ΦC31 integrase, a site-specific recombinase, can efficiently target attB-bearing transgenes to endogenous pseudo attP sites within mammalian genomes. The sequence features of endogenous binding sites will help us to fully understand the site-specific recognition function by ΦC31 integrase. The present study was aimed to uncover the global map of ΦC31 integrase binding sites in bovine cells and analysis the features of these binding sites by comprehensive bioinformatics methods.In this study, we constructed a ChIP-seq method that can be used to uncover the global binding sites by phiC31 integrase. 6740 potential ΦC31 integrase binding sites were identified. A sequence motif was found that contains inverted repeats and has similarities to wild-type attP site. Using REPEATMASKER, we identified a total of 20,183 repeat-regions distributed in 50 repeat types for the 6740 binding sites. These sites enriched in “regulation of GTPase activity” of in the GO category of biological process and KEGG pathway of signal transmembrane transporter activity.This study is the first time to uncover the global map of binding sites for ΦC31 integrase using ChIP-sequencing method and analysis the features of these binding sites. This method will help us to fully understand the mechanism of the site-specific integration function by phiC31 integrase and will potentially boost its genetic manipulations in both gene therapy and generation of transgenic animals.The online version of this article (10.1186/s41065-018-0079-z) contains supplementary material, which is available to authorized users. The Streptomyces phage ΦC31 integrase is a site-specific recombinase that catalyzes recombination between two short DNA sequences, a bacterial attachment site (attB) and a phage attachment site (attP) –5, ΦC31 2–103 pseudo attP sites have been predicted in mammalian cells [Studies on sequence characteristics of pseudo attP sites in many species revealed that sequence characteristic is one of the most important factors for ΦC31 integrase to recognize the pseudo attP sites and mediate site-specific integration in eukaryotic cells –21. Meanan cells , the actChromatin immunoprecipitation assay (ChIP) is a powerful tool for studying the interaction between protein and DNA in vivo. The ChIP-Seq technology, which combines ChIP with second-generation sequencing technology, can efficiently detect genome-wide DNA segments that interact with functional proteins .In this study we establish an expression platform for ΦC31 integrase in bovine cells and identify the whole set of ΦC31 integrase binding sites in these cells by using ChIP-Seq technology. We equally aimed to analyze the characteristics of these binding sites using bioinformatics.In this study, the ΦC31 integrase gene sequence was isolated from pCMV-INT plasmid and inserted into the pcDNA3.1-neo plasmid in order to construct a eukaryotic selection vector for generating ΦC31 integrase cell lines. The constructed expression vector pcDNA3.1-int-neo (7272 bp) was validated by enzyme digestion and sequencing. MDKB cells were transfected with the constructed plasmid pcDNA3.1-int-neo. After G418 selection, PCR analysis using specific primers for ΦC31 integrase gene and only clones harboring the ΦC31 integrase gene were used for further analysis. The presence of ΦC31 integrase gene was further confirmed by sequencing. To verify the effective expression of ΦC31 integrase, western blot was performed for ΦC31 integrase in wild type and engineered MDKB cells. As shown in Fig. We performed the ChIP-Seq experiments in order to uncover the whole genome binding sites for ΦC31 integrase. The depth of sequencing data of the genome was obtained by analyzing the coverage of the reference sequence and the depth information of the genome while the gene profiling and the depth distribution of the upstream and downstream intervals were obtained by analyzing the depth distribution of the gene in the interval between the gene ontology and the upstream and downstream 2 k intervals  whereas 30,675,258 were mapped from the 36,809,270 clean reads generated from the INTMDBK_treat sample (mapping rate of83.34%). These reads with unique alignment to a unique position on the genome was used for subsequent information analysis. The 66 captured sites (markers) information and location was reported in Additional file Based on a certain analysis model in the entire genome range, the peaks (ChIP-Seq enrichment regions) were scanned to obtain the data on peak location in the genome and the peak region sequence information. The peak search output file was saved into wiggle file format and uploaded to the UCSC Genome Browser for visualization. We identified a total of 6740 ChIP-Seq peaks with total peak length of 4,136,812 and peak mean length of 613.77. The total peak depth was 2,035,208 while the peak mean depth was 301.96 for the INTMDBK_treat sample.The distribution characteristics of gene functional elements of peaks showed that peaks were composed of exons, introns, upstream, downstream, intergenic and other functional elements Fig. . The peaTo determine whether our ChIP-seq experiment recovered the DNA motif assumed to be responsible for sequence-specific ΦC31 integrase–DNA binding, we applied de novo motif discovery to the ChIP-seq peak regions. Using the MEME motif discovery tool, we searched all regions for enriched sequence patterns without any assumption. A highly significant motif (E = 1.4e-043) was found in 236 sequences  for biological process, “metal ion transmembrane transporter activity”  for molecular function, and “membrane region”  for cellular component. The Directed Acyclic Graph (DAG) of biological processes, molecular functions and cellular components were subsequently performed in this study to generate the graphical representation of the results of enrichment analysis showing the inclusion relation and the range of functions . The results , “Focal adhesion”  and “Adrenergic signaling in cardiomyocytes” .To uncover the molecular pathways in which are involved the set of 6740 peaks, we performed GO and KEGG analysesby using bioinformatics tools. In GO functional analysis Fig. , the mosTo validate the functional ΦC31 integrase binding to the genome, we performed qRT-PCR assay to verify the binding of ΦC31 integrase to candidate hotspot binding sites, namely BF27, BpsF1, BpsM1 and BF4a that we had found . Indeed,2–103 pseudo attP sites have been predicted in mammalian cells [The development of genetic engineering techniques for the introduction of gene sequences into mammalian cells has many potential important applications, such as gene therapy, mammary gland bioreactor and mechanism study on gene function  The ΦC31an cells , the numan cells , the actAfter having confirmed the effective expression of ΦC31 integrase, we applied the ChIP-Seq method to uncover the global map of ΦC31 integrase binding sites. A total of 6740 binding sites were identified to be significantly enriched ΦC31 integrase binding regions in the MDBK cell genome. This study is the first of its kind to uncover the global map of binding sites for ΦC31 integrase in the bovine genome. By analyzing genomic DNA sequences, we identified a highly significant sequence motif among the binding sites. This motif contains inverted repeats and has similarities to wild-type attP site. We also have found that these sites contain repetitive DNA. These results strongly suggest that there are sequence characteristics that define pseudo attP sites in the bovine genome, and that the recognition of these sequences is an important feature of site-specific ΦC31 integrase activity in MDBK cell. Repetitive elements are presumably recognized as a suitable substitute for attP sequences.Through GO analysis, the most significant GO terms of these genes around the ΦC31 integrase binding sites were “regulation of GTPase activity” in the GO category of biological process, “metal ion transmembrane transporter activity” in the GO category of molecular function, and “membrane region” in the GO category of cellular component. Using KEGG pathway analysis with KOBAS, we found that the genes located in the core significant pathways were associated with “cGMP-PKG signaling pathway” and “Focal adhesion”. These results may explain why the ΦC31 integrase does not necessitate external energy and cofactors as it is able to catalyze the metabolism of energy and microelement pathways. Maybe this hypothesis is wrong because we didn’t have enough direct evidences. Also we don’t know what influence will happen to the location genes expression if a foreign gene is integrated nearby by ΦC31 integrase.The present study has some limitations since we analyzed only a single ChIP-Seq dataset without animal in vivo experimental validation following ChIP-Seq. However, due to the fact that the engineered cells were well characterized and the ChIP-Seq results were validated by real time PCR, it is probable that sequences obtained here are potential ΦC31 integrase target binding sites. This time, our purpose was to validate that the ChIP-seq method can be used to uncover the global binding sites by phiC31 integrase and next time we will take some strategies to optimize our selecting system, in order to get rid of the random binding sites of phiC31 integrase in the bovine genome.Our study uncovered a large set of ΦC31 integrase binding sites in the bovine genome and analysis the features of these binding sites. This method will help us to fully understand the mechanism of the site-specific integration function by phiC31 integrase and will potentially boost its genetic manipulations in both gene therapy and generation of transgenic animals.The pCMV-Int plasmid harboring ΦC31 integrase was a kind gift of Prof. M. P. Calos . The plasmid pCMV-Int was amplified by PCR using specific forward (5-AAGCTTGGTACCGGTCCGGAATTCCC-3) and reverse (5-CTCGCTGGATCCGGGTGTCGCTAC-3) primers. After digestion by HindIII and xhoI restriction enzymes, the ΦC31 gene fragment was isolated from the PCR product and cloned into the recipient plasmid pcDNA3.1-neo