Here, we highlight recent studies that have used these tools to characterize transcription during development, and discuss the mechanisms that contribute to the precision and accuracy of the timing, location and level of transcription. We attempt to disentangle the discrepancies in how physicists and biologists use the term ‘precision' to facilitate interactions using a common language. We also highlight selected examples in which the coupling of mathematical modeling with experimental approaches has provided important mechanistic insights, and call for a more expansive use of mathematical modeling to exploit the wealth of quantitative data and advance our understanding of animal transcription.During development, genes are transcribed at specific times, locations and levels. In recent years, the emergence of quantitative tools has significantly advanced our ability to measure transcription with high spatiotemporal resolution  Summary: This Review highlights how high-resolution quantitative tools and theoretical models have formed our current view of the mechanisms determining precision and accuracy in the timing, location and level of transcription in the Drosophila embryo. In animals, changes in gene regulation are associated with phenotypic changes within and between species . To deciin vivo measurements of animal transcription are a recent innovation and are now being widely deployed  of the transcriptional output differ from their average target value, the transcriptional output is less accurate. The target value can refer, for example, to the average value measured in the wild type. In the field of transcription, biologists often use the term ‘accuracy’ synonymously with the term ‘precision’.Robustness. Robust transcription is the faithful execution of precise and accurate transcription when confronted with a perturbation, usually environmental or genetic.Stability. Stability refers to the tolerance of a feature to a noisy and stochastic molecular environment .Plasticity. We refer to transcriptional plasticity as the ability of a regulatory mechanism to change, while transcriptional accuracy and precision are conserved. For example, when the type or number of transcription factor binding sites within an enhancer changes during evolution, while the output driven by it does not.Here, we present precision and accuracy as core definitions upon which other features, such as robustness, plasticity and stability, depend (see chanisms . UltimatTranscription can be characterized by the following features: (1) the location of transcription, i.e. the expression of a gene in a specific spatial location within an organism; (2) the level of transcription, i.e. the number of transcripts within a given cell at a particular time; and (3) the timing of transcription, i.e. when the expression of a particular gene is turned on/off; this can be defined in absolute time, or relative to a particular developmental event (such as the onset of cellularization), or relative to other cells within an organism. When considering these features, we can also define the precision and the accuracy of transcription: precision in transcription refers to minimizing variation in these features between individual cells or embryos within the same population; accuracy describes how close the average values of these features are between different populations, such as wild-type and perturbed embryos.It is useful to distinguish between precision and accuracy because they can lead to distinct mechanistic insights. For example, we often use measurements of transcriptional accuracy to confirm that a putative target is regulated by a specific transcription factor (TF). This usually involves perturbing the TF – manipulating its level or binding site sequence – and measuring changes in the transcriptional output . This geAlthough defining precision and accuracy is relatively straightforward, linking changes in these parameters to organismal phenotype is not. Development requires the accurate specification of cell fate during differentiation, even when it is subject to genetic and environmental perturbations . BecauseShavenbaby  gene in Drosophila , fluorescence correlation spectroscopy, single-particle tracking, and genetically encoded fluorescent RNA labeling, including the popular MS2/MS2 coat protein system  gene in Drosophila embryos provides an excellent example of how mathematical models can be used to interpret quantitative data. Using smFISH (hb transcription and made mechanistic inferences about the function of the TF Bicoid (Bcd)  . They shid (Bcd) . The desid (Bcd) . A clearid (Bcd) . Identifid (Bcd)  by decipid (Bcd) .Fig. 2.As we highlight below, various mechanisms have been proposed to impact the location, levels and timing of transcription. We note that each one of these features can be characterized by their precision (or noise) and accuracy (deviation from the average), but that this is not how most results are discussed in the literature. Most of the studies we describe address transcriptional accuracy, for example by perturbing a component genetically and measuring the effect on an average value. However, with the recent increased use of quantitative tools, the number of studies that analyze both precision and accuracy to gain mechanistic insights is growing. We also note that some mechanisms are proposed to regulate multiple features. For example, functional interactions between shadow enhancers have been shown to influence the location, level and timing of transcription. However, it is not yet clear how control of the location, level and timing of transcription is mechanistically intertwined or to what extent, if any, these features can be modulated independently. Finally, it should be noted that we focus our discussion on gene regulation via transcriptional enhancers.Drosophila, as this is our area of expertise, although similar quantitative studies have also been conducted in other organisms and some have been recently reviewed in the context of bacterial  enhancer function uncovered shadow enhancers in the eve locus  indicates bursty expression , a key patterning gene in Drosophila embryos, is regulated by a pair of shadow enhancers  shadow enhancers are also regulated by different TFs, leading to an interesting hypothesis that shadow enhancers are not simple duplications but instead work on distinct steps of the transcription cycle or at distinct times DrosophilDrosophila cell culture, Pol II pausing also inhibits new initiation between transcriptional bursts  is known to reshape the chromatin landscape at this critical developmental stage, regulating the transcriptional activation and temporal coordination of a substantial subset of early embryonic genes (Changes in chromatin state, especially at regulatory elements such as promoters and enhancers, play a significant role in regulating both the timing and specificity of transcription during embryogenesis . One of ic genes . Zelda hic genes . Howeveric genes . Severalic genes . These sDrosophila embryos that has attempted to decipher the mechanisms that impinge upon these features. Our summary is in no way comprehensive; indeed, work in other model systems has provided much to the field and our own thinking. We restricted our discussion to the Drosophila embryo, which has long served as a model for quantitative studies of transcription, because genetics, biochemistry and microscopy are well established for this model system and because many of the molecular players are known (In this Review, we sought to define a useful vocabulary for discussing quantitative features of transcription, namely the precision and accuracy of the location, levels and timing of transcription. We discussed recent selected work in re known .Technological advances have provided new tools to further dissect the mechanisms that contribute to precise and accurate transcription. Measuring the timing, location and level of gene expression across a population of cells requires the use of quantitative tools, which are growing in use and scope within developmental biology. This provides an exceptional opportunity for the use of mathematical models to guide the interpretation of quantitative data and the design of further experiments. It will also be worthwhile to contrast mechanistically motivated models, like those we have discussed here, with statistical models, which are widely employed to interpret functional genomics data . Statist


Genome-wide mapping of transcription factor binding is generally performed by chemical protein–DNA crosslinking, followed by chromatin immunoprecipitation and deep sequencing (ChIP-seq). Here we present the ChIP-seq technique based on photochemical crosslinking of protein–DNA interactions by high-intensity ultraviolet (UV) laser irradiation in living mammalian cells (UV-ChIP-seq). UV laser irradiation induces an efficient and instant formation of covalent “zero-length” crosslinks exclusively between nucleic acids and proteins that are in immediate contact, thus resulting in a “snapshot” of direct protein–DNA interactions in their natural environment. Here we show that UV-ChIP-seq, applied for genome-wide profiling of the sequence-specific transcriptional repressor B-cell lymphoma 6 (BCL6) in human diffuse large B-cell lymphoma (DLBCL) cells, produces sensitive and precise protein–DNA binding profiles, highly enriched with canonical BCL6 DNA sequence motifs. Using this technique, we also found numerous previously undetectable direct BCL6 binding sites, particularly in condensed, inaccessible areas of chromatin. Chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) can map transcription factor binding to a given genome. Here, Steube and colleagues devise a ChIP-seq technique based on photochemical crosslinking by high-intensity ultraviolet laser irradiation, and describe its utility. Interacting proteins are chemically crosslinked to their target DNA sequences by formaldehyde (FA), the purified chromatin is sheared and the relevant protein is enriched by immunoprecipitation with specific antibodies. The co-purified genomic DNA is then determined by deep sequencing.Genome-wide profiling of protein–DNA interactions is generally performed by chromatin immunoprecipitation in combination with deep sequencing (ChIP-seq)12. Formaldehyde crosslinking generates protein–protein and protein–DNA formations, thus disallowing for the discrimination between direct and indirect protein–DNA interactions in subsequent analyses  light at relevant wavelengths. UV irradiation results in the formation of covalent “zero-length” crosslinks, which occur exclusively between nucleotide bases and protein amino acids that are in immediate contact  during immune responses and in the initiation and maintenance of B-cell lymphomas27. The genome-wide binding of BCL6 has been extensively studied using conventional FA ChIP techniques, revealing thousands of potential BCL6 binding sites32. Nevertheless, most sites found did not overlap canonical BCL6 DNA sequence motifs. In contrast, the UV-ChIP-seq technique presented here results in the detection of robust and high quality genome-wide BCL6-DNA binding sites with high specificity and resolution. Our technique enables the accurate and precise discovery of many previously undetectable direct BCL6 binding sites, particularly in condensed, inaccessible areas of chromatin.In this study we present the first application of photochemical crosslinking by high-intensity nanosecond-pulsed UV laser irradiation in combination