The PAF complex (Paf1C) has been shown to regulate chromatin modifications, gene transcription, and RNA polymerase II (PolII) elongation. Here, we provide the first genome-wide profiles for the distribution of the entire complex in mammalian cells using chromatin immunoprecipitation and high throughput sequencing. We show that Paf1C is recruited not only to promoters and gene bodies, but also to regions downstream of cleavage/polyadenylation (pA) sites at 3’ ends, a profile that sharply contrasted with the yeast complex. Remarkably, we identified novel, subunit-specific links between Paf1C and regulation of alternative cleavage and polyadenylation (APA) and upstream antisense transcription using RNAi coupled with deep sequencing of the 3’ ends of transcripts. Moreover, we found that depletion of Paf1C subunits resulted in the accumulation of PolII over gene bodies, which coincided with APA. Depletion of specific Paf1C subunits led to global loss of histone H2B ubiquitylation, although there was little impact of Paf1C depletion on other histone modifications, including tri-methylation of histone H3 on lysines 4 and 36 (H3K4me3 and H3K36me3), previously associated with this complex. Our results provide surprising differences with yeast, while unifying observations that link Paf1C with PolII elongation and RNA processing, and indicate that Paf1C subunits could play roles in controlling transcript length through suppression of PolII accumulation at transcription start site (TSS)-proximal pA sites and regulating pA site choice in 3’UTRs. Gene transcription can be regulated through multiple mechanisms, such as histone modifications that create structural changes of the chromatin leading to gene activation or suppression, or regulation of the 3’ cleavage site of the mRNA, known as alternative cleavage and polyadenylation (APA), resulting in the generation of transcript isoforms with various lengths. Here we present genome-wide subunit-specific roles of the PAF complex (Paf1C) related to both mechanisms of transcriptional regulation. Using mouse muscle cells, we show contrasting results with yeast, namely, that depletion of Paf1C subunits does not affect certain histone modifications previously associated with this complex and that the complex exhibits subunit-specific functions. We also discovered a novel role of Paf1C in APA, wherein genome-wide transcript shortening occurs after depletion of three of the subunits. However, APA varies after depletion of certain subunits, reinforcing our conclusions regarding subunit specificity. Furthermore, by comparing depletions of two subunits, we show that the accumulation of RNA polymerase II (PolII) near the transcription start site (TSS) is specifically associated with the activation of TSS-proximal pA sites observed in one depletion but not the other. Paf1C has been intensively explored in yeast, flies, and mammalian cells, which has led to diverse, and sometimes differing, conclusions. The complex was first characterized in yeast as a PolII-associated factor, and extensive use of mutants revealed that it plays a role in transcriptional elongation and chromatin modifications , 3. EarlRobust data suggest that Paf1C plays an important role in acquisition of transcription-associated histone modifications: specific subunits (including Paf1 and Rtf1) were shown to be required to promote H2B ubiquitination (H2Bub), as well as H3 methylation at K4, K36, and K79 in yeast, flies, and humans –16. HoweRtf1, Paf1, and Ctr9, but not Leo1 or Cdc73, led to mRNA transcript lengthening and defects in 3’ end formation of snoRNA, which are not polyadenylated and whose ends are processed via the exosome in concert with other processing factors [Cdc73 impacted 3’ end formation of mRNA [Other roles have also been ascribed to Paf1C. For example, biochemical studies have indicated that Ski8, a component of the SKI complex known to interact with the exosome, also associates with Paf1C , suggest factors . On the  of mRNA . Further of mRNA . Althoug of mRNA –26.Well over half of all mammalian genes contain multiple pA sites that lead to transcript variants with distinct 3’UTRs or coding regions . Alternain vitro assay or led to aberrant transcript lengthening of a human gene [in vitro, or they were based solely on investigations of a small number of individual genes. Thus, a general role for Paf1C in C/P has not been established.Studies have indicated possible genetic and biochemical interactions between Paf1C and factors required for C/P in yeast and mammalian cells. For example, it was shown that yeast Ctr9 could associate with the CPSF160 homolog . Moreoveman gene , 38. HowHere, we have investigated Paf1C function on a genome-wide scale in mouse muscle cells. We find that Paf1C subunits exhibit distinct functions with respect to gene occupancy and chromatin modifications. Moreover, we find that Paf1C was enriched not only at promoters but also downstream of 3’ ends, in contrast with previous observations in yeast. Surprisingly, loss of certain Paf1C subunits, but not others, leads to changes in histone modifications, PolII accumulation over gene bodies, and altered pA site usage. Our results suggest a novel role for Paf1C in suppression of C/P, providing a mechanism underlying APA regulation in muscle cells.We initiated our studies of Paf1C function in skeletal muscle, since the complex has not been extensively characterized in this tissue, and we made use of mouse C2C12 myoblasts, a well-established in vitro differentiation system. Our previous studies indicated that H2Bub dramatically decreased during myogenic differentiation, and this was due, in part, to reduced abundance of the enzyme (RNF20) that deposits this chromatin modification in differentiated myotubes . Since PDrosophila, and S. pombe counterparts [To further investigate the interactions among Paf1C subunits, immunoprecipitation experiments were performed using solubilized chromatin from myoblasts or myotubes. Strikingly, we found that Rtf1 failed to co-precipitate with all other Paf1C subunits . In paraterparts , 16, 40,terparts .Next, we investigated the impact of depleting Paf1C subunits on histone modifications thought to depend on various components of this complex. After verifying the depletion of each subunit, we detected H2Bub, H3K4me3, H3K36me3, and H3K79me3 Figs . InteresIntrigued by the possibility that Paf1C subunits might display unique activities, we comprehensively investigated the genome-wide occupancy of all six subunits in myoblasts and myotubes. To this end, we verified the specificity of each of our anti-Paf1C antibodies and confirmed their ability to enrich specific loci in chromatin immunoprecipitation (ChIP) experiments . Next, wMoreover, average ChIP-seq enrichment profiles revealed notable differences among the subunits. For example, Cdc73 recruitment was most prominent near the TSS, and its profile closely resembled that of RNA PolII. On the other hand, Paf1 and Leo1 showed the strongest enrichment toward the 3’ ends of genes and downstream of the TES. Next, we used clustering to compare localization of the Paf1C subunits, PolII, and the elongation factor, Spt6, with histone modifications at regions surrounding the TSS and TES during myogenic differentiation Figs  and S2B.We noticed that Paf1C occupancy was globally reduced as cells differentiated into myotubes, paralleling or exceeding the reductions observed for PolII recruitment Figs  and S2B.Next, we investigated the extent to which Paf1C subunits bound to common and unique sets of genes by systematically analyzing our ChIP-seq data . ConsistTo investigate potential functional differences among Paf1C subunits, we individually ablated Paf1, Cdc73, and Ski8, since our analyses of factor binding in myoblasts suggested that these proteins might belong to distinct sub-clusters . Through3' region extraction and deep sequencing (3'READS) [Given that Paf1 binding is enriched in the vicinity of the TES  and that3'READS)  to poly(3'READS)  Fig 3A)3' region3'READS) , in whicRemarkably, ablation of all three genes led to pervasive transcript shortening, although the locations of regulated APA sites differed significantly . Within Using a quantitative reverse-transcription-coupled PCR (qRT-PCR) method, we validated our 3’READS results for several genes . ImportaGO analysis indicated that genes that exhibited altered APA were enriched for several terms, including RNA splicing and gene silencing by RNA, confirming that Paf1C not only binds, but also regulates, targets involved in these biological processes . Taken tp-value = 4×10−28 and 4×10−19 for Paf1 and Cdc73, respectively; p-value = 0.02). The expression changes for intronic pA isoforms were plotted against their locations within either the first two TSS-proximal introns , the last two distal introns (-2 and -1), or intervening introns   Figs  and S6C.Since APA involves kinetic competition between different pA sites, it is conceivable that PolII elongation rate can impact pA choice. Indeed, alteration of PolII elongation rate has been implicated in APA . A key qcdk9 loss-of-function, and PAF recruitment required CDK9 activity [Intriguingly, CDK9, a kinase associated with P-TEFb and the Super Elongation Complex (SEC) known to phosphorylate Ser2 of the PolII CTD, is required for maintaining global levels of H2Bub, and its ablation led to altered 3’ end processing and enhanced read-through to an alternative pA site at replication-dependent histone genes, which are not normally polyadenylated . The relactivity , in partactivity , and ablactivity . In contactivity . Since COur work has vastly extended previous studies to show that Paf1C is able to suppress both APA and upstream antisense transcription. We propose a model in which Paf1C regulates mRNA processing through direct control of RNA polymerase II progression during elongation and indirectly via expression of a cohort of cleavage and polyadenylation proteins . It willAspects of Paf1C-mediated APA regulation are reminiscent of recent results in which nuclear poly(A) binding protein (PABPN1), a factor involved in stimulating poly(A) polymerase and controlling poly(A) length, were ablated, triggering activation of TSS-proximal APA and shortening of 3’UTRs , 54, 55.4 cells cm−2 for 48 h transfection. 