LEF/TCFs direct the final step in Wnt/β-catenin signalling by recruiting β-catenin to genes for activation of transcription. Ancient, non-vertebrate TCFs contain two DNA binding domains, a High Mobility Group box for recognition of the Wnt Response Element  and the C-clamp domain for recognition of the GC-rich Helper motif (5′-RCCGCC-3′). Two vertebrate TCFs (TCF-1/TCF7 and TCF-4/TCF7L2) use the C-clamp as an alternatively spliced domain to regulate cell-cycle progression, but how the C-clamp influences TCF binding and activity genome-wide is not known. Here, we used a doxycycline inducible system with ChIP-seq to assess how the C-clamp influences human TCF1 binding genome-wide. Metabolic pulse-labeling of nascent RNA with 4′Thiouridine was used with RNA-seq to connect binding to the Wnt transcriptome. We find that the C-clamp enables targeting to a greater number of gene loci for stronger occupancy and transcription regulation. The C-clamp uses Helper sites concurrently with WREs for gene targeting, but it also targets TCF1 to sites that do not have readily identifiable canonical WREs. The coupled ChIP-seq/4′Thiouridine-seq analysis identified new Wnt target genes, including additional regulators of cell proliferation. Thus, C-clamp containing isoforms of TCFs are potent transcriptional regulators with an expanded transcriptome directed by C-clamp-Helper site interactions. The Wnt signaling pathway is one of several vital developmental pathways conserved in all phyla of the animal kingdom ,2. Wnt pDrosophila and Caenorhabditis elegans, a single TCF carries this responsibility . In vertebrate systems, the LEF/TCF family has expanded in both the number of family members as well as the diversity of isoforms produced from each gene. Diversification and subfunctionalization is due in part to alternative splicing and alternative promoter usage, which interestingly does not introduce newly evolved domains but instead has made ancient, existing domains, such as the N-terminus (β-catenin binding) and C-terminus  an alternative choice  DNA binding domain (DBD) that binds to a eight nucleotide DNA sequence motif frequently called a Wnt Response Element , but other domains can greatly influence DNA binding. For example, alternative splicing of the C-terminus of TCF1 and TCF4 produces an isoform (named an ‘E-tail’ isoform) that is particularly potent in its ability to regulate transcription –17. TCF1in vivo and in vitro  for transcription regulation independent of any obvious canonical WRE motif. We also assessed early changes in gene expression in tandem with ChIP-seq experiments using a metabolic labeling and high-throughput RNA-seq technique called 4′Thiouridine-seq. This technique selectively labels actively transcribed nascent RNAs, and by comparing 4′Thiouridine-seq transcript changes with ChIP-seq data, we identified new C-clamp-dependent Wnt target genes that showed direct regulation by TCF1, including histone genes, and other genes with high relevance to cell proliferation. We conclude that the C-clamp enables gene regulation independent of canonical WRE interactions in modulating the DNA activities and transcriptional output of TCF1E. However, our results also indicate that the DNA binding specificities of the HMG box and C-clamp synergize to control gene expression of a subset of Wnt target genes.The establishment of inducible dnTCF1EWT and dnTCF1Emut DLD-1 cell lines has been described previously .See Supplementary Methods. ChIP-seq data set was deposited to GEO with accession GSE53536.See Supplementary Methods.Construction of TCF1EWT, TCF1Emut, expression plasmids was described previously ,26.COS-1 cells were transiently transfected with BioT transfection reagent according to the manufacturer's protocol (Bioland Scientific LLC). COS-1 cells were plated at a density of 200 000 cells/well in 6-well plates 20 h before transfection. Luciferase reporter constructs (0.4 ug) were cotransfected with β-catenin (0.4 ug), β-galactosidase (0.1 ug) and a LEF/TCF expression vector (0.1 ug). Cells were harvested after 20 h, and luciferase and β-galactosidase activities were determined as described .EMSAs were carried out with 1 ng (∼200 cps) of radioactive oligonucleotide in a final reaction volume of 20 ul containing 10 mM HEPES (pH 8.0), 2.5 mM ethylenediaminetetraacetic acid (EDTA), 10% glycerol, 20 mM KCl, 5 mM MgCl2, 0.024 ug/ul salmon sperm DNA and 20 mM dithiothreitol (DTT). COS-1 cells were transiently transfected with EVR2 and expression vectors for full-length human TCF1EWT. COS-1 cells were prepared 48 h after transfection by swelling cells on ice, immersing them for 15 min in hypotonic lysis buffer , and douncing. The methylated probe was ordered from Fisher Scientific and contains four methylated CpGs (two on each strand in the Helper sites).Most ChIP-seq studies are descriptive studies that focus on establishing the binding profiles of one or several DNA binding proteins. However, ChIP-seq experiments are well suited to testing the functional contribution of individual protein domains within a DNA binding protein. For example, it was shown that the genome-wide binding profile of the E2F transcription factor in MCF-7 breast cancer cells is not mediated through protein–protein interaction domains, but rather almost exclusively through the E2F DBD . Intereset al. to discover that either dnTCF1E or dnTCF4E expression causes a stall in the G1 phase of the cell cycle  much like a false discovery rate value. IDR analysis showed that the top 1000 peaks from 2 h of Doxycline induction had a reproducibility index of 0.1 or better for the dnTCF1EWT replicates and 0.04 for the dnTCF1Emut replicates . Following the ENCODE protocol for ChIP-seq (dnTCF1EWT and dnTCF1Emut (C-clamp mutant) were induced with doxycycline in duplicate cultures for 1 h (undetectable expression), 2 h (low amount of expression) or 9 h (higher amount of expression) Figures  and protChIP-seq , we therP = 4.5E-16; paired t-test). Binding by dnTCF1Emut was also weaker at 9 h post-induction compared to 2 h post-induction, suggesting that the decrease in binding seen at later time points (even with greater amounts of dnTCF1E in the nucleus) is due to a C-clamp-independent phenomenon. To test whether dnTCF1E was inducing a repressive chromatin state that reduces binding at 9 h post-induction, ChIP-quantitative polymerase chain reaction (qPCR) experiments were performed on H3K9acetyl and H3K9me3 chromatin marks . H3K9me3 is associated with a closed chromatin state and decreased access of transcription factor binding  across all samples from this pooled data set shows sharp peaks of occupancy Figure  and know binding , while H binding . InductiSites of dnTCF1EWT and dnTCF1Emut occupancy were chiefly within intergenic and intronic regions, a pattern of binding that has been observed for TCF4, a LEF/TCF family member that is co-expressed with TCF1 in colon epithelial cells Figure  36). Ho. Ho36). Drosophila . For comparison, the incidence of the Helper site in promoters occupied by dnTCF1Emut is not significantly greater than the genome background promoters    and Hypes (RSAT) . These as (RSAT) . For thes Figure , suggestP = 1.6E-11), while dnTCF1Emut had only 4 peaks with 2 Helper sites and 1 WRE (P = 0.021). dnTCF1EWT had 18 peaks with 3 or more Helper sites and 1 WRE, whereas dnTCF1Emut had 0 peaks with 3 or more Helper sites and 1 WRE. This suggests that the C-clamp can utilize multiple Helper sites to contribute to binding as has been previously suggested  of the 733 genes that changed expression after 2 h of induction of dnTCF1EWT showed a decrease in transcription rate Figure , confirmWe assessed the overall degree in which transcription was repressed for genes downregulated at both time points. There was a significant decrease in relative transcription at 9 h versus 2 h post-induction . This same pattern was seen with Wnt target genes  and is consistent with the 4′Thio-RT-PCR results . Interestingly, this trend is directly opposite from the changes in dnTCF1E peak strength (peaks were strongest at 2 h post-induction and declining by 9 h). To investigate this seemingly contradictory result, we counted the number of peaks within 50 kb of the TSS of downregulated genes for the two ChIP-seq time points . We hypothesized that decreased transcription at the 9 h time point was due to a greater number of occupied sites surrounding the gene. This was confirmed for one-third of the downregulated genes where ChIP-seq peaks were easily linked to nearby target genes. This analysis does not take into account the actions of even longer range enhancers that might be involved in the downregulation of additional genes. It is therefore possible that the stable repression of transcription derives from increased involvement of multiple regulatory sites. Additionally, there may be a lag between the initial protein binding event at 2 h post-induction and the greater changes in gene expression seen at 9 h post-induction.The degree to which dnTCF1EWT binding sites were linked to downregulated genes was assessed by comparing the ChIP-seq and 4′Thiouridine-seq datasets Figure . dnTCF1Ebona fide WREs are present in the reporter plasmid . For oud Figure . The remd Figure . The posd Figure . HIST2H4d Figure . Howeverd Figure .To address whether Helper sites are sufficient to regulate transcription on a global level, we divided peaks within 30 kb of the TSS of genes detected in 4′Thiouridine-seq into several categories: peaks with no WREs or Helper sites, those with WREs, those with Helper sites and those with both Helper sites and WREs. As expected, peaks with one or more WREs were significantly associated with downregulated genes compared with peaks with no WREs . In contrast, peaks with Helper sites alone were not significantly associated with downregulated genes, suggesting that WRE-independent Helper site-mediated transcriptional regulation is likely a gene-specific rather than a globalized mode of regulation. However, peaks with two or more Helper sites and a WRE were significantly associated with greater downregulation of genes compared with peaks with only WREs. Taken together, our results indicate that the C-clamp Helper site interaction synergizes with the HMG-WRE interaction for strong transcriptional control of target genes and in special cases, the C-clamp can participate in direct transcriptional regulation independent of a WRE.in vitro selection experiments with oligonucleotides where the relative positions of WRE and Helper site were also variable  showed that a luciferase reporter plasmid with multimerized Helper sites was not responsive, whereas the Helper site greatly augmented activation of a reporter with multimerized WRE motifs   . Therefogure S6) . An altegure S6) . Our datOur analysis also identified a dynamic pattern of genome occupancy where both dnTCF1EWT and dnTCF1Emut binding decreased at 9 h post-induction Figure . Given tIn addition to the dynamic temporal shifts of occupancy, the DNA sequence of the Helper site suggests another way in which TCF binding can be modulated. Embedded in each DNA strand of the Helper site sequence is one CpG dinucleotide, whereas the WRE does not have any CpG pairs. It is possible that the portion of the Wnt transcriptome that depends on C-clamp activity might be rendered inaccessible through DNA methylation. A preliminary test of this notion was carried out by an EMSA of TCF-1EWT binding to an oligonucleotide probe encoding two WREs and two Helper sites . A mutation in the Helper site reduced binding to the oligonucleotide, but not as much as a version of the EMSA probe that contained a methylated CpG dinucleotide in both strands of the Helper site. Binding was strongly reduced even though the consensus, canonical WRE remained the same in all the probes. Thus, targeted methylation of Helper sites in the genome might be one way in which the Wnt transcriptome can be shaped by epigenetic modification.4′Thiouridine-seq was a useful technique for identifying immediate decreases in transcription in response to induction of dnTCF1EWT. Combining this data with our ChIP-seq results enabled us to link hundreds of dnTCF1EWT occupied regions to stably downregulated genes and therefore prompted predictions of additional direct target genes (Supplementary File S4, and also see Table The great majority of the downregulated genes are not known Wnt target genes. This is even true after 2 h post-induction when indirect effects have not had as much time to accumulate and therefore direct gene expression changes should be the most enriched. The identification of downregulated genes that contain closely linked ChIP-seq peaks at 2 h post-induction is strong evidence that our analysis identified new Wnt target genes (see Table Supplementary Data are available at NAR Online.