digested by the same restriction enzymes after overnight connection at 16 °C to generate ΦC31 integrase eukaryotic expression vector pcDNA3.1-int-neo.MDKB cells were obtained from the Cell Bank of the Chinese Academy of Sciences. All reagents were obtained from Invitrogen unless otherwise indicated. The cells were maintained on dulbecco’s modified eagle medium (DMEM). Fresh medium was provided to the cells every day, and the cells were passaged every 4–5 days. One day prior to transfection with Lipofectamine 2000 (Invitrogen), cells were treated with Accutase  and plated on Matrigel in conditioned medium. Lipofectamine 2000 transfection was carried out according to the manufacturer’s protocol. We typically used 4 μg of the ΦC31 integrase expression vector pcDNA3.1-int-neo to transfect 2 million cells. After 14–21 days of selection, individual colonies of MDKB cells transformed with the expression vector were manually picked and expanded for further analysis. DNA was extracted from the transgenic colonies and negative MDKB cells with the phenol chloroform procedure.The ΦC31 integrase expression was validated by western blot analysis. MDBK cells were washed with 3 ml 4 °C precooled PBS and heated at 80 °C for 30 min in RIPA buffer, and then centrifuged for 5 min at 10000 g. Next, 20 μg total proteins were eledectrophoresed in a 10% SDS-PAGE gel, and then electrically transferred onto PVDF membranes using a semidry method. ß-actin was used as endogenous control. Detection was performed using the ECL-plus enhanced chemiluminescence system (ECL Amersham Biosciences) after auto radiographic exposure to HyperfilmTM.ECL (Amersham Biosciences). The program ImageMaster I-D  was used for quantitation of bands by densitometry.7 cells) were inoculated into 10 mL DMEM medium and cultured to reach 80–90% confluence. Thereafter, cells were fixed with 1% formaldehyde for 15 min cross-linking and quenched with 450 μL 2.5 M glycine. After washing cells three times with ice-cold PBS, chromatin was isolated by adding 1 ml SDS lysis buffer  followed by disruption with a Dounce homogenizer. Lysates were sonicated (VCX750) to shear the DNA to an average length of 200–500 bp, followed by centrifugation at 15000 g for 10 min to remove cell debris. 300 μL of supernatant were divided into 3 aliquots of 100 μL: 100 μL for the experimental group into which was added 1–10 μg anti-ΦC31 antibodies, 100 μL for the control group without addition of antibodies and 100 μL to which was added 4 μL 5 M NaCl. The 3 aliquots were submitted to 3 h cross-linking at 65 °C followed by agarose gel electrophoresis for the evaluation of ultrasonic disruption results. Thereafter, 900 μL ChIP Dilution Buffer  and 20 μL 50 × PIC were added in the experimental and control samples, and cleared with 60 μlProtein AAgarose/Salmon Sperm DNA. 20 μL genomic DNA (input) was purified from both aliquots of chromatin and quantified on a Nanodrop spectrophotometer. Extrapolation to the original chromatin volume allowed quantitation of the total chromatin yield. ChIP assays were carried out as follows: An aliquot of chromatin (50 mg) was precleared with protein Aagarose beads (Invitrogen). ΦC31-bound genomic DNA fragments were isolated using an anti-bacteriophage ΦC31 integrase antibody (ab93248). After incubation at 4 °C overnight, protein Aagarose beads were used to isolate the immune complexes. Complexes were washed, eluted from the beads using SDS buffer , and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65 °C, and ChIP DNA was purified by PCR Purification Kit (QIAGEN).MDBK or Int-MDKB cells (expressing ΦC31 integrase) .https://github.com/najoshi/sickle) and SeqPrep (https://github.com/jstjohn/SeqPrep). The sequencing output raw reads were trimmed by stripping the adaptor sequences and ambiguous nucleotides and reads with quality scores less than 20 and lengths below 20 bp were removed.To ensure the accuracy of subsequent bioinformatics analysis, the original sequencing data was filtered to obtain high-quality sequencing data (clean data). Quality control of the sequencing data was performed using Sickle  and specific primers. Amplification reactions were then performed with an Applied Biosystems 7900 real-time PCR system. The primers used for BF27 binding site were 5-GTACTTATTTTTGTATTTGAAACCACCT and TCTGGGTGTTTTTACATT-3; for BF4a, 5-CCATAAAAGGAATATACTTGAAA-3 and 5-ACCAACATGGCAATCGGGGACAT-3; for BPSF1, 5-GGACCTAGAAGGGGCTCATAACT-3 and 5-TGTATACTCACACCACTGTCCTA-3; for BPSM1, 5-GCCGTTTGTTGGGTTAGCTTTTCAGAA-3 and 5-GTCATCCTGCCTTGCAGGCTGTTTCTT-3; for GAPDH, 5-TTCACCACCATGGAGAAGG-3 and 5-GGCATGGACTGTGGTCATGA-3. Use AppliedBiosystems SDS2.4 and RQ manager software to analyze data.http://genome.ucsc.edu). UCSC provides genomic reference sequences for a large number of species, including a large number of gene annotation information and comparative genomics information.To identify significant ΦC31 binding sites in the sequencing data, the whole genome peak scan was performed using the UCSC Genome Browser following the UCSC Genome Browser Instructions (http://meme-suite.org) was ran with the command line summary below: meme INTMDBK_treat.peaks.fa -dna -mod zoops -revcomp -nmotifs3 -maxw30 -minw 6 -maxsize 26,000,000 -oc. /INTMDBK_treat_meme.GO functional enrichment analysis of Peak related genes.The MEME web service (Gene Ontology (GO) is an internationally standardized gene function classification system that provides a set of dynamically updated standard vocabularies to comprehensively describe the properties of genes and gene products in organisms. GO has a total of three ontologies describing the molecular function of the gene, the cellular component in which it is located, and the biological process involved. GO is the basic unit , each term corresponds to a property.http://www.geneontology.org/) and calculate the number of genes for each term. The hypergeometric test was applied to find out significant GO enrichment of peak-related genes based on the entire genome background using the formula [The GO enrichment analysis was used to map all the peak-related genes to terms in the Gene Ontology database , a graphical representation of the results of enrichment analysis of peak-related genes was also equally performed.Where N is the number of genes with GO annotation in the genome; n is the number of peak-related genes in N; M is the number of genes annotated for a particular GO term in the genome; m is the number of peak-associated genes annotated for a particular GO term. The GO term which satisfied the condition was defined as the GO term which was significantly enriched in the peak-related gene if the Bonferroni corrected http://kobas.cbi.pku.edu.cn) for KEGG pathway enrichment analysis. The calculation principle was the same as GO enrichment analysis. Terms with corrected p-value of < 0.05 was considered significant.This study used KOBAS  of “Biological Processes” obtained from GO enrichment analysis. Figure S6. Directed Acyclic Graph (DAG) of “Cellular Components” obtained from GO enrichment analysis. Figure S7. Directed Acyclic Graph (DAG) of “Molecular Functions” obtained from GO enrichment analysis. (ZIP 726 kb)GO functional analysis output file. Additional file 2:Captured ϕC31 integrase binding sites (markers) information and location. (XLSX 15 kb)Additional file 3:The peak calling output file and peak annotation are reported. (XLSX 109 kb)Additional file 4:KEGG functional analysis output file. (XLSX 855 kb)Additional file 5:GO functional analysis output file. (XLSX 29 kb)


RNA polymerase (RNAPII) transcription occurs pervasively, raising the important question of its functional impact on other DNA-associated processes, including replication. In budding yeast, replication originates from Autonomously Replicating Sequences (ARSs), generally located in intergenic regions. The influence of transcription on ARSs function has been studied for decades, but these earlier studies have neglected the role of non-annotated transcription. We studied the relationships between pervasive transcription and replication origin activity using high-resolution transcription maps. We show that ARSs alter the pervasive transcription landscape by pausing and terminating neighboring RNAPII transcription, thus limiting the occurrence of pervasive transcription within origins. We propose that quasi-symmetrical binding of the ORC complex to ARS borders and/or pre-RC formation are responsible for pausing and termination. We show that low, physiological levels of pervasive transcription impact the function of replication origins. Overall, our results have important implications for understanding the impact of genomic location on origin function. The annotation of transcription units has traditionally heavily relied on the detection of RNA molecules. However, in the last decade, many genome-wide studies based on the direct detection of RNA polymerase II (RNAPII) have clearly established that transcription extends largely beyond the limits of regions annotated for coding functional RNA or protein products . The genS. cerevisiae, pervasive transcription accounts for the production of a multitude of transcripts generally non-coding, many of which undergo degradation in the nucleus or the cytoplasm  complex and is responsible for terminating transcription of genes that do not code for proteins. Small nucleolar RNAs (snoRNAs) and cryptic unstable transcripts (CUTs), a prominent class of RNAPII pervasive transcripts, are typical targets of NNS-dependent termination. One important feature of this pathway is its association with proteins involved in nuclear RNA degradation such as the exosome and its cofactor, the Trf4-Mtr4-Air (TRAMP) complex. The released RNA is not exported to the cytoplasm but polyadenylated by TRAMP and nucleolytically attacked by the exosome that trims snoRNAs to their mature length and fully degrades CUTs.Recent studies in yeast and other eukaryotes have shown that constitutive and regulated readthrough at terminators provides a very significant contribution to pervasive transcription . Fail-saThe ubiquitous