (Abcam ab6002) were purchased from commercial sources.To identify new EZH2 interacting proteins, co-immunoprecipitation (Co-IP) assays were performed using EZH2  antibody and normal rabbit IgG. Proteins co-immunoprecipitated by EZH2 antibody were separated by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and then stained by Coomassie blue. The corresponding bands were subjected to in-gel trypsin digestion. The labeled peptides were analyzed by LC-MS/MS using Nano-LC combined with Orbitrap Q Exactive mass spectrometer .MCF7 and SUM159 cells were lysed with NP-40 buffer and lysates were incubated with relevant antibodies overnight at 4°C. Normal rabbit or mouse IgG was used as negative controls. Then 50 μl protein A/G-Sepharose was added to the mixture and incubated for 2 h. The immunoprecipitated complexes were washed with NP-40 buffer for 4× and then subjected to SDS-PAGE and immunoblot analysis with the indicated antibodies. The antibodies used for immuoprecipitation assays were EZH2 (CST #4905), SUZ12 (CST #3737), TFIIIC102 (Santa Cruz sc-101176) and TFIIIC110 (Santa Cruz Santa Cruz sc-81406).Escherichia coli BL21 and purified with Glutathione Sepharose 4B beads (Pharmacia Biotech). Then the Glutathione Sepharose 4B beads and the purified proteins were mixed with lysates of HeLa cells transfected with Flag-TFIIIC102 or Flag-EZH2 plasmids and then incubated at 4°C over night. After being washed for 4× by NP-40 buffer, the co-immunoprecipitated proteins were subjected to western blot analysis.The DNA fragments for the N-terminal region (1–522), Cys-Rich (523–609) domain and SET domain (610–746) of EZH2 were amplified with polymerase chain reaction (PCR). To obtain the glutathione S-transferase (GST) fusion proteins of EZH2, the above DNA fragments were subcloned into pGEX-4T-1 vector . The GST fusion proteins of TFIIIC102 were obtained with similar methods. GST and GST fusion proteins were expressed in 7) were treated with 1% formaldehyde for 10 min at 37°C and then 0.125M glycine was added and incubated for 5 min at room temperature. Cells were lysed with lysis buffer  on ice. The cell lysates were sonicated on ice for 10× with 6 s duration each time and the length of the DNA fragments were controlled at about 200–1000 bp. After centrifugation at 14 000 rpm for 15 min at 4°C, the lysates were diluted with nine volume of dilution buffer . A total of 100 μl of the diluted lysate was spared as input and stored at 4°C until the step for reverse cross-linking. Immunoprecipitation was carried out with antibodies specific to EZH2 (Millipore), H3K27me3 (Abcam) and normal mouse IgG as a control and incubated overnight at 4°C. After immunoprecipitation, 20 μl magnet beads (Invitrogen) were added and incubated for 2 h. The magnet beads were then sequentially washed with TSE I , TSE II  and TSE III , and washed twice with TE buffer before being eluted with the elution buffer (1% SDS and 0.1 M NaHCO3). The eluates were incubated for 6–8 h at 65°C to reverse the formaldehyde cross-linking. DNA fragments were purified by using the PCR Purification Kit (Qiagen). Quantitative PCR (qPCR) was performed using Roche reagents. Each value was normalized to its respective input. The specific PCR primer pairs for ChIP were described in Supplementary Table S2.MCF7, SUM159 or MDA-MB-231 cells  according to the standard protocol and 2 μg total RNA was used for cDNA synthesis with random primers (Genestar). The cDNA was added to a qPCR mixture that contained 2× SYBR Green PCR master mixes (Roche) and 10 μM gene-specific primers. Assays were performed in triplicate. The PCR steps included incubations for 10 min 95°C, followed by 40 cycles, each cycle consisting of 15 s at 95°C and 1 min at 60°C. The expression levels of snRNAs were normalized with acidic ribosomal phosphoprotein P0 (ARPP P0). Primers used were described in Supplementary Tables.http://genome.ucsc.edu/). The ChIP signaling peaks between −1000 to +1000 from the TSS (Transcription start site) of the randomly chosen tRNA genes were captured from USCS genome browser and drawn into graphs.ChIP-seq dataset of histone marks including H3K4me2, H3K4me3, H3K9ac, H3K9me3 and H3K27me3 was acquired from Encyclopedia of DNA Elements (ENCODE) at UCSC  of results from three independent experiments. Statistical significance was determined by the two-tailed Student EZH2 is an important methyltransferase that is frequently overexpressed in breast and prostate cancers and is associated with tumor growth and invasion ,12,29. Ain vitro pull down assays using purified proteins including fragment A (aa 1–522) of EZH2 fused with GST and fragment C (aa 524–886) of TFIIIC102 fused with MBP. It was demonstrated that GST-EZH2 (aa 1–522) interacted directly with the C-terminal fragment C of TFIIIC102 . Collectively, our results indicated that EZH2 interacts with TFIIIC102 and the interaction is mediated by the N-terminal region