STMN1) is a neuronal growth-associated protein that is involved in microtubule dynamics and plays an important role in synaptic outgrowth and plasticity. Given that STMN1 affects fear behavior, we hypothesized that genetic variations in the STMN1 transcriptional regulatory region affect gene transcription activity and control fear behavior. In this study, two single nucleotide polymorphisms (SNPs), g. -327 A>G and g. -125 C>T, were identified in 317 English Springer Spaniels. A bioinformatics analysis revealed that both were loci located in the canine STMN1 putative promoter region and affected transcription factor binding. A statistical analysis revealed that the TT genotype at g.-125 C>T produced a significantly greater fear level than that of the CC genotype (P < 0.05). Furthermore, the H4H4 (GTGT) haplotype combination was significantly associated with canine fear behavior (P < 0.01). Using serially truncated constructs of the STMN1 promoters and the luciferase reporter, we found that a 395 bp (−312 nt to +83 nt) fragment constituted the core promoter region. The luciferase assay also revealed that the H4 (GT) haplotype promoter had higher activity than that of other haplotypes. Overall, our results suggest that the two SNPs in the canine STMN1 promoter region could affect canine fear behavior by altering STMN1 transcriptional activity.Stathmin 1 ( Police dogs play a unique role in preventing and cracking down on crime . In the STMN1 is widely distributed and localized in the cytosol [STMN2, STMN3 and STMN4 are neuron-specific and located on membranes of developing (STMN2 and STMN3) and mature (STMN4) nerve cells [The microtubule destabilizers Stathmin family, includes STMN1, STMN2 , STMN3 (SCG10-like protein) and STMN4 (stathmin-like protein B3) –22. They cytosol , whereasve cells –22. STMNve cells –26.STMN1 is closely related to fear level in humans and animals. Two single nucleotide polymorphisms (SNPs) (rs182455 and rs213641) in the transcriptional control region of STMN1, were found to affect fear and anxiety responses in humans [STMN1 led to abnormal spike-timing-dependent long-term potentiation and defective memory on fear conditioning [Several studies have reported that n humans . The resn humans . In miceitioning –31.STMN1 transcriptional regulatory region have not yet been reported in dogs. Moreover, the distribution, frequency, and function of the STMN1 variations are unclear, and the regulation of STMN1 transcriptional activity by the promoter region remains to be revealed. Therefore, the present study investigated potentially functional genetic variations in the STMN1promoter and explored whether these genetic variations could affect gene transcriptional activity and, consequently, fear behavior.However, genetic variations in the A total of 317 English Springer Spaniels from the Nanjing Police Dog Institute of the Ministry of Public Security were employed in this study . The ages of these dogs ranged from 6 to 10 months with 142 males and 175 females. Blood samples (2 mL) were collected from forelimb vein by a veterinarian and stored at −20°C for DNA extraction. Heart, liver, spleen, lung, kidney, pancreas, muscle, medial prefrontal cortex (mPFC), midbrain, cerebellum, brainstem, amygdala, hippocampus, hypothalamus, hypophysis, spinal cord, medulla oblongata, and olfactory bulb tissues were collected from 15 dogs, within 30min of euthanasia by overdose of pentobarbital, frozen and stored in liquid nitrogen until total RNA extraction. All experimental procedures and sample collections were performed according to the Regulations for the Administration of Affairs Concerning Experimental Animals  and approved by the Ethics Committee of Nanjing Agricultural University. All efforts were made to minimize the number of animals used and their suffering.The fear level test consisted of five separate subtests: the affability and handling test , floor t™ qPCR SYBR Green Master Mix , 0.4 μL ROX Reference Dye II, 0.8 μL (10 μM) of each primer, and 6 μL RNase-free water. PCR program is the same as previously described [−ΔΔCt method for mRNA quantification.Total RNA was extracted from heart, liver, spleen, lung, kidney, pancreas, muscle, mPFC, midbrain, cerebellum, brainstem, amygdala, hippocampus, hypothalamus, hypophysis, spinal cord, medulla oblongata, and olfactory bulb tissues using TRIzol reagent  as previous described . RNA purescribed . The qRTSTMN1 was sequenced and scanned with the P-F1/R primer  with high- or low-fear levels respectively. Two DNA pools for the high- and low-fear groups were prepared by mixing equal amounts of genomic DNA from 12 dogs. The transcription start site was marked as +1. The 5′-flanking transcriptional regulatory region  upstream in R primer . The SNPA>G and g. -125 C>T) in the canine STMN1 promoter. The g. -327 A>G and g. -125 C>T genotypes were examined using the polymerase chain reaction single-strand conformation polymorphism (PCR-SSCP) technique. The F1/R1 and F2/R2 primers , and double distilled H2O up to 20 μL. The cycling protocol is the same as previously described [The sequencing results disclosed two genetic variations  of the pGL3-basic vector . The recombinant plasmids were confirmed by sequencing and denoted pGL3-F1, -F2, -F3, F4, F5, and F6, respectively. To compare the transcriptional activities of the STMN1 promoters between various haplotypes, the regulatory regions from −592 nt to +83 nt containing the H1(AC), H2(AT), H3(GC), and H4(GT) haplotypes were amplified with the P-F5/R primer pair  following the manufacturer’s instructions. The pRL-TK vector is an internal control to correct for differences in transfection and harvesting efficiency. The luciferase assay was conducted as the manufacturer instructed (Promega). Firefly and Renilla luciferase activities were measured with a luminometer . Promoter activity is reported in relative light units and normalized against thatof the empty pGL3-basic vector. All transfections were performed in triplicate and repeated at least three times in independent experiments.Before transfection, 293T cells were seeded in 12-well plates. When cells had grown to 70–80% confluence, 0.05 μg pRL-TK vector (Promega) and 1 μg pGL3 vector were co-transfected with 3 μL LipofectamineP-values < 0.05 were considered significant. All data are expressed as means ± standard error.Genotype frequencies, allelic frequencies, effective number of alleles, heterozygosity, polymorphism information content (PIC), and the chi-square test of goodness-of-fit for the Hardy-Weinberg equilibrium law were calculated using PowerMarker V3.25 , 39. StaSTMN1 expression in 18 tissues  of the dogs. High levels of STMN1 transcripts were detected mainly in brain tissues , and low levels were detected in heart, liver, spleen, kidney, pancreas, and muscle  in the amygdala, mPFC, and hippocampus   within the analyzed population. According to the PIC classification , all loci exhibited intermediate polymorphism at the STMN1 in the analyzed population (We assumed that genetic variations in the 125 C>T) . The two125 C>T) . The genpulation .C>T polymorphism was associated with fear level (P < 0.05). Animals with the g. -125 C>T TT genotype had an obviousgreater fear level than those with the CC genotype (P < 0.05), whereas the g. -327 A>G genotype had no relationship with fear (P > 0.05).The effects of the two SNPs on the fear level of 317 dogs are summarized in AC), H2 (AT), H3 (GC), and H4 (GT). Consequently, there are nine combinations for the population analysis: H1H1 (ACAC), H1H2 (ACAT), H1H3 (ACGC), H1H4/H2H3 (ACGT/ATGC), H2H2 (ATAT), H2H4 (ATGT), H3H3 (GCGC), H3H4 (GCGT), and H4H4 (GTGT). The effects of the nine haplotype combinations on fear level are summarized in GTGT) haplotype combination was dramatically higher than that of dogs with any of the other haplotype combinations.Four haplotypes were constructed from the two detected SNPs: H1  were used to assess the effect of different promoter haplotypes on STMN1 transcriptional activity (P < 0.05). The H4 haplotype showed 81% and 64% higher transcriptional activity than the haplotypes H2 and H1 (P < 0.05), but there was no significant difference between the H4 haplotype and H3 haplotype. Given that the -592 nt and +83 nt region is the core promoter, we speculate that g. -327 A>G and g. -125 C>T may be related in transcriptional regulation of STMN1. Overall, our results indicate that the functional g. -327 A>G and g. -125 C>T variants in the recombined haplotypes play crucial roles in STMN1 promoter.To investigate the impact of the potential functional SNPs on activity . The difSTMN1 core promoter region was located in the -477 nt to +139 nt region. The transcription factor prediction analysis showed that canine STMN1 has a typical TATA box in its promoter region. The putative promoter contained several functional elements, including POU6F1, SRY, c-Ets-2, GATA-3, SP2, MZF1, and E2F7 transcription factor binding sites. Both g. -327 A>G and g. -125 C>T located in the transcription factor binding site of the promoter core region. However, the presence of a mutant G allele at g. -327 A>G created the polyamine-modulated factor 1 (PMF1) (-) transcription factor binding motif, whereas the presence of a mutant T allele at g. -125 C>T created NK6 homeobox 3 (Nkx6.3) (+) and Forkhead Box P 1 (FOXP1) (-) transcription factor binding motifs  motif is conserved in dog, horse and cow. The FOXP1 (-) binding site is conserved in dog, horse, cow and rat. We found that human and rodent species possessed very low levels of homology with dog in the three interest binding sites  were identified in the STMN1promoter region, and animals with g. -125 C>T TT genotype had greater fear levels than those with the CC genotype by association analysis. The g. -125 C>T TT genotype was significantly associated with fear level, whereas the g. -327 A>G GG genotype was not. However, dogs with the H4H4 (H4 = GT) haplotype combination showed relatively higher fear levels. Therefore, the influences of the g. -327 A>G and g. -125 C>T SNPs, specifically the H4H4 haplotype combination, on fear level requires further elucidation. Overall, these results suggest that STMN1 gene is associated with canine fear behavior, and the mutation could be used as the molecular genetic marker to select working dogs and breeding dog with low fear behavior.As y people . Moreovey people . In our STMN1 promoter region, and our results revealed that the P-F6 fragment had the highest promoter activity, which is in accordance with predictions of bioinformatic analysis. As the function of this fragment remains unclear, we speculate that upstream regulatory elements may interact with each other to regulate STMN1 expression. In addition, identifying other signals that indirectly regulate STMN1 promoter activity would be interesting. Haplotypes are more likely to affect traits than SNP [A>G and g. -125 C>T SNPs were located at the STMN1core promoter region, indicating their possible important function in canine STMN1 expression. The H1 (AC), H2 (AT), H3 (GC), and H4 (GT) haplotypes were transfected into 293T cells to examine their impacts on STMN1 promoter transcriptional activity. We found that the pGL3-H4 reporter plasmid had the highest relative luciferase activity among all plasmids. The fact that different haplotypes exhibited different promoter activities, suggests that SNPs may regulate STMN1 expression and further affect the physiological function of STMN1 on fear behavior.We explored the transcriptional activity of the than SNP , 44. In A>G and g. -125 C>T SNPs locate in the putative canine STMN1 promoter region. SNPs in gene coding regions can change the biological character of the encoded protein, while SNPs in non-coding regions may regulate gene expression in an allele-specific manner, and these regulatory polymorphisms stand forcrc a critical but relatively unexplored class of genetic variation. In fact, many reports found that promoter region polymorphisms regulate gene expression levels [Interestingly, the g. -327 n levels . Geneticn levels  and hencn levels .G allele at g. -327 A>G generated a PMF1 (-) transcription factor binding motif, and the existence of the mutant T allele at g. -125 C>T generated the Nkx6.3 (+) and FOXP1 (-) transcription factors binding motifs. PMF-1 doesn’t belong to Maf family, but does possess a DNA binding region and a binding site for the NF-E2 related factor-2 (Nrf-2) protein [PMF-1 binds Nrf-2 to regulate transcription of the spermidine/spermine N"-acetyltransferase gene and the 4E-BP1 gene [Nkx6.3, a member of the NKX6 subfamily, contains an Engrailed-homology domain that may mediate interactions with transcriptional co-repressors [FOXP1 and FOXP2 belong to the FOXP subfamily of transcription factors [A>G and g. -125 C>T mutations in the STMN1 promoter region resulted in recruitment of different transcription factors, which subsequently altered gene expression. However, no direct evidence shows that these predicted transcription factors affect fear behavior. Further research is needed to reveal the molecular mechanisms involved.In our study, the existence of the mutant  protein . PMF-1 bBP1 gene , 50. Nkxpressors . Studiespressors , 53. FOX factors . They fo factors  and are  factors , 57, sugA>G and g. -125 C>T SNPs of STMN1 and the H4H4 (H4 = GT) haplotype combination were associated with canine fear behavior. They were identified in the putative promoter region and affected the STMN1 transcription factor binding sites. Moreover, promoter regions with different haplotypes displayed different promoter activities, suggesting that these SNPs likely modulate STMN1 promoter binding activity and further affect canine fear level. Overall, our data suggest that variations in STMN1 affect the molecular signaling that regulates fear behavior. In addition, our findings demonstrate that the STMN1 genotype is related to the regulation of fear level in canines and the two SNPs might be used as molecular markers to select working dog and breeding dog.In summary, our results suggest that the g. -327 S1 Table(XLSX)Click here for additional data file.


GAL1, or altered genome-wide, we show that non-coding transcription is associated with high H3 acetylation and H3 levels across the gene, while reducing H3K36me3. Set1 is required for these antisense transcription-associated chromatin changes in the gene body. We propose that nascent antisense and sense transcription have fundamentally distinct relationships with chromatin, and that both should be considered canonical features of eukaryotic genes.Genes from yeast to mammals are frequently subject to non-coding transcription of their antisense strand; however the genome-wide role for antisense transcription remains elusive. As transcription influences chromatin structure, we took a genome-wide approach to assess which chromatin features are associated with nascent antisense transcription, and contrast these with features associated with nascent sense transcription. We describe a distinct chromatin architecture at the promoter and gene body specifically associated with antisense transcription, marked by reduced H2B ubiquitination, H3K36 and H3K79 trimethylation and increased levels of H3 acetylation, chromatin remodelling enzymes, histone chaperones and histone turnover. The difference in sense transcription between genes with high or low levels of antisense transcription is slight; thus the antisense transcription-associated chromatin state is not simply analogous to a repressed state. Using mutants in which the level of antisense transcription is reduced at  Secondl= −0.06) . That anSTE11, H3K36me3 may suppress co-transcriptional turnover of nucleosomes , in which the transcription machinery and transcription factors bind, flanked by the −1 and +1 nucleosomes . These pleosomes . Genome-leosomes ,18, and leosomes . Howeverleosomes . H3K79meleosomes ,21.Given that a gene subject to both sense and antisense transcription has two overlapping convergent transcription units, and since antisense transcription generally extends into the 5′ sense promoter ,8, it isGAL1 gene is reduced, we demonstrate that loss of antisense transcription causes expected changes to the chromatin. To validate these links between chromatin modifications and antisense transcription, we used mutant yeast strains with altered levels of antisense transcription around the sense promoter and show that an increase in antisense transcription is concurrent with increased histone acetylation, increased nucleosome occupancy and decreased H3K36me3. We propose that sense and antisense transcription define distinct chromatin architectures, and that these will have distinct effects on gene behaviour.Here we ask how antisense transcription influences chromatin around the sense promoter and across the gene body. Using extensive correlations of genome-wide data sets, we find that nascent sense and antisense transcription are each associated with distinct features of chromatin. Antisense transcription is associated with increased levels of chromatin remodelling enzymes and histone chaperones at the sense promoter, increased nucleosome occupancy and closure of the NDR. Across the gene body, antisense transcription is associated with an increase in histone acetylation and a reduction in levels of H3K36me3, H3K79me3 and H2BK123 ubiquitination. Remarkably, antisense transcription is also associated with increased histone turnover at the sense promoter and gene body. All these changes occur in a stepwise manner across five gene groups with different levels of antisense transcription, thus even small changes in antisense transcription level are associated with changes in chromatin. Using an experimental system in which antisense transcription across the Transcription start (TSS) and end sites were annotated as described previously . If no sAll H3 modifications were normalised to H3 levels before subsequent analysis. To assess the correlation of a given feature with sense and antisense transcription, the level of the feature inside a 10 bp window slid from −1000 bp to +1500 bp relative to the TSS was determined for 5183 genes described in the main text, and the Spearman's correlation coefficient determined for each window step.Genome-wide levels of all proteins were obtained from sources described in the supplementary experimental procedures. P-values were determined using the Wilcoxon rank-sum test and comparing the distribution of occupancy levels between two gene groups. Further details can be found in the supplementary information.The occupancies of 202 different transcription-related proteins at gene promoters were determined using a comprehensive ChIP-chip study in TAP-tagged yeast strains . P-valueNucleosome occupancy levels and coordinates were determined as described previously . The disEstimated levels of H3 turnover genome-wide were obtained from  and 16)16). At aThe levels of numerous modifications were determined within a sliding 50 bp window moved across the transcribed regions for 5183 genes. The modification levels within these windows were then correlated with the level of nascent sense and, separately, antisense transcription in the same windows, in order to calculate a Spearman's correlation coefficient.set2Δ, rco1Δ, eaf3Δ and set1Δ strains was the same. Every gene was then assigned to one of the five antisense transcription classes presented in Figure For each mutant, three gene groups were drawn from the 5183 genes defined above, dependent on whether a given gene's antisense transcription in the 300 bp window increased, decreased or was unchanged in the mutant relative to wild-type. The NET-seq data used were those from , and werP-values were calculated using the Wilcoxon rank sum test. For Figs displaying the average levels of a given factor or modification around the TSS, P-values shown are those calculated by comparing the distributions at the maximum point in the vicinity of the TSS for the highest and the lowest antisense-transcribed groups, or at the minimum points for graphs of nucleosome occupancy, H3K36me3, H3K79me2, H3K79me3 and H2BK123ub.All GAL1 strains used were modified versions of those described previously  with high levels of antisense transcription , a class of 1240 genes (24%) with little or no antisense transcription (0 or 1 read), and three intermediate classes  with another group which had no or low levels of antisense transcription (0 or 1 read) but higher levels of sense transcription  within the same 300 bp window (Supplementary Table S2). Components of the ISW1, ISW2 and INO80 remodelling complexes and the FACT histone chaperone . The increase in nucleosome occupancy occurred in a stepwise fashion between the five gene classes, suggesting that antisense transcription can exert changes in the chromatin even at low levels and in ≈75% of genes. Critically, we found that there is only a very weak correlation between sense and antisense transcription within the 300 bp window . Thus the increase in nucleosome occupancy associated with increasing antisense transcription is likely to be independent of sense transcription.We asked how genes might differ in the positioning, dynamics, and occupancy of their promoter-bound nucleosomes. Using a genome-wide map of nucleosome occupancy , we founw Figure  . These trends remained when the H3K36 methyltransferase SET2 was deleted  transcripts, although in glucose the sense transcript (SUT013) originates from a promoter in GAL10 (ADH1 (TADH1) into the ORF of GAL1 resulted in a redefinition of the transcription unit; the GAL1 sense transcripts (GAL1 and SUT013) terminated at TADH1 while the antisense transcript initiated from it  and when sense transcription is highly induced  . ,37. GAL1e Figure . Mapping) Figure .TADH1 resembling a TATA-box (TATAAAAA)  Figure , and hyp) Figure , in keep) Figure . We used) Figure , confirmGAL1 constructs  and H3K36me3 across both s Figure . ChIP ext Figure . Based ot Figure . These csame genes between different yeast strains. i.e. by investigating how antisense transcription