average coverage of the genome by transcription, coupled to the remarkable stability of the transcription elongation complex, raises the important question of the efficient coordination of machineries that must read, replicate, repair and maintain the same genomic sequences. The crosstalks between transcription and replication are paradigmatic in this respect.Eukaryotic cells faithfully duplicate each of their chromosomes by initiating their replication from many origin sites . To ensuS. cerevisiae origins are specified in cis by the presence of Autonomously Replicating Sequences (ARSs). Within each ARS, ORC recognizes and binds specifically a bipartite DNA sequence composed of the ACS   . We alsoitation) . Both meitation) .We retrieved a total of 228 origins that we oriented according to the direction of the T-rich strand of their proposed ACS . Origins−20, Although the sharp decrease of RNAPII signal immediately preceding the ACS is suggestive of transcription termination, it is possible that RNAPII occupancy downstream of the ACS decreases because of a shorter persistency of the elongation complex in these regions, for instance because of higher transcription speed. We thus sought independent evidence of transcription termination before the ACS. Transcription termination is accompanied by release of the transcript and generally by its polyadenylation. Therefore, we mapped the distribution of polyadenylated RNA 3’-ends around origins as a proxy for transcription termination , blue. BThese observations strongly suggest that the landscape of pervasive transcription is significantly altered by the presence of replication origins. Incoming RNAPIIs are paused with an asymmetric pattern around ARSs and termination occurs upstream of the primary ACS.in vitro for efficient pre-RC assembly and suggested to play an important role for origin function in vivo  at these origins, we examined local transcription by RNAPII CRAC under conditions in which an essential component of either the CPF-CF or the NNS termination pathways is affected, that is in an n method . We reasARS305 (YCL049C or CUT040) and are subjected to roadblock termination at both the main (blue) or the putative secondary ACSs  or at CUT040 upon Nrd1 depletion , leads to increased accumulation of RNAPII at both ACSs and to transcription invading the ARS.In the case of ARS305 , low lev element ), respecARS413  because no additional initiation sites could be detected, even in cytoplasmic and nuclear RNA degradation mutants (data not shown). When the transcription load was increased by affecting the termination of YDL073W and YDL072C in rna15-2 cells at the non-permissive temperature (light green tracks), RNAPII occupancy at the RBs increases and some readthrough within the ARS occurs. This example suggests that both ACSs are occupied by the ORC complex, although it is not clear whether they function in conjunction or alternatively in different cells.Two ACSs were previously proposed for ARS413 : sense AARS413  and antiARS413 . TranscrARS431 . Consistent with the notion that roadblock termination occurs at ARS305, the transcript released was subject to exosomal degradation and was stabilized by deletion of Rrp6 (lane 4). This short RNA disappeared when the ACS sequence was mutated, to the profit of a longer species resulting from termination downstream of ARS305, confirming the ACS-dependency of termination (lane 5). ARS305 contains, in addition to the ACS, two motifs, B1 and B4, required for full origin function  and the B1 motif are additionally required.This notion was further supported by Northern blot analysis of the transcripts produced when a shorter onstruct . A shortfunction . Interesfunction . To asseTo provide independent evidence that ORC bound to the ARS triggers transcription termination by a roadblock mechanism, we took advantage of the finding that many sequences with a perfect match to the ACS consensus do not bind ORC. We used published coordinates of ACSs bound (ORC-ACSs) or not recognized (nr-ACSs) by the ORC complex in ORC-ChIP-seq experiments , and mapARS404 and ARS1004, located downstream of the YDL227C and YJL217W genes, respectively. In both cases, RNAPII signals are present immediately upstream of the T-rich strand of the ACS, presumably because of transcription events reading through the upstream terminator that are roadblocked at the site of ORC binding  against the probability of firing (PF), we identified two classes of origins: the first that aligns almost perfectly on the diagonal  is the product of the probability of licensing (PL) by the probability of firing once licensing has occurred (PF|L), the latter is defined by the ratio PF/PL. We then sought correlations between the total level of transcription over each ARS and the efficiency with which it is activated at the post-licensing step (PF|L). Strikingly, origins that have a high PF|L are generally insensitive to transcription , similar in strength and characteristics to the HSP104 promoter (HSP104 RNA (data not shown), implicating that transcription termination occurs efficiently in this construct. This is expected to allow origin function, even under conditions of the strong transcription levels induced by the TET promoter. Indeed, after deletion of the ARS present in the plasmid backbone (ARS1), the plasmid could still be maintained in yeast cells, showing that it can rely on ARS1206 for replication  or the antisense (pAS) orientation  relative to the antisense (pAS) orientation  and ARS209, also used in these studies, might also contain termination signals from the contiguous TRP1 and HHF1 genes, respectively. Another possibility is that transcription termination occurred both at the roadblock site (the ACS) and internally, but the former was missed because of the poor stability of the RNA produced. As discussed below, we also found evidence of internal termination, but preferentially when examining the fate of antisense transcription (i.e. entering the ARS from the opposite side of the main ACS oriented by its T-rich strand).These findings are seemingly in contrast with earlier reports showing that inserting model ARSs in a context of strong transcription leads to transcription termination lication . One posThe transcriptional footprint observed for antisense transcription shows a large peak when origins are aligned on the main ACS but condenses into a well-defined peak when the alignment is done on the presumed secondary ORC-binding sites  Figure , suggestWe propose that the asymmetrical distribution of RNAPII at ARS borders relates to the ‘quasi-symmetrical’ model for pre-RC assembly on chromatin, as proposed by Coster and Diffley . Earlierin vitro  in a 100nt window located at position −500 from the primary ACS across all available sites. Using this estimate, we calculated the probability of detecting the number of termination events actually observed at every position using the binomial distribution and correcting for the multiple testing factor .ORC-ACSs are defined as the best match to the consensus under ORC ChIP peaks . nr-ACSsFor the boxplot analyses shown in For the correlation analyses shown in Position Weight Matrix) using the function PWM from the R Bioconductor package ‘biostrings’ using default options. We used the ‘matchPWM’ function from ‘biostrings’ to look for the best match for putative secondary ACSs in the range between the position +10 to+400 relative to the main ACS. We then calculated the distribution of distances between the main and the putative secondary ACSs and the distribution of matching scores  plasmids were constructed by inserting a 548 bp fragment containing the wild-type ARS305, as defined in OriDB v2.1.0  in vector pDL454  plasmid was constructed by inserting the HSP104 gene and the downstream genomic region containing the HSP104 terminator and ARS1206 into pCM188  by homologous recombination in yeast. ARS1 was removed from pDL214 by cleavage with NheI and repaired by homologous recombination using a fragment lacking ARS1 to obtain ‘pS’. PTETOFF-HSP104-6021sra (or ‘pAS’) was constructed by reversing ARS1206 orientation in ‘pS’ using homologous recombination in yeast.PRNAs were prepared by the hot phenol method as previously described . NortherACT1 mRNA levels.For RT-qPCR analyses, RNAs were reverse transcribed with 200U of M-MLV reverse transcriptase (ThermoFisher) and strand specific primers for 45 min at 37°C. Reactions were diluted 10 times before qPCR analyses. Quantitative PCRs were performed on a LightCycler 480 (Roche) in 384-Multiwell plates (Roche) in 10 µL reactions that contained 1% of the reverse transcription mix and 0.25 pmoles of each priming oligonucleotides. Quantification was performed using the ∆∆Ct method. ‘No RT’ controls were systematically analyzed in parallel. Each transcription level reported represents the mean of three independent RNA extractions each assayed in duplicate qPCRs. Error bars represent standard deviations. Oligonucleotides used for RT-qPCR are reported in Table 1. Unless indicated otherwise, transcription levels were normalized to ARS1206-borne  plasmid and plated on complete synthetic medium lacking uracile. Single transformants were used to inoculate liquid cultures of CSM −URA that were grown to saturation. Saturated cultures were back diluted into rich medium and maintained in logarythmic phase (i.e. below 0.8 OD600) for the indicated number of generations. Aliquots were pelleted, rinsed with water and seven-fold serial dilutions were spotted on YPD and CSM −URA, starting at 0.3 OD600. Growth on YPD plates was used to infer that the same numbers of cells were spotted, while reduced numbers of cells growing on CSM−URA reflected plasmid loss over the indicated number of generations.Cells were transformed with the indicated Datasets used in this study are available from GEO with accession numbers GSE56435 , GSE7558 In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Bruce Stillman as Reviewing Editor and Kevin Struhl as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Pervasive transcription fine-tunes