with ChIP-seq (UV-ChIP-seq) in living mammalian cells. To evaluate UV-ChIP-seq we investigated genome-wide DNA binding of the sequence-specific transcription factor B-cell lymphoma 6 (BCL6) in human diffuse large B-cell lymphoma (DLBCL) cellsFor photochemical crosslinking of protein–DNA interactions we irradiated human DLBCL cells using a high-intensity nanosecond-pulsed UV laser technique. The experimental setup for UV laser irradiation of living cells and the UV-ChIP-seq workflow is shown in Fig. CISH, AFF3 and FPGT genes. In brief, cells were irradiated by increasing doses of UV light (3.5–35 J cm−2), lysed and chromatin was fragmented by sonication. DNA bound by BCL6 was immunoprecipitated using an anti-Bcl6 antibody, isolated and analyzed by qPCR—amplifying previously validated areas containing canonical BCL6 binding motifs . This control showed no enrichment indicating that non-covalent BCL6-DNA interactions are disrupted by the wash conditions Fig. . Further34. UV-ChIP sequence data exhibited a high level of read clustering  whereas the UV control experiments  showed no enrichment   within UV-ChIP-seq biological replicate data sets relative to UV input DNA data  in human DLBCL cells.To detect BCL6 binding sites (peaks) within the obtained UV-ChIP-seq data we first performed peak calling using the Irreproducible Discovery Rate (IDR) frameworkE-value 2.6e−385) of a DNA sequence matching the canonical BCL6 binding motif previously reported  scan within DNA sequences for the canonical BCL6 motif showed a strong enrichment in the majority of identified binding sites   Fig. . In addi.1) Fig. .Fig. 2Bi37. In order to determine whether BCL6 binding occurs preferentially in accessible or inaccessible chromatin regions, binding sites were correlated to DNaseI hypersensitive sites (DHSs) in human B-cell lymphoma cells. We found that UV-ChIP-seq detected the majority of BCL6 binding sites in DNaseI insensitive regions   of identified BCL6 peaks did not overlap a known DHS  regions in human B cells38. These regions of highly accessible chromatin are believed to be dynamically bound by a great variety of transcription factors and often do not contain specific DNA sequence motifs38. UV-ChIP-seq showed minimal binding of BCL6  in these parts of the genome  were located in promoter regions of annotated genes, 43.4% (n = 3918) in intragenic, and 52.1% (n = 4706) in intergenic regions  Fig. . Interesome Fig. . Analyseons Fig. .Fig. 3UV31. The raw sequence data was analyzed identically using the mapping and peak calling procedures described above. Following peak calling, BCL6 FA ChIP-seq data sets have been merged for comparative analysis to UV-ChIP-seq data. This analysis revealed 14,133 BCL6 binding sites detected by all three FA ChIP-seq experiments in comparison to the 9029 binding sites identified by UV-ChIP-seq  with conventional chemical (FA) crosslinking, we performed BCL6 FA ChIP-seq. Except for the method used for protein–DNA crosslinking, cell lysis and sonication conditions, FA ChIP-seq was performed identically using the same human DLBCL cell line (OCI-LY1) and antibody type (Bcl6 (N-3)) as used for the UV-ChIP-seq experiments. In addition, we collected FA ChIP-seq data from two independent studies elucidating BCL6 binding in OCI-LY1 cells (GSE29282 and GSE46663)seq Fig. . Therebyn = 3497, q-value 0.1) when compared to UV-ChIP-seq data     and HOT regions   of BCL6 binding sites within promoter regions, 50.9% (n = 7199) in intragenic, and 27.9% (n = 3944) in intergenic regions  Fig. . Consequ82) Fig. . Canonic82) Fig. . Remarkaons Fig. . In compons Fig. .CISH, CCND2, SOCS2, and HELB promoter regions  associated with BCL6 binding sites identified by UV-ChIP-seq  was maintained in Iscove’s Modified Dulbecco’s Medium (Gibco) supplemented with fetal calf serum  at 37 °C and 5% COUV laser irradiation of living cells was performed using a Quanta-Ray pulsed Nd:YAG laser  equipped with an HG-2 harmonic generator (Spectra Physics) and dichroic mirrors (DHS-2 Quanta-Ray dichroic harmonic separator) to give monochromatic light at 266 nm. The laser beam was focused by a fused silica lens, deviated by 90° and adjusted to fit the surface of the sample area. The laser energy at the sample position was determined using a Power/Energy Meter  equipped with a Power Thermal Sensor . The parameters for UV laser irradiation were as follows: pulse duration 5 ns, repetition rate 10 Hz, energy per pulse 50 mJ, diameter of the laser beam 6 mm.8 cells were resuspended in 1 ml ice-cold PBS and divided into four aliquots. An open-top, flat-bottom polyethylene sample tube , containing approximately 2.5 × 107 cells in suspension (250 µl) and a magnetic stir bar, was vertically aligned with the laser beam and positioned on a stir plate. UV laser irradiation was performed at 4 °C under constant stirring .For UV-ChIP experiments, OCI-LY1 cells were counted, collected, and washed twice in ice cold PBS (pH 7.4). 1 × 10g for 7 min, the pellet was resuspended in 250 µl ChIP lysis buffer . Chromatin was sheared on ice  into fragments averaging 250 bp and centrifuged at 16,000 × g for 10 min. Fragmentation of chromatin was analyzed with purified DNA using agarose gel electrophoresis and a 2100 Bioanalyzer (Agilent Technologies). An aliquot of sheared chromatin was used for UV input DNA sequencing and UV-ChIP-qPCR analysis. The supernatant was pre-cleared for 1 h using pre-washed protein A magnetic beads according to the manufacturer’s instructions . A ChIP-grade anti-Bcl6 antibody (Bcl-6 (N-3), sc-858, Lot# A3013, Santa Cruz) was used for ChIP. The specificity and suitability for ChIP was confirmed by immunoblot analysis. For UV IgG control ChIP, normal rabbit IgG  was used. Clarified chromatin extracts were incubated for 12 h with 10 µg of specific anti-Bcl6 antibody and control IgG antibody, respectively. Nucleoprotein-antibody complexes were precipitated using pre-washed beads for 1 h. The supernatant was removed and beads were sequentially washed twice in low salt , high salt , LiCl , 250 mM LiCl, 10 mM Tris-HCl pH 8.0, 1 mM EDTA) and TE  buffer. For elution of immunoprecipitated nucleoprotein-antibody complexes, beads were resuspended in 0.1 M citrate buffer (pH 2.2) for 2 min at room temperature. The supernatant was removed and transferred into Tris-HCl (pH 8.0) for neutralization. After RNase A treatment  for 1 h at 37 °C and Proteinase K treatment  for 12 h at 50 °C, genomic DNA was purified by phenol/chloroform/isoamylalcohol extraction and ethanol precipitation.Immediately after irradiation, four aliquots of OCI-LY1 cell suspensions were combined and added to 10 ml of ice cold PBS (pH 7.4) containing protease inhibitors according to the manufacturer’s recommendations . All subsequent steps were performed at 4 °C. After centrifugation at 1000 × 3) and crosslinking was reverted by addition of 0.3 M NaCl and incubation for 5 h at 65 °C.Formaldehyde (FA) ChIP was performed as described above except for the following steps. OCI-LY1 cells were fixed with 1% FA for 10 min under rotation at room temperature and the reaction was quenched by addition of 125 mM glycine for 5 min. Cells were collected, washed twice with ice-cold PBS (pH 7.4) and lysed in RIPA buffer . For elution of immunocomplexes, beads were resuspended in elution buffer  and the StepOnePlus Real-Time PCR System (Applied Biosystems) as recommended by the manufacturer. Variations across samples were adjusted by normalization to input DNA. The ∆Ct values were calculated for anti-Bcl6 and IgG control immunoprecipitated DNA fragments relative to input DNA by ΔCt [normalized ChIP] = (Ct [Input]−Log2 (100))−Ct [ChIP]. The percentage of input (% Input) values were calculated by % Input = 2ΔCt [normalized ChIP]. The % Input value represents the enrichment of BCL6 on specific genomic regions. The enrichment of BCL6 relative to control regions and the UV IgG control enrichment at specific sites was calculated.UV dose titration was performed by UV-ChIP and quantitative real-time PCR (UV-ChIP-qPCR). UV dose  for photochemical crosslinking of BCL6 to DNA ranged 3.5–35.3 J cm40. The effect of UV laser irradiation on amplificability of DNA fragments was estimated by high-intensity UV laser irradiation of OCI-LY1 cells with increasing UV doses. Cells were irradiated (energy density 0 to 106 J cm−2), total DNA was isolated and qPCR analysis of genomic regions A (295 bp) and B (124 bp) was performed as described. Ct values were normalized to input DNA  and delta Ct values were calculated by ΔCt = Ct [UV]−Ct [−UV].Target primer pairs span BCL6 binding sites and corresponding control primer pairs (specific control region) are located 2.4–3.2 kb upstream the BCL6 interaction siteSequencing libraries were prepared from UV-ChIP, FA ChIP and control DNA fragments according to the manufacturer’s protocol (NEBNext Ultra DNA Library Prep Kit). In brief, DNA fragments were end-repaired and the blunt, phosphorylated ends were treated with Klenow DNA polymerase and dATP to yield a 3′ A base overhang for ligation of adapters. After adapter ligation, DNA was PCR amplified (15 cycles). Libraries were size-selected to achieve ChIP DNA fragment lengths of 200–300 bp. The purified DNA was captured on an Illumina flow cell for cluster generation and libraries were sequenced on a HiSeq 2500 (Illumina) instrument according to manufacturer’s protocol.Total RNA was isolated from OCI-LY1 cells . RNA concentration, purity and integrity was verified using a 2100 Bioanalyzer (Agilent Technologies). Libraries were generated using the mRNA-seq sample preparation kit (Illumina). Briefly, mRNA was selected by two rounds of purification using magnetic polydT beads and then fragmented. First strand synthesis was performed using random oligonucleotides and SupersciptIII (Invitrogen). After second strand synthesis a 200 bp paired-end library was prepared following Illumina paired-end library preparation and sequencing on a HiSeq 2500 (Illumina) instrument according to manufacturer’s protocol.http://www.ncbi.nlm.nih.gov/geo) under accession no. GSE103125.Raw sequence data files are publicly available at NCBI Gene Expression Omnibus (GEO)  in the human genome (https://sites.google.com/site/anshulkundaje/projects/blacklists) and overlapping reads were removed33. Strand cross-correlation (SCC) analysis and computation of NSC  and RSC (relative strand coefficient) values was performed using phantompeakqualtools  with default parameters (−s = −500:5:1500)47. For the Irreproducible Discovery Rate (IDR) framework, we applied peak detection by SPP  on replicate and pseudo-replicate (pooled and subsampled reads from replicates) data and used peak calling results ranked by signal value for consistency analysis (IDR 0.01)48. Code and detailed instructions using the IDR framework are available at https://sites.google.com/site/anshulkundaje/projects/idr. MACS2 was used for peak calling in sequence data with default parameters 35. For further analysis, a window of 250 bp around the peak summit (base position of maximum enrichment) was defined and peaks were ranked based on their peak calling p-value. For comparative studies, we used published ChIP-seq raw data (GSE29282 and GSE46663) and performed data processing as described above31. DNA sequences  were generated from chromosome coordinates produced by peak detection and windowing using BEDTools49. De novo motif analysis was performed using MEME (-dna -mod zoops -nmotifs 5 -revcomp -minw 5 -maxw 30) on DNA sequences of windowed top ranked