Here we have analyzed changes in nucleosome occupancy at the enhancers as well as at the promoters of three pro-inflammatory genes when they are induced by bacterial lipopolysaccharides (LPS) in primary mouse macrophages. We find that nucleosomes are removed from the distal enhancers of IL12B and IL1A, as well as from the distal and proximal enhancers of IFNB1, and that clearance of enhancers correlates with binding of various cis-regulatory TFs. We further show that for IFNB1 the degree of nucleosome removal correlates well with the level of induction of the gene under different conditions. Surprisingly, we find that nucleosome occupancy at the promoters of IL12B and IL1A does not change significantly when the genes are induced, and that a considerably fraction of the cells is occupied by nucleosomes at any given time. We hypothesize that competing nucleosomes at the promoters of IL12B and IL1A may play a role in limiting the size of transcriptional bursts in individual cells, which may be important for controlling cytokine production in a population of immune cells.Chromatin is thought to act as a barrier for binding of  S. cerevisiae have indicated that promoter regions are relatively depleted of nucleosomes compared to the surrounding regions PHO5 and GAL1/10 genes of yeast, it was found that removal of promoter nucleosomes is required for gene induction and is mediated by nucleosome remodelers (e.g. the Swi/Snf complex) that are recruited to these regions by specific TFs GAL1/10 promoters these nucleosomal sites are only lowly occupied prior to induction and low promoter nucleosome occupancy is at least partly determined by the underlying DNA-sequence and facilitates rapid nucleosome removal when the inducer galactose is added cis-regulatory TFs and the transcriptional machinery. However, at least at one site of binding of a transcriptional activator, the UASg of the GAL1/10 locus, it was shown that the consensus site-containing piece of DNA is part of an, albeit unusual, nucleosome that apparently accommodates activator binding on its surface Genome-wide studies in cis-regulatory TF XBP when IFNB1 was induced by LPS and thapsigargin (TPG), an inducer of ER-stress that enhances expression of certain pro-inflammatory cytokines through the action of XBP. Furthermore, a minimal region of 305 bp that encompasses consensus-sites for XBP and IRF3 was shown to enhance transcription of a reporter gene confirming this region as a bona fide enhancer. Similar studies of the IL12B gene performed mostly by Stephen Smale's laboratory identified a distal enhancer located 10 kb upstream of its TSS Pro-inflammatory cytokines are expressed by macrophages as part of the innate immune response to various pathogens . Our results indicate that promoter nucleosomes are not stably evicted but instead are bound to a fraction of promoters in the population of cells at any given time. Furthermore, we find that PolII and TBP are only associated with nucleosome-free promoters and we discuss the potential role of competing nucleosomes at the promoters of these cytokine genes in limiting their expression in a population of immune cells.We find that nucleosomes in the distal enhancers of IL12B, IL1A and IFNB1 are rapidly evicted when the genes are induced. Nucleosomes are also removed from the proximal enhancer of IFNB1, which leads to clearance of the adjacent TATA-box and TSS as had been described for the human gene Procedures to obtain primary cells from mice were performed under IACUC oversight (#07/12-113-00).E. coli strain EH100 (Ra mutant)(Sigma) to serum-starved BMDMs for the indicated times. Serum starvation was done by growth of cells in incomplete IMDM medium for 1 h. Other inducers were ISD (interferon stimulatory DNA) derived from Listeria monocytogenes; poly(I:C), synthetic dsRNA that acts as a TLR3 agonist; and poly(dA:dT), a synthetic analog of B-DNA . 1 μg/ml of either of these inducers was given to BMDMs by transfection with Lipofectamine 2000 (Invitrogen) in an equal volume mixture Primary cells where isolated from 8–12 week old C57BL/6 mice (NCI). Bone marrow derived macrophages (BMDMs) were generated as described Total RNA was isolated from BMDMs or B-cells using Trizol (Invitrogen/Lifetech). In brief, Trizol was added to cells growing in culture, and Trizol lysates were collected. 400 μl of chloroform was added per 1 ml Trizol lysate, the aequous phase was extracted, 170 μl isopropanol was added and the mixture was further purified on ReliaPrep RNA Cell Miniprep System columns according to the manufacturer's protocol (Promega). RNA was converted into cDNA according to the protocol described 6 cells per antibody that had been cross-linked with 0.5% formaldehyde for 10 min was isolated by sonication with a Branson sonifier (10 pulses of 10″ at setting 4) in Lysis buffer  and centrifugation for 10′ at 21,000×g. To increase the resolution of ChIP experiments when detecting histones or histone modifications, and to differentiate nucleosome binding from PolII and TBP binding, the isolated chromatin was digested with 0.5 or 1 U MNase (NEB) for 1 h 30′ in the presence of 0.15 mM CaCl2, and the digestion reaction was stopped by addition of 20 mM EDTA. Digested chromatin was diluted 3-fold with High Salt ChIP buffer ) to yield 600 μl total volume and incubated overnight at 4°C with either 5 μl of anti-H3 , 4 μg of anti-H2A.Z , 1 μg of anti-H3K4me1 , 1 μg of anti-H3K4me3  or 1 μg of H3K27ac . For all other ChIP experiments isolated chromatin was directly diluted with High Salt ChIP buffer and incubated with either 1 μg of anti-PolI antibody (sc-56767), 6 μg anti-TBP (sc-204), 4 μg anti-PU.1 (sc-352), 4 μg anti-C/EBPβ (sc-150), 6 μg anti-NFκB (sc-372), 5 μg anti-c-Jun (sc-45), 6 μg anti-p300 (sc-585) or 10 μg anti-IRF3 (sc-9082) all from Santa Cruz Biotechnologies. 