replication origin activity" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This paper aims to define how transcription influences replication origin function in budding yeast. It is known that transcription through origin sequences can influence DNA replication, but it is unclear how many origins are affected. mRNA abundance, measured at steady state, is a poor indication of ongoing transcription so Candelli et al. use a newer method to map nascent RNA and RNA polymerase occupancy across the genome. Their most prominent conclusions are: (1) that replication origins function as strong transcription terminators; (2) termination sites are correlated with ACS locations; (3) most origins utilize two ACS sequences – by implication two sites for ORC loading; (4) ORC, Cdc6 or the entire pre-RC mediates transcription termination; (5) transcription through some replication origins influences origin activity.Taken together this report provides a solid advance for our understanding of how transcription may influence origin activity, or how assembly of proteins at origins of DNA replication affect transcription. The major finding, which is convincingly supported by the data, is the demonstration that proteins assembled in an ORC- and Cdc6-dependent manner can inhibit transcriptional elongation. This result is likely to be of broad interest. The second important finding is that the level of transcription across origins may influence origin activity. Beyond these findings the paper does not elaborate a more comprehensive understanding of how transcription may influence origin function. The role of the second ACS/ORC binding site is not particularly well supported; how and why some origins are more sensitive to transcription is also not defined.The paper is of sufficient interest and the authors are requested to respond to specific issues, some relating to individual conclusions. The following are specific issues that the reviewers have raised about the manuscript that need to be addressed before publication.Essential revisions:1) It is clear that transcription can terminate in the vicinity of the primary ACS, however the data showing termination near the secondary ACS is unconvincing. The data in Figure 1D, E are noisy (see point 2 below) and the small peak near the secondary ACS could be an artifact from a single over-represented site.Further investigation of the second ACS site in the paper is also not particularly convincing. Thus, the conclusions about these data supporting the model that multiple ORC proteins assemble the pre-RC are not warranted.2) Related to point 1, the authors use the technique of Schaughency et al., 2014 for measuring RNA reads at genomic loci. The data in Schaughency et al., 2014 show mean reads of 30-60 , but the data in Figure 1 A show mean number of RNAPII reads of 2-7 near origin sequences. How significant are these reads? Is there anywhere in the genome that does not have reads of at least 2-7 ? The low reads could be experimental background and the dip could be because a protein (ORC-Cdc6) is bound at the origin. Again, what are the mean reads near poly(A)+ sites analyzed in Figure 1B. Only a summary of the location of these reads is shown. Showing the mean Poly(A)+ reads over the origin in addition to the summary analysis would be more convincing. Figure 1D: Again, the number of reads is very low and this could be due to random noise. What is convincing about the peaks, particularly at secondary ACS sites?3) The data in Figure 2 is interpreted to show significant roadblock of RNA polymerase II at ACS sites. However, given that the mapped signal fluctuates significantly across the regions shown, it is unclear how the authors can conclude that one specific site is a "roadblock" whereas another nearby site isn't? Specifically, Figure 2A and subsection “RNAPII pausing and transcription termination occur at ARS borders”. The light green track in Figure 2A show that in the mutant, many peaks of RNAPII pausing are observed, but the authors only point to the ACS. Why? What about all the other peaks in the track? Same for Figure 2B, Figure 2C and Figure 2D and yet the authors point out that ACSs are occupied by ORC. But ORC or the pre-RC is most likely not at the other pauses.4) The Abstract states, " We provide evidence that quasi-symmetrical binding of the ORC complex to ARS borders is responsible for pausing/termination." This is too strong a statement since they have not shown that ORC does this. Indeed, it is shown that Cdc6 mutants also compromise termination. It could be that loaded pre-RC or the Mcm2-7 double hexamers are the terminator, not ORC. Please clarify the abstract, otherwise it will become accepted that ORC causes terminationeLife. Your revised article has been favorably evaluated by Kevin Struhl (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Pervasive Transcription Fine-tunes Replication Origin Activity" for further consideration at eLife, with some modifications or a brief response to issues raised below. The paper does show that proteins assembled at ARS origins can contribute to termination of transcription through the origin. The problem remains in the interpretation of the results and here the authors even propose contradictory ideas. In re-reading the paper, there are some miss-statements or contradictions that could be corrected.The revised paper has been improved and the authors have addressed the questions raised by the reviewers. As such it is now appropriate for publication in These are:Introduction, the authors should reference that Steve Bell's laboratory, who performed the cited single molecule experiments, but has published subsequent research consistent with the two ORC-Cdc6 model. Thus, the paragraph about a controversy should be toned down and their reference cited in the Introduction [Warner et al. (2017)].Introduction. The paragraph starts of by stating that "Studies have proposed that transcription might activate replication origins" and then in the second sentence Marahrens and Stillman, 1992 is cited as evidence. Marahrens and Stillman, 1992 did not propose that transcriptional activators activate replication by via transcription, but by activators likely modulating chromatin structure. Thus, this paper does not support the hypothesis by the authors. Even Stagljar et al., 1999 did not show that transcription activates an origin. These papers are not "in apparent contrast with the demonstration that strong transcription through ARSs is detrimental for their function" (Introduction). While it is true that transcription is detrimental for replication initiation, accurate citing of references is needed.Subsection “Topological organization of replication origin factors detected by transcriptional footprinting” and later in subsection “Functional implications for pervasive transcription at ARS”. The authors argue that transcription may "play an important role" in the initiation of DNA replication by pushing the first loaded Mcm2-7 hexamer away from the ORC binding site. This is clearly not the case since initiation of DNA replication in vitro is very efficient and does not require transcription, even on chromatin templates . It seems the authors are stating on one case that ARSs and their binding proteins terminate transcription and yet then state that transcription may play an important role in replication initiation. This, based on the data presented, does not seem likely (at least it has not been demonstrated). Indeed, subsection “Functional implications for pervasive transcription at ARS”, the authors state " We propose that transcription though origins might induce similar changes that are susceptible to outcompete binding of ORC and/or pre-RC formation." They cannot have it both ways. Essential revisions:1) It is clear that transcription can terminate in the vicinity of the primary ACS, however the data showing termination near the secondary ACS is unconvincing. The data in Figure 1D, E are noisy (see point 2 below) and the small peak near the secondary ACS could be an artifact from a single over-represented site. Further investigation of the second ACS site in the paper is also not particularly convincing. Thus, the conclusions about these data supporting the model that multiple ORC proteins assemble the pre-RC are not warranted.2) Related to point 1, the authors use the technique of Schaughency et al., 2014 for measuring RNA reads at genomic loci. The data in Schaughency et al., 2014 show mean reads of 30-60 , but the data in Figure 1 A show mean number of RNAPII reads of 2-7 near origin sequences. How significant are these reads? Is there anywhere in the genome that does not have reads of at least 2-7 ? The low reads could be experimental background and the dip could be because a protein (ORC-Cdc6) is bound at the origin. Again, what are the mean reads near poly(A)+ sites analyzed in Figure 1B. Only a summary of the location of these reads is shown. Showing the mean Poly(A)+ reads over the origin in addition to the summary analysis would be more convincing. Figure 1D: Again, the number of reads is very low and this could be due to random noise. What is convincing about the peaks, particularly at secondary ACS sites?Essential revisions #1 and #2 are very much related and will be answered together below. For matching questions and answers we added to each question or group of related questions a letter  that refers to the answer.A) On the significance of RNAPII pausing at the primary ACSsmean levels of reads but the median, which was done precisely in order to undermine the contribution of highly represented sites. With a less stringent analysis, i.e. when plotting the mean values  and clearly due to outliers that do not represent the overall population (in this case probably a site of initiation after the origin).Use of the median is more stringent and generally more appropriate for representing distribution that deviate from normality.We considered that good evidence for the existence of a significant signal at the roadblock would be loss of that signal immediately