5 μl RNAiMax in 2 ml medium and 50 nM siRNA were applied to cells by using fast-forward method. siRNA target sequences are listed below.C2C12 myoblasts (Sigma) were grown and induced to differentiate into myotubes as described . Stable siPaf1: GCUAUGAGGAGAACUAUUUsiCdc73: GAUCCUACAUUGCGUACAAsiSki8: GGUGUUGAAUGUUGCGUUCp < 0.05 or **p < 0.01.All qChIP and RT-qPCR experiments were repeated at least twice independently except as noted, and the data are presented as mean ±SD and ± SEM for two and three or more repeats, respectively. Statistical significance was determined using Student’s t test, and is presented as *Ab: Paf1; Source: Affinity purified from Paf1 anti-serum ; ApplicaAb: Leo1; Source: A300-175A-1, Bethyl; Application: ChIP, IP and western blotting.Ab: Cdc73; Source: A300-170A (Lot # A300-170A-2) Bethyl; Application: ChIP, and IP.Ab: Cdc73; Source: Kind gift from the Handa lab ; ApplicaAb: Ctr9; Source: A300-BL679, Bethyl; Application: ChIP and western blotting.Ab: Ctr9; Source: A301-395A, Bethyl; Application: IP and western blotting.Ab: Rtf1; Source: A300-179A (Lot # A300-179A-1), Bethyl; Application: ChIP, IP and western blotting.Ab: Ski8; Source: Affinity purified from Ski8 anti-serum ; ApplicaAb: RNA PolII; Source: Kind gift from the Bentley lab ; ApplicaAb: RNA PolII; Source: 8WG16, Covance; Application: ChIP.Ab: CPSF100; Source: Kind gift from the Manley lab; Application: ChIP and western blotting.Ab: H3; Source: ab1791, Abcam; Application: ChIP and western blotting.Ab: H3K4me3; Source: 39159, Active Motif; Application: ChIP and western blotting.Ab: H3K36me3; Source: Ab9050, Abcam; Application: ChIP and western blotting.Ab: H2Bub; Source: pw8810, Biomol International; Application: Western blotting.Ab: α-tubulin; Source: T5168, Sigma; Application: Western blotting.A solubilized chromatin fraction was prepared as described . BrieflyChIP was performed as described , followePrimers for qChIP:Spp1+1000F: TGAAATTGCCCTTTTCCTTGSpp1+1000R: GCACCACTAGATCACCACCARan+1500F: ATGCTCACGTGCTTCCTCTTRan+1500R: CTGGCCCATCAAAGTTCATCMyog_+350_F: GAAAGTGAATGAGGCCTTCGMyog_+350_R: AGGCGCTCAATGTACTGGATMyh3+25K F: GGTCCAGGAAGTGTCTCTGCMyh3+25K R: CAGGAGGTCTTGCTCACTCCmmGenedesert ChIP 5´: CCTCTGTAGCTGCCTCTCGTmmGenedesert ChIP 3´: GTGTTGGGCAAGACTCTGCTPcbp2+150 F: CTCCCCTTTTCCCCTCAGTCPcbp2+150 R: AAGAAGGATGTCACGAGTGGVim+110F: TCCCTTGTTGCAGTTTTTCCVim+110R: GGTAGGAGGACGAGGACACAMyh9+700F: TCCAAGGTTGAATGAGGTCAGMyh9+700R: TCAGAGTGCGTGGGAAAAGSnx18+250F: CTCGCTGCACAGGCTCAGSnx18+250R: CGGGCGCTCTACGACTTTADhx9+100F: CTTACCCTCCTCCGCTTTTCDhx9+100R: GTGTGTTCTTTCGCCGTTCTCsde1+300F: AGGCGTCCCTTTTTCACCCsde1+300R: CTGGCCTCACCTCCTCACTAAnkrd1+50F: CATACCAGCTCCTCTACTCTCAGAnkrd1+50R: CAGGGGTTCATCCACAAGAGCdh13+70F: GGCTCCCACGGAAAATATGCdh13+70R: GAGTTCTCGGCTGCATCTTGDld TSS F: GCTGAACGCCTGGTAAGACTDld TSS R: GACGAAGCGACCGGAAAGAnxa2+250F: CAAAGTGTCCCGCAAGTGACAnxa2+250R: GAAACCTCAAGGGGAAGCACCol12a1-30 F: TACGAAACGCCTGAGAAGGTCol12a1-30 R: TGCACACGTTCCCAAAAGTATotal RNA extraction and cDNA synthesis using anchored oligo d(T) primers have been described previously . PrimersFor RNA-seq, ribosomal RNA was removed using RiboMinus Eukaryote System v2 (Ambion) , and strand-specific cDNA library preparation and sequencing were performed as described . 3’READSPrimers for RT-qPCR:Tomm20_M-150F: TCAGGCAAACATGCAAATTCTomm20_M-150R: GGGTGTTCCATCTCAGCATTTomm20_L-150F1: TGGAAGAAGAAATGGGTGTGTomm20_L-150R: AATAATGCTGATGGCGCTCTDdx46_M-150F: TACCCCATACCCATCCAAAADdx46_M-150R: CAAAATTCCAATGTCTTCCAGADdx46_L-250F: CCCGGAAACTCCATACTGGTDdx46_L-250R: CACAAACAGAAAACGTGATACGAPdcd6ip_F-150F: AAATCAATAAACCAAAAGAGAAAGAAAPdcd6ip_F-150R: TAAGATGCAGTTGGGACAAAGAPdcd6ip_L-150F: TTTTCCCCCTAAGAGAAATGAAPdcd6ip_L-150R: TTATCACACCGGAGATTTTAGATTB3galtl_F-150F: AGGGAAACTGGGAAAAATCGB3galtl_F-150R: GGTGGGCTAAAATAGGCACAB3galtl_L-150F: GGCTTTGTTCCTGCTGTCTTB3galtl_L-150R: GGTGACTTGTTTAGAGGGCTTGGpc6_ex1_F: CTCTCACTGGCTCCCTCAGTGpc6_ex1_R: GTGTGTGCGTGGAGGTATGTGpc6_L_F2: CATGAGCAACACCTTAACGAGpc6_L_R1: TGCACACATTTATCCTGCACArsk_F-130F: TGATGGACAGAAGCAGACACTTArsk_F-130R: CCGGATGAGCTAACAAACATTArsk_L-350F: GACTGGGTTGGGCTGCTAArsk_L-350R: ATTGGCAGAGAGATCCAAGGDld_F F: CATTGACTCGTTTCCAATATGADld_F R: TTGACAGGAAGGAGCTGGAADld_L F: TGGTGTCTTCATTCCCTGGTDld_L R: TTGCTGGCAGTTTAAGAGGTCdh13_ex1_F: GGCTCCCACGGAAAATATGCdh13_ex1_R: ACAGGGTGAGCGGAGTTCTCdh13_L_F: GTTTCAACCCCACACACAGTCdh13_L_R: GCGGAAGGCAAACTCAAAZfhx4_I F1: AAGTGTTAAAGGGTGCAAGGAAZfhx4_I R1: TTCTGTTTGGGTCATTCTTGTTTZfhx4_S F1: GATGTCTCAAATGCACCGTAZfhx4_S R1: CCAGAAGAATAAGTTCAAATACCATCCol12a1_5'-Exon1F: CCGCTCCCTCTGTCACCTCol12a1_5'-Exon1R: GGGAAGCCTGGTCTGCATCol12a1_L F1: TCAGTAGTTTTATTGAAAAATGCCTCTCol12a1_L R1: AGAATTGCTGCTTTTATATTTTAACTGPcbp2 Exon5 F: GGTGCACGTATCAACATCTCAPcbp2 Exon5 R: AAGGCTTTGAAGATGGCATTPcbp2 Exon14 F: CGCCAAAATCAATGAGATCCPcbp2 Exon14 R: ATGGTAACCTGCCGATCAGTMyh9_ex1_F: CTTCCGAGTGGACTTTCTCGMyh9_ex1_R1: CTCAAGAACCTGACCTGCTGMyh9_L_F: TGACGCTCAGTGGAAACATCMyh9_L_R: AAAGGCGCTGTCATAAAGGAVim_ex1_F: CCTCATTCCCTTGTTGCAGTVim_ex1_R: GAGGACACAGACCTGGTAGACAVim_L_F: CAGCTTTCAAGTGCCTTTACTGVim_L_R: CGTCTTTTGGGGTGTCAGTTIrs1_ex1_F: CGATTCCCGAGGCAAATTAIrs1_ex1_R: GAGGAGAAGGAGGAGGGAGAIrs1_L_F: CCAGACAGAATTGGGGGTAAIrs1_L_R: TTTAGACGTGTTTCACTTTTCCAAp-value<0.01 (Fisher’s exact test).Reads were mapped to the mouse genome (mm9) using bowtie2 . Uniquely mapped reads with MAPQ score>10 were used. RefSeq gene model was used to calculate gene expression level. For protein-coding genes, only reads mapped to coding region were used to eliminate the effect of 3’UTR-APA in gene expression calculation. Reads per kilobase per million total uniquely mapped reads (RPKM) value was calculated to reflect gene expression level. Significantly regulated genes were selected based on these criteria: >1.4-fold difference of RPKM between Paf1C siRNA treated cells and siRNA control cells and 3’READS library preparation was carried out as previously described , 43. 3’RReads were mapped to the mouse genome (mm9) using bowtie2. Reads non-uniquely mapped (MAPQ≤10) were discarded. Multiple reads mapped to same genomic loci  were collapsed to one.2 ratio of RPM between IP sample and input sample. The relative genomic location was binned  before calculating enrichment score and then plotted in R.To calculate enrichment score of a genomic locus in a meta-gene plot, the first nt at the 5’end of a read was counted for all genes in a group and converted to read per million total reads (RPM) value. The enrichment score was defined by logThe correlation of ChIP-seq data among different Paf1C factors was calculated based on the Pearson correlation of RPM values of four sub-regions of genes . Only sub-regions with at least 50 reads in at least one sample were used for the calculation.For visualization of ChIP-seq data, all uniquely mapped and collapsed reads were used. Reads were extended from its 5’end mapped position by 250 nts (reflecting the average fragment size in ChIP-seq DNA libraries) and converted to bigwig format files using an in-house perl script and bedGraphToBigWig program from UCSC genome browser. The bigwig files were visualized in IGB.GO annotations of genes were obtained from the NCBI Gene database, and GO analysis was carried out using the Fisher’s exact test .ChIP-seq, RNA-seq and 3’READS data generated for this study were deposited under the primary accession GSE72574. Previously published data re-analyzed for this study include GSE44119 , and GSS1 Fig(A) Co-immunoprecipitation of Flag-tagged Cdc73 and Ski8, but not Rtf1, from nuclear extracts of myoblasts. (B) qRT-PCR verification of siRNA-mediated depletion of Leo1, Ctr9, and Rtf1 in myoblasts. Transcript levels of Paf1C subunits were normalized to Gapdh.(TIF)Click here for additional data file.S2 Fig(A) Metagene analysis shows average ChIP-seq enrichment profiles of indicated Paf1C subunits and PolII. A region from 3 kb upstream or downstream of the TSS or TES was analyzed. (B)k-means clustering (k = 10) of ChIP-seq data reveals common and distinct localization of Paf1C subunits, Spt6, histone modifications, and PolII, and correlation between Paf1C occupancy and levels of gene expression. Each row represents a single 4 kb genic region surrounding the TES. A heatmap representing RNA-seq analysis of myoblasts and myotubes. (C) qChIP validation of Cdc73 specific enrichment, and a representative genome browser track example of distinct target regions of Paf1C subunits. The y-axis represents normalized read density in reads per million (RPM). MB, myoblasts; MT, myotubes. (D) Cluster analysis of co-occupancy of biological replicates of Paf1C subunits on chromatin regions. Values are Pearson correlation coefficients. Correlations of RPM values encompassing genomic regions (TSS/TES upstream/downstream 1kb) were analyzed.(TIF)Click here for additional data file.S3 Fig(A) GO analysis of Paf1C target genes. The y-axis indicates biological process. The x-axis represents significance score  [(B) Heatmap of RNA-seq data after depletion of Paf1, Cdc73, or Ski8. KD, Paf1C-depleted cells; Ctrl, control cells. (C) Genes exhibiting changes in expression after siRNA-mediated depletion of Paf1C subunits were classified according to GO analysis as described in S3A Fig. Genes that showed greater than 1.4-fold change in expression  were analyzed. The y-axis indicates biological process, and the x-axis represents the significance score .ct test) . MB, myo(TIF)Click here for additional data file.S4 FigGenes with significant APA regulation, SAAP method, FDR<0.05, were analyzed as in (TIF)Click here for additional data file.S5 Fig(A) Genome browser tracks depict enrichment of Paf1C subunits by ChIP-seq (top) for a control gene (Dld) that does not show changes in APA, as detected by 3’READS analysis, after depletion of Paf1C subunits. This gene serves as a control for comparison with genes (Ddx46 and Pcbp2) that show changes in APA in B) The percentage of regulated intronic CDS pA sites in Paf1-, Cdc73- or Ski8-depleted cells. (C) The average enrichment profiles of Paf1, Cdc73, and Ski8 were plotted over genes that showed regulated or unaffected CDS-APA after depletion of Paf1, Cdc73 or Ski8. Genes exhibiting low levels of expression are also shown. Regions from 3 kb upstream of TSS to 3 kb downstream of TES were analyzed. (D) The average enrichment profiles of Paf1, Cdc73, and Ski8 were plotted over genes that showed shortening of 3’UTRs or that were unaffected after depletion of Paf1, Cdc73, or Ski8. Regions from 2 kb upstream or downstream of the two most regulated 3’UTR pA sites were analyzed.(TIF)Click here for additional data file.S6 Fig(A) siRNA-mediated depletion of Paf1 did not affect the levels of CPSF100. siCtrl, non-specific control siRNA. (B) Representative genome browser tracks demonstrate increased PolII occupancy immediately downstream of the TSS of genes that showed CDS-APA up-regulation after Paf1 depletion. The gray rectangle shows a blown-up portion of the entire gene, Myh9, to the left. The y-axis represents normalized reads in reads per million mapped (RPM). Gene name and the maximum value of y-axis are indicated. siCtrl, non-specific control siRNA. (C) The ratio of PolII ChIP-seq signals in Paf1 or Ski8 versus control siRNA-treated cells was plotted over genes that were unaffected or showed 3’UTR shortening after depletion of Paf1 or Ski8. Regions from 2 kb upstream or downstream of the two most de-regulated 3’UTR pA sites were analyzed .(TIF)Click here for additional data file.