20 μl of Protein A/G magnetic beads (Pierce) were added to the reaction and incubated at 4°C for 2 h. Beads were washed with 280 μl each of TSE buffer , TSE250  and TSE500 , Wash buffer III  and TE  all containing Complete protease inhibitors. Antibody complexes were eluted from the beads with 2×100 μl Elution buffer  by incubation for 30′ (and 10′) at 55°C. Eluates were combined and the cross-link was reversed by incubation at 65°C for 4 h. DNA was purified using a Qiagen PCR purification kit, and analyzed on a Lightcycler 480 (Roche) using primer pairs in the regions indicated. Sequences of the primers used can be given upon request.Chromatin from 5×107 cells isolated as described for ChIP experiments was incubated in Lysis buffer containing 140 mM sodium chloride with 22 increasing concentrations of MNase  in the presence of 0.15 mM CaCl2 for 1 h 30′. DNA was purified as described and quantified using a Roche Lightcycler 480. Digestion data was analyzed using two-state exponential curve-fitting as described The assay was performed essentially as described in Genomic DNA was isolated from RAW264.7 macrophages as described m-curve was performed as a quality control for each primer pair at the end of each qRT-PCR run. We also found that addition of 1.5% PEG400 (Fluka) to the qRT-PCR reaction greatly enhanced performance for many primer pairs and led to a greater linear range of the qRT-PCR measurements.DNA and cDNA was quantified on a Lightcycler 480 (Roche) as described Panels C and D of To determine the earliest timepoint of nucleosome removal at the distal enhancers of IL12B and IL1A we analyzed nucleosome occupancy in the centers of the two enhancers 15′, 30′, 60′ and 90′ after LPS induction. The minimal enhancer of IL12B was previously shown to bind C/EBPβ and Oct1/2 upon induction and consensus-sites for these TFs were identified in this region Student's T-tests , p(dA:dT)) either alone or in combination with LPS. However, the strongest increase in IFNB1 expression was seen when cells were pre-treated with the ER-stress inducer TPG prior to LPS induction  and increased recruitment of the transcriptional machinery.To determine binding of To determine whether PolII and TBP might bind to the promoters of IL12B and IL1A in the presence of nucleosomes or whether the transcriptional machinery is only associated with the fraction of promoters that is nucleosome-free we performed the experiment shown in bona fide enhancer activity by previous studies  that remain bound by nucleosomes. Future studies will show whether removal of nucleosomes from consensus-sites can be used as a criterion to distinguish functional binding-sites for specific cis-regulatory TFs in the genome from sites that remain associated with nucleosomes and may therefore not be accessible.Our analysis of nucleosome occupancy at the regulatory regions of three pro-inflammatory genes revealed that the distal enhancers of IL12B and IFNB1 were rapidly cleared of nucleosomes when the genes were induced. The regions that became nucleosome-free include the respective minimal regions that had been shown to have dies see  and 4A[2dies see [19]. In dies see  and 5. Tdies see , 2A, 4A.The most surprising result of our study was the finding that the promoters of IL12B and IL1A were not cleared of nucleosomes when the genes where expressed, while nucleosomes were rapidly removed from the associated distal enhancers. Thus, we found that the TSS of IL12B was occupied in about 70% of the population prior to induction and remained essentially unchanged, while the distal enhancer became nucleosome-free in about 90% of the population see . We founcis-regulatory TFs. We did not find significant binding of NFκB or C/EBPβ to this region upon LPS induction compared to the strong binding we found at the 10 kb upstream enhancer  using sensitivity of chromatin to MNase followed by indirect end-labeling or ligation-mediated PCR to determine nucleosome binding ncer see . Nor didncer see , which wncer see . It is pe.g. a region 7 kb upstream of the TSS of IL12B) showed no binding of these factors  independent experiments performed with various antibodies as described in the legend of. All the measurements at the 2–4 locations in each enhancer or promoter, as well as the measurements at a single location in each ORF or in the IL12B intervening region were pooled from each experiment and Student's Tests  were performed on each dataset. (TIFF)Click here for additional data file.Table S2Student's tests were performed as described in the legend for .(TIFF)Click here for additional data file.