after the ACS. Therefore, we compared the reads levels in the 100nt before and after the ACS for every region . The distributions of these values for the 100 origins with the highest levels of surrounding transcription are now shown in Figure 1—figure supplement 2B under the form of Box Plots. It appears clearly that the median signal is higher before the ACS and drops immediately after, and that the loss of signal is highly significant according to both parametric and non-parametric tests. A strong statistical significance is observed even when all the origins are considered (data not shown).rna15-2 or NRD1::AID).This signal derives from the crosslinking of the nascent RNA to the polymerase, and the absence of signal (and nascent RNA) can only derive from the failure of RNAPII from actively transcribing that particular region. The drop in the RNAPII signal occurs thereforebecause of the presence of a protein complex bound at the ACS. This is not a technical artefact as the one that could be expected from ChIP datasets, in which the absence of crosslinking could be due to the steric hindrance due to another complex bound at the same location. Lastly, we would also like to stress that the poor signal around ACSs cannot be ascribed to the poor "mappability" of reads derived from such AT rich regions, because: i) similar AT-rich regions elsewhere in the genome have signals and ii) a signal at origins can be detected when incoming transcription is "forced" in transcription termination mutants (e.g. B) On the significance of RNAPII pausing at the secondary ACSsWe agree with the referees that we did not provide a strong experimental support for the existence of a second ORC complex bound to the secondary ACS. We did our best to tone down these claims and we only claim consistency with this hypothesis in the revised manuscript.Concerning the analyses of transcription around the putative secondary ACSs, we did not intend to claim that secondary ACSs induce transcription termination and apologize if this was not sufficiently clear in the manuscript. In Results section and subsection “Topological organization of replication origin factors detected by transcriptional footprinting”of the original manuscript we had proposed that the best interpretation of the data shown in Figure 1D-E is that RNAPII pauses at the secondary ACS but that termination occurs later on, which is actually the basis of the asymmetry that we observed. We have added a few sentences to strengthen this notion in the revised version of the manuscript. We also added a probability profile of termination around secondary ACS  from which it is clear that statistically significant termination only occurs after the ACS.median number of reads and not the average. By definition, the peak in the median profile upstream of the secondary ACSs cannot be due to the contribution of only a single (or a few) overrepresented values as it depends on half of the values of the distribution. To further support the significance of RNAPII pausing upstream of the secondary ACSs, we compared the distributions of RNAPII levels before and after the aligned, putative secondary ACSs. Here again we found a very significant decrease in the signal .Concerning the significance of the RNAPII occupancy peak upstream of the putative secondary ACSs, we plotted in Figure 1D the C) On the significance of termination at primary ACSsFinally, the referees request the metaprofile of the mean level of RNA 3’-ends around ACSs. We would like to stress that the question addressed in Figures 1B,1E, Figure 4A and 4B was whether termination occurred upstream of origins, using as a proxy the presence/absence of RNA 3’ ends in the regions analyzed. Because the RNAs produced can have different stabilities, the average 3’-ends signal (as opposed by the 3’-ends count) is strongly influenced by the steady state level of the RNAs. Using this indicator for termination might introduce a major bias, as one or a few RNAs with high steady state levels would dominate the signal, which would be artefactual. This was particularly relevant at origins because many of the RNAs produced in these regions are poorly abundant, and because roadblocked transcription events tend to produce mainly non-coding and unstable RNAs .Since it is the occurrence of termination events that we profile independently of the RNA steady state levels, profiling the average 3’-ends signal would therefore not be appropriate.Nevertheless, to convince the referees that there is increased occurrence of termination events immediately preceding the average ACS, we calculated the statistical significance of the observed number of termination events at the ACS peak. To do so, we adopted the H0 hypothesis that termination occurs with equal frequency in the whole region of alignment (-500 to +500 from the ACS), and calculated a p-value for each position based on the frequency observed in the first 100nt window (position -500) and on the actual values observed at every position.upstream of the ACS, demonstrating that termination occurs with higher frequency in this region (p<10-20). We also performed the same analysis around secondary ACSs , from which it is clear that termination occurs with high significance only after the ACS.As shown in Figure 1—figure supplement 2D, the frequency of termination events is not significantly different in most of the region. However, a prominent peak of very low p-value is seen immediately We conclude from these analyses, which have been included in the revised version of the manuscript, that transcription termination occurs immediately upstream of the primary ACSs with high statistical significance.3) The data in Figure 2 is interpreted to show significant roadblock of RNA polymerase II at ACS sites. However, given that the mapped signal fluctuates significantly across the regions shown, it is unclear how the authors can conclude that one specific site is a "roadblock" whereas another nearby site isn't? Specifically, Figure 2A and subsection “RNAPII pausing and transcription termination occur at ARS borders”. The light green track in Figure 2A show that in the mutant, many peaks of RNAPII pausing are observed, but the authors only point to the ACS. Why? What about all the other peaks in the track? Same for Figure 2B, Figure 2C and Figure 2D and yet the authors point out that ACSs are occupied by ORC. But ORC or the pre-RC is most likely not at the other pauses.majority of origins; otherwise it would not be detected by the median. Also, note that RNAPII pausing peaks at ACSs often appear after a region of low signal, which is consistent with an accumulation of polymerases fed by low levels of upstream readthrough transcription. To better highlight this point we have modified Figure 2 by adding an inset at panel 2A.We thank the referees for giving us the opportunity to clarify this point. The snapshots in Figure 2 are only shown with the purpose of illustrating the behavior of RNAPII around specific origins. These snapshots alone do not demonstrate that the pausing that is observed is specifically due to the presence of an origin. Indeed, many additional sites of pausing are observed within genes and sometimes in the downstream region, at a distance from origins. What is telling us that ACS sequences induce pausing is the aggregate signal , in which other sites of pausing are averaged out while pausing immediately preceding the ACSs remains visible. This implies that pausing occurs at the 4) The Abstract states " We provide evidence that quasi-symmetrical binding of the ORC complex to ARS borders is responsible for pausing/termination." This is too strong a statement since they have not shown that ORC does this. Indeed, it is shown that Cdc6 mutants also compromise termination. It could be that loaded pre-RC or the Mcm2-7 double hexamers are the terminator, not ORC. Please clarify the abstract, otherwise it will become accepted that ORC causes termination.We agree, we did not show that ORC alone is sufficient for termination, only that is necessary. We modified the abstract as requested.The revised paper has been improved and the authors have addressed the questions raised by the reviewers. As such it is now appropriate for publication in eLife, with some modifications or a brief response to issues raised below. The paper does show that proteins assembled at ARS origins can contribute to termination of transcription through the origin. The problem remains in the interpretation of the results and here the authors even propose contradictory ideas. In re-reading the paper, there are some miss-statements or contradictions that could be corrected.We thank the editors for re-evaluating favorably our revised manuscript. We briefly answer below to the last concerns.These are:Introduction, the authors should reference that Steve Bell's laboratory, who performed the cited single molecule experiments, but has published subsequent research consistent with the two ORC-Cdc6 model. Thus, the paragraph about a controversy should be toned down and their reference cited in the Introduction [Warner et al. (2017)].This reference had been included in the original manuscript subsection “Topological organization of replication origin factors detected by transcriptional footprinting”) to discuss the possible sliding of an intermediate during helicase loading. Results presented in Warner et al. are consistent with the quasi-symmetrical model, as the authors suggest, but do not prove that ORC binds to the B2 element when sliding is prevented. We therefore cited the work as "see also", together with the Coster et al. article. We also eliminated the notion of controversy, but we believe that the single molecule studies of the Bell and Greene laboratories should still be referenced.Introduction. The paragraph starts of by stating that "Studies have proposed that transcription might activate replication origins" and then in the second sentence Marahrens and Stillman, 1992 is cited as evidence. Marahrens and Stillman, 1992 did not propose that transcriptional activators activate