Together, they provide a framework to integrate, summarize and compare the ChIP-Seq enrichment signal from complex experimental designs. Those packages identify and quantify similarities or dissimilarities in patterns between large numbers of ChIP-Seq profiles. We used metagene to investigate the differential occupancy of regulatory factors at noncoding regulatory regions (promoters and enhancers) in relation to transcriptional activity in GM12878 B-lymphocytes. The relationships between occupancy patterns and transcriptional activity suggest two different mechanisms of action for transcriptional control: i) a “gradient effect” where the regulatory factor occupancy levels follow transcription and ii) a “threshold effect” where the regulatory factor occupancy levels max out prior to reaching maximal transcription. metagene, Imetagene and similaRpeak are implemented in R under the Artistic license 2.0 and are available on Bioconductor.ChIP-Sequencing (ChIP-Seq) provides a vast amount of information regarding the localization of proteins across the genome. The aggregation of ChIP-Seq enrichment signal in a metagene plot is an approach commonly used to summarize data complexity and to obtain a high level visual representation of the general occupancy pattern of a protein. Here we present the R package  PLOS Computational Biology Software paper.This is a Understanding the global regulation of gene expression programs is an important goal of functional genomics studies. To this end, it is now standard procedure to survey the occupancy of regulatory proteins genome-wide using chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) . AffordaIn a typical ChIP-Seq analysis, reads are first aligned using an aligner of choice and peaks are called using peak calling algorithms, such as MACS  or PICS ngs.plot [ChIPseeker [seqPlots [Current approaches to compare and summarize enrichment signals for groups of regions rely on visual representations of the average enrichment at a specific position. These representations are known as metagene plots  facilitate the integration of signal from many datasets linked by complex experimental designs, 2) statistically compare the enrichment profiles of groups of genomic regions and 3) provide visual representations of the data to facilitate interpretation. Here we used the metagene package to investigate how regulatory factors contribute to the transcriptional output of noncoding regulatory regions. Indeed, recruitment of regulatory factors to noncoding regulatory regions, including enhancer and promoter regions, modulates the transcriptional response of each gene. Using the metagene and similaRpeak package, we identified the similarities and dissimilarities in the recruitment patterns of these factors at enhancer and promoter regions. Our results demonstrate that there are two distinct mechanisms of action for transcriptional regulators. Indeed, we discovered that the level of the regulatory factors either correlates with the transcriptional activity or saturates prior to maximal transcriptional activity of the regulatory region. We termed those patterns “gradient effect” and “threshold effect”.We developed the metagene package builds upon Bioconductor scalable data structures for representing annotated ranges on the genome [metagene supports the most common genomic file formats such as bam, bed and narrowPeak/broadPeak. The number of files used in a single analysis is only limited by the computer memory available. To reduce memory usage, metagene produces coverages only for the genomic regions of interest and stores this information in Run-length encoding. It is possible to compare multiple region groups and multiple experiments in a single analysis. To increase the analytical power, metagene uses the controls to estimate the signal-to-noise ratio and remove background signal. The datasets are also normalized for an accurate comparison. Furthermore, the directionality of the genomic regions (i.e. the strand) is usable to highlight asymmetric enrichment patterns. In the final graphical output, the metagene plot, each curve summarizes the information of multiple genomic regions (termed region groups) from a single experiment. When used with the similaRpeak package, our approach allows the comparison of multiple samples and gives the possibility to statistically compare the results with metrics adapted to different profile features. The Imetagene package offers a simple graphical interface to manage complex experimental designs. A workflow of a typical metagene analysis is provided in The e genome . Additiometagene uses the Normalization of ChIP-seq (NCIS) approach [metagene by converting the raw coverage values in read per millions aligned. It is also possible to change the orientation of each genomic region on the negative strand to represent every region in the 5’→3’ orientation. The profile of each group defined in the design is calculated using either an average or median profile, as specified by the user. A confidence intervals of the estimators (mean or median) is computed at each base pair using bootstraps (1000 times by default) for each group profile. To reduce the effects of extreme coverage values, a data binning strategy with customizable bin sizes, is applied before bootstrapping. Visually, the confidence interval is represented by a ribbon which includes an editable percentage (default 95%) of the sampled values (see metagene). Using the Imetagene package, it is also possible to preview the regions as an interactive heatmap , in function of the transcriptional activity –32. As eWhile Pol II and GTFs activities are directly linked to the transcriptional output, the importance of each individual regulatory factor for the transcription process is not well understood. To assess the quantitative recruitment of transcription factors, cofactors and chromatin regulators at cis-regulatory elements as a function of the transcriptional activity, we evaluated the occupancy of regulatory factors, histone modifications and DNAse hypersensitive sites in GM12878 cells. Interestingly, we observed two distinct recruitment patterns at promoter and enhancer regions. Indeed, a “gradient effect” was observed when the occupancy level of a factor correlated with the transcriptional activity  while a Differential recruitment of regulatory factors at promoter and enhancer regions raises mechanistic questions. We are proposing different models to explain the “gradient” and “threshold” effects. For the “threshold effect”, mostly observed at enhancer regions, the regulatory factors are potentially working as “on/off” switches. In that model, once a predetermined level is achieved for a specific transcription factor or cofactors, the transcriptional contribution is maximized . Extrapometagene package will be usable for multiple applications. For instance, the metagene package will be suitable to study differential recruitment in different classes of regulatory elements. For instance, enhancers and promoters regions could be stratified by functional types instead of expressions levels, such as the chromatin states [metagene to provide molecular insights into the mechanism of action. Additionally, the dynamic of transcription factors recruitment could be studied using time course datasets. Future studies will reveal new details on the mechanisms of recruitment of regulatory factors and will help in understanding the similarities and dissimilarities between the various classes of regulatory elements.In addition to the current study, the n states . The enrmetagene package, the graphical interface Imetagene, and the companion package similaRpeak are available on Bioconductor with documentation and an example dataset. These packages perform a thorough evaluation of the similarities or dissimilarities of the aggregated signal of region groups. For the current version, the region groups are based on annotations in order to test specific scientific hypotheses. Next, we will work on refinement to the bootstrapping strategy and we will be implementing clustering algorithms (as a part of a machine learning strategy) to cluster regions based directly on their occupancy patterns to provide an exploratory approach.The S1 FigImetagene package can be used to explore the matrix-associated with each experiment to visualize the coverages of the regions.After the matrices are computed, the (PDF)Click here for additional data file.S2 FigThe x-axis is centered on enhancers and promoters ±1000bp. The y-axis represents the mean occupancy normalized in reads per million (RPM). Each line represents the mean occupancy of POLR2Aphosphos2. Groups of transcriptional activity of enhancers or promoters are identified by different colors Click here for additional data file.S3 FigThe x-axis is centered on enhancers and promoters ±1000bp. The y-axis represents the mean occupancy normalized in reads per million (RPM). Each line represents the mean occupancy of POLR2Aphosphos5. Groups of transcriptional activity of enhancers or promoters are identified by different colors Click here for additional data file.S4 FigThe x-axis is centered on enhancers and promoters ±1000bp. The y-axis represents the mean occupancy normalized in reads per million (RPM). Each line represents the mean occupancy of TBP. Groups of transcriptional activity of enhancers or promoters are identified by different colors . The ribbons represent the 95% confidence interval of the mean calculated using 1000 bootstraps.(PDF)Click here for additional data file.S5 FigThe x-axis is centered on enhancers and promoters ±1000bp. The y-axis represents the mean occupancy normalized in reads per million (RPM). Each line represents the mean occupancy of the histone mark. Groups of transcriptional activity of enhancers or promoters are identified by different colors . The ribbons represent the 95% confidence interval of the mean calculated using 1000 bootstraps.