of EZH2 and the C-terminus of TFIIIC102.To map the binding regions between EZH2 and TFIIIC102, EZH2 was divided into three fragments and fused with GST separately Figure . FragmenTFIIIC complex is composed of five main subunits including TFIIIC220, TFIIIC90, TFIIIC102, TFIIIC110 and TFIIIC63, which are named according to their molecular weight . To examTyr, 5S rRNA and 7SL RNA  did not influence the expression of TFIIIC components in neither of the two breast cancer cell lines MCF7 and SUM159 , indicating EZH2 could not regulate TFIIIC expression. EZH2 has been reported to mediate trimethylation of histone 3 lysine 27 at the target gene promoters, thereby leading to chromatin condensation and transcriptional repression ,7. This A Figure  and c. IA Figure  and c. CA Figure  and d. TA Figure ,35, as wA Figure  and f. TTo examine whether occupancy of EZH2 at Pol III targets can be affected by TFIIIC complex, components of TFIIIC complex including TFIIIC63, TFIIIC102 and TFIIIC110 were knocked down separately with small interfering RNAs and then ChIP assays using an EZH2 antibody were performed followed by qPCR analysis. Results showed that EZH2 occupancy on Pol III transcribed genes was largely reduced upon TFIIIC knockdown Figure , d and fTRNA_Tyr and the active tRNA gene TRNA_Leu  and serum-starved (0% FBS) HeLa cells and checked the expression of EZH2 and tRNAs. We found that when HeLa cells were serum-starved, the expression of tRNAs and EZH2 were both decreased (Supplementary Figure S5c, Figure Tyr, 5S rRNA and 7SL RNA (Figure The presence of EZH2 and H3K27me3 at promoters of Pol III transcribed genes, especially on the inactive genes, strongly suggested that EZH2 may regulate Pol III directed gene transcription through trimethylation of histone H3 at lysine 27. To test this hypothesis we examined the role of EZH2 in both gain- and loss-of -function experiments. For gain-of-function experiment, ectopic expression of wild-type EZH2 led to reduced production of Pol III target genes while the Flag-EZH2ΔSET mutant displayed no significant effect on Pol III transcription Figure  and b. TA Figure  and d. TThe aforementioned findings suggested a requirement of EZH2 methyltranferase activity for inhibition of Pol III target gene transcription. We therefore scrutinized this possibility by using DZNep, a commonly used potent EZH2 inhibitor . DZNep hSUZ12 has been indicated to play a role in guiding the PRC2 complex to the target gene promoters and is essential for the methylation function of PRC2 complex ,38. GiveTo test whether SUZ12 is required for trimethylation of H3K27 by EZH2 at promoters of Pol III transcribed genes, we transfected SUM159 cells with control or SUZ12 siRNAs and then performed ChIP assays to detect the occupancy of EZH2 and H3K27me3 at the target gene promoters. Interestingly, knockdown of SUZ12 greatly affected EZH2 and H3K27me3 occupancy at the promoter of Pol III target genes Figure , suggestTyr, 5S rRNA and 7SL RNA. It was known that epigenetic marks H3K4me2, H3K4me3 and H3K9ac were enriched at the promoters of active tRNA genes; whereas at the promoters of inactive tRNA genes, epigenetic marks H3K27me3 and H3K9me3 were enriched (In the present report we uncovered a novel role of EZH2, as a key epigenetic regulator, in controlling transcription of a subset of small RNA genes transcribed by RNA polymerase III including tRNA, 5S rRNA and 7SL RNA. EZH2 has been known to repress the expression of RNA polymerase II transcribed genes –43. For enriched . In lighenriched . This moenriched , suggestEpigenetic regulation of RNA polymerase II has been well established. However, epigenetic regulation of transcription mediated by RNA polymerase III is still unclear although researchers have found epigenetic marks are also present at promoters of Pol III transcribed genes. C-Myc, for example, was found to be able to trigger Pol III transcription through recruiting acetyltransferase GCN5 and TRRAP  Figure . H3Ser10p16INK4a (It was demonstrated that forced reduction of c-Myc expression results in increased expression of H3K27me3 mark in mouse and human prostate cancers . Meanwhip16INK4a . Furtherp16INK4a ,21 and pp16INK4a ,21. ThesIt was reported that the expression level of TFIIIC components and production of tRNAs were both elevated in tumors than in normal tissues, noted that tumors require more materials for protein synthesis and cell growth ,49. GeneIn summary, we demonstrated that PRC2 component EZH2 and SUZ12 repress RNA polymerase III transcribed non-translated RNA gene transcription through interacting with and being recruited by transcriptional factor IIIC. Both EZH2 and H3K27me3 are present at the promoters of Pol III target genes and suppress their expression. Our findings provide evidence that PRC2 complex regulate RNA polymerase III transcribed gene expression in mammalian cells.Supplementary Data are available at NAR Online.