replication by via transcription, but by activators likely modulating chromatin structure. Thus, this paper does not support the hypothesis by the authors. Even Stagljar et al., 1999 did not show that transcription activates an origin. These papers are not "in apparent contrast with the demonstration that strong transcription through ARSs is detrimental for their function" (Introduction). While it is true that transcription is detrimental for replication initiation, accurate citing of references is needed.We had cited these papers for reporting that transcription activators binding is required for efficient origin firing . Indeed, these studies do not show that transcription is induced at the studied origins, but they do not prove either that it is not. In the Stagljar et al. paper, this is actually suggested. This is why we considered this in apparent contradiction with the notion that strong transcription inactivates origins. We clarified this point and also deleted the first sentence that was associated to incorrect references. The reference to the Knott et al. study was associated to the references describing the importance of transcription factors at origins.Subsection “Topological organization of replication origin factors detected by transcriptional footprinting” and later in subsection “Functional implications for pervasive transcription at ARS”. The authors argue that transcription may "play an important role" in the initiation of DNA replication by pushing the first loaded Mcm2-7 hexamer away from the ORC binding site. This is clearly not the case since initiation of DNA replication in vitro is very efficient and does not require transcription, even on chromatin templates . It seems the authors are stating on one case that ARSs and their binding proteins terminate transcription and yet then state that transcription may play an important role in replication initiation. This, based on the data presented, does not seem likely (at least it has not been demonstrated). Indeed, subsection “Functional implications for pervasive transcription at ARS”, the authors state " We propose that transcription though origins might induce similar changes that are susceptible to outcompete binding of ORC and/or pre-RC formation." They cannot have it both ways.Our analyses are based on the average, negative effect of pervasive transcription on replication initiation. They do not exclude that in individual cases, pushing of the DH by transcription might also favor initiation of replication (even though this is not required in vitro). This is the reason of the apparent contradiction. Nevertheless, we agree that this is only a point of discussion and that we did not address here this possibility. Therefore, we eliminated the claim that transcription might favor firing by deleting this sentence.Lastly, we also added a missing reference to the recent Soudet et al. paper.


Using ChIP-Seq, we identified the Dal80 GATA factor targets in yeast. Our data reveal Dal80 binding to a large set of promoters, sometimes independently of GATA sites, correlating with nitrogen- and/or Dal80-sensitive gene expression. Strikingly, Dal80 was also detected across the body of promoter-bound genes, correlating with high expression. Mechanistic single-gene experiments showed that Dal80 spreading across gene bodies requires active transcription. Consistently, Dal80 co-immunoprecipitated with the initiating and post-initiation forms of RNA Polymerase II. Our work suggests that GATA factors could play dual, synergistic roles during transcription initiation and post-initiation steps, promoting efficient remodeling of the gene expression program in response to environmental changes.GATA transcription factors are highly conserved among eukaryotes and play roles in transcription of genes implicated in cancer progression and hematopoiesis. However, although their consensus binding sites have been well defined  GATA transcription factors are highly conserved among eukaryotes and play key roles in cancer progression and hematopoiesis. In budding yeast, four GATA transcription factors are involved in the response to the quality of nitrogen supply. Here, we have determined the whole genome binding profile of the Dal80 GATA factor, and revealed that it also associates with the body of promoter-bound genes. The observation that intragenic spreading correlates with high expression levels and exquisite Dal80 sensitivity suggests that GATA factors could play other, unexpected roles at post-initiation stages in eukaryotes. Upstream Activating/Repressing Sequences), generally directly adjacent to the core promoter. The generated regulatory signals converge at the core promoter where they permit the regulation of Pol II recruitment via the ‘TATA box-binding protein’ and associated general transcription factors [in vivo.In eukaryotes, gene transcription by RNA polymerase II (Pol II) is initiated by the binding of specific transcription factors to double-stranded DNA. The yeast transcription factors target regulatory regions called UAS or URS  coordinating a zinc ion followed by a basic carboxy-terminal tail [GATA factors constitute a family of transcription factors highly conserved among eukaryotes and characterized by the presence of one or two DNA binding domains which consists of four cysteines -sensitive genes, the expression of which is repressed in the presence of a preferred nitrogen source  and derepressed when only poor nitrogen sources  are available [DAL80 and GAT1 is also NCR-sensitive, which implies cross- and autogenous regulations of the GATA factors in the NCR mechanisms [DAL80 is highly induced [In yeast, the family of GATA transcription factors contains over 10 members . Four ofvailable –38. In achanisms . Under n induced , and Dal induced ,39,42. A induced ,38, the  induced .In vitro, the Gln3 and Gat1 activators bind to single GATA sequences, presumably as monomers [In vivo, GATA factor binding site recognition also appears to require repeated GATA motifs within promoters, as shown for the NCR-sensitive DAL5 promoter [UASNTR, consisting in two GATA sites located close to one another to present a binding platform for GATA factors [UGA4 promoter results in the loss of the Dal80-repressive activity while affecting moderately Gln3- and Gat1- activation capacity [The sequence conservation among the four yeast NCR GATA factors is remarkable and the residues involved in contacts with the DNA, thus specificity determination, are 100% conserved. In this respect, the binding sites of Dal80 on target DNA are likely to be recognized also by Gln3, Gat1 and Gzf3 . In vitrmonomers , like thmonomers ,20,39,44promoter –47. This factors –47. Fina factors , althoug factors . The ant factors , and inaIn summary, although NCR-sensitive genes are recognized to contain at least one GATA site, and often more, a precise definition of the minimal element required for binding and transcriptional regulation is still lacking.in vivo [In yeast, genome-wide ChIP analyses have allowed gaining insights into the GATA factor gene network through the identification of direct targets –53. Howein vivo .Here, we provide the first genome-wide identification of Dal80 targets in yeast, in physiological conditions where Dal80 is fully expressed and active. Using a ChIP-Seq approach combined to a bioinformatic peak-calling procedure, we defined the exhaustive set of Dal80-bound promoters, which turned out to be much larger than anticipated. Our data indicate that at some promoters, Dal80 recruitment occurs independently of GATA sites. Strikingly, Dal80 was also detected across the body of a subset of genes bound at the promoter, globally correlating with high and Dal80-sensitive expression. Mechanistic single-gene experiments confirmed the Dal80 binding profiles, further indicating that Dal80 spreading across gene bodies requires active transcription. Finally, co-immunoprecipitation experiments revealed that Dal80 physically interacts with active form of Pol II.foci when tagged by GFP [13-tagged strain and the isogenic untagged strain, as a control  in wild-type cells according to the nitrogen source provided  genes differentially expressed  in proline-grown dal80Δ cells compared to wild type , the promoter of which is bound by Dal80 (MEP2), correlating with Pol II occupancy levels  and especially revNCR-sensitive (325) genes are significantly enriched in Dal80-bound genes  those bound by Dal80 at the promoter only  those showing both promoter and intragenic binding  those bound across the ORF only  the unbound genes  thond genes . InteresP” class .Most of the genes of the “O” class are not Dal80-sensitive , we aimed to get a more robust control for the specificity of Dal80 within gene bodies. Our rationale was to evaluate how similar and/or specific two close GATA factors could share/distinguish this “so called” artefactual hyper-ChIPability property. We performed a similar ChIP-Seq analysis using another GATA factor, the Gat1 activator [MEP2, a well-characterized NCR-sensitive gene, the promoter of which is bound by the two GATA factors, but only Dal80 is found within the gene body.However, since these ‘hyper-ChIPable’ ctivator , using tTo summarize, Dal80 occupancy across the gene body correlates with high expression levels. In a substantial proportion of cases, intragenic occupancy was found to be specific for Dal80, as another GATA factor also recruited to the promoter in the same experimental conditions was not detected within the gene body.MEP2, an NCR-sensitive gene of the “P&O” class  and is strongly repressed by Dal80 in the absence of the inducer [UGA4 without inducer, a Dal80-specific deletion in the C-terminal leucine zipper domain was generated, impairing Dal80 repressive activity without affecting its binding capacity [ΔLZ-Myc13 strain , we noted a difference in Dal80 binding levels to the adjacent URA3 ORF, while those of Pol II remain similar across the two coding regions, suggesting that Pol II level might not be the only factor that control Dal80 occupancy.In order to test if the presence of