peaks (500)50. TOMTOM was used to compare identified motifs against the JASPAR database51. The position weight matrix (PWM) of the canonical BCL6 motif was mapped onto DNA sequences of windowed peaks using FIMO 52. The distance of canonical BCL6 motifs  relative to the corresponding peak summit  was calculated and plotted over all peaks. Conservation analysis was performed using the ChIPseeqerCons module of ChIPseeqer53. Conservation scores centered at the peak summit were computed as the mean placental mammal conservation level (phastCons) extracted from hg19 phastCons46way.placental track (UCSC Genome Browser database)54. Randomly selected sequences were used as a control. For DNaseI hypersensitive site (DHS) analysis we used data (bed format) from human B-cell lymphoma cells (OCI-LY7)  and the DNaseI Hypersensitive Site Master List (125 cell types) from ENCODE/Analysis (http://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeAwgDnaseMasterSites) and the overlap was determined to windowed peak lists using BEDTools33. Highly Occupied Target (HOT) regions were used from human B cells (GM12878) and processed equally (http://encodenets.gersteinlab.org/metatracks)38. Data for histone marks H3K27ac (GSM763424) and H3K4me3 (GSM763420) in OCI-LY1 cells were used from Hatzi et al. (GSE29282) and processed equally30. Peak locations were analyzed based on human RefSeq gene annotations (GRCh37/hg19). Peaks localized ± 2 kb of the transcription start site (TSS) of annotated genes were defined as promoter peaks, peaks localized to intronic, exonic and UTR regions were defined as intragenic peaks and peaks away from intragenic and promoter regions were defined intergenic peaks. Target gene prediction based on detected binding sites was performed using BETA (http://cistrome.org/BETA) with default parameters42. Read density profiles  were generated as pileup signal files   and fold enrichment tracks were generated by normalized ChIP over input DNA pileup signal files using MACS235. Profiles were visualized by the Integrative Genomics Viewer (IGV)55.Sequence data from UV-ChIP, FA ChIP and control experiments were used for further analysis. Reads were mapped onto the human genome reference (GRCh37/hg19) using Bowtie (−l 50–3 1 −n 2 −m 1 --best --strata)56. The processed paired-end RNA sequencing reads were aligned to the human genome using TopHat (2.1.1) with default parameters and the human reference genome and its annotation files were obtained from the Illumina iGenomes collection (Ensembl GRCh37) (http://cole-trapnell-lab.github.io/cufflinks/igenome_table)57. The mapped reads were assembled using Cufflinks (2.2.1) with default parameters to determine gene and transcript expression 58. A t-test was performed to test for significant differences between the mean FPKM values. For differential gene expression (DGE) analysis we used RNA-seq data 48 h after BCL6 or control small interfering RNA (siRNA) transduction in OCI-LY1 cells (GSE29282)30. The processing of the paired-end RNA sequencing reads was performed as described and differentially expressed genes  were identified between control (siNT) and BCL6 knockdown (siBCL6) RNA-seq data using Cuffdiff with default parameters58. Paired t-test was performed to test for significant differences between the mean FPKM values.RNA sequencing reads were trimmed by Trimmomatic (0.36) to remove low-quality sequences and reads that were shorter than 36 baseshttp://www.ncbi.nlm.nih.gov/geo) under accession no. GSE103125.Raw sequence data and processed files are publicly available at NCBI Gene Expression Omnibus (GEO) (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


VEGF stimulates RNAPII pause release by stimulating acetylation of ETS1, a master endothelial cell transcriptional regulator. In endothelial cells, ETS1 binds transcribed gene promoters and stimulates their expression by broadly increasing RNAPII pause release. 34VEGF enhances ETS1 chromatin occupancy and increases ETS1 acetylation, enhancing its binding to BRD4, which recruits the pause release machinery and increases RNAPII pause release. Endothelial cell angiogenic responses in vitro and in vivo require ETS1-mediated transduction of VEGF signaling to release paused RNAPII. Our results define an angiogenic pathway in which VEGF enhances ETS1–BRD4 interaction to broadly promote RNAPII pause release and drive angiogenesis.Release of promoter-proximally paused RNA polymerase II (RNAPII) is a recently recognized transcriptional regulatory checkpoint. The biological roles of RNAPII pause release and the mechanisms by which extracellular signals control it are incompletely understood. Here we show that  et al. show that this process is essential in angiogenesis by demonstrating that the endothelial master transcription factor ETS1 promotes global RNAPII pause release, and that this process is governed by VEGF.Promoter proximal RNAPII pausing is a rate-limiting transcriptional mechanism. Chen  However, the mechanisms by which VEGF influences gene expression remains poorly understood.Angiogenesis, the growth of blood vessels from existing vasculature, is central to mammalian organ development and disease. Nearly all steps of angiogenesis are controlled by vascular endothelial growth factor (VEGF), which signals through VEGF receptor 2 (VEGFR2) to activate, among others, the MEK–ERK kinase cascade. Ultimately, VEGF stimulation alters endothelial cell (EC) gene transcription to enable vessel formationETS1 impaired vascular development during embryogenesis5. The ETS motif is found in nearly all angiogenic transcriptional enhancers, and we previously found that ETS1 and the co-activator p300 co-localize at EC enhancers6. However, the mechanism by which ETS1 controls EC gene expression and its potential role in angiogenic signal transduction and downstream transcription remain undetermined.V-Ets Avian Erythroblastosis Virus E26 Oncogene Homolog 1 (ETS1), the founding member of the E26 transformation-specific sequence (ETS) transcription factor family, is a master regulator of EC gene transcription. Upregulation of ETS1 in quiescent ECs was sufficient to convert them to an angiogenic state, and depletion of 8. RNAPII pause release requires Positive Transcription Elongation Factor-b (P-TEFb), a kinase which phosphorylates pausing factors and RNAPII on serine 2 of its C-terminal domain. Bromodomain-containing protein 4 (BRD4) and certain transcription factors, such as MYC and NFKB, recruit P-TEFb to transcriptional start sites (TSSs) to release paused RNAPII12. Although many environmental stress-responsive and developmental genes have promoter-proximally paused RNAPII15, the molecular mechanisms that link environmental stimuli to altered RNAPII pausing at these genes are incompletely understood.The transition of RNAPII from a promoter-proximally stalled state to active elongation has recently been identified as a key checkpoint for the transcription of many genesETS1 as a new RNAPII pausing regulator that globally promotes pause release in ECs. This role of ETS1 to stimulate RNAPII pause release was regulated by VEGF and essential for VEGF angiogenic activity. Together, our study implicates VEGF-stimulated RNAPII pause release as an important regulatory step in angiogenesis. More broadly, our study provides a new and possibly widely applicable mechanistic model by which extracellular stimuli influences RNAPII pausing and gene transcription.In this study, we identified 5. To unveil the transcriptional mechanisms by which ETS1 regulates anigogenesis, we analyzed its chromatin occupancy in human umbilical vein endothelial cells (HUVECs) by ChIP-seq before and after VEGF stimulation , ETS1 modRNA globally and significantly reduced PI of genes with ETS1 promoter occupancy  on most loci , a highly efficient and non-toxic technique to rapidly express gene products in difficult-to-transfect cells including HUVECogy Fig. . To meas20. ETS1 overexpression markedly increased RNAPII-pS2, without affecting overall levels of RNAPII or the key pause release regulators CDK9, HEXIM1, ELL2, BRD4, or AFF4  by CDK9, a subunit of the essential pause release complex P-TEFb or by BRD4, a protein that recruits P-TEFb and that was recently reported to directly phosphorylate RNAPII-S2FF4 Fig. . The ETSFF4 Fig. . JQ1, a FF4 Fig. . These d11. To determine if ETS1 had similar activity in ECs, we measured RNA abundance per cell. Total RNA and rRNA content per cell were not significantly different between ETS1 and control modRNA transfection .Genome-wide increase of RNAPII pause release is able to increase RNA content globallyion Fig. , unlike ion Fig. . To furtlls Fig. , consistCollectively, these data demonstrate that ETS1 globally increases mRNA transcription in ECs through P-TEFb and BRD4-mediated RNAPII pause release.21. To explore mechanisms by which ETS1 stimulates RNAPII pause release, we scrutinized the interaction between ETS1 and these factors. In 293T cells, Flag-ETS1 co-precipitated CDK9, CCNT2, and BRD4  and its positive , and negative  regulatory factorsRD4 Fig. . NegativRD4 Fig. , demonstRD4 Fig. , demonstRD4 Fig. . In contRD4 Fig. , indicatETS1 or GFP modRNA-transfected HUVEC cells. ETS1 and BRD4 chromatin occupancy generally correlated within promoter as well as distal regions , pointed (PNT), acidic, exonVII, and ETS domainsins Fig. . We founNext, we mapped the region of BRD4 that binds ETS1. BRD4 has three known functional domains, two bromodomains (Bromo1 and Bromo2) and a long C-terminal domain that binds P-TEFb. In Co-IP assays, either Bromo1 or Bromo2, but not the C-terminal domain, bound ETS1 Fig.  or the E23. We therefore hypothesized that it binds acetylated lysines within the ETS1 NT domain. To map ETS1 acetylated lysine residues, we co-expressed Flag-tagged ETS1 and CBP in 293T cells. Mass spectroscopy of affinity purified ETS1 showed that lysine residues at positions 8 and 18, within the NT domain, were the most heavily acetylated 25. To test whether CBP catalyzes K8;18 acetylation, we co-transfected CBP and ETS1 into 293T cells and detected ETS1 acetylation by using acetyl-lysine specific antibody. CBP increased acetylation of both full length ETS1 and ETS1-NT and stimulated their binding to BRD4  in HUVECs using lentivirus , whereas it had relatively weak effect on the early response genes at 1 h  had a stronger dominant negative effect, while the acetylation-deficient peptide ETS1-NT(KR) did not have a significant inhibitory effect. These data indicate that ETS1 stimulation of EC migration depends upon its interaction with BRD4 via acetylation at K8 and K18. The ETS1 DNA-binding domain (DBD), previously shown to have dominant negative activity25, also blocked EC migration, underscoring the importance of ETS1 in this process. Together, these results indicate that RNAPII pause release and ETS1–BRD4 interaction is functionally relevant for EC migration in response to VEGF.We further probed the role of ETS1–BRD4 interaction in VEGF-induced EC migration, using both the transwell assay Fig.  and the VEGF by measuring both cell number , while lentiviral ETS1-NT expression significant inhibited it. Again, the KQ mutation potentiated dominant negative activity, whereas the KR mutation abrogated it. Lentiviral expression of ETS1 DNA binding domain (DBD) also inhibited angiogenesis in this assay, consistent with its dominant negative activity . BRD4 inhibition by JQ1 or lentivirally expressed shRNA , a type of endothelial progenitor cell that robustly forms vessels in this assay  mouse