Chromatin looping plays a pivotal role in gene expression and other biological processes through bringing distal regulatory elements into spatial proximity. The formation of chromatin loops is mainly mediated by DNA-binding proteins (DBPs) that bind to the interacting sites and form complexes in three-dimensional (3D) space. Previously, identification of DBP cooperation has been limited to those binding to neighbouring regions in the proximal linear genome (1D cooperation). Here we present the first study that integrates protein ChIP-seq and Hi-C data to systematically identify both the 1D- and 3D-cooperation between DBPs. We develop a new network model that allows identification of cooperation between multiple DBPs and reveals cell-type-specific and -independent regulations. Using this framework, we retrieve many known and previously unknown 3D-cooperations between DBPs in chromosomal loops that may be a key factor in influencing the 3D organization of chromatin. The formation of chromatin loops is mainly mediated by DNA-binding proteins (DBPs) that bind to the interacting sites and form complexes in 3D space. Here, Zhang et al. present an algorithm integrating ChIP-seq and Hi-C data to systematically identify both the 1D- and 3D-cooperation between DBPs. The human genome is tightly packaged into chromatin and forms complex structures of which the functional outputs, such as gene expression, depend on local chromatin states and chromatin three-dimensional 3D) organization12D organiz45678910These studies have shown that the cooperation of multiple DBPs is critical to orchestrate loop formation. However, there still lacks a systematic method to investigate the role of combinatorial regulation between DBPs in chromatin loop formation. Previous studies have focused on identifying DBPs binding to proximal genomic regions181920The ENCODE project has generated hundreds of ChIP-seq data sets to map binding sites of DBPs in multiple cell lines1922To tackle this challenge and comprehensively catalogue DBP cooperation, we present here a new model to construct networks that represent both 1D- and 3D-association between DBPs. Analysing these networks in GM12878 and K562 has revealed complex cooperative relationships among TFs, histone modifications, chromatin-remodelling enzymes and chromatin architectural proteins. Through the identification of communities and cliques in the DBP cooperation network, we have uncovered many DBP interactions in the chromatin loop regions. Intriguingly, many of these 3D-cooperative DBPs directly interact with one another, which suggests their binding may be important for loop formation or stabilization in 3D space. Furthermore, we performed a comparative network analysis between GM12878 and K562, and revealed cell-type-specific cooperation between DBPs that are critical for regulating cell-type-specific functions.μ and covariance matrix Σ. Let Σ−1 be the inverse of covariance matrix. If the ijth component of Σ−1 is zero, then variables i and j are conditionally independent given all other variables in the networket al.et al.To systematically identify DBP cooperation, we analysed DBP ChIP-seq data using Gaussian graphical model (GGM)ijth component a non-zero covariance if node i and j were connected in the simulated graph. All other components were then set to zero. We next drew samples from a multivariate Gaussian distribution parameterized by a zero mean vector and the constructed covariance matrix. These samples were used as input for network re-construction. As a comparison, we selected ARACNEin silico simulator that employs a dynamic model to simulate gene regulatory networks. The ground truth were subnetworks taken from yeast gene regulatory network with size 50 and 100, respectively. For each size, we performed 10 different simulations . To generate samples according to the simulated graph, we constructed a covariance matrix by assigning each O(N3+N2M2), where N is the number of variables or nodes in the network, M is the number of samples; as it scales with M2, it is not suitable for our application where we have more than 10,000 samples (the number of ChIP-seq peaks). In contrast, GGM, with a time complexity O(N3+N2M), can easily handle a large number of samples. In practice, we observed that the GGM was 50–100 times faster than ARACNE on the synthetic data sets  in the GGM network are also present in the PPI network . In GM12878, we found roughly the same numbers of 1D and 3D edges, 413 and 417 respectively. We noticed a great overlap between 1D and 3D edges . We thusz-score: −0.081 and 1.301), respectively. Indeed, a number of independent studies have shown that EP300 and MYC can cooperate to regulate gene transcription and the physical interaction between them has been previously reported39A 1D-dominant association cooperation between two DBPs represents a frequent co-occurrence in linear space but not in the long-range interacting loci that form loops in the 3D space. In this category, we recovered some previously known interactions such as the RNA Pol II–TAF1 interactionU-test to compare the significance level of enrichments of 1D-dominant, 3D-dominant and 1D–3D cooperative DBPs with that of uncooperative DBP pairs, the P values are 1.6e-3, 5.2e-28 and 2.0e-45, respectively. These results suggest that DBP cooperation has important functional implications in a variety of diseases. Therefore, we anticipate that the binding sites of cooperative DBPs can be used to prioritize genotype variations to identify causal associations.To further confirm the importance of DBP cooperation, we analyzed the enrichment of genotype variations in regions bound by cooperative DBPs. The disease-associated genotype variations were downloaded from the NHGRI-EBI GWAS databaseModularity is an important property of biological networks. Characterization of the modularity in DBP cooperation networks can illuminate how multiple DBPs cooperate to carry out complex regulation. Modularity