changes upon mutation, and whether these changes are mirrored by the changes in the chromatin features discussed above.Thus far, we have shown associations between antisense transcription and chromatin features across different genes in a wild-type background. We hypothesised that similar associations would be observed when comparing the SET2, RCO1, EAF3 and SET1 deletion strains , as antisense transcription was differentially altered between them—i.e. a gene classed as ‘increased’ in one mutant could well be classed as ‘decreased’ or ‘unchanged’ in another. Genome-wide levels of sense expression were generally similar between all four mutants and wild-type, save for eaf3Δ, in which the average level of sense transcription at a gene was three-quarters that of wild-type. Crucially, and in support of our earlier analyses, the correlation between the change in sense and antisense transcription at the 300 bp window in the deletion mutants relative to wild-type was similarly small for all four strains  (set2Δ and rco1Δ strains only)  . H4ac lens only) . We predns only) –D. Superns only) . For exas Figure . We noteHere we have shown that genes subject to high levels of antisense transcription show pronounced differences in a broad range of chromatin features, both at the sense promoter and within the gene body. Generally, features associated with newly deposited dynamic chromatin—histone H3 acetylation, turnover, chromatin remodelling enzymes and histone chaperones—are increased, while features associated with established chromatin—H3K79me3, H3K36me3 and H2B123ub—are reduced. These are distinct from the features associated with sense transcription Figure . Moreovetranscription are somewhat at odds with the current perception of the field supporting an antagonistic relationship between sense and antisense transcripts, based on single gene studies , H3K9ac  and H3K18ac , suggesting that Rpd3 has an HDAC complex- independent function related to antisense transcription. Furthermore, although histone modifications, antisense transcription and histone turnover are strongly correlated with each other, histone modification is not thought to cause changes in histone turnover, nor is histone turnover thought to change the histone modifications , an alternative explanation might be that these new transcripts are responsible for the reduced levels of H3K36me3 and increase acetylation. This observation illustrates one of the potential difficulties of working with non-coding transcripts/transcription—that manipulations of the genome to remove one transcript often give rise to novel spurious transcripts such as described here at the GAL locus or at the HMS2 locus (Some regulatory non-coding transcripts, including cription –24, whilcription  does notS2 locus .plastic—i.e. genes with the capacity to be extensively regulated. Transcriptionally plastic genes possess occupied promoters, have higher histone turnover rates and are more extensively regulated by chromatin remodelling enzymes (How might the observed changes to chromatin affect gene behaviour? The promoters of genes with antisense transcription also share common dynamic chromatin features with the promoters of genes that are highly  enzymes . Antisen enzymes  but, unt enzymes . The ass enzymes .without changing the overall level of expression itself, thus antisense transcription could raise noise without changing the level of sense transcription, in line with the lack of correlation observed between the two processes. Intrinsically noisy genes are subject to a distinct mode of transcription initiation in which a gene switches between an off state and an active, rapidly re-initiating state, referred to as transcription bursting (Genes that show marked variation in expression levels within a population of genetically identical cells are described as noisy ,52. Stribursting ,54. Antibursting . Use of


Drosophila. It is shown that the core promoter is a critical determinant of circadian mRNA expression in both species. A distinct core promoter class, strong circadian promoters (SCPs), is identified in mouse liver but not Drosophila. SCPs are defined by specific core promoter features, and are shown to drive circadian transcriptional activities with both high averages and high amplitudes. Data analysis and mathematical modeling further provided evidence for rhythmic regulation of both polymerase II recruitment and pause release at SCPs. The analysis provides a comprehensive and systematic view of core promoters and their link to circadian mRNA expression in mouse and Drosophila, and thus reveals a crucial role for the core promoter in regulated, dynamic transcription.Circadian rhythms in transcription are generated by rhythmic abundances and DNA binding activities of transcription factors. Propagation of rhythms to transcriptional initiation involves the core promoter, its chromatin state, and the basal transcription machinery. Here, I characterize core promoters and chromatin states of genes transcribed in a circadian manner in mouse liver and in  Circadian rhythms switch gene expression on and off with a daily rhythm in most tissues in mammals and other animals. Typically, thousands of genes are affected, and the functions of these rhythms include preparing and adjusting various physiological functions in tissues to meet time-of-day dependent requirements optimally. The controllers of the rhythms are often transcription factors (proteins which regulate transcription), which are relatively well known. However, there is a layer between transcription factor action and transcriptional activity whose role in circadian transcription has not previously been characterized: the core promoter. The core promoter acts as a template for the assembly of the intricate machinery that governs initiation of transcription. There are different types of core promoters that are typically used for different types of genes. It is not known which types of core promoters are used for the rhythmically induced genes. Here, it is shown that there are specific characteristics of core promoters driving rhythmic transcription in mice and flies. Furthermore, it is shown that there is a class of strong circadian promoters in mice that drive strong rhythms with very intense average transcriptional rates. These results help understanding the regulatory systems governing circadian transcription, and ultimately, aid the understanding of many diseases resulting from disturbed circadian rhythms. In many metazoans, transcription of numerous genes in most cell types occurs in a rhythmic fashion with a period of ~24 hours, also if the organism is held under constant conditions; these rhythms are termed circadian transcriptional rhythms . They arDrosophila melanogaster, the CLOCK homolog CLK has a similar function, also binding to E-boxes [The cellular circadian clock becomes manifest in an output of rhythmic mRNA expression of typically hundreds or thousands of clock-controlled genes (CCGs) . The CCG E-boxes . RhythmiDrosophila have served as useful model systems for investigating transcriptional rhythms, and by for instance comparing nascent transcript to mature mRNA abundances, the propagation of rhythms from transcriptional activities to transcript abundances can be monitored [Circadian transcriptional rhythms have been investigated by combined analysis of transcript abundances and CTF binding to corresponding promoters. Mouse liver and onitored –12. Analonitored ,14. Circonitored –17. HoweCore promoters often contain specific binding sites (core promoter elements) for general transcription factors (GTFs) such as TFIID and TFIIB ,19. GTFsDbp transcript, including observed rhythmic CLOCK/BMAL1 binding, histone H3 Lys4 trimethylation (H3K4me3) and Lys9 acetylation modifications, as well as rhythmic gross H3 abundance at the TSS [Eukaryotic core promoters are often thought to have an inactive ground state, assured by a tendency for nucleosomes to cover promoter DNA. In this ground state, the TSS is assumed to be covered by a nucleosome which constitutes a certain barrier for Pol II to penetrate –24. Tran the TSS . However the TSS ,28. The  the TSS . There a the TSS –32.in vitro and in vivo evidence collected to date suggest CpG dinucleotides help instruct the formation of nucleosome-depleted regions of mammalian promoters [There are several correlated hallmarks of promoters with active ground state, including nucleosome-depleted regions immediately upstream of the TSSs, absence of TATA box, high levels of Pol II and the H3K4me3 mark immediately downstream of the TSS, as well as high levels of the H2A.Z histone variant at the first nucleosome downstream of the TSS ,33–39. Iromoters –51. Besiromoters ,50,52. Iromoters .Based on these myriad features and mechanisms, two main metazoan promoter classes, type I and type II promoters, have been proposed . Type I Both type I and type II promoters may govern inducible transcription, however implemented in different manners, as suggested by two studies of LPS-induced transcription of ~50 genes in mouse macrophages ,59. PrimDrosophila, by integrating promoter sequence features (CpG ratios and core promoter elements) with a wealth of genome-wide data measuring chromatin state, transcriptional activities, and CTF binding (This report describes the core promoters of genes transcribed in a circadian manner (hereafter: circadian promoters) in mouse liver and in  binding . It is sTo investigate whether there is a preferred core promoter architecture for circadian promoters in mammals, a collection of mouse transcripts assignable to unique TSSs and promoters was established . The transcriptional activities producing these transcripts were estimated by reanalyzing Nascent-Seq data . These dTo characterize core promoters, areas in the vicinities of the TSSs were scanned for the presence of TATA boxes, BREu, and BREd motifs , although not as strongly enriched as silent promoters. However, circadian promoters driving transcription with high amplitudes and high average activity were exceptionally strongly enriched for TATA boxes, with around 35% TATA box containing promoters , but were significantly more nucleosome-depleted than silent promoters , but silent promoters had much lower CpG ratios than circadian promoters . Circadian TATA box promoters were furthermore depleted of the BREd motif compared to constitutive TATA box promoters , or when comparing circadian to constitutive promoters overall . No enrichment or depletion was detected for the BREu motif in circadian promoters compared with constitutive promoters . Thus, the BREd motif appears to favor constitutive mRNA expression.