model of this disease31  occupancy of the promoters of most expressed genes; (2) correlation between promoter occupancy, RNAPII occupancy, and gene expression; and (3) broad stimulation of RNAPII pause release with resulting broad increase in transcript abundance. We found that ETS1 shares these characteristics, indicating that it is a transcriptional amplifier that enhances productive RNAPII elongation in ECs and possibly other cell types in which it is expressed, including blood and prostate cancer cells. Our data indicate that ETS1 co-occupies promoters with MYC. Future studies will determine whether these factors act synergistically or independently to stimulate RNAPII pause release.Recently, a new class of transcription factors has been identified that broadly regulates gene expression by promoting RNAPII pause release11. However, ETS1 amplified mRNAs but not rRNA, suggesting that ETS1 more selectively impacts RNAPII transcription rather than transcription by other RNA polymerases. Another difference is that MYC is broadly expressed in many cell types, whereas ETS1 is restricted to select cell types, most notably endothelial and hematopoietic lineages. Thus, ETS1 is an example of a cell-type restricted transcriptional amplifier. Finally, MYC transcription is dependent upon BRD4-mediated RNAPII pause release, making MYC expression susceptible to inhibition by JQ132, the small molecule inhibitor of BRD4, and suggesting a potential positive feedback loop in which MYC induces its own expression through release of paused RNAPII. ETS1 expression, on the other hand, was not affected by JQ1, indicating that this type of positive feedback loop is not a general feature of transcriptional amplifiers. The relationship between RNAPII pause release and MYC, and co-occupancy between MYC and ETS1, suggests possible complex regulatory circuits which are not disentangled by our current study and which will be a fruitful area for further investigation.While ETS1 and MYC share many properties, they also have interesting differences. MYC upregulated total RNA, as it increased rRNAs and tRNAs in addition to mRNAs33. However, the molecular pathways linking these events to RNAPII pause release are not well understood. In this study, we identified two mechanisms by which VEGF influences RNAPII pause release through ETS1. First, VEGF activates the MAPK pathway, and ERK, a major effector of this signaling pathway, phosphorylates ETS1 at T38 and S4125, resulting in ETS1 recruitment of CBP, a lysine acetyltransferase25. CBP then acetylates ETS1 at K8 and K18, enhancing ETS1–BRD4 binding. This interaction recruits P-TEFb to ETS1-occupied chromatin sites, which are highly enriched within promoter regions of expressed genes, positioning P-TEFb to stimulate release of promoter-proximal paused RNAPII. BRD4 has also been reported to be an atypical kinase that itself phosphorylates RNAPII-S220, suggesting an additional mechanism by which ETS1–BRD4 stimulates RNAPII pause release.Promoter proximal RNAPII pausing has been suggested to regulate genes responsive to environmental stress, immunological stimulation and developmental cues. Previously, we and others showed that VEGF promoted RNAPII pausing release on a genome-wide scaleBRD4 to stimulate RNAPII pause release. Alternatively, increased ETS1 chromatin occupancy could recruit CBP to enhance local acetylation of histones and other proteins, thereby stimulating traditional enhancer activity or BRD4-mediated RNAPII pause release. We observed increased ETS1 chromatin occupancy primarily at later time points (hours 4 and 12). However, ETS1–BRD4-mediated stimulation of RNAPII pause release operated as early as 1 h. One interpretation of these data is that ETS1 phosphorylation and acetylation of pre-bound ETS1 leads to rapid BRD4 recruitment and release of paused RNAPII, resulting in rapid upregulation of ERGs. At later time points, VEGF-stimulated ETS1 chromatin occupancy contributes to upregulation of LRGs, potentially through both traditional transcriptional enhancers that increase RNAPII initiation and through enhanced RNAPII pause release.Second, at later time points VEGF stimulated ETS1 chromatin occupancy genome-wide without altering overall ETS1 protein level, and this correlated with gene upregulation. Increased ETS1 chromatin occupancy could directly recruit Our experiments suggest that manipulation of this VEGF–ETS1–BRD4 transcriptional regulatory pathway may be a new avenue to ameliorate retinopathy, cancer, and other diseases of excessive angiogenesis. However, further studies will be required to more comprehensively understand the in vivo significance of VEGF regulation of RNAPII pause release for vessel formation and maintenance. On the other hand, our study also highlights the need to be alert for adverse effects of chronic angiogenic suppression that may result from BRD4 inhibition for treatment of other diseases. In this regard, the ETS1–BRD4 interaction may represent a more selective therapeutic target than BRD4 itself, with potential applications to ameliorate diseases caused by excessive angiogenesis, such as retinopathy of prematurity.Foxn1nu/J, Jax #000711) at the age of 6–8 weeks from JAX were used for Matrigel Plug Assay. Wildtype C57BL6 mice of both genders were used for the OIR experiment at P7–P17. All experiments with mice were performed under protocols approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University and Boston Children’s Hospital.Male nude mice  were transduced with lentivirus for 48 h and then collected by trypsinization. 0.8 million ECFC and 2 million human mesenchymal stem cells  were mixed with 200 µl ice-cold matrigel  and injected under the abdominal skin of nude mice. After 7 days, the gel plugs were dissected out and fixed in 4% paraformaldehyde overnight and preserved in paraffin. Paraffin sections were incubated for 30 min in boiled antigen retrieval buffer  and then blocked with 1% BSA plus 10% serum that matched the host species of the second antibody . Cell proliferation was measured using Ki-67 antibody . Cell apoptosis was measured using anti-activated Caspase 3 antibody . Blood vessels formed were stained by UEA , a lectin that only recognizes human ECs. Proliferative or apoptotic cell numbers and vascular area were measured using ImageJ.The matrigel plug assay was performed as described31. Briefly, neonatal mice (129S) were exposed to 75% oxygen from postnatal day 7 to 12, along with their nursing mothers. At postnatal day 12, mice were returned to room air. Where indicated, 0.5 µl lentivirus (107 per eye) were intravitreaously injected into the retina of anesthetized pups at postnatal day 1, or JQ1 (5 mg kg−1 in 10% hydroxypropyl beta-cyclodextrin) was delivered to the mice daily by IP injection from postnatal day 12 to 16. At postnatal day 17 the mice were anesthetized, retinas were dissected and The retina was whole-mount stained with Alexa Fluor 594 isolectin GS-IB4 , which labels ECs. Retinas were imaged using a Zeiss AxioObserver.Z1 microscope and AxioCam MRm camera. QPathological neovascularization was quantified as a percentage of total retinal areas performed using ImageJ with the SWIFT NV plug-in32.Oxygen induced retinopathy (OIR) was performed as describedVEGF stimulation, cells were cultured overnight in basal culture medium (EBM2 plus 1% FBS). The hour 0 sample was collected just prior to addition of 50 ng ml−1VEGF (R&D Systems). Stimulated cells were collected at 1, 4, and 12 h for ChiP-seq or RNA-seq experiments. JQ1  was used at 500 nM.Primary HUVEC cells at 3–6 passages were purchased from Lonza (CC-2519) and grown in EGM2 full medium  for regular maintenance. Prior to −1, Qiagen; sequences in Table SHUVEC cells were transfected with siRNAs , and then loaded onto the upper side of the transwell. 30 ng ml−1VEGF in 600 µl migration medium was added to the lower chamber to induce migration. Migrated cells were stained with crystal violet  and counted with the “Particle Analysis” function of ImageJ.HUVEC in vitro transwell assay was performed as describedETS1 and domain deletion variants fused with 3×Flag were cloned into the NheI site of Phage-CMV-zsGreen vectors by Gibson assembly. Where indicated, a nuclear location sequence (NLS) was added to ensure nuclear entry. To make ETS1 and BRD4 knock down constructs, we used PLKO.1-Puro to express ETS1 and BRD4 specific shRNA. These vectors were transfected to 293 T cells together with VSV-G expressing envelope vector (PDM2.G) and packaging vector (psPAX2) to generate lentivirus particles. The virus particles were purified and concentrated with PEG6000 from cell culture medium and titered with Lenti-X p24 Rapid Titer Kit (Clontech).−1 of hexadimethrine bromide. The virus-transducing medium was replaced with fresh EGM2 medium 4 h after infection. The transfection efficiency was monitored by zsGreen expression to ensure that infection efficiency each time was >90%.HUVEC or ECFCs were transduced with lentivirus at one to two multiplicity of infection in the presence of 8 µg mlETS1 and GFP protein coding sequences were cloned into a TOPO vector with T7 promoter and 5′ UTR (Gift from Jian Ding & Da-Zhi Wang) and then PCR amplified using Phusion high fidelity polymerase (New England Biolabs) to generate a linear DNA template containing the T7 promoter, 5ʹUTR and polyT for in vitro transcription (see Table S−1 with TE buffer.−1) was added to HUVEC cells for 2 h to limit type I interferon activation. One well of a six well dish was transfected with 500 ng of modRNA using Lipofectamine RNAiMAX. GFP and ETS1 expression were examined by fluorescent microscopy and Western blotting 12 h after transfection.For modRNA transfection, B18R  was added to each sample of extracted RNA. mRNA was selected using the Dynabeads mRNA Purification Kit (Life Technologies). The cleavage, adaptor-tagging, and reverse transcription of purified mRNA was performed with Scriptseq V2 RNA library kit (Epicentre). The concentration of tagged-mRNA and spike-in RNA was measured by real-time PCR using primers complementary to library adapters and Sybr Green chemistry. RNA-seq libraries were sequenced on an Illumina Hiseq 2500 (50 nt paired end reads).Total RNA from 2 × 10ETS1 SC-350, 1:1000; CDK9 SC-281, 1:1000; Abcam: RNAPIIS2 ab5095, 1:1000; ETS1 (pT38) ab59179 1:500; Millipore: RNAPII 05-623, 1:2000; Bethyl Laboratories: BRD4 A301-985A50, 1:2000; Cell signaling: HEXIM1 12604S, 1:1000; ELL2 and AFF 1:1000, gifts from Ali Shilatifard38).Protein expression was measured by western blotting. Uncropped western blot images are shown in Supplementary Fig. −1 PMSF, 1× Protease Inhibitor Cocktail (Roche)) and then incubated with 30 µl M2 Flag antibody magnetic beads (Sigma) overnight at 4 °C. For IP experiments in HUVECs, nuclear extract was incubated with antigen-specific antibody (1 µg per 10 mg input protein) and then pulled down by Protein G Dynal beads (Thermo Fisher).For co-IP experiments in 293T cells (ATCC), expression constructs were cotransfected into 293 T cells with PEI . Total protein was extracted with IP buffer , 10 mM sodium butyrate, 50 µg mlETS1 acetylation sites, after pulldown with 100 µl M2 Flag antibody beads, ETS1-3×Flag was eluted with Flag peptide  for 1 h with shaking at 4 °C. Eluted proteins were resolved by 10% SDS PAGE and stained using SimplyBlue SafeStain (Thermo Fisher). Gel bands were excised, cut into ∼1 mm3 pieces, and digested with 50 mM ammonium bicarbonate solution containing 12.5 ng µl−1 modified sequencing-grade trypsin (Promega) at 4 °C. Peptides were later extracted by 50 mM ammonium bicarbonate solution and followed by one wash with a solution containing 50% acetonitrile and 1% formic acid. Samples were further resolved in a nano-scale reverse-phase HPLC capillary column and analyzed using a LTQ Orbitrap Velos Pro ion-trap mass spectrometer (Thermo Fisher) to generate a tandem mass spectrum of specific fragment ions for each peptide. Peptide identity was determined using the software package Sequest (Thermo Fisher). Data were filtered to between a one and two percent false discovery rate.For detection of BRD4 expression construct was transfected into 293T cells. Whole cell extracts were prepared in IP buffer 2 days after transfection and incubated with 2 µg of biotin-labeled peptide (synthesized by LifeTein) at 4 °C for 2 h. Peptide and bound proteins were pulled down with streptavidin Dynal Beads for 2 h. The Dynal beads were washed four times with IP buffer and then eluted with 1× SDS Laemmli Buffer.For peptide affinity pulldown assays, V5-tagged ETS1 was cloned into pET32b vectors and expressed in BL21 bacterial by induction with 0.1 µM IPTG (Sigma). His-ETS1 was purified with immobilized cobalt affinity resin (Clontech). TNT Quick Coupled Transcription/Translation System was deployed to express Flag-BRD4. Purified His-ETS1 was incubated with in vitro translated Flag-BRD4 overnight at 4 °C. After the pull down with cobalt resin, interacting BRD4 were detected by immunoblot with M2 Flag monoclonal antibody .For the in vitro pulldown assay, His-7 cells (ChIP of modified histones) or 5 × 107 cells (ChIP of transcription factors) were cross-linked with 1% formaldehyde for 10 min at room temperature and stored in −80 °C. The nuclei were extracted with Hypotonic Buffer ) and sonicated in Sonication Buffer  using a Misonix Sonicator 3000 .ChiP-seq was performed as described previously with minor modifications. 2 × 10ETS1 SC-350; P300 SC-585; MYC SC-40. Abcam: H3K27ac ab4729; H3K36me3 ab9050; H3K4me1 ab8895; H3K9me3 ab8898. Active Motif: H3K4me3 39159. Bethyl Laboratories: BRD4 A301-985A50. Millipore: H3K27me3 17-622; RNAPII 05-623B) at 4 °C and then incubated with BSA-blocked Dynabeads Protein G for 4 h. Bead-bound DNA was rinsed with three to five times in RIPA Buffer , de-crosslinked in a 65 °C water bath overnight, digested with RNAase and Proteinase K to remove RNA and protein, respectively, and then purified with the Qiaquick PCR purification kit (Qiagen).Sheared DNA was incubation with 5–10 µg primary antibody (Santa Cruz: ChiP-seq library preparation was perform as described with NEBNext ChIP-seq Library Prep Master Mix Set for Illumina. Libraries were sequenced (50 bp single end) on an Illumina HiSeq 2500.39. We use MACS 1.440 to call ChIP-seq peaks. The read density (reads/nt) around ETS1-induced peaks was calculated using Homer41 and further normalized by region length and total reads number per library (reads per million). To identify differentially occupied genomic regions, MACS was used with the bdgdiff option.FASTQ files of ChiP-seq data were aligned to hg19 using bowtie242, supplemented with ERCC-spike in sequences (http://tools.invitrogen.com/downloads/ERCC92.fa). The FPKM (fragment per kilobase of transcript sequence per million base pairs sequenced) of each transcript was computed with Cufflinks 2.043. The FPKM of Spike-In sequences was used to renormalize gene expression with loess regression using the loess.normalization function in the R affy package44.For RNA-seq, sequenced reads were aligned to hg19 using Tophat2ETS1 and RNAPII ChiP-seq reads within promoters were normalized by library size, and input read density was subtracted. The ETS1 promoter density was correlated with RNAPII promoter density or gene expression (FPKM). The trend lines were generated using smoothed cubic splines (via smooth.spline in R) with no degrees of freedom. In Fig. ETS1 promoter occupancy  was correlated with fold change of gene expression  at same time point upon VEGF stimulation. The bin size of ETS1 fold change used for Fig. For calculating signals within promoter regions, we used TSS ± 1 kb. 45. Plots of set intersections were generated using UpSetR.Gene ontology analyses were performed using GREAT13. Briefly, the ratio of normalized RNAPII ChiP-seq reads in the TSS region  was divided by the reads in the gene body (+300 bp to 3 kb past the transcription end site): where L1 and L2 are the lengths of the TSS and gene body regions.The PI was calculated based on RNAPII ChiP-seq. PI was calculated as described previouslyThe RNAPII and input reads were uniquely aligned to the Refseq genes and normalized against the length of the counting regions and the library size (mapped reads per million bps). For each Refseq region, the normalized RNAPII ChIP-seq reads minus the normalized input reads were used for the final PI calculation. PI was calculated for all genes with transcriptional activity, except where RNAPII ChiP-seq signal was lower than input.https://b2b.hci.utah.edu/gnomex/. ChIP-Seq and RNA-Seq data have also been deposited at GEO under accession code GSE93030.High throughput data are available on the Cardiovascular Development Consortium server at Supplementary InformationPeer Review File


To quantify regulatory divergence, we compared CRE activity across species by testing differential ChIP-seq read depths directly measured for orthologous sequences. We show that the primate regulatory landscape is largely conserved across the lineage, with 63% of the tested human liver CREs showing similar activity across species. Conserved CRE function is associated with sequence conservation, proximity to coding genes, cell-type specificity, and transcription factor binding. Newly evolved CREs are enriched in immune response and neurodevelopmental functions. We further demonstrate that conserved CREs bind master regulators, suggesting that while CREs contribute to species adaptation to the environment, core functions remain intact. Newly evolved CREs are enriched in young transposable elements (TEs), including Long-Terminal-Repeats (LTRs) and SINE-VNTR-Alus (SVAs), that significantly affect gene expression. Conversely, only 16% of conserved CREs overlap TEs. We tested the cis-regulatory activity of 69 TE subfamilies by luciferase reporter assays, spanning all major TE classes, and showed that 95.6% of tested TEs can function as either transcriptional activators or repressors. In conclusion, we demonstrated the critical role of TEs in primate gene regulation and illustrated potential mechanisms underlying evolutionary divergence among the primate species through the noncoding genome.Gene regulation shapes the evolution of phenotypic diversity. We investigated the evolution of liver promoters and enhancers in six primate species using ChIP-seq (H3K27ac and H3K4me1) to profile  We geneMicrocebus murinus), bushbaby (Otolemur garnettii), marmoset , rhesus macaque (Macaca mulatta), chimpanzee (Pan troglodytes), and human (Homo sapiens) . We identified H3K27ac and H3K4me1 peaks in the human liver, treating all human individuals as replicates in the peak calling procedure with MACS2 (FDR < 5%)  . The samDR < 5%) . OverlapSupplemental Table S8). Finally, we tested the regulatory activity of 1-kb fragments from 276 predicted CREs in HepG2 cells using a novel parallelized reporter assay , demonstrating that the majority of the CREs predicted from our ChIP-seq data are likely functional regulatory elements in the human liver.Several lines of evidence indicate that the regions of histone modification we have identified represent active CREs. First, 66.1% of ENCODE HepG2 H3K27ac regions overlapped one of our human peaks . MoreoveWe identified regions orthologous to human peaks from the genomes of nonhuman primates using the Ensembl multiple sequence alignment (MSA) database . We cataSupplemental File S4; Supplemental Fig. S1). Additionally, we compared our H3K27ac data to a recent study of liver CREs in mammals .After extracting ChIP-seq read counts for the six species from the 39,710 regions, we assessed evidence of differential histone modification with DESeq2 , using C mammals . Only 10Supplemental Table S7). As an additional control, we performed a chimpanzee-centric analysis for regions orthologous to chimpanzee H3K27ac consensus peaks and demonstrated that 54.1% of these regions were not differentially histone modified in any of the pairwise comparisons, indicating that species-specific bias is unlikely.The majority of the 39,710 human CREs  did not exhibit significant differential histone modification in any of the tested pairwise comparisons . This suP < 2.2 × 10−16) . Together, our differential histone state analysis results are broadly supported by several studies that have consistently suggested a high degree of regulatory element conservation between closely related species in metazoans  , as obse× 10−16) . On the × 10−16) . We deteetazoans . We noteP > 0.05 for both of the comparisons), suggesting that the restriction of our analyses to six-way orthologous CREs did not significantly bias the results.To assess the extent to which restricting this analysis to the set of six-way-orthologous CREs could affect our results, we re-performed the same analysis for H3K27ac, only considering human, chimpanzee, and rhesus macaque. Of human H3K27ac CREs, 86.9% have an aligned ortholog in both species. In total, 98.3% and 97.5% of the tested CREs were conserved between human and chimpanzee  and between human and rhesus macaque , respectively. Results between the two analyses are thus comparable , and (3) the per-nucleotide pairwise sequence divergence for each species with respect to humans for each of the 39,710 unique orthologous CREs.For each human-centric species × species comparison, we estimated the following: (1) the fraction of differentially modified CREs, (2) the fraction of differentially expressed genes from a set of 10,243 genes with six-way orthologs (P < 2.2 × 10−16).Differential histone modification ranged from 0.79% in the human × chimpanzee to 28.4% in the human × mouse lemur comparisons A. SimilaP = 6.8 × 10−8; human × rhesus macaque, P < 2.2 × 10−16; human × marmoset, P = 1.3 × 10−8; human × bushbaby, P = 3.3 × 10−4; human × mouse lemur, P = 2.4 × 10−4) . Moreover, functionally conserved CREs were significantly more likely to overlap a placental mammal phastCons element (P < 2.2 × 10−16). Together, these data demonstrate that CREs with conserved nucleotide sequence are significantly more likely to have conserved regulatory activity and are associated with conserved gene expression, as previously suggested  or lincRNAs (Fisher's exact test P = 8.9 × 10−14) (P = 0.30)  (P < 2.2 × 10−16). These data demonstrate increased selective pressure to maintain regulatory activity in the vicinity of protein-coding genes.To understand the mechanisms responsible for CRE conservation and turnover, we identified genomic features associated with conserved regulatory activity. CREs associated with protein-coding genes were significantly more conserved than CREs associated with either pseudogenes (Fisher's exact test × 10−14) A. For cl = 0.30) B. Howeve× 10−16) B. IntronP = 6.2 × 10−15) (P < 2.2 × 10−16) .Multiple genomic features indicative of broad regulatory element activity were significantly associated with regulatory conservation. Promoters and enhancers overlapping regions of chromatin accessibility in many cell types  were sig× 10−15) C. Simila× 10−16) D. A GeneSupplemental File S2), such