can be studied at different levels. For instance, cliques highlight local modules in the network while community structure is a more global view of the modularity. Communities are groups of nodes in a network that are more densely connected internally than with the rest of the network1744To reveal the functions of a particular community, for each protein in the community we analysed its ChIP-seq peaks. We then ranked genomic loci by the number of proteins that bind to them and selected the top 5,000 loci as input to GREAT analysis to search for enriched GO terms. We found that the green community is linked to mRNA metabolic processes and translation-related functions . The samFurthermore, GREAT analysis of binding sites of DBPs in the red community revealed that ‘immune response', ‘leukocyte activation' and ‘lymphocyte activation' are the most enriched GO terms. These terms are highly specific to the B cell , suggestk-component DBP clique, we randomly selected k DBPs and did the same analysis. We took 50,000 samples and used them as a null model for enrichment and empirical P value calculation. As a result, all the DBPs in the cliques share a significant amount of peaks that occur in loops  common binding sites in K562 and GM12878 , which ide novo motif analysis using MEME–ChIPde novo motifs found in K562 and GM12878 were very different. Encouragingly, the motifs of several co-factors identified from ChIP-seq experiments were also retrieved from the de novo motif analysis. These results suggest that the STAT5A–CEBPB–PML complex indeed has different regulatory mechanisms in different cell types. To further interrogate the regulatory mechanisms of their cooperation, we used Spamode novo motifs. As a result, in GM12878 we found two de novo motifs, corresponding to STAT5A and MEF2, showing a statistically significant spacing constraint with the MEF2 motif occurring 13 bp downstream of the STAT5 motif  of DBP cooperation is a mixture of 3D and 1D events. Furthermore, we observed enrichment of disease-associated genotype variations in DBP cooperative binding sites, which suggests the functional importance of DBP cooperation.Identification of cooperation between multiple DBPs has been a challenging problem. Combinatorial approaches are limited to consider cooperation between a small number of DBPs because of the exponential increase of the possible combinations. In contrast, our model can easily search combinatorial cooperation in thousands of DBPs. By identifying modules and cliques in the network, we have uncovered closely collaborated DBPs, particularly those associated through 3D interactions in chromatin loops that may be crucial for loop formation or stabilization.et al.Our comparative analyses between GM12878 and K562 reveals different mechanisms of achieving cell-type specificity: using different combinations of DBPs or using the same protein complex but collaborating with different partners. Interestingly, we also found spacing constraints between the binding sites of certain partners, which implies higher-order regulatory rules for not only 1D but also 3D DBP cooperation. One major limitation of this work is that it relies on high resolution Hi-C data that is only available in a limited number of cell types. Furthermore, the chromatin loops used in this project are taken directly from the study of Rao et al.BAM files of ChIP-seq experiments in K562 and GM12878 were downloaded from the ENCODE project websiteλlocal=max{λBG, λ14k, λ24k} where λBG is the average RPKM of the whole genome; λ14k and λ24k are average RPKM of 14 and 24 kb windows. We used a larger window size than MACS's default size and a loose P value (0.01) to call peaks to increase the sensitivity for detecting broad peaks. For each 1 kb region, if it was called as a peak, we used the fold enrichment as its ChIP-seq enrichment score; otherwise, a zero score is assigned to that region. After computing the enrichment scores for every protein, we removed regions with low variation of scores by requiring the s.d. of scores to be at least 1. This excludes some unwanted artefacts from our analysis. For instance, regions with low mappability or an abnormally high signaln loops, denoted by L1, L2,…, Ln and each loop Li consists of two interacting loci Li, we first binned A and B, for every loop, we first compared the enrichment scores of A on the two interacting loci. We considered the interacting locus with larger enrichment score of protein A as A's primary binding locus, and the other interacting locus as the primary binding locus of protein B. The enrichment scores of primary binding loci for each protein were then used to compute the correlation coefficient.We divided each chromosome into consecutive 1-kb regions. For each protein, we computed the Reads Per Kilobase per Million (RPKM) mapped reads on these regions. The fold enrichment was calculated using MACS's algorithmμ and covariance matrix Σ. Let Σ−1 be the inverse of covariance matrix. If the ijth component of Σ−1 is zero, then variables i and j are conditionally independent given other variables. Therefore, each non-zero component represents an edge in the network. To efficiently and accurately estimate the inverse of the covariance matrix using DBP ChIP-seq data, we employed the Graphical lasso algorithmWe adapted the GGM to construct the DBP cooperation networks. GGM assumes that the observations have a multivariate Gaussian distribution with mean We used Eppstein's algorithm9 times to generate the background distribution, which was then used to calculate the P values.Protein-protein interaction data was obtained from the BioGrid databaseG model. This model specifies an n-node network, in which each edge is included with a probability P independent from every other edge. We used P=0.2 in this paper, which gives rise to a sparse network. We follow the procedures given in ref. To generate an Erdős–Rényi random graph, we used the We used Genetweaver 3.1 