−15, −15). However, although circadian promoters had higher nucleosome occupancy on average compared to constitutive promoters  known to potently induce rhythmicity in transcription of CCGs. CTFs considered were REV-ERB α and REV-ERB β (which bind to ROR elements), E4BP4 (which binds to D-boxes) and BMAL1 (which binds to E-boxes), and promoters were scanned ± 3000 bp of the TSS for CTF binding events (Methods). The reason for this stratification was that CTFs levels exhibit strong circadian rhythms in most tissues. Promoters not binding CTFs but still driving circadian transcriptional rhythms in mouse liver are probably controlled by other rhythmic TFs or cofactors that could be induced partly by rhythmic external cues in an inducible, tissue-specific manner . Thus, c−10, odds ratio 1.44). Further, CTF-binding circadian promoters (n = 900) were enriched for TATA boxes, compared to circadian promoters not binding CTFs . Unexpectedly, however, CTF binding alone was not associated with high transcriptional amplitude . In contrast, CTF binding was strongly associated with high average transcriptional activities , independently of TATA boxes or LCpG. These effects were additive: Promoters with both CTF binding and a TATA box or LCpG were associated with very strong transcription, with high amplitudes as well. Note that the scale is logarithmic and that the fold differences in mean transcriptional activities are quite large. The same effects were observed when stratifying for TATA boxes or CpG content separately  to match the distribution of average nascent mRNA read counts of SCPs. This resulted in an artificial group of constitutive TATA box promoters with the same statistical distribution of transcriptional activities as SCPs. A bootstrapping test procedure was then employed to probe whether nucleosome occupancies of the matched constitutive type I promoter population are similar or different to those of SCPs (Methods). This analysis  showed t−/− animals [Could the low nucleosome occupancy of SCPs be due to BMAL1 binding, since this CTF is known to act as a pioneering factor and induce eviction of nucleosomes ? A reana animals  yielded −15, median ratio 0.81). Thus, since SCPs are CpG-poor and enriched for TATA boxes, but on the other hand have low nucleosome occupancies, it is not obvious if SCPs should tend to be focused or dispersed: the general rules are in conflict. An analysis showed that SCPs with TATA box were mainly focused with a smaller dispersed subpopulation, but that TATA-less SCPs (consequently with LCpG) tended to be much more dispersed . Conversely, focusing on circadian promoters, CTF binding in conjunction with LCpG was associated with TFIID-dependence. 12% of LCpG SCPs were strongly TFIID-dependent, compared to 0.03% for circadian promoters with neither CTF binding nor TATA box or LCpG .Indeed, SCPs had higher promoter-proximal Pol II coverage than any other promoter group, including circadian or constitutive type II promoters, even though the latter two promoter groups had slightly lower nucleosome occupancies as SCPs . These h−15, median ratio 2.3, Although there also was a general negative correlation between nucleosome occupancy and paused Pol II, SCPs consistently displayed atypically high levels of paused Pol II for a given nucleosome occupancy , but notOne could thus ask whether there are widespread oscillations in the competition between a nucleosome-occupied state and a Pol II-bound nucleosome-depleted state at circadian promoters, and at SCPs in particular. If so, rhythms in nucleosome densities and Pol II levels should be observable in MNase-Seq and Pol II ChIP-Seq data. Since both the Pol II ChIP-Seq data  and the −15). However, the assumption of only higher average Pol II recruitment rates for SCPs is not an attractive explanation for their high Pol II signals in the promoter-proximal regions, since SCPs drive strong rhythms. Instead, the situation for SCPs is better explained by invoking the alternative scenario  must be assumed to explain the high transcriptional activities of SCPs . Such hiHow might the predicted lower Pol II release rate be achieved? A possible mechanism could be depletion of the histone variant H2A.Z from the first (+1) nucleosome downstream of the TSS. Incorporation of the histone variant H2A.Z is recognized as a general route to transcriptional activation via various mechanisms . In gene−6).To address this, mouse liver H2A.Z ChIP-Seq data were reanalyzed. For all mouse promoters, the H2A.Z signal around the +1 nucleosome  was computed, and normalized to the MNase-Seq based nucleosome signals. Indeed, there was a negative correlation between H2A.Z signal and levels of promoter-proximal paused Pol II for promoters with marked levels of paused Pol II . In part−15).This is consistent with the model prediction that SCPs have lower Pol II release rates, given the notion that H2A.Z facilitates transcription through the +1 nucleosome. Depletion of H2A.Z might thus enable tighter regulation of Pol II release, exploited at promoters of genes transcribed in a strongly circadian manner. In line with the idea that H2A.Z might be more associated with constitutive transcription, there was a negative correlation in general between circadian transcriptional amplitudes and H2A.Z levels . The lower H3K4me3 levels of SCPs apparently occur despite their higher promoter-proximal Pol II coverages: Normalizing each promoter-proximal Pol II level to its TSS H3K4me3 level (hereafter: P/M ratio) likewise resulted in significantly higher P/M ratios for SCPs compared to circadian type II CTF binding promoters . Again, the bootstrapping test procedure (Methods) verified that the TSS H3K4me3 levels and P/M ratios of SCPs indeed are significantly lower and higher, respectively, than for highly expressed type I promoters (p < 10−6 in both cases). This relationship apparently runs counter to the previously observed positive association between the H3K4me3 mark and promoter-proximal Pol II levels [−6). There was also a general negative correlation between average TSS H3K4me3 levels and circadian transcriptional amplitudes , and consistent with this and the results regarding H2A.Z and amplitudes outlined above, the previously observed correlation between H2A.Z and H3K4me3 levels [In order to clarify the role of H3K4me3 at SCPs and other promoter classes, the H3K4me3 ChIP-Seq data  were reaI levels , which w3 levels  was nota3 levels ,54. AlthA possible interpretation of these observations is that the observed intermediate H3K4me3 levels of SCPs reflect Pol II-mediated H3K4 trimethylation, pushing H3K4me3 levels up to intermediate rather than low levels. This intermediate level might be low enough to avoid a constitutively permissive chromatin state that would weaken rhythmic regulation. A non-causal role of H3K4me3 in circadian transcriptional activities has also been proposed to account for the later phases of rhythmic H3K4me3 levels with respect to transcriptional activities . This woThe results reported so far established that mouse liver circadian promoters are strongly enriched for TATA box promoters, and further uncovered a special class of promoters: SCPs. These circadian promoter traits could be specific for mammals, or they could represent more universal constraints on core promoters for circadian regulation of transcription. A comparison with an evolutionary distant animal may throw light on the universality of circadian core promoters.Drosophila is besides mouse the model organism where the mRNA expression output pathways of the circadian clock have been best studied. Data from various studies of the Drosophila clock were compiled, resulting in a data set that, bar tissue-specificity, almost rivaled the data compiled for mouse liver. Time-resolved nascent and mature RNA-sequencing [Drosophila transcript annotation (Methods). As was the case for mouse liver, Drosophila circadian transcripts turned out to be enriched for TATA boxes, compared to constitutive transcripts, especially transcripts with high amplitudes .quencing , and ChIquencing  of fly hquencing  as well quencing  were complitudes . These rDrosophila generally had high nucleosome occupancies . There was a negative correlation between paused Pol II and nucleosome occupancy immediately upstream of the TSSs of expressed transcripts , as previously described [However, there were only 2 promoters both containing a TATA box as well as binding CLK. In fact, there was no evidence of a promoter class corresponding to mouse SCPs. Circadian promoters in escribed , which iDrosophila.These results suggest that the following traits: 1. high nucleosome occupancies, 2. low Pol II pausing levels, 3. depletion of the H2A.Z variant downstream of the TSS, and 4. a TATA box-based promoter architecture, are universally correlated to circadian regulation of transcription, albeit with moderate average mRNA expression levels. The evolution of SCPs–TATA box promoters with high Pol II pausing levels and low nucleosome occupancies that drive rhythms with both high averages and amplitudes–seems to not have occurred in The results presented here show that the core promoter and its chromatin state are fundamental determinants of circadian transcriptional rhythms. This should motivate a broadening of focus for circadian transcriptional regulation studies to routinely cover aspects of the core promoter. Such efforts could also initially provide validation of the results of the present study. Further, when studying clock output mechanisms in cells or tissues, deletions of CTF binding sites, or over-expression of core clock genes, may abolish rhythmicity in expression of clock controlled genes, but also strongly influence average levels of gene product . This maTwo major classes of circadian promoters emerged in the analysis. The most peculiar of these represented strong circadian promoters (SCPs), driving circadian transcription with both high amplitudes and high average rates. SCPs appear to represent a class of promoters that has not previously been characterized, combining traits of type I and type II core promoter classes as commonly defined , and comDrosophila since it provides a binding platform for circadian TFs CLOCK/BMAL1 and CLK/CYC, respectively. However, the E-box was originally discovered in the context of constitutive or transient activation of transcription, affecting processes involved in differentiation and development [The important role played by the core promoter may help explain why only some CTF binding promoters drive circadian transcription. The E-box is strongly associated with the generation of circadian rhythms in mammals and elopment . Thus, telopment ,77. Whatelopment .What properties of the TATA box make it suitable for circadian regulation of transcription? TATA boxes can facilitate the assembly of a transcription reinitiation scaffold, consisting of TFIIA, TFIID, TFIIE, TFIIH, and Mediator, which remains on the promoter after Pol II promoter escape and makes rapid reassembly of the PIC possible ,79. SuchThe TFIIB-binding element BREd was underrepresented among circadian promoters. This element might thus rather be conducive to constitutive basal levels of TFIIB binding to promoters, especially in the scaffold scenario. TFIIB has been implicated in stimulating release of paused Pol II in via interactions with TFIIF –84, and Why are promoters driving high amplitude circadian