as RFX5, SMAD1, and EOMES. The rapid evolution of immune response genes and TFs is supported by many studies in vertebrates and in Drosophila melanogaster, demonstrating that while the central machinery of immune responses is strongly conserved, components of the extended molecular networks can evolve rapidly or diversify as a consequence of evolutionary competition between hosts and pathogens . Among these master regulators, HNF4A is essential for the differentiation of human hepatic progenitor cells , nearly all of which were SINE-VNTR-Alus (SVAs), and LTRs (mostly ERV1) . While only 0.01% of the human TEs overlap a human CRE, when restricting the analysis to the 39,710 six-way-alignable CREs, 73.5% of TE insertions are found within a differentially modified CRE (Fisher's exact test P < 2.2 × 10−16) .To quantify the contribution of TEs to the regulation of liver gene expression, we annotated each liver CRE based on overlap with RepeatMasker elements : 9877 ofly ERV1) . As we fAlu elements), whereas the remaining 25.0% were Eutherian-specific or older . Given that young TE families are likely depleted from our alignments due to alignment limitations, we believe these estimates are likely conservative. TE enrichment analyses were based on the expected proportion of CRE that contain each kind of TE. Thus, SVAs, and also LTRs, were significantly more abundant than expected, regardless of the TE lineage specificity, despite being among the least common classes of repeats in the human genome .The majority (75.0%) of enriched TE families were relatively young, and specific to humans (SVA-F), Hominidae , Hominoidea (the LTR12 subfamily), Simiiformes (LTRs), or primates (P < 2.2 × 10−16). We therefore investigated whether these recent TE insertions altered the expression patterns of nearby genes in primates. We focused on SVAs and LTR12-C,D,E TE categories present only in human and chimpanzee. Of genes associated with CREs overlapping either SVAs or LTR12-C,D,Es, 17.6% were differentially expressed between apes and the other tested primates. In contrast, genes associated with primate-specific TEs or not associated with any TE are significantly less likely to be differentially expressed between apes and non-ape primates (Fisher's exact test P < 0.002). Genes whose CREs recently acquired TE insertions did not exhibit greater within species expression variability than genes without TE insertions. This suggests that genes that acquire TE insertions are not simply more tolerant to variable expression .In at least one pairwise comparison, 57.3% of CREs harboring TEs were differentially active. Among these, SVAs (2.5%) were overrepresented . In contrast, only 16.0% of evolutionarily conserved CREs contain an annotated TE. LTRs (in particular LTR-12C) and SVAs are the most common TEs overlapping newly evolved CREs   . The regGRIN3A. This gene is involved in physiological and pathological processes in the central nervous system and has been associated with several complex human diseases, including schizophrenia  . Further, our differential histone modification analysis identified an ape-specific ChIP-seq peak overlapping a 1-kb-long ape-specific insertion . This insertion, located 13 kb from the TSS of GRIN3A, is entirely derived from an LTR-12C. The insertion drove strong enhancer activity upon transfection into HepG2 cells (Wilcoxon rank-sum test P = 0.00017) , suggesting that the TE insertion results in a functional enhancer at the GRIN3A locus.The contribution of LTRs to gene regulation has been proposed in previous studies . An examophrenia . GRIN3A P = 0.00117), suggesting that an SVA has a higher probability of becoming a CRE if it inserts near gene promoters .SVAs are a hominid-specific family of composite retrotransposons active in humans , with moJARID2. This gene is an accessory component of Polycomb Repressive Complex-2 (PRC2), recruits PRC2 to chromatin, and is involved in liver, brain, neural tube development, and embryonic stem cell differentiation  . SVAs-Fs overlapping a CRE exhibit significant enrichment for binding sites of known transcriptional repressors such as PAX5, FEV, and SREBF1 (JARID2 SVA-F insertion leads to significantly decreased expression in HepG2 reporter assays (Wilcoxon rank-sum test P = 0.00275) . Furthermore, 78.3% of the genes associated to CREs with human-specific SVA insertions exhibit significantly decreased expression relative to their nonhuman orthologs (Fisher's exact test P < 1.6 × 10−6). This, along with additional validation assays presented below, supports the role of SVA insertions as transcriptional repressors.Among the SVAs with significant histone modification, we identified an intronic CRE for the gene ntiation . Our difd SREBF1 . Indeed,cis-regulatory activity of 69 TE subfamilies, covering all major classes and families of primate TEs . TEs from these families overlap 3897 of our predicted CREs. We synthesized the mammalian consensus sequence for 69 different TE families, cloned them into a luciferase reporter vector with a minimal promoter, and transfected into HepG2 cells to perform luciferase reporter assays. Luciferase expression levels for 66 of the 69 (95.6%) tested TE families were significantly different from the negative control . Strikingly, only 17 (25.7%) of these, mostly LTRs and DNA transposons, increased gene expression  from Texas Biomedical Research Institute and from Duke University Lemur Center. Samples were collected and flash-frozen immediately.We obtained liver tissue samples for three or four individuals belonging to each of the studied species . Libraries were pooled in two different pools based on barcode compatibility, and each pool was sequenced on two Illumina HiSeq 2500 lanes, producing on an average of 42.1 million single-end 100-bp reads per sample.We processed samples from all species in random batches of four to minimize batch effects. We used 4 µg of total RNA to produce barcoded RNA sequencing libraries using the Illumina TruSeq stranded mRNA kit . We used 5–15 ng of input and immunoprecipitated DNA to generate sequencing libraries using the NEBNext Ultra ChIP-seq library kit. Libraries were multiplexed, pooled, and sequenced on a total of 16 Illumina HiSeq 2500 lanes, producing on an average of 40.6 million SE 100-bp reads per sample.We processed samples in six randomly assigned groups in order to minimize batch effects (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). We trimmed sequencing adapters and low-quality base calls using Trim Galore! v0.4.1 .We assessed standard QC measures on FASTQ files using FASTQC v0.11.3 . We used featureCounts  using STAR v2.5, in 2-pass mode .We analyzed differential gene expression levels using read counts, normalized by feature length with DESeq2 , with thSupplemental Table S3; We used a set of 10,243 genes annotated as orthologs in the six species according to Ensembl (BioMart v. 0.9) , using Burrows–Wheeler alignment tool (BWA), with the MEM algorithm . Alignedhttps:// github.com/taoliu/MACS/wiki/Call-differential-binding-events): --no model --ext size 147 -B. We performed QC on peaks called for each specimen using metrics recommended by ENCODE . Samples that did not pass the three main QC metrics  were excluded for any downstream analysis. We called human consensus peaks for H3K27ac and H3K4me1 using MACS2 and the above-described parameters. All human samples passing QC were considered as replicates for the consensus peak calling. The human consensus H3K27ac and H3K4me1 peaks were used to perform all human-centric downstream analyses.We called peaks for each individual using MACS2, at 5% FDR, with parameters recommended for histone modifications . Within each of the loci defined by the 276 liver eQTLs, we predicted a 1-kb CRE, and tested their functionality as described in Supplemental File S4.We obtained a list of 334 putative 1-kb-long CREs overlapping liver eQTLs . Two hunSupplemental File S4.We mapped orthologous sequences using all identified human consensus ChIP-seq peak regions in both the H3K27ac and H3K4me1 experiments using the 40 Eutherian mammals Ensembl MSA. The detailed pipeline is illustrated in design = ∼assay + condition + assay:condition, where the assay indicates either IP data or input data, and condition indicates the species or the group of species.By use of the above-described procedure, for both H3K27ac and H3K4me1, we produced a single matrix including the human peaks having an ortholog in each of the studied species, as well as the associated read count for each histone mark plus the input in all of the six species. Read counts were used for differential ChIP-seq analysis with DESeq2, performing an interaction analysis between the histone marks read counts and their associated input values, using the Wald statistic: Differential histone mark analysis included the same species × species and group × group comparisons described for RNA-seq (FDR < 10%). Further, different FDRs (up to 50%) were tested to assess the robustness of our approach. We initially analyzed differential histone modifications for the two marks independently. Then, overlapping CREs were merged.outliers .TE enrichment analysis was performed using the Supplemental File S4.We compared activity of two predicted functional CREs with the empty pGL4.23 vector as a negative control, as described in the Supplemental File S4.TE constructs were built by synthesizing (GenScript) the Dfam  consensuhttps://github.com/ypar/cre_evo_primates.git) and in the Supplemental File S5.All statistical analyses were performed using R v3.3.1 . Figureshttps://www.ncbi.nlm.nih.gov/bioproject/) under accession numbers PRJNA349047 (RNA-seq) and PRJNA349046 (ChIP-seq).All raw sequence data from this study have been submitted to the NCBI BioProject database (BioProject;


Double-strand breaks (DSBs) result from the attack of both DNA strands by multiple sources, including radiation and chemicals. DSBs can cause the abnormal chromosomal rearrangements associated with cancer. Recent techniques allow the genome-wide mapping of DSBs at high resolution, enabling the comprehensive study of their origins. However, these techniques are costly and challenging. Hence, we devise a computational approach to predict DSBs using the epigenomic and chromatin context, for which public data are readily available from the ENCODE project. We achieve excellent prediction accuracy at high resolution. We identify chromatin accessibility, activity, and long-range contacts as the best predictors.The online version of this article (10.1186/s13059-018-1411-7) contains supplementary material, which is available to authorized users. Double-strand breaks (DSBs) arise when both DNA strands of the double helix are severed. DSBs are caused by the attack of deoxyribose and DNA bases by reactive oxygen species and other electrophilic molecules . DSBs arγ-H2AX, and concentration of DNA-repair proteins at the site of damage ; AUPR = 0.982; Additional file p values associated with the coefficients. We found that all variables, except H3K79me2, H3K9ac, and H4K20me1, were significantly associated with DSBs  found by BLESS, while it also successfully identified an additional 63 587 out of 66 591 DSB sites (95.49%) found by DSBCapture that were not detected by BLESS. The model misclassified only 1552 out of 83 225 predicted DSB sites (1.86%). However, this previous prediction comparison should be carefully interpreted, because the model was learned from DSBCapture and then used to predict DSBCapture and BLESS DSBs.We