to extract random subnetworks with different sizes (50 and 100 nodes) from the yeast gene regulatory network provided by the software. For other parameters, we used the software's default setting.To draw the receiver operating characteristic (ROC) curve, we counted the number of true positives, false positives, true negatives and false negatives. If a predicted edge is present in the true network, it is a true positive, otherwise it is a false positive. Edges that are present in the true network but not identified by the algorithm are defined as false negatives. True negatives are edges that are not present in either predicted or true networks.http://wanglab.ucsd.edu/star/DBPnet/index.html.The code used in this study is available at https://www.encodeproject.org with accession codes listed in Chromosome loops in GM12878 and K562 were available in Gene Expression Omnibus (GEO) repository with the accession code GSE63525. ChIP-seq data sets analysed in this study were available from How to cite this article: Zhang, K. et al. Systematic identification of protein combinations mediating chromatin looping. Nat. Commun. 7:12249 doi: 10.1038/ncomms12249 (2016).Supplementary Figures 1-2 and Supplementary Tables 1-2Simulated networks and expression data generated by Genetweaver 3.1.Centrality of DBPs in GM12878 network.A list of 1D, 3D and 1D-3D cooperation in GM12878.All DBP cliques found in GM12878.Cell-type-specific DBP cliques and common DBP cliques in GM12878 and K562.Accuracy of different TFs in discriminating the cell-type-specific binding of the STAT5A-CEBPB-PML clique.ENCODE accession codes of ChIP-seq experiments analyzed in this study.


Pseudomonas aeruginosa largely represent insulated functional modules which provide a critical level of biological organization involved in general adaptation and survival processes. Analysis of the operational state of the sigma factor network revealed that transcription factors functionally couple the sigma factor regulons and significantly modulate the transcription levels in the face of challenging environments. The threshold quality of newly evolved transcription factors was reached faster and more robustly in in silico testing when the structural organization of sigma factor networks was taken into account. These results indicate that the modular structures of alternative sigma factor regulons provide P. aeruginosa with a robust framework to function adequately in its environment and at the same time facilitate evolutionary change. Our data support the view that widespread modularity guarantees robustness of biological networks and is a key driver of evolvability.The focus of modern molecular biology turns from assigning functions to individual genes towards understanding the expression and regulation of complex sets of molecules. Here, we provide evidence that alternative sigma factor regulons in the pathogen  Pseudomonas aeruginosa is a Gram-negative bacterium that can be found in various and challenging habitats346P. aeruginosa can be attributed to its large metabolic versatility and environment-driven flexible changes in the transcriptional profile. Sequencing of the P. aeruginosa reference strains revealed a large genome with highly abundant global regulators and signaling systems that form a complex and dynamic regulatory networkP. aeruginosa) are of exceptional importance as they provide promoter recognition specificity to the RNA polymerase core enzyme and mediate cellular responses to environmental cues through redirection of transcription initiation91011Controlling the rate of gene transcription is a fundamental biological process, ultimately dictating the cellular phenotype and bacterial adaptive processes to diverse environmentsP. aeruginosa alternative sigma factor regulons are discrete functional modules that exhibit only limited overlap in their transcriptional response to external stimuliP. aeruginosa. By linking experimental data with in silico testing we uncovered a general principle of how one of the largest bacterial genomes is structured. We argue that connectivity among the subunits of functional sparsely connected alternative sigma factor governed modules via global transcription factors enable P. aeruginosa to reconcile robustness and flexibility to a large variety of resources and habitats.A long-standing question in biology is how populations are capable of adapting to novel and challenging environments and thus conquer new niches. In this context, it has been repeatedly arguedP. aeruginosa genome it became apparent that alternative sigma factor regulons are discrete functional modules that exhibit only limited overlapP. aeruginosa type strain PA14 grown under a plethora of different environmental conditions have been analyzed previouslyBacillus subtilis under various environmental conditions indeed uncovered a modular expression structure that was reflecting sigma factor regulonsP. aeruginosa alternative sigma factors  were observed within the adaptive transcriptome , all of which are known to regulate large numbers of genes. We recorded the transcriptional profiles of the respective mutants of the Harvard Medical School PA14 transposon mutant libraryP. aeruginosa genome by calculating an enrichment factor (EF) . Interestingly, we found an enrichment of RpoS-controlled genes in the regulon of the transcription factor LasR (EF = 1.63) consistent with previous studies which uncovered the contribution of RpoS, LasR-LasI and RhlR-RhlI to the complex architecture of the quorum sensing regulon in P. aeruginosa181920P. aeruginosa revealed FleQ and RpoN on top of the regulatory cascade, while FliA is required for the expression of effector genes such as fliC-fleL, cheAB-motAB-cheW, cheVR, flgMN and cheYZrhlI24P. aeruginosaP. aeruginosa29303132333435373839To analyze which genes are affected by global In conclusion, the composition of global transcription factor profiles in respect to the affiliation of their genes to specific