(PDF)Click here for additional data file.S6 FigThe RATIO INTERSECT was calculated using the moderate CAGE signal and high CAGE signal groups.(PDF)Click here for additional data file.S7 FigThe x-axis is centered on enhancers and promoters ±1000bp. The y-axis represents the mean occupancy normalized in reads per million (RPM). Each line represents the mean occupancy of SMC3. Groups of transcriptional activity of enhancers or promoters are identified by different colors . The ribbons represent the 95% confidence interval of the mean calculated using 1000 bootstraps.(PDF)Click here for additional data file.S8 FigThe x-axis is centered on enhancers and promoters ±1000bp. The y-axis represents the mean occupancy normalized in reads per million (RPM). Each line represents the mean occupancy of EP300. Groups of transcriptional activity of enhancers or promoters are identified by different colors . The ribbons represent the 95% confidence interval of the mean calculated using 1000 bootstraps.(PDF)Click here for additional data file.S1 TableExperiment accession: unique identifier of the experiment. File accession: unique identifier of the file. Target: the name of the factor that was targeted for immunoprecipitation. Controls: the experiment accession of the recommended controls. Biosample name: the cell type. Assembly: the version of the genome used for the alignment. Href: the URL to download the file.(CSV)Click here for additional data file.S2 TablePseudometric: the name of the pseudometric. Definition: the description of the metric. Threshold: criteria that can be set by the user to avoid calculating the value of a pseudometric that would return nonsensical results .(XLSX)Click here for additional data file.S3 TableThe classification of the 106 regulatory factors in “gradient” or “threshold”. Target: the name of the target. Type: enhancer or promoter. RATIO_INTERSECT: the RATIO_INTERSECT score calculated using the moderate and high CAGE signal groups. Class: “gradient” or “threshold”.(CSV)Click here for additional data file.S1 TextMetagene and similaRpeak.Bootstrap: Description of the bootstrapping steps. Permutation: Details of the permutation procedure in (PDF)Click here for additional data file.


Xenopus oocytes. RNA-seq analyses reveal that reprogramming by oocytes results in a selective switch in transcription toward an oocyte rather than pluripotent type, without requiring new protein synthesis. Time-course analyses at the single-nucleus level show that transcriptional reprogramming is induced in most transplanted nuclei in a highly hierarchical manner. We demonstrate that an extensive exchange of somatic- for oocyte-specific factors mediates reprogramming and leads to robust oocyte RNA polymerase II binding and phosphorylation on transplanted chromatin. Moreover, genome-wide binding of oocyte-specific linker histone B4 supports its role in transcriptional reprogramming. Thus, our study reveals the rapid, abundant, and stepwise loading of oocyte-specific factors onto somatic chromatin as important determinants for successful reprogramming.Nuclear transfer to oocytes is an efficient way to transcriptionally reprogram somatic nuclei, but its mechanisms remain unclear. Here, we identify a sequence of molecular events that leads to rapid transcriptional reprogramming of somatic nuclei after transplantation to   •Xenopus oocytes induce an oocyte transcription pattern in mouse nuclei in 2 days•Reprogramming requires a switch from somatic to oocyte transcriptional components•Unusually high amounts of oocyte-derived RNA polymerase II drive reprogramming•The pattern of oocyte linker histone binding to somatic chromatin is revealed Xenopus oocytes. Transcription is switched selectively to an oocyte-type program in a process involving stepwise loading of oocyte-specific factors onto transplanted chromatin.Jullien et al. identify a sequence of events leading to rapid transcriptional reprogramming of somatic nuclei after their transplantation to  Xenopus first meiotic prophase oocyte NT experiments, several hundred mouse somatic cell nuclei are injected into the specialized oocyte nucleus , leading to changes in transcription of the incoming somatic nuclei within a few days in the absence of cell division , for the transcription of 10,000 somatic nuclei, yet only a very small fraction of RPB1 is phosphorylated and actively transcribing the oocyte lampbrush chromosomes , have been implicated in the reactivation of a few pluripotency genes such as Sox2 , but theomosomes .To understand the changes leading to the reprogramming of somatic nuclei by NT to the oocyte, we have used time-course analyses at the single-nucleus level, defining different steps of reprogramming and demonstrating that the somatic transcriptional machinery is exchanged for that of an oocyte in a hierarchical manner, which does not require new protein synthesis, and leads to a greatly increased level of Pol II binding and phosphorylation in transplanted nuclei. Using genome-scale gene expression analysis to specifically profile newly synthesized transcripts from transplanted somatic nuclei, we demonstrate that oocytes induce extensive, rapid, and specific transcriptional patterns distinct from the somatic type. We further demonstrate by chromatin immunoprecipitation sequencing (ChIP-seq) analyses that the binding of oocyte linker histone B4 contributes to transcriptional reprogramming in transplanted nuclei.Xenopus oocytes, we determined how the transcriptome of mouse somatic cells changes after NT into the germinal vesicle of oocytes. Specifically, we compared the polyA+ messenger RNAs (mRNAs) accumulated in cultured immortalized mouse embryonic fibroblasts (MEFs) to those produced during the 2 days after transplantation of MEF nuclei to Xenopus oocytes. Transcripts produced exclusively after NT were selectively labeled using 5-bromouridine 5′-triphosphate (BrUTP), which we injected into oocytes, allowing newly transcribed mRNAs to be immunoprecipitated before the generation of RNA-sequencing (RNA-seq) libraries using a protocol adapted for low cell number . These results suggest that NT to Xenopus oocytes induces a rapid, extensive shift in transcription from a somatic- to oocyte-type.Gene ontology (GO) analysis showed that reprogrammed genes after NT include GO categories relevant to oocyte functions, such as those for transcription and development, while downregulated genes are mostly enriched in signaling pathways C, which  oocytes  and defi oocytes D. In mouXenopus oocyte differs from a mouse embryonic stem cell (ESC) transcriptional pattern. We performed a hierarchical clustering of gene expression level in mouse ESCs, two-cell-stage mouse embryos, Xenopus oocytes (mouse orthologs) and in MEFs before and after NT. ESCs and two-cell-stage embryos clustered away from other samples, while NT samples clustered together with Xenopus oocyte mouse orthologs, away from MEFs before NT -RPB1 donor nuclei , important for the formation of a preinitiation complex, is exchanged for oocyte TBP2 during reprogramming . GFP-TBP2-cherry . Donor G2-cherry . We concXenopus oocytes is characterized by an extremely high rate of transcription  prevented gene reactivation, even in the presence of aAmR donor somatic RPB1  form of RPB1 . We conftic RPB1 B. Thus, Xenopus follicle cells, hundreds of which surround each oocyte, and those of mouse chromatin coming from transplanted nuclei. We therefore compared sequence reads that mapped either to the mouse or Xenopus genome. We found that 73% of the H3 ChIP-seq reads mapped to the mouse genome, while 27% to the Xenopus genome. In contrast, 97% of B4 ChIP-seq reads mapped to the mouse genome, consistent with the exclusive expression of B4 in the oocyte and not in follicle cells, a result that confirms the specificity of the B4 antibody in ChIP analysis. In general, the binding of B4 to transplanted mouse chromatin was widespread across the mouse genome. However, B4 was depleted at transcription start sites (TSSs) compared to adjacent regions , in good agreement with the linker histone H1 binding pattern described in mouse ESCs  analyses were performed to detect reprogrammed transcripts from transplanted mouse nuclei. Four whole injected oocytes were pooled as one sample, and RNA was extracted from the samples using QIAGEN RNeasy columns. After RNA extraction including on-column DNase I digestion, reverse transcription was performed using SuperScript III with gene specific primers. Real-time PCR was performed as SYBR Green assays on an ABI 7300 Real Time PCR Cycler using a standard ABI cycling condition. Primers used in this assay are shown in the Xenopus oocytes 2 hr after transplantation with MEFs. Oocytes were collected 48 hr after NT, and RNA was extracted using a QIAGEN RNeasy kit (eight oocytes per column). RNA immunoprecipitation (RIP) was performed using a protocol adapted from the previous report  was injected to the cytoplasm of s report : BrUTP-lNewly transcribed RNAs in NT oocytes were isolated by RIP. These RNAs were reverse-transcribed and complementary DNAs (cDNAs) amplified following a published protocol . AmplifiDetails of ChIP analysis of NT oocytes were described elsewhere . BrieflyE. coli DNA polymerase I  treatment before end repair reaction. The samples were then amplified by following the library preparation protocol described for RNA-seq (20 PCR cycles). The ChIP-seq libraries were sequenced on an Illumina HiSeq 2000.For ChIP-seq analyses, 364 and 140 NT oocytes were used for histone B4 and H3 library preparation, respectively. Antibodies used include a rabbit polyclonal B4 antibody  and a raWestern blots were performed following standard protocols. Anti-mouse, -rabbit, or -goat immunoglobulin G Alexa Fluor 680 (Invitrogen) and/or anti-mouse or -rabbit IRDye 800CW (LICOR) were used as a secondary antibody, and bands were detected using the LI-COR ODYSSEY imaging system. Primary antibodies used were as follows: rabbit polyclonal anti-Pol II CTD repeat YSPTSPS (phosphoS2) , mouse monoclonal anti-Pol II CTD repeat YSPTSPS (8WG16) , mouse monoclonal anti-HA .See MEFs were derived from embryos (embryonic day 13.5) hemizygous for the X-GFP transgenic allele  as descrThe number of biological replicates are shown as n. In transcriptional assays by RT-qPCR, the statistical difference was calculated by ANOVA. Error bars represent SEs. Statistical tests in RNA-seq and ChIP-seq analyses are described in J.J., K.M., V.P., and J.B.G. designed experiments, analyzed data, and performed almost all experiments. V.P., K.M., and J.B.G. wrote the manuscript with help from J.J. G.E.A. and C.R.B. analyzed genome-wide data. R.P.H. performed experiments. N.J.G., H.K., and K.O. generated reagents.