The transcriptome and its regulation bridge the genome and the phenome. Recent RNA-seq studies unveiled complex transcriptomes with previously unknown transcripts and functions. To investigate the characteristics of neural transcriptomes and possible functions of previously unknown transcripts, we analyzed and compared nine recent RNA-seq datasets corresponding to tissues/organs ranging from stem cell, embryonic brain cortex to adult whole brain.We found that the neural and stem cell transcriptomes share global similarity in both gene and chromosomal expression, but are quite different from those of liver or muscle. We also found an unusually high level of unannotated expression in mouse embryonic brains. The intronic unannotated expression was found to be strongly associated with genes annotated for neurogenesis, axon guidance, negative regulation of transcription, and neural transmission. These functions are the hallmarks of the late embryonic stage cortex, and crucial for synaptogenesis and neural circuit formation.Our results revealed unique global and local landscapes of neural transcriptomes. It also suggested potential functional roles for previously unknown transcripts actively expressed in the developing brain cortex. Our findings provide new insights into potentially novel genes, gene functions and regulatory mechanisms in early brain development. It is well known that total gene numbers are similar among multicellular eukaryotes, and genome size does not correlate with organism complexity, which differs greatly in terms of development, physiology and behavior among eukaryotes . The traRecent high-throughput RNA-seq  technoloIt has been reported that undifferentiated human stem cells have elevated expression of unannotated TARs compared with differentiated neural progenitor cells . Our recTo further investigate the transcriptome dynamics and to better understand the possible roles of unannotated TARs in early neural development, we have analyzed the RNA-seq datasets from embryonic and postnatal mouse brain cortices that we generated recently , as wellThrough a systematic analysis of these nine datasets, we found several unique characteristics of the transcriptomes in early neural development. We found that, although the genome was not as pervasively transcribed as previously reported , most ofTo examine the genomic distribution of transcriptomic reads, we mapped all RNA-seq data by the TopHat software , which wDespite a dataset size up to 1,000 Mbp (AMB), only about 2% of the mouse genome had been mapped with RNA-seq reads, unlike a previous report that suggested a more pervasively transcribed genome from detailed analyses of ~1% of the human genome . A recenAfter further dividing the genome into 10 kb intervals, most of the 10 kb intervals had no detected transcription Figure . Most ofR = 0.43) and intergenic expression level (R = 0.37) in the same region . However, at 100 kb resolution, the aforementioned correlation became very weak .We found that expression level was positively correlated with the exon contents of a given interval. The higher exon percentage an interval had, the higher the number of detected RNA-seq reads in the interval. At 1 Mb interval size, the Pearson's Correlation Coefficient between exonic read number and exonic content percentage was 0.60; in contrast, the Pearson's Correlation Coefficient between intronic read number and exonic content percentage was only 0.18. The Pearson's Correlation Coefficient between intergenic read number and exonic content percentage was also quite low, at 0.26 Figure  and 3B. et al. , adult mouse samples: [SRA: SRA001030], brain cortices samples: [SRA: SRP007262].this file includes figure S1 to S7.Click here for filethis file includes supplemental data and analysis results relevant to this study, table S1 to S6, and figure legends for figure S1 to S7.Click here for file


Chromatin-modifying enzymes and ATP-dependent remodeling complexes have been intensely studied individually, yet how these activities are coordinated to ensure essential cell functions such as transcription, replication, and repair of damage is not well understood. In this study, we show that the critical loss of Sas3 and Gcn5 acetyltransferases in yeast can be functionally rescued by inactivation of ISWI remodelers. This genetic interaction depends on the ATPase activities of Isw1 and Isw2, suggesting that it involves chromatin remodeling activities driven by the enzymes. Genetic dissection of the Isw1 complexes reveals that the antagonistic effects are mediated specifically by the Isw1a complex. Loss of Sas3 and Gcn5 correlates