alternative sigma factor regulons is i) transcription factor specific and ii) does not reflect the overall composition of the alternative sigma factor dependent genes throughout the genome. These results imply that by creating connectivity among alternative sigma factor regulons the transcription factors govern a distinct composition of genes, which has been selected from only a sub-fraction of well approved composite genes of the genome and rely on the well proven, combat-ready alternative sigma factor functional modules.Incremental changes in coding and non-coding sequences are the key drivers of genome evolution and allow for adaptations to new and challenging environmental conditions. However, it has been assumed that especially those genes which exhibit central and pleiotropic functions experience a great deal of evolutionary limits and constraints. It thus might be expected that alternative sigma factors have undergone stabilizing selection, and are therefore conserved and limited in their evolutionary response to future environmental changes.Genome evolution can also be driven by the emergence of new genes. There is a growing interest in novel taxonomically restricted genes that are free to evolve new functions without suffering from the constraining effect of pleiotropy. New genes commonly arise through the duplication of existing genes and may maintain similar functions to the parental gene over a long evolutionary period or may undergo a process of diversification until a completely new function is evolved.We hypothesized that the organization of the genome in distinct alternative sigma factor governed structural modules which govern pleiotropic phenotypes limits the space for the evolution of alternative sigma factors but facilitates the evolution of novel transcription factor regulons that create connectivity between the alternative sigma factor regulons in a way that allows organisms to adapt to new challenges.P. aeruginosa strains41We therefore determined the sequence variation within the coding sequence of the ten major alternative sigma factors  and the housekeeping sigma factor RpoD as well as of the six global transcription factors  across the previously profiled 151 clinical in silico predictions. These results imply that evolution of transcription factors is accelerated and facilitated by the organization of the genome in functional modules and is further reinforced if the newly evolved transcription factor regulon resorts to a selected choice of genes within distinct alternative sigma factor regulons. This result is remarkable as in most systems speed and stability of evolutionary processes are mutually exclusiveTo test whether the organization of the genome provides a level of biological organization that is critical for the evolution of new transcription factors, we simulated the generation of an optimized transcription factor (represented as a set of gene expression levels between 0 and 1) by using an evolutionary algorithmin silico simulations predicting increased robustness and speed of the evolution of new alternative sigma factors have been derived from simulations with purely random transcription factors. They could show a structural advantage of the modular organization in alternative sigma factors without assuming a particular structure of the regulons. To test whether or not this advantage is also observable in the six transcription factors whose transcriptional profiles and sigma factor association were studied here, the same simulations were repeated with the experimentally determined gene expression values, regulons, and sigma factor usage for all six transcription factors. Despite the varying number of genes in the different regulons, the simulation results were comparable in all cases and clearly showed an evolutionary advantage in all cases , PA14_30650 , PA14_44490 , PA14_50220 , and PA14_62540  from the Harvard Medical School PA14 transposon mutant librarylasR) and recorded their transcriptional profiles. As a wild-type control we used the PA14_12430  transposon mutant as the PA14 strain carries an inactivated ladS gene.We selected PA14_19120  and deletion (lasR) mutants. For each of those cultures three individual main cultures grown in LB medium at 37 °C with shaking at 180 rpm were pooled and cells were harvested at the early stationary phase (OD600 = 2.0). RNA extraction, cDNA library preparation and Illumina sequencing were performed as previously describedGSE55328. There we also provide a quality control report showing gene expression scatter plots, heatmaps of highly expressed genes and principal component analysis of the samples.RNA was isolated from two independent cultures  of each of the 6 and 14.1 × 106 single-end reads of 50 bp length, the sample sequenced on the Genome Analyzer II-x had read length of 36 bp. Sequence reads were separated according to their barcodes and barcode sequences were removed. Reads were mapped to the genome sequence of the reference strain P. aeruginosa PA14 wild-type using stampyladS inactivated strain and their Benjamini-Hochberg corrected P value was maximally 0.05. The lists of differentially expressed genes are provided in the GSE55328 archive.The sequencing runs yielded between 3.1 × 10P. aeruginosa PA14, over- or under-representation was calculated by comparing normalized PseudoCAP classes experimentally detected and normalized PseudoCAP classes annotated using the following equation: EF = /. As previously describedUsing the PseudoCAP annotation available for nRPKshclust function. We computed the pair-wise Pearson correlation between the normalized read counts for each gene under each condition and performed hierarchical gene clustering by progressively grouping them: at each step of the iterative algorithm the two genes or gene clusters that have the smallest distance were merged to form a new cluster, and two branches of a growing tree were joined. We used the average linkage rule; this means