Drosophila embryos. This long-range repressor mediates histone acetylation and methylation in large blocks, with highly context-specific effects on target genes. Most strikingly, Hairy exhibits biochemical activity on many loci that are uncoupled to changes in gene expression. Rather than representing inert binding sites, as suggested for many eukaryotic factors, many regions are targeted errantly by Hairy to modify the chromatin landscape. Our findings emphasize that identification of active cis-regulatory elements must extend beyond the survey of prototypical chromatin marks. We speculate that this errant activity may provide a path for creation of new regulatory elements, facilitating the evolution of novel transcriptional circuits.Metazoan transcriptional repressors regulate chromatin through diverse histone modifications. Contributions of individual factors to the chromatin landscape in development is difficult to establish, as global surveys reflect multiple changes in regulators. Therefore, we studied the conserved Hairy/Enhancer of Split family repressor Hairy, analyzing histone marks and gene expression in DOI:http://dx.doi.org/10.7554/eLife.06394.001 The genes encoded in DNA contain the instructions to make proteins and other molecules important for cell behavior. Only a fraction of genes are ‘expressed’ at any particular time; proteins called transcriptional repressors keep many in a silent state. One such repressor in the fruit fly is called Hairy, and its activity is essential for embryos to develop correctly. Similar Hairy-related proteins are crucial regulators of development in mammals.A central mechanism of controlling gene expression involves the wrapping of DNA around histone proteins to form a structure called chromatin. Attaching chemical tags to histones changes how accessible the genes are within the chromatin—the more accessible the genes are, the more likely they are to be active. Some tags promote gene activation, while other tags block expression. Previous research showed that Hairy reduces gene expression by influencing which tags are added to, or removed from, the chromatin.Drosophila fruit fly embryos. This revealed the genes that Hairy directly targets and the corresponding effects this targeting has on chromatin structure. Hairy altered chromatin chemical tags over large blocks of DNA on silenced genes, with some of the changes being specific to particular genes. However, many areas of chromatin activity were not associated with changes in gene expression. Instead, many genes ignore Hairy-mediated changes in their vicinity, while in other cases chromatin changes occurred on genes that were already silent.Kok et al. have now tracked the effects of the Hairy protein on the entire genome of Previous studies have suggested that regulatory factors like Hairy frequently bind to many sites on the genome and have no function. Kok et al. now suggest that these sites—previously regarded as representing ‘inert’ sites—are biochemically very active. Genomic studies that label regulatory sites solely by changes to their chromatin modifications may be fooled by the apparent activity of such ‘errantly targeted’ sites, assuming that they are critical for gene regulation. At the same time, these sites may represent regions that are particularly likely to evolve regulatory properties. Kok et al. therefore propose that errant targeting by Hairy may help new regulatory elements to evolve that could eventually influence how genes are expressed.DOI:http://dx.doi.org/10.7554/eLife.06394.002 Drosophila embryo, localized transcriptional repressors provide essential patterning information that establishes the primary anterior-posterior and dorsal–ventral axes of the organism. The action of transcriptional repressors is heterogeneous and can exhibit context effects; one of the most striking aspects involves the different classes of repressors that mediate distinct chromatin changes on target genes. Short-range acting proteins Snail and Knirps interfere with transcription only when their cognate binding sites are located within close range of the activator binding sites , that can oligomerize to spread over large areas of the genome, and colocalization of HDAC to the target genes resulting in deacetylation of specific lysine residues in histones H3 and H4  that constitute the basis of animal development. Molecular studies have demonstrated that the acquisition or loss of binding sites or entire regulatory modules appears to drive significant changes in gene expression that initiate critical evolutionary transitions, such as elaboration of novel limb structures . Signifirganized . The exiDrosophila blastoderm embryo in a seven stripe pattern, which is important in controlling downstream pair rule genes that direct segmentation  binding regulated by Hairy in the blastoderm embryo using an inducible system as described previously to capture direct effects with high temporal resolution  Figure . Hairy ientation . Here, wion . GO categories for the set of upregulated genes were of lower statistical significance, and included reproductive processes (p < 0.03)  .Expression of the majority of genes bound by Hairy did not change , consistcis-regulation; H4Ac, H3K27Ac, and H3K4me1 at promoters and enhancers; H3K4me3 at transcription start sites (TSS); H3K36me3 at gene body regions; and H3K9me3 at repressed regions of chromatin  of chromatin marks that are often correlated with specific features of hromatin . The meaandscape . As was andscape .10.7554/ftz locus  they are ‘shovel ready’ and not constrained by complex  genomes . Other s genomes . In gene genomes . Importa genomes . Of courOur study provides a new perspective on these previous observations, in that essentially trivial biological interactions may have consequences in evolutionary time. We show that Hairy is engaged apparently in errant targeting of chromatin on many loci during the period when it is expressed, and demonstrate that in many cases, little distinguishes the types of chromatin effects observed on functionally repressed targets compared to ‘non-functional’ interactions on other loci . Thus, uhairy gene was created by introducing a multiple cloning site containing Kozak sequence, initiator ATG and HindIII/BglII sites into the 5′ portion of the hairy ORF in the pCaSpeR-hsh using EcoRI/BstEII sites as described previously , and reversed transcribed using a High Capacity cDNA Reverse Transcription Kit from Invitrogen/Applied Biosystems. The cDNA was then analyzed by real-time PCR using the primer pairs located at transcription units. Data was normalized to Drosophila Genome Oligo Microarrays (Agilent) according to the manufacturer's instructions. Slide image data was quantified using Agilent's Feature Extraction software. Four biological replicates were performed for each sample. Differential gene expression analysis was performed with the GeneSpring program (Agilent). Functional annotation of down- and up-regulated genes was done using the Database for Annotation, Visualization and Integrated Discovery . Samples were amplified and labeled using the Quick AMP Labeling kit  and hybridized to 8 × 15K Customized iscovery . DiffereHeat shocks and ChIPs were performed as described previously , with thDNA from chromatin immunoprecipitation (10 ng) was adapter-ligated and PCR amplified (18 cycles) as described in We detected the regions where chromatin states are changed upon induction of Hairy by comparing the level of histone marks at particular genomic locations. Differentially changed genomic regions were identified using the diffReps program , which uHypergeometric Optimization of Motif EnRichment (HOMER) was used for peak finding and downstream data analysis .Using HOMER with default settings, peaks for histone marks and Flag tagged Hairy protein were identified using signals from H3 ChIP and input respectively as background.Regions detected by diffReps or peaks called by HOMER were associated with genes by identifying the nearest RefSeq TSS and annotated to a genomic feature such as intergenic, intron, exon etc.Normalization of ChIP-seq tags for histograms, heatmaps, and scatter plots: We normalized the total number of mapped tags to 10 million for each sample using HOMER so that the read densities were comparable.ChIP-seq densities of a 4 kb window centered at affected regions detected by diffReps were determined using HOMER. The program normalizes the output histogram such that the resulting units are per bp per region with bin size of 10 bp. Plots were generated using matplotlib .Data matrices were generated using HOMER by counting total tags in a 5 kb window around affected regions or peaks and normalizing to 10 million reads with bin size of 25 bp. Data was visualized using Java Tree View .Tag densities were calculated by counting the tags at regions defined by peak coordinates of the first experiment (x axis) and compared to the second experiment (y axis). Data was log2 transformed and plotted using matplotlib. Pearson's Correlation Coefficients were calculated to determine the extent of similarity between samples.mergePeaks program of HOMER was used to find overlapping sites between differentially changed regions of different histone marks upon Hairy induction. These regions were considered as overlapped if changed regions from each experiment share at least 1 bp. Significance of co-occurrence of regions was indicated by natural log p-values using the hypergeometric distribution. Positive values signify divergence.Affected regions for chromatin marks were considered as Hairy bound if the nearest gene has at least one Hairy peak. The occupancy of the induced Hairy protein was compared to that of endogenous Hairy binding by conducting ChIP-Seq analysis using the Flag epitope on the inducible protein; a large fraction (40%) of these binding sites were also found in the ChIP–chip study (p = 2.15e-159). Similar Hairy binding motifs were enriched in both data sets, indicating that the induced Hairy protein has similar targeting specificity to the endogenous protein .k-Nearest Neighbors (KNN), Naive Bayes and Random Forests) were employed. To perform this analysis, we wrote Python and Java codes