with defective RNA polymerase II (RNAPII) occupancy at actively transcribed genes, as well as a significant loss of H3K14 acetylation. Inactivation of the Isw1a complex in the acetyltransferase mutants restores RNAPII recruitment at active genes, indicating that transcriptional regulation may be the mechanism underlying suppression. Dosage studies and further genetic dissection reveal that the Isw1b complex may act in suppression through down-regulation of Isw1a. These studies highlight the importance of balanced chromatin modifying and remodeling activities for optimal transcription and cell growth. In eukaryotes, essential processes such as transcription, replication, and repair of damage occur in the context of chromatin. The structure of chromatin is tightly regulated during the cell cycle by chromatin-modifying enzymes, including acetyltransferases, and ATP-dependent remodeling complexes. Although there has been extensive characterization of their individual functions, little is known about how their activities are coordinated to maintain cell viability. In this study, we show that the critical loss of Sas3 and Gcn5 acetyltransferases can be functionally rescued by inactivation of ISWI remodelers. At a molecular level, the effects on cell viability tightly correlate with the recruitment of RNA polymerase II (RNAPII) at active genes, suggesting that transcriptional regulation may be the mechanism underlying cell viability rescue. Our genetic analyses reveal distinct roles for the two Isw1a and Isw1b complexes; in particular, the antagonistic effects are mediated specifically by the Isw1a complex. These studies highlight the importance of balanced chromatin modifying and remodeling activities for optimal transcription and cell growth. Two major classes of enzymes regulate the architecture of chromatin and are thereby critical for DNA-templated processes such as transcription, replication, and repair of damage. Remodeling enzymes use the energy of ATP hydrolysis to alter the structure or position of nucleosomes  at active genes. Together these studies provide new evidence for functional distinctions between the families of chromatin remodeling activities and point to critical interactions between the Sas3 and Gcn5 acetyltransferases, ISWI remodeling machines, RNAPII recruitment, and chromatin compaction.Functional links have not yet been established between Sas3 and chromatin remodeling, or the loss of viability that results when both Sas3 and Gcn5 activities are compromised. We describe here a critical interaction between Sas3 and Gcn5 acetyltransferases and ISWI chromatin remodelers. Strikingly, and in contrast with nucleosome disrupting remodelers such as SWI/SNF and Chd1, inactivation of Isw1 or Isw2 relieved conditional lethality in a Among the major H3 acetyltransferases, either Gcn5 or Sas3 is required for cell viability: loss of both enzymes leads to death. To understand this loss of viability, we asked if other chromatin-modulating activities contributed to it, and in particular if there was a role for ATP-dependent chromatin remodeling activities.ino80Δ lethality appears restricted to the W303 genetic background There are four distinct families of biochemically defined chromatin remodeling complexes: SWI-SNF, ISWI, CHD, and INO80 gcn5Δ sas3 conditional mutant described earlier We began by evaluating the effects of ATP-dependent chromatin remodeling activities when Sas3 and Gcn5 activities were compromised, using the temperature-sensitive gcn5Δ sas3 double mutant was exacerbated upon deletion of CHD1  at transcriptionally active target genes  in the gcn5Δ strain, and more dramatically in the gcn5Δ sas3 strain  cleavage patterns of chromatin prepared from wild-type and mutant strains .In Drosophila, H4K16 acetylation regulates chromatin compaction by reducing the ability of ISWI to bind chromatin PYK1 gene. Of note, nucleosome occupancy appears slightly reduced in the gcn5Δ sas3 mutant, at the predicted nucleosomes +1 and +3  in chromatin fibers gcn5Δ sas3 mutant An additional possibility for the ISWI-dependent chromatin remodeling rescue in gcn5Δ sas3, the Isw1b complex antagonizes the function of the Isw1a complex. Early on, functional characterization of these two complexes revealed distinct roles in vivoAn unexpected finding uncovered by our genetic analysis is the opposing functions of Isw1a and Isw1b complexes in relation to the H3 HATs. Specifically, we observed that rescue mediated by Ioc2 and Ioc4 requires Ioc3, but this requirement is not reciprocal. This suggests that, at least in the context of Together, the results from this study deepen