transcription (SCPs and circadian type I promoters) depleted of H3K4me3, relatively seen? Perhaps high levels of these nucleosome features should be viewed as hallmarks of high constitutive (or perhaps poised) transcription . CircadiAs was the case for the H3K4me3 mark, high levels of the H2A.Z nucleosome variant were also associated with lower amplitudes. These two observations are probably related, since the degree of correlation between the H3K4me3 mark and H2A.Z levels was notable, which corroborates earlier findings ,68. The This may be the case for circadian nucleosomal occupancy dynamics in general. There are certainly cases where induced transcription involves nucleosome rearrangement ,59,89. HImportantly, observed averages in tissue samples translate to probabilities of nucleosome occupancy in the single cell. There, nucleosomal occupancy is a dynamic phenomenon, where nucleosome sliding, binding and unbinding continuously takes place –94. It tSCPs and circadian type I promoters have, by definition, low CpG ratios on average. This sequence feature is often considered to increase the nucleosome forming potential . HoweverDrosophila circadian promoters, where no counterpart of mouse SCPs was found. This could mean that SCPs are a relatively late evolutionary invention, and that CCGs originally employed the type I promoter architecture thought to be common for heavily regulated genes.The establishment of a nucleosome-depleted chromatin state is apparently not widespread for The short nascent mRNA produced by Pol II prior to entering the paused state might act as a recruitment platform for regulators of transcription . Thus, fMouse promoters were compiled from the UCSC mm9 RefSeq annotation ,97. ThenThen, transcript sets with identical start and end coordinates were reduced to all but one transcript, leaving 27274 transcripts. Further, for TSSs corresponding to more than 1 resulting transcripts, only the shortest transcript was kept, 26251 transcripts now remained. Finally, a set of transcripts unambiguously assignable to a TSS was needed to conduct the present study, since it combines core promoter and transcript properties. For this, any overlapping transcripts with different TSSs needed to be discarded. Finally, transcripts shorter than 500 bp and non-protein coding transcripts (RefSeq annotation starting with "NR") were excluded, leaving a set of 17686 promoters and transcripts used for the following analysis.Drosophila promoters were compiled from the UCSC dm3 refGene table, which corresponds to the FlyBase transcript annotation [Drosophila promoters.notation . The samDrosophila CAGE peaks classified as "peaked" (= focused) or "broad" (= dispersed) were obtained from a study of fly embryos [ embryos . The neaMouse liver Nascent-Seq and RNA-Seq data from a study of mice kept under 12 hr/12 hr light-dark cycles  were obtMicroarray data from samples of livers of mice kept under similar light-dark conditions  were obtDrosophila head Nascent-Seq and RNA-Seq data [Seq data  were obtDrosophila heads, data spanned two days, with samples taken every 4 hrs, yielding 12 samples for each Nascent-Seq or poly(A)+ RNA-Seq data set. Averages and relative amplitudes were quantified by harmonic regression using the HarmonicRegression R package [Drosophila). Silent transcripts were defined as having mean nascent RPKM values of < 0.01 in both organisms. Mouse circadian transcripts were defined as expressed transcripts having Benjamini-Hochberg corrected RAIN p values < 0.2 and circadian amplitudes > 0.1 as estimated by harmonic regression. Drosophila circadian transcripts were defined as expressed transcripts having Benjamini-Hochberg corrected RAIN p values < 0.25 (the laxer constraints on Drosophila transcripts were necessary in order to obtain a number of circadian transcripts large enough for reliable statistics). Highly expressed transcripts were defined as the upper 25% quantile of mean transcriptional activities of expressed transcripts. High amplitude transcripts were defined as the upper 25% quantile of transcriptional amplitudes of circadian transcripts.Circadian rhythms in nascent or mature transcript abundances were detected using the RAIN algorithm and R package . For bot package . Expresshttps://bioconductor.org/packages/profileScoreDist). The package is generally applicable to any position count matrices.Position count matrices (PCM) for the TATA box, BREu, BREd DNA sequences were obtained from JASPAR . To discDrosophila TSSs, and when plotting the positions of all mouse matches , the maximal median phastCons score was reported.Scans were made for TATA box PCM matches between −50 (start position) and −10 bp (end position) of mouse and position ), the faposition  was recoor mouse . Hence, s scores  were obtCpG ratios (observed/expected CG dinucleotides) were determined for mouse promoters using the standard formula CG×N/(G×C) , where N10.1371/journal.pbio.1000595.s019. Normalized tag counts for each time point as given in this file were used to estimate BMAL1 binding phases (ZT) using the HarmonicRegression package.Mouse liver ChIP-Seq peaks for BMAL1 (E-box binding)  were obtMouse liver ChIP-Seq peaks for REV-ERB α and β (ROR element binding)  were obtMouse liver ChIP-Seq peaks for E4BP4 (D-box binding)  were obtDrosophila ChIP-chip calls for CLK (E-box binding) [Drosophila dm3 (BDGP5) assembly.binding)  were obtDrosophila CLK peaks, which were matched to ±2000 bp regions around the TSSs, following the original study. Promoter were classified as CTF binding if they had one or more CTF peak center within these regions.ChIP-Seq and ChIP-chip peaks were narrowed to their center coordinates, which were matched to regions ±3000 of the set of TSSs, except for ChIP-Seq reads were aligned to the mouse mm9 genome assembly using the BWA aligner . DuplicaReads were mapped to the promoter regions using the GenomicAlignments package ; the "coFor statistical tests and calculation of circadian phases (ZT), mouse liver promoter-proximal pausing region Pol II levels were quantified for each of the 7 time points as RPKM counts of ChIP-Seq reads overlapping the region +21 to +100 bp of the TSS. Gene body Pol II was quantified as RPKM counts of reads overlapping the positions +301 to +1300 bp downstream of the TSS for transcripts 1300 bp or longer, otherwise between positions +301 and +500 for the few short transcripts. PIs were computed as proximal/gene body Pol II signals.Promoter-proximal (TSS) H3K4me3 (ChIP-Seq data spanning the same 7 time points as the Pol II data) was quantified in the same way using the +1 to +200 bp interval, downstream gene body H3K4me3 was quantified using the +801 to +1000 bp interval.As in the original study , Pol II −/− knockout mice [Aligned and normalized mouse liver MNase-Seq and H2A.Z Chip-Seq reads for livers of WT and BMAL1out mice  were obtout mice . PileupsFor statistical tests, mean nucleosome pileups for the regions between −101 and −1 bp of the TSS were averaged to represent nucleosomal coverages.https://cran.r-project.org/web/packages/peakPick/index.html).The locations of the first (+1) nucleosome peak downstream of the TSSs in the pileups were detected exactly as described in the original report . The proH2A.Z and nucleosomal pileups were averaged over intervals between −80 and +80 bp of the +1 peaks, then the H2A.Z signals were normalized to the nucleosomal signals. This value was used as the H2A.Z signal for downstream analysis.Drosophila S2 cell MNase-Seq, H2A.Z ChIP-Seq, and 3'NT-Seq data [Seq data  were obtDrosophila TSSs were averaged to represent nucleosomal coverages.For statistical tests, averaged nucleosome pileups for the regions between −100 and −1 bp of the The locations of the first (+1) nucleosome peaks and the H2A.Z signals normalized to the MNase-Seq signal for these +1 peaks were computed exactly as for the mouse liver TSSs described above.Drosophila Pol II promoter-proximal pausing region signals were quantified as 3'NT-Seq normalized pileups averaged over the region +1 to +100 bp of the TSS. Gene body Pol II signals were quantified as normalized pileups averaged over the region +250 –+650 bp downstream of the +1 nucleosome. Due to a few outliers, top and bottom 1% quantiles were left out of the averaging. Finally, Drosophila PIs were computed as the ratios between these two signals. The original study [For statistical tests, al study  analyzedMean transcriptional activities were binned into 55 bins spanning the logarithmic scale. Non-CTF binding constitutive promoters with TATA box (constitutive type I) had probabilities assigned to each bin. These probabilities were weighted according to the counts of SCPs with transcriptional activities falling within the corresponding bin, then normalized. This enabled sampling of non-CTF binding constitutive promoters to obtain populations with the same mean transcriptional activity distributions as SCPs. Properties of SCPs compared to those of the sampled population of "non-SCPs with SCP-like transcriptional activities" could then be analyzed to distinguish SCP features that are not merely epiphenomena of high transcriptional activities.For this, standard bootstrapping procedure was employed: The SCP properties and constitutive type I promoter properties to compare (such as CpG ratio) and their probability distributions  were pooled, samples of the same size as the SCP group and the constitutive type I promoters group, respectively, were drawn randomly with replacement from the pool, and a rank sum test statistic was computed each time. This was done 1,000,000 times. The resulting empirical test statistic probability distribution was then used when repeatedly drawing samples from each population separately, each time with sample sizes corresponding to the population sizes, rank sum test statistics and median location differences were computed. Then, two-sided p values could be estimated; 1000 such p values were computed for each property, median p value was reported .lox/lox-AlbCreTaf10 mice were obtained from Supplementary Table 1 of the original article [Taf10 gene postnatally between days P15 and P22.Gene symbols for up- (at least 2-fold) and down-regulated (at least 0.5-fold) transcripts at Postnatal day 30 (P30) in livers of  article , then maS1 Fig−15. A least squares regression line is plotted to highlight the negative correlation. C. Low CpG ratios are associated with high amplitudes. The overall bimodal CpG ratio distribution evident in panel D below motivated the binary classification around CpG ratio = 0.5. Amplitudes were computed for all circadian promoters, and promoters with high CpG ratios had significantly lower amplitudes . Median and 95% confidence intervals are visualized. D. CpG ratio distributions. CpG ratios were stratified according to promoter class. Data are presented as kernel densities, which are smooth analogues to histograms computed with the standard R kernel density algorithm. E. Nucleosome occupancies  immediately upstream of the TSSs plotted against CpG ratios