to partition our dataset into 10 parts to perform feature selection and 10-fold cross validation classification utilizing the Weka machine learning software (http://www.cs.waikato.ac.nz/ml/weka/). To increase the robustness of our results we performed 50 iterations of the above procedure and combined the predicted classes for each gene to create a new aggregate predicted class for that gene. Here we took the class that has been predicted more than 50% of the 50 iterations as the predicted class of the gene. We have applied every combination of the four feature selection algorithms and four classification algorithms to the data to obtain the optimal classification methodology for our dataset. The results of our analysis are summarized in the main text.We used the differential changes of H4Ac, H3K27Ac, H3K4me1, H3K4me3, H3K36me3, H3K9me3 and Pol II in response to Hairy as features for our analysis here. The genomic blocks detected as significantly altered by diffReps are annotated to closest TSS. We considered four features for each of the ChIP-seq data; number of blocks linked to the same gene, range of blocks, fold change of ChIP-seq signal at blocks, and distance of blocks to closest TSS. Four features from ChIP–chip data sets of Hairy , CtBP, a review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , a Reviewing editor, and three peer reviewers, one of whom, Michael Eisen, has agreed to share his identity. The decision was reached after discussions between the reviewers. Based on our discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.Thank you for choosing to send your work entitled “Genome-wide futile cycling by Hairy repressor suggests mechanism for evolution of gene regulatory networks” for consideration at This manuscript provides an extensive ChIP-seq data set to study how Hairy regulates gene expression. Using a Hairy over-expression system, authors find that Hairy effects gene expression in a context dependent manner on a subset of target genes. However, on the majority of the bound sites Hairy is able to induce chromatin changes without altering gene expression. Based on these and other observations, authors also speculate about how novel transcription circuits could evolve.The overall consensus among the reviewers was that although this manuscript does contain some novel observations, it does not provide sufficient mechanistic insights that would explain why only a fraction of Hairy bound sites show a functional correlation and how Hairy induces widespread chromatin changes without changes in gene expression. The fact that the major conclusions are based purely on an over-expression system also poses a major problem. For example, it would be advisable that the authors use other experimental approaches such as depletion experiments to further potentiate their results. The reviewers were also not convinced by evolutionary claims made in the manuscript. The manuscript also lacked a number of controls that made it difficult to assess the quality and strength of the observations.Reviewer #1:D. melanogaster early embryonic transcriptional repressor Hairy. This paper uses a lot of ChIP-seq data to make a fairly simple point: when Hairy binds to sites where it does not appear to be regulating transcription it nonetheless induces changes in chromatin that are similar to the changes it makes when it does have an effect on transcription. This paper makes a valuable contribution. The main point the authors seem to be trying to make – that Hairy affects chromatin even when it's not affecting expression – is lost in the clutter. I think this could be an eLife paper, but not in its current form.This is the latest in a series of papers from the Arnosti lab looking at the mechanism of action of the What they did:Used a previously generated transgenic line that expresses Hairy ubiquitously in the embryo upon heat shock, and characterized changes in expression in the Hairy-ub line compared to wt. Compared gene expression in induced Hairy-ub line to control (unclear what control was). Carried out ChIP-seq with H3, H4Ac, H3K27Ac, H3K4me1 and H3K4me3 on wt and Hairy-ub embryos. Carried out similar experiments using a mutant form of Hairy that does not interact with CtBP.What they claim:Hairy binding is associated with transcriptional repression, histone deacetylation and changes in histone methylation.The effects of Hairy are mediated to some extent by its interaction with CtBP.There are significant alterations upon induction of Hairy in regions where the binding of Hairy does not seem to effect transcription.Genes could come under Hairy control by the acquisition of activator binding to regions already poised for Hairy repression .Concerns:1) Maybe I'm missing something (though I've looked extensively), but as far as I can tell, nowhere in the paper does it say what the control for gene expression and ChIP experiments was. There are at least three possibilities I can think of, and all of them differ in important ways. Wild type embryos grown under normal conditions? Wild type embryos heat shocked in parallel with the transgenic ones? Transgenic embryos that weren't heat shocked?2) A related issue is that the paper doesn't say in the Methods how long the heatshock was, other than that it was short. It's known that heatshock accelerates development. Since gene expression and chromatin marks at this time are known to be highly dynamic, any chance in the stages present with and without heatshock could produce anomalous results. Same could be true for changes that are the result of the induction of h. It would be nice to see some data on the stages represented in the two samples.3) ChIP-seq data was normalized to 10,000,000 mapped tags for each sample. However it's easy to imagine that altering the levels of an important transcriptional repressor across the whole embryo could have a global impact on the levels of some of these marks. This really calls for some kind of reference standard that would allow a quantitative comparison of overall levels not just their relative distributions, which is what is being done here.4) There are many claims made associated with 5) I didn't really get the point of the machine learning section. I'm not saying it should be removed, just that the authors should try to explain why they did it and what they learned from it a bit better.6) I'm not a fan of the use of the term “futile cycling” here, since there is no direct observation of cycling here. What the authors have shown is that the ectopic induction of Hairy leads to an alteration of chromatin state in places where Hairy binding doesn't seem functional. However it seems quite possible that what is happening at these sites is that Hairy is preventing the creation of a chromatin state that would otherwise occur in its absence, rather than altering a previously existing chromatin state. And thus it's not really cycling.7) I'm all for speculation in papers, but the evolutionary model could use some fleshing out – especially what makes it interesting and novel. It seems pretty obvious that you could make a new enhancer by adding an activator to a previously existing repressor. This idea that enhancer evolution is easier if you add an activator to a repressor rather than a repressor to an activator has been around for as long as I can remember.Reviewer #2:Drosophila embryos. One of the main findings is that the effects of Hairy binding are context-dependent: some genes that are bound by Hairy are repressed, and many others are not. This is not surprising, in fact it is true of every transcription factor for which regulated genes and genomic binding sites have been compared . What is much more surprising is that the H3K36me and H3K9me3 modifications, which are typically strongly associated with actively expressed and silenced genes respectively, were not correlated with Hairy-induced changes in gene expression in this study. The authors suggest that Hairy works through non-canonical mechanisms to regulate gene expression without changing the characteristic markers of gene expression and repression. If so, this is by far the most novel and significant finding of the study, because it overturns the conventional view of the distribution of these histone modifications. However, the authors quickly leave this aspect of the analysis and return to the other modifications, some of whose changes make more conventional sense – assuming that, unlike H3K36me and H3K9me3, their presence or absence can be interpreted in the usual way.In this manuscript, Kok and colleagues report the genome-wide effects of misexpression of the transcriptional repressor Hairy on five histone modifications, RNAPolII binding, and gene expression in Another unexpected finding is that most genes that are transcriptionally affected by Hairy overexpression (either directly or indirectly) show no change in PolII binding, either at the promoter or in the gene body. A lack of binding at the promoter could be explained by the idea of “promoter escape” mentioned by the authors (subsection “RNA polymerase II and silencing by Hairy”), but the lack of change of PolII in the gene body cannot. It is possible that these targets are regulated post-transcriptionally (and therefore indirectly), so that transcription is unaffected  but RNA levels are reduced (which would be consistent with the microarray data)? Or could the result be due to the significant heterogeneity of the cells in the whole-embryo samples?hairy used in this study: they are ChIP-chip data from the endogenous protein  are not bound by Hairy. They consider the 167 genes that are both bound and repressed to be directly repressed by Hairy, but this is probably a significant overestimate. Since the vast majority of Hairy binding sites are apparently nonfunctional, is it safe to assume that all of the Hairy sites in down-regulated genes are functional, especially since many other genes are regulated indirectly? How many of these binding sites in the 167 regulated genes are located within active enhancers? How many of the known direct target enhancers of Hairy  are bound in this experiment? Compounding the uncertainty, the Hairy binding sites shown in the figures are not from the over-expressed hs- protein . Only 40HLHm7 by H-mut-CtBP  or in The Discussion section is far too bold in its claims, relative to the results, especially in its evolutionary speculations. To flatly state that “essentially trivial biological interactions have consequences in evolutionary time” is to go far