understanding of the essential roles for H3 HATs. Not only do the HATs positively promote gene-specific transcriptional activation, as has been well established, they also have a critical role in balancing the activities of ATP-driven chromatin remodelers. The functional antagonism between Isw1a and the Sas3 and Gcn5 acetyltransferases further defines biological distinctions between the Isw1 enzyme's catalytic activities in its two structurally distinct complexes. Thus, in addition to interactions between histone modifications defining transcriptional output  on gcn5Δ::kanMX. The strains expressing Ioc3-13Myc and Isw1-13Myc from their chromosomal loci were constructed as described in LEU2). pLP645 was constructed by inserting a BamHI-SalI fragment containing SAS3 into Yep351 opened with SalI and BamHI (J. Lowell). pLP1524 was obtained from a genomic library generously provided by S. Roeder GCN5 gene . To construct Yep351-IOC2 (pLP2234), a HindIII fragment (4.6 kb) containing IOC2 was subcloned from pLP2170  into Yep351. To create Yep351-ISW1 (pLP2256), a BamHI-PstI fragment was subcloned from pRS416-ISW1 (a generous gift from T. Tsukiyama) into Yep351. Plasmid Yep351-IOC4 (pLP2260) was constructed by PCR amplification of IOC4 (−395 bp from start codon to +412 bp from stop codon), and cloned into Yep351 using HindIII-PstI restriction sites. Plasmid Yep351-IOC3 (pLP2266) was constructed by PCR amplification of IOC3 (−700 bp from start codon to +400 bp from stop codon), and cloned into Yep351 using HindIII-PstI restriction sites. Integrity of the constructs was confirmed by DNA sequencing.Strains used are listed in 600 of 1 and plated in fivefold serial dilutions onto SC or selective medium, supplemented with 1 M sorbitol where indicated, and incubated for 4 days at the indicated temperatures prior to data collection.Cultures were grown for 2 days in SC or appropriate selective medium at permissive temperature. Cells were diluted to an A600 of 0.7–0.9 and cross-linked with 0.86% formaldehyde for 40 min. Immunoprecipitations (IP) were either pre-cleared with CL4B Sepharose beads (Sigma) for 1 hour at 4°C, then incubated overnight at 4°C with anti-RNAPII  or directly incubated overnight at 4°C with antibodies against H3 , acetylated H3K14 , or the myc epitope (9E10). DNA was purified using PCR purification columns (Qiagen) and analyzed by real-time PCR (MJ Research Opticon2 system). Primer sequences are listed in Chromatin immunoprecipitation assays were performed as described previously 600 of 0.7–0.9. Then ∼2×109 cells were harvested, washed in 1 ml sorbitol 1 M, resuspended in 1 ml of zymolyase solution  and incubated for 1.5 min at room temperature. Spheroplasts were then washed twice in 1 M sorbitol and gently resuspended in 1.6 ml of cold buffer A . The cell slurry was divided into 400 µl aliquots, and each was added to a microfuge tube containing the MNase  and incubated at 37°C for 4 minutes. The reaction was stopped by addition of 40 µl of stop buffer . DNA purification was performed as described in PHO5 TATA region Extracts from MNase digestions were prepared as described The protein immunoblotting, mRNA quantification, and chromatin analysis techniques used in Figure S1gcn5Δ sas3 mutant. Five-fold serial dilutions of cells were plated onto SC medium supplemented when indicated with 1 M sorbitol, and grown for 4 days at the indicated temperatures  Inactivation of the Chd1 chromatin remodeling enzyme exacerbates the growth defects of the see also . (B) Ina(TIF)Click here for additional data file.Figure S2IOC2 and IOC4 does not affect IOC3 expression. WT and gcn5Δ sas3 cells expressing Ioc3-Myc were transformed with IOC2 or IOC4 in the 2 µm plasmid and grown at 37°C. Ioc3-Myc levels were determined by immunoblotting using anti-Myc, normalized using anti-tubulin and further normalized to empty vector control for relative quantification. Shown is a representative blot of three experiments.Overexpression of Isw1b subunits does not affect Ioc3 protein levels. Increased gene dosage of (TIF)Click here for additional data file.Figure S3IOC3 does not restore bulk levels of H3K14 acetylation in gcn5Δ sas3 cells. Whole cell protein extracts from wild-type, gcn5Δ sas3, gcn5Δ sas3 ioc3Δ cells were immunoblotted with anti-H3K14ac, and anti-H3 as a control for histone levels. Quantification of H3K14Ac was normalized to H3 levels with WT level set to 1.Deletion of (EPS)Click here for additional data file.Figure S4PYK1 gene (A) or at the 5′ regions of PMA1, RPL10, RPS5, CDC25 and UBP7 genes (B) were assayed by ChIP analysis of cells grown in SC medium at 34°C. RNAPII occupancies