that the distance between two clusters is computed as the mean of all the distances between the genes in the first cluster and the genes in the second cluster. In the expression tree depicted in 796 genes in PA14 have been defined to be highly variable under 14 environmental conditionsAn optimal TF for a new challenge is randomly defined by attribution of gene expression levels between 0 and 1 to each of the The target TF is (tried to be) evolved using two strategies:Genome-based: A set of NP randomly chosen TFs is defined by randomly choosing expression levels between 0 and 1 for all Alternative sigma factor-based: 11 alternative sigma factors with randomly attributed measured sizes are defined. These are mutually exclusive and cover the whole genome. For each of the alternative sigma factors, random gene expression levels between 0 and 1 are attributed to all genes associated with the respective alternative sigma factors. These are evolved using the DE algorithmquality index, QI) reaching values below 0.1%.The number of generations required to reach a success criterion is monitored in both evolution strategies. The success criterion is defined as the average per gene distance of the best TF in a generation from the target TF  on an unstructured genome is developed and compared to the same evolutionary process with a genome structured by alternative sigma factors. In this investigation, any functional relevance of alternative sigma factors is ignored. Further, the fact that TFs use only a small subset of alternative sigma factors is not considered unless stated otherwise. Any of these observations would increase the advantage of having alternative sigma factors such that this is a worst case investigation. However, the fact that newly evolved TFs do not simply take alternative sigma factors as they are but modulate the expression of each gene associated with the alternative sigma factors enters the investigation. The general idea of the simulation is to start from a vector of In addition, the CPU time used for both strategies is monitored, where in the alternative sigma factor-based strategy the time required for all alternative sigma factors is added up for comparison. Furthermore, the success of further evolution is controlled in both strategies by ensuring that the quality index decreases by at least CR = 0.4 and F = 0.1. These parameters are most efficient in approaching the target TF with both, the alternative sigma factor-based and the genome-based strategy (F = 0) was used .


Embryonic stem (ES) cells derived from the inner cell mass of developing embryos have tremendous potential in regenerative medicine due to their unique properties: ES cells can be maintained for a prolonged time without changes in their cellular characteristics in vitro , while sustaining the capacity to give rise to all cell types of adult organisms (pluripotency). In addition to the development of protocols to manipulate ES cells for therapeutic applications, understanding how such unique properties are maintained has been one of the key questions in stem cell research. During the past decade, advances in high-throughput technologies have enabled us to systematically monitor multiple layers of gene regulatory mechanisms in ES cells. In this review, we briefly summarize recent findings on global gene regulatory modes in ES cells, mainly focusing on the regulatory factors responsible for transcriptional and epigenetic regulations as well as their modular regulatory patterns throughout the genome. Embryonic stem (ES) cells are in vitro cultural counterparts of the inner cell mass   or epiblDuring the early era of mouse ES cell studies, establishments of ES cells were limitedly successful in only few mouse strains , 5. TherOver the last decade, understanding the underlying regulatory mechanisms enabling the unique characteristics of ES cells has been one of the major goals in the field of stem cells and developmental biology , 8. BesiIn this review, we provide an overview of recent advances in understanding global gene regulatory mechanisms in mouse ES cells. We summarize key regulatory factors and their roles in transcriptional and epigenetic regulations. Another focus is on global gene regulations mediated by long-range interactions among multiple chromatin domains in ES cells. We also discuss ES cell-specific modular regulations where functionally separable sub-networks are tightly intertwined to maintain self-renewal and pluripotency of mouse ES cells.cis-regulatory elements  and trans-acting factors . Thtivators  and trantivators –19.Oct4 . . 100]. TThe two most well-known histone marks (H3K27me3 and H3K4me3) are generally observed near the proximal promoters, and generated by PRC and Trithorax-group (TrxG) proteins, respectively , 104. PoThe active histone mark H3K4me3 is catalyzed by TrxG group proteins such as Set1A/B or Mll1/2 (mixed lineage leukemia 1/2), forming a SET1/MLL complex , 112. WdEnhancers act over a long distance and enhance the activity of target gene promoters to govern the identity of specific cell types by connecting tissue-specific master TFs, mediators, and RNA PolII machinery . The forFor successful differentiation of ES cells, active enhancers controlling ES cell-specific genes must be inactivated. Histone H3K4/K9 demethylase Lsd1 , the first mammalian histone demethylase identified , has beecis-regulatory elements, and eventually determine local gene activity. ATP-dependent chromatin remodeling complexes can be divided into several groups, such as SWI/SNF, ISWI, and CHD, with different types of ATPase core subunits [ATP-dependent chromatin remodelers provide open or closed chromatin structures by rearranging nucleosome compositions or repositioning nucleosomes , 13. TheES cells have a distinctive SWI/SNF complex so-called esBAF comprised of Baf155, Baf60A, Baf250a, and the ATP-dependent helicase Brg1 , 128. DeMammalian ISWI ATP-dependent chromatin-remodeling complexes containing