beyond the data. That statement may be true, it probably is true at least some of the time, but it is not a finding or a reasonable interpretation of this study, and there is nothing in this study to support it. I am also confused by the concept of “futile cycling”: I'm not clear on exactly what is cycling or being cycled. If it is chromatin, is there any evidence for higher chromatin turnover dependent on Hairy binding? Another notable statement is that “little distinguishes the types of chromatin effects observed on repressed genes compared to ‘non-functional’ interactions on other loci” . That is indeed a finding of the study, but at least for me, it is more likely to reflect a problem with the data than to prove that most of what we know about gene expression, silencing, and their associated chromatin modifications is wrong. If the latter is true, the authors have a tremendously important result on their hands, but that will require a much higher level of proof.cis-regulatory element.” However, (1) no experimental support for this idea, direct or indirect, is provided here or cited from other studies , and (2) this evolutionary model does not depend on any of the results shown here, since widespread, apparently non-regulatory binding sites have been commonly observed since the first days of ChIP-chip . This is a group of talented, rigorous, and thoughtful investigators, but in this case their large biochemical and evolutionary claims are not well supported by the data.Finally, it is quite easy to propose that non-functional Hairy binding sites are “an off-the shelf module that only needs the addition of a few activator binding sites to emerge as a fully fledged Reviewer #3:cis-regulatory modules through the acquisition of additional transcription factor binding sites. In general, I think this is a well-executed study that brings out some important new ideas. I have a few questions that I would like the authors to address:Kok and co-workers have carried out an extensive genome-wide analysis of the effect of Hairy on the chromatin landscape in an effort to correlate these effects with transcriptional repression by Hairy. The ChIP-seq analysis in comparison with the transcriptome analysis shows that Hairy binds to many more genes than it represses. Analysis of the chromatin landscape shows that repression is largely associated with histone deacetylation and H3K4me1 demethylation in regions that include the Hairy binding sites and spread from these sites. Perhaps surprisingly, however, genes that are not repressed by Hairy, but that bind Hairy, often show similar changes in the chromatin landscape . This shows that deaceylation and H3K4me1 demethylation are not sufficient for repression and even suggests that they could be a consequence as opposed to a cause of repression. Various machine learning protocols are used in an effort to determine what features might be most predictive of repression and some moderate success is achieved. The authors propose that these non-functional Hairy binding sites could facilitate the evolution of new 1) The authors have carried out the analysis of Hairy targets by comparing Hairy-overexpressing with wild-type embryos. This has both advantages and drawbacks. The advantage, of course, is that it is technically feasible. But the disadvantage is that Hairy is being expressed in cells in which it is never normally present and that this abnormal context could have functional consequences that we can't easily account for. This could potentially account for the lack of correlation between changes in the chromatin landscape and repression. While the ChIP-seq analysis is probably technically out-of-reach in loss-of-function mutants, have the authors considered at least carrying out the RNA-seq analysis in loss-of-function (RNAi knockdown?) embryos. It would be worth knowing how many of the repression targets identified by overexpression are also identified by loss-of-function.2) I found the lack of correlation between the Pol II ChIP and repression to be puzzling. The authors have not clearly explained how they account for this. Has this lack of correlation between Pol II ChIP and transcription levels been observed in other contexts?3) I don't fully understand the model in [Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your revised article has been favorably evaluated by Diethard Tautz (Senior editor), a Reviewing editor (Asifa Akhtar), and two reviewers. The reviewers appreciated that the manuscript has significantly improved upon revision and therefore we would like to go forward with the manuscript. However, before acceptance we would like you to address the remaining concerns (below) adequately in the text so that the readers are aware of the limitations and the conclusions that can be drawn an over-expression system.Thank you for resubmitting your work entitled “Genome-wide errant targeting by Hairy” for further consideration at Reviewer #1:I think the authors did an excellent job addressing our concerns. The revised manuscript is far more direct and readable, and I think paints a more accurate picture of what the data do and do not say. There are aspects of it that I would change if it were my manuscript, but I don't think it's the role of reviewers to shape manuscripts entirely to our tastes, so I will refrain from pointing them out.eLife level contribution. But I can see it either way.I think there one overarching question remains, which is that, to me, none of what they report in the manuscript is surprising. It's very much consistent with lots of data from lots of labs over the past decade. However, what the authors show here has not been demonstrated experimentally yet, and, as such, I think it is an Reviewer #3:The authors have addressed many of the criticisms in the initial review through additional data analysis and reference to the literature. I have two concerns:1) The original review asked for loss-of-function data to confirm the results of the overexpression studies. In response, the authors cited previous literature showing that hairy loss of function results in derepression of many of the genes that are repressed upon hairy overexpression. However, this does not address the more important point that pertains to the genes that exhibit changes in the chromatin marks and yet show no repression upon hairy overexpression. This is the class of genes upon which the claim to novelty is based and therefore, this is the class of genes whose behavior needs to be verified in loss-of-function studies.2) I find the machine learning section of the proposal a little puzzling. On the one hand the authors claim that repressed and non-repressed genes often show similar changes in histone marks upon Hairy overexpression. However, the machine learning section seems to suggest that the changes in chromatin marks are not really the same for repressed and non-repressed genes. How do the authors reconcile these apparently disparate claims? [Editors’ note: the author responses to the first round of peer review follow.]eLife readers.Thank you very much for guiding the editorial process of our recent manuscript. We read the reviews carefully, and discussed the main points with two individuals who agreed to be identified. The comments were quite helpful in focusing and clarifying the work; additional analysis on our part was able to address each of the points raised in the review process. […] A major new point that came from responding to questions in this review process was the consideration of how the “errant targeting” and biochemical activity of Hairy, as well as potentially many other factors, calls into question our assumptions in calling enhancers from genomics datasets. This, in addition to consideration of evolution of CREs, will be of great interest to We address the comments from Reviewers 1, 2 and 3 below.Reviewer 1 found the presentation complicated and confusing; in part because we presented two parallel narratives. One dealt with the implications of genome-wide chromatin modifications by Hairy, while a separate one focused on the biochemistry of Hairy and role of the CtBP corepressor. We agree with the reviewers that the main focus was on the former story, and accordingly we changed the structure of the paper to reflect this focus, removing data about analysis of the CtBP binding mutant which overly complicated the presentation. To facilitate presentation of our extensive chromatin modification data sets, we now present individual points in separate figures  to make Reviewer 1 raised a concern about the controls used for inducible gene expression in the ChIP experiments. In the old version of the manuscript, we had cited the controls used for embryo collection and heat-shock treatment . To clarAuthor response image 1.We plotted genomic data using reads normalized to the sequencing depth . Reviewer 1 asked whether induction of Hairy might cause a global impact on histone marks, which would be missed by after normalization. We considered this possibility; in fact, we find that whether or not Hairy is induced, the genome features for multiple chromatin marks are virtually identical, except in very discrete regions where there are significant changes, as shown in Reviewer 1 noted that the tendency for Hairy-bound regions to show loss rather than gain of acetylation/methylation marks may be influenced by the starting level of these chromatin marks. If Hairy is overrepresented in regions highly enriched for these marks, then there might be a bias. However, this is not the case. For H4Ac, the majority of Hairy reduced regions were not in the ∼2700 regions called as peaks by HOMER, suggesting that Hairy is not preferentially acting in “hot spots” for this modification . FurtherReviewer 1 asked that we better explain the purpose of carrying out machine learning on our chromatin data, and what we learned from it. The variety of chromatin alterations induced by Hairy did not contain a simple pattern, such as magnitude of change or width of affected area, predictive of transcriptional repression. Clearly, the ability to repress or not might hinge on where activators are bound; we didn’t measure the location of all activators, as this is not currently available knowledge. However, using machine learning, we did test the hypothesis that certain combinations of factors relating to position, size of chromatin block, magnitude of changes, occupancy of Pol II and other factors would tend to differentiate “real” regulatory events from “simulations”. The “Random Forests” method, shown in Reviewer 1 was not in favor of using the term “futile cycling” for the biochemical changes observed on non-functional Hairy-bound sites. Although we noted in the manuscript that the changes are indeed impermanent “Hairy repression is readily reversible, with chromatin reverting to its previous state minutes after depletion of the overexpressed repressor” we agree that this term may be misleading. Therefore we use “genome-wide errant targeting by Hairy” as now employed in the revised title and text.cis-regulatory elements is not new. We agree that this part of the argument was weak; removing the CtBP – related data allows us to focus directly on this point. We now present in the Discussion (and provide relevant background information in the Introduction) two major points; first, previous studies had suggested that Hairy as a repressor only functions in the context of an intact and active enhancer. Our work demonstrates that this protein is able to mediate its biochemical activity in most bound regions, indicating that there is little context necessary for the protein to go to work. Second, much molecular biology research has emphasized the high degree of cooperativity necessary for metazoan transcription factors to work well. Enhanceosomes, patterning elements and other enhancers give aberrant readouts if correct stoichiometries and spacings are not respected. These findings suggest that random individual sites are less likely to generate a suitable transcriptional readout. At least for the repressor side of the equation, we see here that the demands for generating biochemical activity are lower than anticipated – our work challenges our colleagues in the field to determine the generality of previous assumptions, and measure the independent potential for many activators binding to “off target” sites.Reviewer 1 urged us to flesh out the evolutionary model, noting that the idea of evolutionary appearance of binding motifs to make new Reviewer 2 notes that one of the main findings presented is that Hairy interacts with many “off target” sites, with relatively few linked to transcriptional function. The reviewer notes that such observations have been made previously, and this finding is hardly surprising. We completely agree, and in fact cited previous studies discussed this point . As emphDrosophila occurs without increases in H3K36me3 . 1. The reviewer asked whether it is safe to assume that all of the Hairy sites in downregulated genes are functional. Indeed, to assume that every single bound site must be functional is unlikely, however, importantly, our study does not rest on that assumption, only that the large majority of target genes are bona fide Hairy targets based on the criteria below. 2. The reviewer asked how many of the Hairy binding sites on the 167 genes scored as direct targets overlap with active enhancers. High-quality data about validated enhancers remains to be determined for most of these genes, but if we use ChIP occupancy of transcription factors as a proxy for “active enhancer” (which is an admittedly soft proposition), and map Hairy sites within these elements, we find that 163 of these 167 genes has Hairy associated with a putative regulatory module . There are no other reports of Hairy interacting with natural enhancers, where the role of Hairy has been described at a molecular level; our study provides the first elucidation of other possible targets. 4. The reviewer asked whether the use of ChIP data for non-overexpressed Hairy was suitable, as only 43% of our Flag epitope signals for the overexpressed Hairy protein overlapped with the ChIP-chip data. We selected the ChIP-chip data for our analysis because the Flag epitope gave low signals overall, although high-confidence functional targets such as ftz, Impl2, odd, h, 18w, wg, tup, pros, nht, and en were found. Practitioners of the trade know that there are often discrepancies in the exact sets of genes captured by ChIP studies, but the bulk of evidence from this study strongly supports the set of genes as a whole being likely Hairy targets; first, the overlap between repressed genes and genes bound in vivo is highly significant (p=3.8e-95), second, down-regulated genes are significantly enriched in classes consistent with known Hairy function  can pick up cis-regulatory elements from a variety of exapted ancestral or novel DNA elements. Alternatively, very beautiful individual cases have been described showing how subfunctionalization or neofunctionalization of cis-regulatory regions plays a role in evolution of gene expression. These studies fail to provide much information about which factors are present on individual elements, or how their activities may be relevant to generating the new element. Our study highlights the need to consider the activity of “off target” sites in generating novel elements, particularly because for Hairy at least (and likely other factors that employ the same cellular machinery) they are “shovel ready” and not constrained by complex cis-regulatory grammar. The new version of this manuscript provides a more balanced discussion of what we know and what we don’t know, and now develops a second theme that was stimulated by considering your comments. That is, genomic consideration of chromatin marking must not equate changes in some active marks with enhancers – there appears to be a significant possibility for false positives if one relies on the simple correlations that are widely employed.Reviewer 2 raised questions about how we handled the discussion of evolutionary implications of this study, noting that we do not show examples of how these “off target” biochemical interactions may facilitate the generation of novel The reviewer was confused by the term “futile cycling”. We recognize that this was not a helpful designation, and have altered this, as described above.hairy to distinguish this from Hairless, which is another gene. We have modified the figure accordingly.“H” was used in Reviewer 3 noted that induction of Hairy in cells in which it is never normally present could induce artifacts, accounting for the low correlation between changes in the chromatin landscape and repression. We agree that it is possible that some loci identified are not real Hairy targets, but only when the protein is overexpressed. However, for hundreds of the loci considered where chromatin changes are taking place, ChIP data from both Biggin as well as Kevin White confirm that Hairy can be found binding to these genomic regions. The early point in development selected for our study means that there are fewer cell-specific modifications, Polycomb repressed regions, and other markers of differentiated or senescent cells; thus the heterogeneity question is of lesser concern. We now do make a remark about this consideration in the Discussion, however.h loss of function mutants cause derepression of genes such as ftz, en, edl, Impl2, and prd as shown in previous studies, and these same genes are down-regulated upon Hairy induction in our hands , consistent with broadly expressed activators. These scenarios are shown in In the first round of review, Reviewer 3 noted that overexpression used here means Hairy will be expressed in (interstripe) cells not normally containing the protein. There, the Reviewer asked if knockdown studies might indicate “how many of the repression targets identified by overexpression are also identified by loss of function”. We interpreted this point as asking whether the repressed genes found in our study were corroborated by other methods, and cited previous literature supporting the identification of a number of Hairy targets. In response, in the second round the reviewer noted that “this does not address the more important point that pertains to the genes that exhibit changes in the chromatin marks and yet show no repression upon hairy overexpression”. Based on this comment, I think that we misinterpreted the thrust of the question in the first place, so here we address the question of the unaffected genes experiencing chromatin modification by Hairy. We have previously discussed data indicating that many of such elements are known to be occupied by endogenous Hairy. The reviewer asked that the behavior of this class be verified in loss-of-function studies. As previously addressed, the ChIP-seq/chromatin change studies require the quick kinetics that overexpression but not knockdown allow, but what might depletion of Hairy reveal? We thought about the possible mechanistic models possible in this case. 1) A gene normally bound by Hairy in the nuclei within the blastoderm stripes whose global expression is unaffected despite induction of Hairy may have separate enhancers that are far from Hairy sites. Even when Hairy binds in the interstripe nuclei, the enhancers would still escape regulation. 2) Alternatively, the gene may be silent, in which case Hairy binding to the gene in interstripe nuclei would not alter its silence. 3) A third possibility is that within the normal Hairy stripe regions, there are special activators that are only present in these stripes, but the endogenous Hairy is at high enough levels so that expressing more Hairy has no effect on transcription – the observed chromatin changes would come only from interstripe nuclei. Only in this last case would we mistakenly “call” a gene as “not regulated transcriptionally” but still having chromatin modifications. We think that there are few genes that fit this third category; known activators at this stage tend to be more broadly expressed, and loss of function Reviewer 3 pointed out the apparent contradiction between our assertions that Hairy action on repressed and non-repressed loci was similar, yet machine learning methods can differentiate promoters that are transcriptionally repressed from others.The two settings for these conclusions are somewhat different. For machine learning, we classified the gene sets as repressed and nonrepressed (activated and unaffected), whether or not they are bound by Hairy or affected at the chromatin level in a particular manner. The machine learning algorithms predict gene expression with up to 75% accuracy, not perfect but much better than random guessing. Separately, in the Discussion, we pointed out the similarity of chromatin changes on targets bound by Hairy, whether or not they were transcriptionally regulated. We address these differences in the revised manuscript, where we clarify these points in the text and