in (A) and (B) were normalized to 5′ region of PYK1 and the PMA1 gene respectively, arbitrarily set to 1. The values represent the means from two or more independent experiments, with error bars reflecting standard deviations. (C) cDNAs from WT, gcn5Δ sas3, and gcn5Δ sas3 ioc3Δ cells grown at 34°C were analyzed by quantitative PCR. Expression values are relative to ACT1 and normalized to WT. The values represent the means from three independent experiments, with error bars reflecting standard deviations.Sas3 and Gcn5 acetyltransferases and Isw1a antagonistically regulate RNAPII recruitment to active genes, but do not alter gene expression. RNAPII occupancy over the (EPS)Click here for additional data file.Figure S5PYK1 gene. MNase analysis of PYK1-specific chromatin in wild-type, gcn5Δ sas3 and gcn5Δ sas3 ioc3Δ cells. Chromatin was probed for PYK1 following digestion with MNase at concentrations of 0, 60, 150 and 400 U/ml, and EcoRI digestion. Marker restriction digests (Marker) are positioned relative to schematic maps of the PYK1 gene. Restriction site positions are relative to the transcriptional start site of PYK1. The black line corresponds to the probe used for indirect end labeling. Densitometric scans of chromatin digested with 60 and 150 U/ml of MNase for the wild-type and mutant strains, respectively, were generated using ImageJ software .Loss of H3 HATs does not lead to major changes in nucleosome positioning at the (EPS)Click here for additional data file.Figure S6gcn5Δ and gcn5Δ sas3 growth defects. (A) Increased gene dosage of IOC2 did not rescue the temperature sensitivity of the gcn5Δ mutant. The gcn5Δ mutant was transformed with the indicated plasmids. Strains were plated on SC–Leu medium and grown for 4 days. (B) Suppression and exacerbation of temperature sensitivity mediated by overexpression of Isw1a and Isw1b complexes, respectively, require the Isw1 ATPase. The gcn5Δ sas3 isw1Δ mutant was transformed with the indicated plasmids. Transformed strains were plated onto SC–Leu medium and grown for 4 days at the indicated temperatures.Requirements of Isw1 complex subunits for restoration of (EPS)Click here for additional data file.Table S1Yeast strains used in this study.(DOC)Click here for additional data file.Table S2ChIP primers used in this study.(DOCX)Click here for additional data file.Text S1Supporting Methods.(DOCX)Click here for additional data file.


Epstein-Barr virus (EBV) immortalizes resting B-cells and is a key etiologic agent in the development of numerous cancers. The essential EBV-encoded protein EBNA 2 activates the viral C promoter (Cp) producing a message of ∼120 kb that is differentially spliced to encode all EBNAs required for immortalization. We have previously shown that EBNA 2-activated transcription is dependent on the activity of the RNA polymerase II (pol II) C-terminal domain (CTD) kinase pTEFb (CDK9/cyclin T1). We now demonstrate that Cp, in contrast to two shorter EBNA 2-activated viral genes (LMP 1 and 2A), displays high levels of promoter-proximally stalled pol II despite being constitutively active. Consistent with pol II stalling, we detect considerable pausing complex (NELF/DSIF) association with Cp. Significantly, we observe substantial Cp-specific pTEFb recruitment that stimulates high-level pol II CTD serine 2 phosphorylation at distal regions (up to +75 kb), promoting elongation. We reveal that Cp-specific pol II accumulation is directed by DNA sequences unfavourable for nucleosome assembly that increase TBP access and pol II recruitment. Stalled pol II then maintains Cp nucleosome depletion. Our data indicate that pTEFb is recruited to Cp by the bromodomain protein Brd4, with polymerase stalling facilitating stable association of pTEFb. The Brd4 inhibitor JQ1 and the pTEFb inhibitors DRB and Flavopiridol significantly reduce Cp, but not LMP1 transcript production indicating that Brd4 and pTEFb are required for Cp transcription. Taken together our data indicate that pol II stalling at Cp promotes transcription of essential immortalizing genes during EBV infection by (i) preventing promoter-proximal nucleosome assembly and ii) necessitating the recruitment of pTEFb thereby maintaining serine 2 CTD phosphorylation at distal regions. Epstein-Barr virus (EBV) is associated with the development of a number of human cancers including Burkitt's lymphoma, Hodgkin's lymphoma, nasopharyngeal carcinoma and post-transplant lymphoproliferative disease. The virus infects B cells rendering them immortal through the production of a small number of viral proteins in the latently infected cell. Many of the viral proteins