for all promoters corresponding to expressed transcripts. Spearman's rho = −0.45, p < 10−15. F. Mean transcriptional activities  plotted against nucleosome occupancies (as in panel C) for promoters corresponding to all expressed genes.A. Positions of the first "T" of all TATA box position count matrix hits in all mouse promoters scanned. B. Transcriptional amplitudes  plotted against CpG ratios for all circadian promoters. Spearman's rho = −0.24, p < 10(PDF)Click here for additional data file.S2 Fig−/− mice, respectively. SCPs were here limited to those SCPs with BMAL1 ChIP-Seq peaks, in order to show that these promoters still have low nucleosome occupancy upstream of the TSSs. Pileups were computed from MNase-Seq data (Methods) and averaged over the promoters for each position relative to the TSS . BMAL1 SCPs = SCPs with at least one BMAL1 ChIP-Seq peak. D. CAGE peak width distributions (Methods) for different promoter classes. Const. = Constitutive. E. Fractions of TFIID-dependent promoters (Methods), and other promoters driving expressed transcripts, respectively, with TATA boxes or LCpG and CTF ChIP-Seq peaks, respectively . Error bars represent 95% confidence intervals assuming binomial distributions. Bar groups: n = 10899 and 130, respectively. F. Reproduction of A. Amplitudes and averages of transcriptional activities as measured by Nascent-Seq were quantified for the transcript corresponding to each promoter. Stratifications were made either according to TATA box presence in the promoter, or according to CpG ratio. B. Nucleosome occupancies immediately upstream of the TSSs plotted against CpG ratios for all circadian promoters (upper panel). SCPs (red dots) had on average lower nucleosome occupancies than other circadian promoters. This effect dominates for low CpG ratios, as evident in the lower panel, where medians and 95% confidence intervals are shown for given CpG ratio intervals. C. Nucleosome occupancies around TSSs for SCPs, as measured in liver samples from wild type and BMAL1(PDF)Click here for additional data file.S3 Figgb), and between paused Pol II and PI (Δ ZTPI), respectively; details of computation method are given in the Methods section. The observed combination of small Δ ZTgb phase differences and large Δ ZTPI phase differences is only compatible with combined circadian regulation of Pol II recruitment and pause release. F. Fractions of different circadian promoter classes with rhythmic PI. Harmonic regression p values for PI oscillation were computed for each promoter (Methods), and the fractions of rhythmic promoters in each promoter group for different p value cutoffs are plotted. SCPs exhibit larger fractions independently of cutoff. Abbreviations as for panel A. G. Levels of Pol II in the promoter-proximal pausing region plotted against normalized H2A.Z levels at the +1 nucleosome (Methods). A population of promoters with both low Pol II levels and H2A.Z levels may be contrasted against a population of promoters with higher Pol II and H2A.Z levels. SCPs are mainly found in the population with higher levels. For that population (here defined as having paused Pol II greater than 10), there was a negative correlation between paused Pol II and H2A.Z levels . H. Promoter-proximal H3K4me3 levels plotted against levels of Pol II in the pausing region. I. Promoter-proximal H3K4me3 levels plotted against H2A.Z levels at the +1 nucleosome. J. Promoter-proximal H3K4me3 levels plotted against CpG ratios. K. Phase differences between transcriptional rhythms (as estimated from Nascent-Seq data) and promoter-proximal H3K4me3 rhythms, as estimated from H3K4me3 ChIP-Seq data (Methods). Positive phase differences mean that the H3K4me3 level has an earlier phase than transcriptional activity. Circ. = Circadian promoters with, in addition, rhythmic H3K4me3 levels . Data are presented as kernel densities. Phase differences were different for CTF (circular mean: −0.9 hrs) and non CTF binding promoters .Note that the RPKM Pol II and H3K4me3 values given in the scatter plots here are not equivalent but proportional to the per-base pileup values in (PDF)Click here for additional data file.S4 FigDrosophila. A. Nucleosome occupancies immediately upstream of the TSSs (Methods), stratified for circadian TATA box promoters associated with highly expressed  transcripts and the other 75%, respectively. All Drosophila circadian promoters, including highly expressed had high nucleosome occupancies, compared to highly expressed constitutive promoters, which had low nucleosome occupancies. On the other hand, in a corresponding plot for mouse liver circadian promoters, the SCP TATA box population is visible with its low nucleosome occupancies. Circ. = Circadian, Const. = Constitutive. B. Comparing nucleosome occupancies immediately upstream of the TSSs for circadian promoters with TATA boxes reveals a subpopulation of mouse promoters with low nucleosome occupancies, but not for Drosophila promoters. Kernel densities of MNase-Seq data (Methods) are visualized. C. For mouse circadian promoters with TATA box, there is a visible subpopulation with high transcriptional activities. Such a subpopulation is not apparent for Drosophila promoters. Kernel densities of Nascent-Seq data (Methods) are visualized. D. For mouse circadian promoters with TATA box, there is a visible subpopulation with high Pol II occupancies in the pausing region immediately downstream of the TSSs . Such a subpopulation is not apparent for Drosophila promoters .Distributions of nucleosome occupancies, promoter-proximal pol II levels, and transcriptional activities hint at the SCP population in mouse liver, but not in (PDF)Click here for additional data file.S5 Fig−15). A standard linear least squares regression line visualizes the correlation.Stall fractions (SFs) measure the percentage of positions with stalled Pol II in the 100 bp upstream of the first nucleosome downstream of the TSS. SFs and nucleosome positions were computed exactly (as far as possible) according to the description in the original article: Stalled positions are defined as 3' end nascent seq peaks rising significantly above their immediate surroundings. The algorithms (SF computation and nucleosome peak detection) were incorporated into the R package "peakPick" (Methods). A. Promoters were classified as having high (SF > 0.6) or low (0.05 < SF ≤ 0.3) SFs. Low SF promoters exhibit slightly lower nucleosome occupancies, which can be interpreted that stalled Pol II is associated with nucleosome barriers. B. Low SF promoters exhibit higher H2A.Z levels , which may be interpreted as H2A.Z being associated with less stalling. C and D. Expressed transcripts were binned along SF intervals 0.1–0.3, 0.3–0.5, 0.5–0.7, and 0.7–1. Averaged normalized H2A.Z levels (C) and nucleosome levels (D) at the +1 nucleosome peak are plotted for each bin. Due to outliers, 20% top and bottom values were left out of the averaging for the H2A.Z levels. Error bars refer to SEM. The increased +1 nucleosome levels associated with high SFs must not be confused with the negative correlation between PI and nucleosome occupancies immediately upstream of the TSSs, demonstrated with the same data set as outlined in the main text. E. PI is correlated to SF. For promoters with PI and SF greater than 0, these two measures of Pol II pausing and stalling, respectively, were correlated Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S1 Interactive Text(NB)Click here for additional data file.


Kaposi’s sarcoma-associated herpesvirus (KSHV) primarily persists as a latent episome in infected cells. During latent infection, only a limited number of viral genes are expressed that help to maintain the viral episome and prevent lytic reactivation. The latent KSHV genome persists as a highly ordered chromatin structure with bivalent chromatin marks at the promoter-regulatory region of the major immediate-early gene promoter. Various stimuli can induce chromatin modifications to an active euchromatic epigenetic mark, leading to the expression of genes required for the transition from the latent to the lytic phase of KSHV life cycle. Enhanced replication and transcription activator (RTA) gene expression triggers a cascade of events, resulting in the modulation of various cellular pathways to support viral DNA synthesis. RTA also binds to the origin of lytic DNA replication to recruit viral, as well as cellular, proteins for the initiation of the lytic DNA replication of KSHV. In this review we will discuss some of the pivotal genetic and epigenetic factors that control KSHV reactivation from the transcriptionally restricted latent program. Kaposi’s sarcoma-associated herpesvirus (KSHV) or human herpesvirus 8 (HHV 8) is one of the seven human oncogenic viruses, and is the etiologic agent of Kaposi’s sarcoma , as well as certain B-cell lymphomas, including primary effusion lymphoma (PEL) and multicentric Castleman’s disease (MCD) ,2,3. KSHSince its initial discovery, there has been intense research on understanding the molecular biology of KSHV-mediated tumorigenesis . KSHV is-FLIP), and ORFK12/Kaposins, along with 18 mature miRNAs (at last count) and viral interferon regulatory factor-3 (vIRF3) . KSHV h i.e., lytic reactivation. This phase predominantly consists of: (1) KSHV Reactivation from latency, followed by (2) Lytic DNA Replication and virion production. Upon reactivation from latency, a full repertoire of lytic viral genes are activated in a temporally regulated manner, leading to the transcriptional activation of three classes of lytic genes, namely, immediate early (IE), early (E), and late (L) genes [Global analysis of the viral gene expression of KS tumor cells indicated that KSHV predominantly expresses viral latent transcripts with only a few percent of cells being lytically active at any specific given time . The latL) genes ,15,16. Tl-lytic genes that appear after 48 h post infection consist of viral structural proteins, including membrane glycoproteins (gB and K8.1), and a small viral capsid antigen required for assembly and maturation of the virions [Members of all three classes of lytic viral genes encode for proteins that assist in the formation of infectious virions . The IE- virions .RTA plays an important role as both an initiator and a controller of KSHV lytic DNA replication . Unlike Despite the induction of lytic cycle following KSHV infection, there is a rapid inhibition of RTA promoter that further decelerates the full-blown KSHV reactivation . The mecKSHV reactivation is an extremely complex process that involves a combination of both viral and cellular factors including but not limited to, temporary or permanent immune suppression, oxidative stress, inflammatory cytokines, hypoxia, viral co-infection and treatment with chromatin modifying agents. Thus far, a number of factors have been reported to stimulate or inhibit major viral proteins, however, the physiological relevance of these stimuli or repressors is far from being fully elucidated. In the following sections