an NCR-sensitive promoter could confer intragenic Dal80 binding across the body of a non-NCR-sensitive gene, we placed the by Dal80 . When drpression , correlaURA3 ORF . In thesoss URA3 . Similarpromoter  or undernd PVMA1 , reinforURA3 sequence, the Dal80 occupancy displays distinct features depending only on the promoter characteristics to be classified as P, P&O or unbound, reflecting transcriptional strength. We propose that Dal80 presence within the ORF could be attributed to a spreading mechanism, controlled by Pol II complex and Dal80-promoter recognition capacity. These results exclude strongly DNA motif(s) as a main determinant for Dal80 spreading into ORF but rather raise the question of the direct implication of Pol II itself.In conclusion, these results show that for the same rpb1-1 strain [13 binding along MEP2 in WT and rpb1-1 cells. When rpb1-1 cells were shifted at 37°C for 1h, MEP2 mRNA and Pol II levels showed a 2-fold , controlling gene expression in response to nitrogen source availability. One of them, the Dal80 repressor, itself NCR-sensitive, acts to modulate the intensity of NCR responses.in vivo binding and function of the GATA factors. The difference between the number of promoters with ≥2 GATA sites and the number of Dal80-bound promoters suggests the existence of a selectivity for Dal80 recruitment. This selectivity could rely on promoter architecture and/or chromatin structure, conditioning the requirement for auxiliary DNA binding factors that would stabilize Dal80 at some promoters. Moreover, although we observed a significant correlation between Dal80 binding and regulation, the expression of most of the Dal80-bound genes was not affected in a dal80Δ mutant strain. Again, Dal80-dependence for transcribing these genes, as well as their NCR sensitivity, could require the presence of yet unknown cofactors which are not produced or inactive under the tested growth conditions. In mammals, GATA factors also display an extraordinary complexity in the relationships between binding and expression regulation. Like Dal80, GATA-1 and GATA-2 only occupy a small subset of their abundant binding motif throughout the genome, and the presence of the conserved binding site is insufficient to cause GATA-dependent regulation in most instances [Over the past decade, a number of studies have screened the genome aiming at gathering an inventory of genes regulated by the nitrogen source. Although >500 genes have been shown to be differentially expressed upon change of the nitrogen source ,64, the nstances . GATA-1 nstances .in vitro binding experiments on naked DNA. Nonetheless, of the 1269 bound promoters, 48% contained at least two GATA sites, a proportion that is not different from that observed among unbound promoters, and the amount of GATA sites per promoter was not different between the two groups either. In addition, Dal80 recruitment was found to occur independently of the presence of GATA sites in 20% of Dal80-bound promoters, as also previously observed in mammalian cells [ALD6 . This consensus has been largely validated in the past using gene reporter experiments, mutational analyses and an cells ,73. FutuALD6 see . HoweverP<1.6e-13), through the Gcn4 activator. Interconnections between NCR and GAAC have already been demonstrated, mostly at the level of nitrogen catabolism control: 1-a large number of non-preferential nitrogen sources leads to increased transcription of GAAC targets [Unexpectedly, although Dal80 has always been described as a repressor, we identified 314 genes that are positively regulated by Dal80  genes are statistically more enriched within the “P&O” class, compared to the “P” class , further indicating that Dal80 spreading across gene bodies depends on active transcription. Consistently, we detected a physical interaction between Dal80 and transcriptionally active forms of Pol II. Together, our data lead us to propose a model where Dal80 could travel from the promoter of highly expressed, NCR-sensitive genes through the gene body by accompanying the elongating Pol II complex  as described [PMEP2-URA3 allele in strains FV806-808, and PTDH3-URA3, PVMA1-URA3, PALD6-URA3 alleles in strains FV1105-1107, respectively, were created by amplification of the URA3 gene using the same strategy, with primers listed in Experiments were conducted using escribed  using pr660nm = 0.5) in YNB (without amino acids or ammonia) minimal medium containing the indicated nitrogen source at a 0.1% final concentration, glucose (3%) and the appropriate supplements  to cover auxotrophic requirements.Cultures were grown at 29°C to mid-log phase (A6 cells/ml) were treated with 1% formaldehyde for 30 min at 25°C and mixed by orbital shaking. Glycine was then added to a final concentration of 500 mM and incubation continued for 5 min. The cells were collected, washed once with cold 10 mM Tris-HCl, pH 8, washed once with cold FA-SDS buffer , and resuspended in 1 ml of cold FA-SDS buffer. An equal volume of glass beads (0.5 mm in diameter) was added, and the cells were disrupted by vortexing for 30 min in a cold room. The lysate was diluted into 4 ml of FA-SDS buffer, and the glass beads were discarded. The cross-linked chromatin was then pelleted by centrifugation , washed for 60 min with FA-SDS buffer, resuspended in 1.6 ml of FA-SDS buffer for 15 min at 4°C, and sonicated three times for 30 s. each , giving fragments with an average size of 250–300 bp. Finally, the sample was clarified by centrifugation at 14,000 × g for 30 min and diluted 4-fold in FA-SDS buffer, and aliquots of the resultant chromatin containing solution were stored at –80°C. Pol II and Myc13-tagged proteins were immunoprecipitated by incubating 100 μl of the chromatin containing solution for 180 min at 4°C with 2 μl of mouse anti-Pol II and anti-Myc antibodies, respectively  prebound to 10 μl of Dynabeads Pan Mouse IgG  according to the manufacturer's instructions. Immune complexes were washed six times in FA-SDS buffer and recovered by treating with 50 μl of Pronase Buffer  at 65°C with agitation. Input (IN) and immunoprecipitated (IP) fractions were then subjected to Pronase treatment  for 60 min at 37°C, and formaldehyde cross-links were reversed by incubating the eluates overnight at 65°C. Finally, the samples were treated with RNase (50 μg/ml) for 60 min at 37°C. DNA from the IP fractions was purified using the High Pure PCR Product Purification Kit (Roche Applied Science) and eluted in 50 μl of 20 mM Tris buffer, pH 8. IN fractions were boiled 10 min and diluted 500-fold with no further purification prior to quantitative PCR analysis.Cell extracts and chromatin immunoprecipitations were conducted as described  using pr18 primers from Fermentas using the manufacturer's recommended protocol. cDNAs were subsequently quantified by RT-PCR using the Maxima SYBR green qPCR master mix from Fermentas.Quantitative RT-PCR was performed as described previously  using prCultures (100 ml) were harvested, washed once in 50 mM Tris, pH 8, and resuspended in 1ml of buffer . Lysis was performed by shaking with 425–600 μm acid-washed glass beads (Sigma) on an IKA Vibrax VXR orbital shaker at maximum speed for 30 min at 4°C. Cell debris and glass beads were removed by centrifugation. Immunoprecipitation was performed by incubating 200 μl of total cell extracts with 20 μl of Dynabeads PAN mouse immunoglobulin G (Invitrogen) that were preincubated with anti-HA , anti-CTD , anti-Ser2P  or anti-Ser5P  antibodies and 20 μl of 1% phosphate-buffered saline-bovine serum albumin for 2 h under orbital shaking (800 rpm) at 30°C. Immune complexes were washed three times in lysis buffer, eluted by boiling in sodium dodecyl sulfate (SDS) sample buffer, and loaded on SDS-polyacrylamide gel for anti-Myc Western blotting.DAL80-MYC13) and FV034 (GAT1-MYC13) cells. Lysis and chromatin extraction was as described above. The average fragment length of sonicated fragment was 300–350 bp. For each condition, libraries were prepared from 10 ng of “input” or “IP” DNA using the TruSeq ChIP Sample Preparation Kit (Illumina). Single-read sequencing (50 nt) of the libraries was performed on a HiSeq 2500 sequencer.ChIP-Seq analysis was performed from two biological replicates of proline-grown 25T0b (no tag), FV078  and quantified using a NanoDrop 2000c spectrophotometer. Ribosomal RNAs were depleted from 1 μg of total RNA using the RiboMinus Eukaryote v2 Kit (Life Technologies). After concentration using the Ribominus Concentration Module (Life Technologies), rRNA-depleted RNA was quantified using the Qubit RNA HS Assay kit (Life Technologies). In parallel, rRNA depletion efficiency and integrity of both total and rRNA-depleted RNA were checked by analysis in a RNA 6000 Pico chip, in a 2100 bioanalyzer (Agilent). Strand-specific total RNA-Seq libraries were prepared from 125 ng of rRNA-depleted RNA using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina), following manufacturer’s instructions. Paired-end sequencing (2 x 50 nt) of the libraries was performed on a HiSeq 2500 sequencer. Sequenced reads were mapped to the reference genome using version 2.0.6 of TopHat [P-value ≤0.01.For each strain and condition, total RNA was extracted from two biological replicates using standard hot phenol procedure, ethanol-precipitated, resuspended in nuclease-free Hf TopHat , as descf TopHat . Tags def TopHat . DiffereStatistical details can be found in the corresponding figure legends. Error bars correspond to standard error. Statistical significance tests were carried out using the Student’s t test when indicated.http://vm-gb.curie.fr/dal80.Sequence data can be accessed at the NCBI Gene Expression Omnibus using accession numbers GSE86307 and GSE86325. Genome browsers for visualization of processed ChIP-Seq and RNA-Seq data are accessible at igeoris@ulb.ac.be). Bioinformatics and genome wide dataset requests could also be addressed to antonin.morillon@curie.fr for rapid processing.Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Isabelle Georis , dal80Δ (FV080) and DAL80-MYC13 (FV078) cells were grown in glutamine- (Gln) or proline- (Pro) containing medium to mid-log phase. After total RNA isolation, levels of DAL5 mRNA were quantified by qRT-PCR  and normalized on SPT15  mRNA levels (primers SPT5O1-O2). Histograms represent the average of at least 2 independent experiments and the associated error bars correspond to the standard error.(A) Functionality of Dal80-Myc(B) Box-plot of the distance between the annotated TSS and ORF start site  for protein-coding genes.(C) Proportion of Dal80-bound and -unbound genes containing at least a GATA cluster in the promoter . A GATA cluster is constituted by at least two GATA sites , 15–35 bp apart.(D) Orientation of GATA sites in the clusters defined above in Dal80-bound and -unbound promoters. The proportion of clusters containing GATA sites in head-to-head (H-H), head-to-tail (H-T), tail-to-head (T-H) and tail-to-tail (T-T) is shown for each class of promoters.locus (ALD6). Densities (tag/nt) are shown for the untagged (black line) and DAL80-MYC13 (blue line) strains. Genes are represented as grey arrows. The region (70 bp) showing the maximum of Dal80-Myc13 binding is highlighted using the dashed box, and the corresponding sequence is shown below. The degenerated GATA sites (1 mismatch/motif) are highlighted in red, and stars indicate the residues that differ from the consensus. The snapshot was produced using the VING software [(E) Snapshot of ChIP-Seq signals along a GATA-less software .(PPTX)Click here for additional data file.S2 FigDal80 recruitment to promoters correlates with nitrogen- and Dal80-sensitive gene expression.DAL80 gene in WT-cells grown in glutamine- containing (Glu) or proline-containing (Pro) medium, and in dal80Δ cells grown in proline-containing medium. RNA-Seq signals are visualized as a heatmap. The upper and lower panels show the signals for the + and—strands, respectively. The color turns from yellow to dark blue as the signal increases . DAL80 is highlighted using a dashed red box. The snapshot was produced using the VING software [(A) Snapshot of RNA-Seq signals for the software .P < 0.00001 upon Chi-square test of independence.(B) Contingency table showing the number of Dal80-activated, -repressed and -insensitive genes among the (rev)NCR-sensitive and -insensitive genes. The results that were experimentally observed and those that are expected in case of independence are indicated in bold and in brackets, respectively. P < 0.00001 upon Chi-square test of independence.(C) Contingency table showing the number of NCR-sensitive, revNCR-sensitive and unaffected genes among the Dal80-bound and unbound genes. The results that were experimentally observed and those that are expected in case of independence are indicated in bold and in brackets, respectively. P < 0.00001 upon Chi-square test of independence.(D) Contingency table showing the number of Dal80-activated, Dal80-repressed and -insensitive genes among the Dal80-bound and unbound genes. The results that were experimentally observed and those that are expected in case of independence are indicated in bold and in brackets, respectively. (PPTX)Click here for additional data file.S3 FigDal80 recruitment to promoters correlates with nitrogen- and Dal80-sensitive gene expression.MEP2 gene in WT-cells grown in glutamine- containing (Glu) or proline-containing (Pro) medium, and in dal80Δ cells grown in proline-containing medium. RNA-Seq signals are visualized as described in MEP2 is highlighted using a dashed red box. The snapshot was produced using the VING software [(A) Snapshot of RNA-Seq signals for the software .MEP2 locus. WT (23344c) and dal80Δ (FV080) cells were grown in glutamine- (Gln) and/or proline-containing (Pro) medium. Anti-Pol II (CTD4H8) ChIP-qPCR analysis was performed using MEP2P5-P6, MEP2P9-P10, MEP2O11-O12 and MEP2O9-O10 primers. Histograms represent the averages of at least 2 independent experiments and the associated error bars correspond to the standard error.(B) Pol II occupancy at the (PPTX)Click here for additional data file.S4 FigDal80 spreading across gene bodies correlates with high expression levels.P < 0.00001 upon Chi-square test of independence.(A) Contingency table showing the number of NCR-sensitive, revNCR-sensitive and unaffected genes among the “P”, “P&O” and unbound genes. The results that were experimentally observed and those that are expected in case of independence are indicated in bold and in brackets, respectively. P < 0.00001 upon Chi-square test of independence.(B) Contingency table showing the number of Dal80-activated, -repressed and–insensitive genes among the “P”, “P&O” and unbound genes. The results that were experimentally observed and those that are expected in case of independence are indicated in bold and in brackets, respectively. (C) Density-plot of RNA-Seq signal  in WT cells grown in proline-containing medium, for genes of the “unbound” , “P”  and P&O”  classes. Y-axis: proportion of genes for each class. The highlighted areas correspond to the 75 (2%) and 170 (15%) genes of the “unbound” and “P” classes, respectively, showing a signal higher than the median of the “P&O” class. A box-plot representation of the same RNA-Seq signals is shown on the top of the density-plot.(D) Same as above, highlighting the 949 (21%) and 632 (56%) genes of the “unbound” and “P” classes, respectively, showing a signal higher than the first quartile value for the “P&O” class.loci previously defined as hyper-ChIPable [(E) Venn diagram showing the number of genes of the “P” class  vs the ChIPable .(F) Same as above for the “P&O” class.loci previously defined as hyper-ChIPable [(G) Venn diagram showing the number of promoters bound by Dal80 and Gat1. Within each group, the number of ChIPable  is indicloci previously defined as hyper-ChIPable [(H) Venn diagram showing the number of genes showing promoter and gene body binding (“P&O”) for Dal80 and Gat1. Within each group, the number of ChIPable  is indic(PPTX)Click here for additional data file.S5 FigDal80 occupancy across gene bodies requires active transcription and correlates with Pol II occupancy.UGA4 locus. Densities (tag/nt) are shown for the untagged  and DAL80-MYC13  strains. Genes and tRNA are represented as grey and black arrows, respectively. The snapshot was produced using the VING software [(A) Snapshot of ChIP-Seq signals along the software .UGA4 expression in a DAL80ΔLZ mutant strain. WT (25T0b), DAL80-MYC13 (FV078), dal80Δ (FV080) and DAL80ΔLZ-MYC13 (FV136) cells were grown in proline-containing medium. Total RNA was isolated and UGA4 mRNA levels were quantified by qRT-PCR using UGA4O1-O2 primers as in (B) UGA4 locus by Pol II. WT (25T0b), DAL80-MYC13 (FV078), dal80Δ (FV080) and DAL80ΔLZ-MYC13 (FV136) cells were grown in proline-containing medium. ChIP-qPCR analysis was performed as described in O1-O2 primers.(C) Occupancy of the (PPTX)Click here for additional data file.S6 FigDal80 occupancy within gene bodies requires NCR promoter binding and correlates with Pol II occupancy. URA3 expression was determined in untagged wild type (25T0b), PMEP2-URA3 (FV806), DAL80-MYC13 wild type (FV078) and PMEP2-URA3 (FV808) cells grown in glutamine- or proline-containing medium. RT-qPCR analysis was performed as described in O1-O2 primers.(PPTX)Click here for additional data file.S7 FigMEP2 requires active transcriptionDal80 binding across .MEP2 expression. WT (FV673) or rpb1-1 (FV675) DAL80-MYC13 cells were grown in glutamine- (Gln) or proline- (Pro) containing medium at 29°C to mid-log phase, then shifted at 37°C for one hour. Total RNA was isolated and SPT15-normalized MEP2 mRNA levels were quantified by qRT-PCR using MEP2O9-O10 primers as in (A) Effect of Pol II elongation defects on MEP2 locus in rpb1-1 cells. Wild type (FV673) or rpb1-1 (FV675) DAL80-MYC13 cells were grown to mid-log phase at 29°C in the presence of glutamine (Gln) or proline (Pro) as unique nitrogen sources, and shifted at 37°C for one hour. ChIP analysis was conducted as described in O9-O10 primers.(B) Pol II occupancy at the (PPTX)Click here for additional data file.S1 TableList of 1269 gene promoters bound by Dal80.(XLSX)Click here for additional data file.S2 TableGO term analysis of 1269 Dal80-bound promoters. Overlap of genes identified in our screens and in previous genome-wide expression screens.(XLSX)Click here for additional data file.S3 TableLists of genes identified in ChIP-Seq (Column A) and RNA-Seq (Column C) analyses, or identified in previous screens (Column G + references in columns H-P), and their overlap .(XLSX)Click here for additional data file.S4 TableList of the 754 genes upregulated  and 928 downregulated  in a wild type (WT) grown in glutamine (G) or proline (P)-containing medium. Gene counts (Columns B-C and G-H), fold change (Columns D and I) and p values (Columns E and J) are indicated. Genes identified in previous screens (Column M + references in columns N-V) are indicated, and their overlap with our data (Columns K-L)(XLSX)Click here for additional data file.S5 TableList of 546 Dal80-regulated genes on proline: 314 genes are activated (Column A) and 232 genes are repressed (Column B). The list of genes having Gcn4 binding sites in their promoter is indicated (Column C), as well as its intersection with the activated gene list (Column D).(XLSX)Click here for additional data file.S6 TableGene ontology term analysis of the 232 genes repressed by Dal80 on proline.(XLSX)Click here for additional data file.S7 TableGene ontology term analysis of the 314 genes activated by Dal80 on proline.(XLSX)Click here for additional data file.S8 TableInventory of gene promoters and ORFs bound by Dal80 (Columns A-E) and their intersection with susceptibility to Dal80 regulation (Columns G-K).(XLSX)Click here for additional data file.S9 Table13 (Columns A-B) or Gat1-Myc13 (Columns C-D) and their intersection (Columns E-F). The list of hyper-ChIPable genes is also provided (Column G) and intersection with Dal80- or Gat1-bound gene lists (Columns H-K).List of gene promoters and P&O bound by Dal80-Myc(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.