required for B-cell immortalization are produced from a very long protein-coding RNA message that initiates at the main viral latency promoter C, and our results provide important new information on how this message is produced. Specifically we show that the production of this long RNA is driven by the recruitment of the elongation factor (pTEFb) to paused transcription complexes at the C promoter. We show that pTEFb is recruited by the chromatin-associated protein, Brd4. Treatment of cells with a recently developed Brd4 inhibitor and inhibitors of the pTEFb elongation factor inhibits production of transcripts derived from the long EBV message thereby highlighting Brd4 and pTEFb inhibitors as potential anti-EBV agents. It contains 52 heptapeptide repeats -proximal and downstream regions and requires pTEFb for activation of both Cp and LMP1p Interestingly, our experiments revealed a large peak of pol II accumulation at Cp consistent with significant pol II stalling despite the fact that Cp was constitutively active . In contWe have previously demonstrated that EBNA 2-activated transcription requires pTEFb activity To further investigate pol II stalling at Cp, we examined the association of the pausing complexes NELF and DSIF with the promoter. We detected high-levels of the NELF-A subunit of the NELF complex and the Spt5 subunit of the DSIF Spt4-Spt5 heterodimer at Cp in Mutu III cells  consisteTo determine whether polymerase stalling, high level pTEFb recruitment and large increases in serine 2 CTD phosphorylation were evident at other shorter EBNA 2-responsive transcription units, we performed ChIP assays using primers specific for the EBNA 2-activated LMP genes . TranscrTo exclude the possibility that low-level pol II and transcription factor association with the LMP gene locus simply reflected low-levels of LMP transcription in the cell-lines under study, we used real-time PCR to determine the levels of Cp-initiated EBNA 2 and EBNA 1 transcripts and compared these to levels of LMP1 transcripts in Mutu III cells and two EBV-infected LCLs . We founTaken together, our data indicate that pol II accumulation and high-level pTEFb recruitment is not a general characteristic of EBNA2- activated promoters, but is specific to Cp. Moreover, the level of promoter-associated pol II does not simply reflect the level of gene transcription from Cp and LMP1p.Pol II stalling has recently been implicated in the promotion of gene activity through the maintenance of a promoter-proximal nucleosome-free region http://genie.weizmann.ac.il/software/nucleo_prediction.htmlGene-specificity of polymerase stalling may be directed by the ability of promoters to recruit high levels of the general transcription factor TFIID and thus high levels of polymerase molecules Although pTEFb can be recruited to promoters via association with activators, we have been unable to demonstrate binding of EBNA 2 to pTEFb (data not shown). To investigate the mechanism of recruitment of pTEFb to Cp further, we examined the association of the pTEFb binding protein, Brd4, with the promoter. Brd4 is recruited via binding of its bromodomains to acetylated lysines in Histones H3 and H4 Interestingly, the essential EBV replication and transcription factor EBNA 1 has been shown to recruit Brd4 to a region of the latent origin of replication (OriP), that functions as an EBNA 1-dependent Cp enhancer We next sought to obtain direct evidence that Brd4 binding is required for Cp but not LMP1 transcription by treating Mutu III cells with the novel small molecule Brd4 bromodomain inhibitor, JQ1, previously shown to block Brd4 association with acetylated histones in vivo show little detectable pTEFb recruitment  essential for immortalization. Following initial production of EBNA-LP and EBNA-2 from a cellular factor-driven promoter (Wp) after primary infection Our results document the presence of stalled RNA polymerase at an actively transcribing viral gene locus, unlike the situation observed at heat-shock genes, where genes temporarily in the ‘OFF’ state maintain promoter-proximally paused pol II to enable a rapid transcriptional ‘ON’ response to heat-shock that results in a re-distribution of polymerase along the gene Significantly, we demonstrate that regulation of Cp is distinct from the regulation of the latent membrane protein promoters, where only low levels of pol II, pTEFb, NELF and DSIF are detectable and serine 2 CTD phosphorylation does not substantially increase at distal regions. The EBNA 2-dependent LMP1, LMP2A and 2B genes encode transcripts of 2.8 kb, 11.7 and 8.4 kb in length (in the B95-8 EBV genome