then compared predictions with BLESS at the genome-wide level Fig. b. We obs%) that were detected by DSBCapture but not by BLESS, and it identified only 605 DSBs out of 73 363 predicted DSBs (0.82%), which may be false positives not detected by DSBCapture and BLESS , and a second model was learned from BLESS and was used to predict DSBCapture DSB sites (the BLESS-trained model). We found that both models had very good prediction performance . Here, we assessed model predictions by training random forests on one biological replicate and by testing prediction accuracy on a second biological replicate. For this, we used the two available DSBCapture biological replicates . AccordiTo assess if the high predictive accuracy of the model was inflated due to the way we selected non-DSB sites (the negative class), we devised different strategies. We first focused on gene promoters and built a random forest classifier to discriminate between promoters with DSBs (16 801 sites) and promoters without (48 838 sites). As previously done, we computed the ROC curve but we also included the PR curve to account for class imbalance. We obtained very good performance for both the ROC curve  to predict DSBs in another cell type (U2OS). For this, we used data that were available for both NHEK and U2OS cells: DNA-seq, CTCF, H3K4me1/3, H3K9me3, H3K27ac, H3K27me3, H3K36me3, and POL2B. The validation is illustrated in Additional file To evaluate prediction accuracy, we used the DSB data  and 77 510 bins with a low DSB score (predicted controls). As expected, we found a high enrichment of both DSBCapture and BLESS reads at predicted DSBs compared to predicted controls Fig. c. On aveak point , 39. Accols Fig. c, and weAdditionally, we performed genome-wide DSB predictions in two other cell types for which endogenous DSB data were available, namely KBM7 (chronic myelogenous leukemia) and MCF-7 (breast cancer). For KBM7 cells, we used DNase-seq, CTCF, H3K4me1/me3, and H3K9me3 for prediction and BLISS for validation . The modAll these results revealed that the strongest predictors including DNase and CTCF were the same in two different cell types, and that accordingly, a random forest learned in one cell type can efficiently predict DSBs in another cell type.%: ; AUPR = 0.910; Fig. %: ; AUPR = 0.919; Fig. β=3.22), corresponding to OR=25 , in agreement with previous predictions based on ChIP-seq data (see above). We also found motifs recognized by factors involved in heavy metal response , in oxidative stress response , in endoplasmic reticulum stress , and in estrogen-induced DNA damage . To assess the significance of those motifs, we built a logistic regression model without any regularization including all motifs with β>0.5. We found that most motifs (22/29) were significantly associated with DSBs . By using state-of-the-art computational models, we achieve excellent prediction accuracy, paving the way for a better understanding of DSB formation depending on developmental stage or cell-type specific epigenetic marks. Thus, our computational approach should allow the genome-wide mapping of DSBs in numerous cell lines and tissues using the ENCODE and Roadmap Epigenomics databases.There are multiple perspectives for this work. Recent developments from deep  neural networks , 46 can https://github.com/taoliu/MACS). The peaks obtained from two biological replicates were intersected to yield more reliable DSB sites for model predictions.All double-strand DNA break data used are summarized in Table We used double-strand DNA breaks mapped by DSBCapture and BLESS in AID-DIvA cells, a U2OS cell line  expressing the AsiSI restriction enzyme fused to a modified estrogen receptor ligand-binding domain . Upon taWe used double-strand DNA breaks mapped by BLISS in KBM7 cells (human myeloid leukemia) from NCBI Sequence Read Archive at SRP099132 . We alsohttps://genome.ucsc.edu/encode). We also used p63 ChIP-seq of keratinocytes from GEO accession GSE59827 [All ChIP-seq and DNase-seq data used are summarized in Table GSE59827 .Table 2http://chip-atlas.org/). We used XRCC4 and γ-H2A.X ChIP-seq for tamoxifen-treated DIvA cells from ArrayExpress accession E-MTAB-1241 [For U2OS cells, we used DNase-seq and H3K27ac ChIP-seq peaks from GEO accession GSE87831 . We usedTAB-1241 .https://genome.ucsc.edu/encode). For MCF-7 cells, we used H3K4me1/me3, H3K9ac/me3, and H3K27me3 ChIP-seq without treatment (DMSO) from GSE23701 [For KBM7 cells, we used DNase-seq from the ChIP-Atlas database, and H3K9me3 ChIP-seq from GSE60056 . InsteadGSE23701 , 52. We http://jaspar.genereg.net). We called transcription factor binding sites over the human genome using the position weight matrices and a minimum matching score of 80%.We used motif position frequency matrices for transcription factor binding sites from the JASPAR 2016 database (https://bioconductor.org/packages/release/bioc/html/DNAshapeR.html).We predicted four DNA shape features using Monte Carlo simulations: minor groove width (MGW) and propeller twist (ProT) at base pair resolution and roll (Roll) and helix twist (HelT) at base pair step resolution using R package DNAshapeR (https://cran.r-project.org/web/packages/ranger) to compute the random forest classification efficiently [https://cran.r-project.org/web/packages/gkmSVM). To learn the model, we mapped epigenomic data, DNA motifs, and DNA shape as follows. For epigenomic data including ChIP-seq and DNase-seq data, we used peak genomic coordinates of a feature  and considered the presence (x=1) or absence (x=0) of the corresponding feature at the DSB site. If a feature peak overlapped only 60% of the DSB site, then x=0.6. For DNA motifs, we computed the number of motif occurrences within DSB and non-DSB sites. For DNA shape, we computed four features including MGW, ProT, Roll, and HelT of DSB sites at base resolution. For each DNA shape feature, we then computed 12 predictors, including quantiles  and the variance to describe the distribution of the feature within a DSB site. The DSB data were next split into two sets: the training set used for learning the model and a test set used for assessing prediction accuracy. We also used R package glmnet (https://cran.r-project.org/web/packages/glmnet/index.html) to compute lasso logistic regression with cross-validation. To assess the prediction accuracy of random forest and lasso regression, we computed the ROC curve and AUROC. To estimate the confidence interval for AUROC, we used the pROC R package (https://cran.r-project.org/web/packages/pROC). We also computed the PR curve and AUPR to assess prediction accuracy when the classes were very imbalanced, especially for genome-wide analyses. For this, we used the PRROC R package (https://cran.r-project.org/web/packages/PRROC).We used R package ranger Additional figures and tables.


After analysis of genomewide distribution of wild-type and RpoZ-defective RNAP by the ChIP-chip method, we found alteration of the RpoZ-defective RNAP inside open reading frames, in particular, of the genes within prophages. For a set of the genes that exhibited altered occupancy of the RpoZ-defective RNAP, transcription was found to be altered as observed by qRT-PCR assay. All the observations here described indicate the involvement of RpoZ in recognition of some of the prophage genes. This study advances understanding of not only the regulatory role of omega subunit in the functions of RNAP but also the regulatory interplay between prophages and the host E. coli for adjustment of cellular physiology to a variety of environments in nature.The 91-amino-acid-residue small-subunit omega , participates in subunit assembly by supporting the folding of the largest subunit, β′, but its functional role remains unsolved except for its involvement in ppGpp binding and stringent response. As an initial approach for elucidation of its functional role, we performed in this study ChIP-chip (chromatin immunoprecipitation with microarray technology) analysis of wild-type and rpoZ-defective mutant strains. The altered distribution of RpoZ-defective RNAP was identified mostly within open reading frames, in particular, of the genes inside prophages. For the genes that exhibited increased or decreased distribution of RpoZ-defective RNAP, the level of transcripts increased or decreased, respectively, as detected by reverse transcription-quantitative PCR (qRT-PCR). In parallel, we analyzed, using genomic SELEX , the distribution of constitutive promoters that are recognized by RNAP RpoD holoenzyme alone and of general silencer H-NS within prophages. Since all 10 prophages in E. coli K-12 carry only a small number of promoters, the altered occupancy of RpoZ-defective RNAP and of transcripts might represent transcription initiated from as-yet-unidentified host promoters. The genes that exhibited transcription enhanced by RpoZ-defective RNAP are located in the regions of low-level H-NS binding. By using phenotype microarray (PM) assay, alterations of some phenotypes were detected for the rpoZ-deleted mutant, indicating the involvement of RpoZ in regulation of some genes. Possible mechanisms of altered distribution of RNAP inside prophages are discussed.The RNA polymerase (RNAP) of IMPORTANCE The 91-amino-acid-residue small-subunit omega (the rpoZ gene product) of Escherichia coli RNA polymerase plays a structural role in the formation of RNA polymerase (RNAP) as a chaperone in folding the largest subunit , but except for binding of the stringent signal ppGpp, little is known of its role in the control of RNAP function. After analysis of genomewide distribution of wild-type and RpoZ-defective RNAP by the ChIP-chip method, we found alteration of the RpoZ-defective RNAP inside open reading frames, in particular, of the genes within prophages. For a set of the genes that exhibited altered occupancy of the RpoZ-defective RNAP, transcription was found to be altered as observed by qRT-PCR assay. All the observations here described indicate the involvement of RpoZ in recognition of some of the prophage genes. This study advances understanding of not only the regulatory role of omega subunit in the functions of RNAP but also the regulatory interplay between prophages and the host E. coli for adjustment of cellular physiology to a variety of environments in nature. Escherichia coli, the RNAP core enzyme is composed of four subunits, α (RpoA), β (RpoB), β′ (RpoC), and ω (RpoZ), in the stoichiometry of α2ββ′ω  in supporting the folding of the longest polypeptide, β′ (RpoC), of 1,407 residues in size. RNAP purified from the rpoZ-defective mutant is associated with GroL, indicating the participation of GroL chaperone in place of ω in the process of RNAP formation  is the key enzyme of transcription. In > α2ββ′ω , 4. In t> α2ββ′ω , which tormation . In agres viable . The cry analysis of the distribution of RNAP along the genome in the presence and absence of RpoZ subunit. Results of the ChIP-chip analysis indicated that the distribution of RNAP lacking RpoZ in growing E. coli K-12 cells is altered compared with that of wild-type RNAP mostly in the middle of the open reading frame (ORF) of a specific set of target genes. Surprisingly, the majority of these genes are located within the cryptic prophages. This finding was confirmed by measuring mRNA for these genes. These unexpected findings raise an interesting possibility of the involvement of RpoZ in the recognition of prophage genes. This could make a breakthrough in the identification of the functional role of RpoZ.In this study, we