Snf2h or Snf2l ATPase facilitate the sliding of nucleosomes by disrupting the interactions between histone proteins and DNA . The impChromodomain helicase DNA-binding (CHD) family chromatin remodelers also have been known to play critical roles in sustaining pluripotency. Chd1 recognizes H3K4me2/3  and occuSo far, we discussed the factors responsible for the transcriptional and epigenetic regulations in ES cells and their functions occurring through local changes in chromatin structures of the genome. More recently, studies have revealed that chromatin structures are further organized into specific higher-order architectures depending on cell types and developmental stages . A growiThe chromatin structure of an ES cell is largely open with less heterochromatin composition, loosely compacted compared to the chromatin of differentiated cells, and transcriptionally hyperactive due to active chromatin-remodeling enzymes and general TFs , 144. ThHoxb locus from its chromosomal territories coincides with its activation. This observation brought up the idea that chromosomal localization may affect gene activity. Thanks to the development of chromosome conformation capture (3C) technology, local chromatin structures and small-scale physical interactions between distant genomic regions have been studied in depth [Early studies of chromatin conformations relied on microscopic observation via DNA fluorescence in situ hybridization (FISH) for a limited number of genomic loci. Chambeyron et al.  showed tin depth , 155. Toin depth ) and higin depth ). Additiin depth , Hi-C [1in depth ). More rcis- and 336 trans-acting chromatin interactions [It has been shown that long-range interactions and higher-order chromatin structures are mediated by specific proteins  such as ractions , and anoractions . Ctcf loractions .Oct4, Sox2, Nanog, and Klf4) are interconnected within close physical proximity in ES cells. However, in neural stem cells, Sox2 gene is connected with Olig1 and Olig2 genes that play important roles in decision of neural cell fate, implying that gene relocation and replacement of interaction partners coincide with differentiation. On the other hand, regulatory elements of Oct4 gene lacking PolII-mediated chromatin interactions in somatic cells obtain intrachromosomal interactions during somatic cell reprogramming [Additionally, ChIA-PET assays of PolII disclosed interactions between multiple active promoters (P–P interactions) and pervasive interactions between promoters and enhancers (P–E interactions) , 167. Nogramming , highligNanog locus in ES cells by physical interaction with Nanog proteins, in turn activating other pluripotency genes [Mediators are major culprits in linking promoters and enhancers as a complex with cohesin, bridging two different chromosomal regions by encircling them with cohesin rings . Depleticy genes .Oct4 enhancer before the activation of endogenous Oct4 during somatic cell reprogramming [Oct4 enhancers and promoters, indicating that Klf4 also involves high-order chromatin architecture to induce pluripotency [Nanog locus also emphasized the interactions between Nanog locus and other pluripotency-specific genes [The fact that pluripotency factors such as Oct4 and Nanog particularly bind to the enhancers of ES cell-specific genes suggested that pluripotency factors may also play important roles in organizing chromatin configurations. A recent 4C-seq study has revealed that Klf4 proteins recruit cohesin onto the gramming . Similaripotency . Anotheric genes . Importaic genes . Particuic genes  that areic genes .cis- and trans-regulatory elements.Research in the past decade aided by modern systems biology tools have demonstrated that the global gene expression program in mouse ES cells is controlled by multiple layers of regulatory steps, such as transcriptional and epigenetic regulations. Emerging patterns of tightly interconnected regulatory pathways are more evident with recent demonstrations of long-range interactions among various cis-regulatory elements becomes functional only when they are occupied by regulatory factors with accompanying chromatin and epigenetic statuses. Now it is clear that these associations between epigenetic modifications and TF occupancies are collectively formed within separate chromosomal territories [Epigenetic signatures generated by the recruitment and action of histone modifiers , 173 andritories  with lonritories . Table 1cis-regulatory elements to form higher-level chromatin interactions, in turn changing cellular characteristics [Notably, analysis of accumulated TF or DBP occupancy data suggested that a group of TFs or regulators tend to share similar chromosomal targets as a functionally separable regulatory subunit as shown in mouse ES cells: the Core, MYC and PRC classes . The co-eristics , 181. Theristics . As disceristics . As mosteristics . This aleristics –184.Recently attained knowledge on chromatin conformation in ES cells suggested important biological significances. Considering limited cellular resources, spatial segregation of active, repressive and silent regions may enable more efficient way of gene regulation . In addiMoreover, results from the enhancer-promoter interactome studies disclosed that multiple enhancers interact not only with a single promoter, but frequently with multiple promoters to form clusters of multiple co-expressed genes , 167. Mocis-elements and three-dimensional chromatin interactions should be considered together. Aligned with these comprehensive studies linking diverse aspects of gene regulations, conventional genetics studies aiming to understand the functions of each regulatory factor in early development or in vivo should also be continued.To our surprise, some of the newly-attained genome-wide data seemed somewhat inconsistent with our conventional understanding of the functions. For example, the genomic targets of some previously known repressor proteins such as Lsd1 , 186 and