of this review, we will summarize recent studies that highlight the activation of KSHV lytic cycle and replication and will primarily focus on the relevant physiological, environmental, cellular, and viral regulatory factors involved in the regulation of KSHV’s biphasic life cycle, gene expression, and viral infection.de novo infection. Therefore, the molecular transition between latency and lytic reactivation is controlled by the interplay between LANA and RTA proteins in KSHV-infected cells.The two major KSHV proteins-LANA and RTA are shown to interact with each other and control the switch between latency and lytic reactivation . Studieso-tetradecanoylphorbol-13-acetate (TPA) or 5-Azacytidine (5-AzaC) caused demethylation of lytic promoters and induced KSHV lytic phase in vitro [de novo methyltransferases DNMT3a/DNMT3b and establishment of methylation marks exclusively on RTA promoter, leading to gene silencing during latency [de novo infected endothelial cells using high resolution tiling microarrays together with immunoprecipitation of methylated DNA (MeDIP) [DNA methylation or CpG dinucleotide methylation, associated with the transcriptional silencing, also plays a key role in the induction of KSHV lytic cycle as the treatment of PEL-derived cell lines with DNA methyltransferase inhibitor, 12-in vitro . Bisulfi latency ,31,32. RInterestingly, post-translational modifications of LANA, such as arginine methylation, phosphorylation and SUMOylation, have been shown to down-regulate the expression of lytic genes during the establishment of latency ,33,34,35de novo infection/during latency and interacts with several transcriptional repressors  and chromatin-remodeling proteins , it is evident that LANA helps to silence lytic gene expression and promotes KSHV latency through epigenetic control [The early stage of KSHV infection is defined by the constitutive expression of latent genes, as well as temporally ordered expression of viral lytic genes. Recent genome-wide ChIP-seq studies described the epigenetic map of KSHV episomes during latency and indicated that chromatin of the KSHV genome is enriched with both active (H3ac or H3K4me3) and repressive histone marks (H3K9me3 and H3K27me3) ,41. Base control ,45,46,47Thus far, several PEL-infected cells, endothelial cells, CV-1, human fibroblasts and HEK cells are known to maintain KSHV in the latent form that can be induced to enter the complete productive cycle of KSHV, following treatment of cells with the broad-spectrum protein kinase C-activator (TPA) or histone deacetylase inhibitor (NaB) (reviewed in ). As a rWhile KSHV infection appears to be necessary for the development of KS, the immunodeficiency appears to be another significant factor, as the immunosuppressed patients are often susceptible to many other infectious agents . Severalet al. have demonstrated that LANA interacts with a new host nuclear protein and hypoxia-sensitive chromatin remodeler, KAP1 (KRAB-associated protein 1), through its SUMO-2 interacting motif (LANASIM), and recruits it to the lytic promoter region of the KSHV genome for transcriptional repression [As an important co-factor, hypoxia (low tissue oxygen concentration) is physiologically linked with the initiation and progression of KSHV-associated cancers and known to induce the accumulation of hypoxia-inducible factors (HIF-1α/2α) (reviewed in ). Hypoxipression . Inhibitpression . In KSHV2O2), mediate KSHV reactivation from latency (reviewed in [2O2) through both autocrine and paracrine signaling [2O2 is sufficient for inducing and mediating KSHV lytic replication in KS tumors by activating ERK1/2, JNK, and p38 mitogen-activated protein kinase (p38 MAPK) pathways [2O2 scavengers; N-acetyl-l-cysteine (NAC), catalase and glutathione peroxidase inhibits KSHV lytic replication and tumor progression in vivo and slows down the development of KSHV-induced lymphoma in a mouse xenograft model [As all forms of KS are characterized by increased levels of inflammation and oxidative stress, it is postulated that reactive oxygen species (ROS), such as hydrogen peroxide (Hiewed in ). A receignaling . H2O2 ispathways . Signifift model . Anotherft model . Additioft model . In addift model . These rN-acetyl lysine amino acids in histones/proteins and play an important role in the regulation of gene expression. As mentioned earlier, during latency, IE and E-lytic genes possess bivalent chromatin associated with both repressive (H3K9me3 and H3K27me3) and activating -histone marks. In addition, previous studies showed that demethylation of H3K27me3 using UTX or dissociation of the histone methyltransferase EZH2, counteracts PRC2 repression of the RTA promoter [Several research groups have reported that HDAC Class I, II, and III can regulate KSHV reactivation, and activation of lytic gene expression can be triggered by treatment of KSHV latent cells with HDAC inhibitors ,67,68. Hpromoter ,41. In oAdditionally, Gao’s research group recently determined the role of Class III HDACs inhibitors or sirtuins (SIRTs) on the KSHV life cycle and reactivation by treatment of KSHV-positive PEL cell lines  with three distinct HDACi, namely-nicotinamide (NAM), sirtinol and NaB . The stude novo KSHV infection. Elevated cellular Egr-1 expression is reported to enhance viral RTA expression in a Raf > MEK > ERK-dependent manner [A recent report determined that Resveratrol (Rev), an important dietary supplement, inhibits KSHV reactivation by altering the interactions between early growth response-1 (Egr-1) and the RTA promoter . Electrot manner . Furthert manner .KSHV lifecycle undergoes a transition between a dormant, latent phase and an active lytic replication phase ,59. KSHVκ, which competes with RTA for binding to RBP-Jκ [ in vitro showed a number of RTA direct binding targets [KSHV encoded ORF50/ RTA (replication and transcription activator), is a key regulator for the lytic reactivation from viral latency ,73. Expro RBP-Jκ . Lytic ro RBP-Jκ . Genome-o RBP-Jκ ,94. In a targets , such as targets ,93. Addi targets ,95. Furt targets ,97,98. H targets . Several targets ,101. Sim targets . In addi targets ,104.9GG-like RTA responsive elements (RREs) for lytic reactivation and enhanced virion production [α binding site [RTA transactivation of viral promoters also depends on its interactions with other cellular proteins. RTA recruits CREB binding protein (CBP), the SWI/SNF chromatin-remodeling complex, and the TRAP/mediator coactivator into viral promoters . RTA binoduction . Furtheroduction ,109,110.oduction . During oduction . Studiesoduction ,111. KAPoduction . Additiooduction . K-RTA ioduction ,113,114.ing site . The proing site . K-RTA iing site ,118. in vitro and in vivo. Thus, K-RTA is an ubiquitin ligase, preferentially targeting SUMO-containing proteins for ubiquitylation; including sumoylated K-bZIP and promyelocytic leukemia (PML) nuclear bodies [Recent studies showed that K-RTA activity is regulated by its association with cellular peptidyl-prolyl cis/trans isomerases (PPIase), Pin1. Pin1 binds specifically to phosphorserine or phosphorthreonine-proline (pS/T-P) motifs in the K-RTA and enhances K-RTA transactivation . Additior bodies . TogetheORF57 is a viral early protein, which favors viral intron-less transcript accumulation, transports, and enhances splicing of intron-containing viral RNA transcripts . MTA is MTA modulates a cascade of viral gene expression and accumulation of specific viral and cellular mRNAs during lytic replication . PhysicaK-bZIP is a basic leucine zipper-containing protein encoded by KSHV K8 . The K-bA genome-wide analysis of K-bZIP’s transcriptional regulation on KSHV gene promoters showed that RTA activated 34 viral promoters whereas K-bZIP alone activated 21 promoters . Nonethede novo primary infection. ORF59 forms a homodimer in the cytoplasm and associates with ORF9 to translocate it to the nucleus during lytic DNA replication [Kaposi’s sarcoma-associated herpesvirus (KSHV) ORF59 plays a critical role in viral lytic DNA replication as a DNA processivity factor to the viral DNA polymerase (ORF9) ,83. ORF5lication . ORF59 alication . This stlication . In a relication .KSHV ORF6, a delayed-early gene encodes for a 126 kDa ssDNA binding protein that has been shown to participate in origin-dependent DNA replication ,154,155.de novo infection, KSHV has been shown to induce MEK/ERK, JNK, and p38 MAPK pathways in human umbilical vascular endothelial cells (HUVEC). This, in turn, regulates AP-1 to facilitate its entry into the target cells and initiate a productive lytic replication at the early acute stage of infection [Several cellular signaling pathways are identified to be involved in the reactivation of KSHV from latency, such as PKCd , b-Raf/Mnfection ,174. Addnfection ,159,175.KSHV lifecycle is also controlled by the viral protein, K-RTA by altering the Notch signaling pathway through binding with RBP-Jκ ,101. AddKSHV has evolved multiple mechanisms to manipulate cellular anti-apoptotic and survival pathways and disruption of these pathways reactivates KSHV ,189,190.Lytic reactivation results in an expression of several KSHV lytic proteins . Many ofKSHV employs diverse mechanisms for controlling both IFN production and signaling as IFN is a potent antiviral defense that is critical for KSHV persistence . The genMany of the KSHV K-gene encoded lytic proteins have also been shown to modulate KSHV infection and pathogenesis. Multifunctional transmembrane glycoprotein K1 encoded by the first ORF of KSHV can constitutively activate multiple pro-growth signaling pathways in KSHV-infected cells. . OligomeFurthermore, KSHV-ORF4-encoded inhibitor of the complement system, designated as KSHV complement Control Protein (KCP) ,249,250,It has been also been shown that KSHV encoded tegument protein, ORF75 is an essential protein as a new viral effector  for the Kaposi’s sarcoma associated herpesvirus (KSHV) modulates various cellular pathways by which it is able to establish and maintain persistent infection in the host to initiate tumorigenesis. Several of these latent viral and lytic proteins are known to transform host cells, linking KSHV with the development of severe human malignancies. These virus-induced cancers pose a large threat to global public health, specifically in areas that are still struggling with malignancies associated with HIV-AIDS with limited treatment options. Over the years, tremendous progress has been made in elucidating the molecular mechanisms of KSHV latency and lytic replication. Nonetheless, there are still vast aspects of viral infection and transformation that are not well explored. With the help of rapid advancements in modern technology, it is presumed that a thorough knowledge of the KSHV life cycle will be achieved over the next few years. Further understanding of the unique mechanisms that KSHV adopts for the establishment of successful lifetime persistence in the infected host will eventually pave the way for novel therapeutic approaches for the treatment of KSHV diseases.