made attempts to find the regulatory role of RpoZ subunit in the function of RNAP and transcription. As a shortcut approach to get insights into the overall functional role of RpoZ subunit in the transcription of the E. coli K-12 wild-type strain BW25113 and its rpoZ-deleted mutant JW3624 from the Keio collection .To examine the possible influence of the presence and absence of RpoZ on the distribution of RNAP along the genome, we performed ChIP-chip analysis , 29 for llection . The abs10.1128/mSystems.00172-17.1FIG S1 E. coli JW3624. E. coli K-12 BW25113 (wild type) and its rpoZ-defective mutant strain, JW3624, were grown in LB medium until logarithmic phase. Cell lysates were prepared and subjected to Western blotting using anti-RpoA, anti-RpoB, anti-RpoC, and anti-RpoZ. Download FIG S1, PDF file, 0.4 MB.The absence of RpoZ in Copyright © 2018 Yamamoto et al.2018Yamamoto et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the rpoZ-defective JW3624 strains were treated with formaldehyde at a final concentration of 1% for cross-linking between proteins and genomic DNA. After 30 min, lysates were prepared, sonicated for DNA fragmentation, and then subjected to immunoprecipitation with anti-RpoA antibody. RNAP-conjugated DNAs in immunoprecipitates were digested by pronase, and then free DNA fragments were purified using a QIAquick PCR purification kit (Qiagen). Recovered DNA fragments were amplified by PCR using a pair of random primers. DNA from wild-type BW25113 was labeled with Cy3, while that from rpoZ-defective JW3624 was labeled with Cy5. The DNA mixture was subjected to tiling array analysis for mapping DNA segments along the E. coli genome. The ratio of Cy3 and Cy5 fluorescence intensity bound to each probe was plotted along the E. coli K-12 genome, and thus, the relative intensity of Cy3/Cy5 represents the relative distribution between wild-type and RpoZ-defective mutant RNAP at each probe position. RNAP-bound DNA fragments of about 250 bp in size should bind to two or more 60-bp-long probes aligned at 105-bp intervals, and thus, a single peak was estimated to be a background noise.In the middle of exponential growth phase, both wild-type BW25113 and E. coli K-12 genome  to 43 (CP4-6) genes, but the functions are not known for most of these genes. The gene functions of prophages have been predicted based on the sequence similarity of the related original phages .The relative distributions between wild-type and RpoZ-defective mutant RNAP were similar along the entire 2 genome . One surrophages , 32, i.eetic map , altogetetic map , 34. The10.1128/mSystems.00172-17.5TABLE S1 Escherichia coli K-12. Download TABLE S1, PDF file, 0.04 MB.List of prophages in Copyright © 2018 Yamamoto et al.2018Yamamoto et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the rpoZ-deleted mutant strains. The marked increase in the distribution of RpoZ-defective RNAP was observed for about 30 positions by setting a cutoff level of 5 (occupancy relative to that of wild-type RNAP) (flu gene encoding the Ag43 autotransporter (ymfN (putative transcription factor [TF]), ymfK (putative repressor), ycfK (unknown protein), intE (predicted integrase), and ymfM (unknown protein) in e14 prophage; ydaY (pseudogene), stfR (predicted tail fiber protein), and ydaV (putative replication protein) in Rac prophage; ykfC (conserved protein) and ykfI (YkfI-YafW T-AT toxin) in CP4-6 prophage; and yjhH (predicted lyase) and yjhG (d-xylonate dehydrogenase) in KpLE2 prophage  . The gennsporter . The relnsporter . The incnsporter . The setprophage , implyinprophage . Includi10.1128/mSystems.00172-17.6TABLE S2 rpoZ mutant RNAP in growing cells of E. coli K-12. Download TABLE S2, PDF file, 0.1 MB.Increased level of distribution of the Copyright © 2018 Yamamoto et al.2018Yamamoto et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli K-12 genome  and the transcript level was more than 2-fold higher for 6 genes   . This fiyagM, yffL, yagN, yjfJ, yagL, and intF  or the phage-encoded RNAP (in the case of T7-type phages) is responsible for the transcription of late genes  screening system. RNAP holoenzyme was reconstituted from the sigma-free core enzyme and 4-fold excess of purified RpoD sigma. The constitutive promoters were predicted based on the binding sites of this reconstituted RpoD holoenzyme alone in the absence of other DNA-binding proteins (E. coli K-12 genome (argF gene encoding ornithine carbamoyltransferase (CP4-6), the lit gene encoding protease for cleavage of EF-Tu in collaboration with Gol protein (e14), and the trkG gene encoding K+ transporter (Rac), respectively. E. coli K-12 contains two genes, argF and argI, both encoding the ornithine carbamoyltransferase involved in the synthesis of l-citrulline from carbamoyl phosphate and l-ornithine along the pathway of arginine biosynthesis. Thus, the argF gene is considered to be integrated into the prophage CP4-6, together with its promoter, after duplication or transposition of the original argI gene  Location of the binding sites of H-NS silencer along the entire genome of E. coli K-12. Genomic SELEX screening was performed to search for H-NS binding sites as described in Materials and Methods. Download FIG S2, PDF file, 0.6 MB.(A) Location of the constitutive promoters of RNAP RpoD holoenzyme along the entire genome of Copyright © 2018 Yamamoto et al.2018Yamamoto et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli carries the gene silencing system, in which H-NS  assay.To see whether the differential occupancy of genes by RpoZ-less RNAP influences the phenotype of the cell, we studied the growth phenotype of the RpoZ-deleted strain with various carbon substrates and its tolerance to different environmental stresses like osmotic stress, pH, and antibiotic, using phenotype microarray (PM). PM technology allows us to find altered functions of genes by testing mutants for a large number of phenotypes simultaneously . An alterpoZ-defective strain was remarkably sensitive toward sodium iodonitrotetrazolium (NT) violet  and phenethicillin . Gallic acid, a type of phenolic acid, is known as a hydrated natural product of tannin and is commonly used in the pharmaceutical industry to produce a psychedelic alkaloid. Against bacteria, gallic acid is a toxic agent, a mutagen, and a modulator of amyloid formation   violet . NT viol) violet , indicatormation , 52. Theormation , ultimat F05-09) . E. coli as TetM . Resista as TetM .rpoZ-defective mutant to these antibiotics detected by PM assay was then confirmed using individual liquid cultures in the presence of different concentrations of tetracycline  Influence of penicillin on the growth of E. coli K-12 wild-type BW25112 and the rpoZ-deleted mutant JW3624. Cells were grown in LB medium in the absence or presence of the indicated concentrations of penicillin for 24 h. Download FIG S3, PDF file, 1 MB.(A) Influence of tetracycline on the growth of Copyright © 2018 Yamamoto et al.2018Yamamoto et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00172-17.4FIG S4 E. coli K-12 wild-type BW25112 and the rpoZ-deleted mutant JW3624. Cells were grown in LB medium in the absence or presence of the indicated concentrations of tetracycline. After 72 h, the metabolism was estimated by measuring the reduction of tetrazolium. (B) Influence of penicillin on the metabolism of E. coli K-12 wild-type BW25112 and the rpoZ-deleted mutant JW3624. Cells were grown in LB medium in the absence or presence of the indicated concentrations of penicillin. After 72 h, the metabolism was estimated by measuring the reduction of tetrazolium. Download FIG S4, PDF file, 0.04 MB.(A) Influence of tetracycline on the metabolism of Copyright © 2018 Yamamoto et al.2018Yamamoto et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli RNAP functionally differentiates through two steps of protein-protein interaction: first, seven species of the sigma subunit, and second, more than 300 species of transcription factors (TFs)  . TFs intrs (TFs) –60. As aE. coli RNAP indicated that the direct binding of ppGpp to RpoB affects the catalytic activity of RNA polymerization  and 14 TFs exist , of whicprophage .intA regulator) (intA gene for excision of the CP4-57 gene (d-xylose. The main pathway for utilization of d-xylose by E. coli K-12 depends on the xylFGH and xylAB operons. XylR is a TF that activates d-xylose import (xylFGH) and catabolism (xylAB) genes  on CP4-6 prophage regulates only the adjacent yagEF genes on the same CP4-6 prophage  and prophage TFs (XynR and DicF) collaborate, thereby contributing to the stable maintenance of prophages CP4-6 and Qin. Likewise, DLP-12-encoded AppY is induced under anaerobic conditions and, in collaboration with ArcA, plays a role in induction of the expression of the hydrogenase 1 operon (hyaABCDEF)  , DPL-12-gulator) , and CP4gulator) . Regulat-57 gene . The polB) genes . The expB) genes . E. coliprophage , indicataABCDEF) . ArcA isWe conclude that not only structurally but functionally as well, RpoZ is crucial for transcription as it guides the transcription machinery to express the genes necessary for viability under various environmental stresses. Our results indicate a future need to explore the role of RpoZ in alteration of the RNAP functions, including the interaction with as-yet-unidentified TFs, including H-NS and Rho.Escherichia coli strains BW25113 (parent strain) and JW3624 lacking the rpoZ gene (600).poZ gene  were use600) of 0.2 and then incubated in M9 medium containing formaldehyde  for 30 min for cross-linking between proteins and genomic DNA. After termination of the cross-linking reaction by the addition of glycine, cells were collected, washed, and lysed by addition of lysozyme. The lysates were sonicated using a digital Sonifier (Branson) to fragment the genome. After centrifugation, the supernatant of the whole-cell extract was subjected to immunoprecipitation with the anti-RpoA antibody (Neoclone)-coated protein A-Dynal Dynabeads (Invitrogen). RNAP-conjugated DNAs in immunoprecipitation fractions were digested by pronase (Roche), and then the free DNA fragments were purified using a QIAquick PCR purification kit (Qiagen). Recovered DNA fragments were amplified by PCR using a pair of random primers. The amplified DNA fragments from the wild-type strain were labeled with Cy3, while another sample from the rpoZ mutant was labeled with Cy5. The labeled DNAs were mixed and hybridized to a 43,450-feature E. coli tiling array (Oxford Gene Technology)  , 81. AftE. coli K-12 W3110 genome was prepared after sonication of purified genome DNA and cloned into a multicopy plasmid, pBR322. In each SELEX screening, the DNA mixture was regenerated by PCR. For SELEX screening, 5 pmol of the mixture of DNA fragments and 10 pmol reconstituted RNAP RpoD holoenzyme or purified H-NS were mixed in a binding buffer . RNAP RpoD holoenzyme was prepared by mixing the purified sigma-free core enzyme and a 4-fold molar excess of the overexpressed and purified RpoD sigma (Escherichia coli (TEC) at the National Institute of Genetics, Mishima, Japan .The genomic SELEX screening was carried out as previously described , 83. A moD sigma  while H-oD sigma . The seqoD sigma , 83. SELE. coli cell as previously described , and quantitative PCR (qPCR) was conducted using SYBR green PCR master mix (Applied Biosystems) as previously described , a high-throughput technology for simultaneous testing of a large number of cellular phenotypes, was employed according to the manufacturer’s instructions (BioLog) , 87. In