sequence NC_007605.1) so these shorter transcription units may therefore be less dependent on elongation factor function. It is worth noting that the LMP 2 transcription units can increase in size due the presence of varying numbers of ∼500 bp terminal repeat (TR) elements that are present within these genes. On entry into host cells, the EBV genome is initially in its linear form and the TR region is the site of recombination-directed genome circularization. Although the B95-8 genome sequence we have used for transcript annotation contains 4 TRs spanning 2.1 kb, TR regions of up to ∼12 kb have been reported, indicating that LMP2A transcripts can be up to 25 kb in length in vivo and thus pausing factor and elongation factor requirements at Cp may not have been faithfully recapitulated. The results presented in this manuscript show that nucleosome occupancy is likely to be the critical determinant that sets up Cp-specific polymerase pausing and pTEFb recruitment on endogenous EBV templates. It would be interesting to test whether Cp-specific regulatory features can be conferred by Cp promoter sequences alone by generating recombinant viruses in which LMP promoter regions are replaced with Cp promoter regions.The specificity of pol II stalling at Cp appears to be driven by the presence of DNA sequences upstream of Cp that are less favourable for nucleosome assembly. These sequences encompass the TATA box and therefore allow increased access to TBP resulting in high-level recruitment of pol II in association with the pausing factors NELF and DSIF. Once pol II stalling is established at Cp, a more extensive region around the promoter is then maintained in a nucleosome-depleted state. We have previously demonstrated that pTEFb activity is required for the activation of both Cp and LMP1 promoter-reporter constructs by EBNA 2 in vitro or in certain cell backgrounds. Importantly, the W promoter is present in multiple copies in the EBV genome  and transcription initiation from a number of W promoters may be required to generate sufficient levels of downstream transcripts. The fact that Cp deletion is a rare event however, supports the notion that Cp plays a crucial role during EBV immortalization and infected cell growth in vivo.Since the studies described here have exclusively examined the nature of constitutive Cp transcription in established cell-lines, it will be interesting to investigate the kinetics of the establishment of polymerase stalling during primary B-cell immortalization, when transcription switches from Wp to Cp approximately 6 days post-infection Since we detected the presence of Spt5 at distal regions, it is also possible that the recruitment of DSIF may play a positive role in Cp transcription, as documented for HIV transcription. pTEFb-mediated phosphorylation of the Spt5 subunit of DSIF at promoter-proximal regions converts DSIF into a positive-acting elongation factor that travels with polymerase to promote processivity and inhibit further pausing EBV strains can be classified into two virus types  largely based on sequence differences between the EBNA 2 genes, which share only 55% homology. Despite the prevalence of type 2 viruses in Africa and their association with BL, type 2 viruses transform resting B-cells much less efficiently than type 1 strains and differences between the EBNA 2 genes appear to be the major determinant of this property Since we have been unable to detect an interaction between EBNA 2 and pTEFb to date, we investigated alternative mechanisms for the recruitment of pTEFb to Cp. The double bromodomain protein, Brd4, has been shown to bind to the active form of pTEFb and recruit it to promoters to stimulate elongation In summary, we demonstrate that polymerase stalling may play a role in facilitating immortalization by the tumour virus EBV. High-level recruitment of pol II and associated pausing factors to the viral C promoter maintains nucleosome depletion and necessitates pTEFb recruitment to overcome pausing. This provides high levels of pTEFb to promote the distal serine 2 CTD phosphorylation required for production of the long viral transcript encoding key EBV immortalizing genes.5 cells/ml and incubated in the presence of DMSO, JQ1/SGCBD01 ER/EB 2.5 cells 5 cells/ml in medium without β-estradiol. After 3 days 1 µM β-estradiol (Sigma) was added for 5 hours and chromatin prepared as described previously ER/EB 2.5 cells were washed and resuspended at 5×105 cells/ml 24 hrs prior to chromatin preparation and resuspended at 1×107 cells/mL in fresh media before crosslinking. Cells were treated with 500 µM DRB for 2hrs prior to chromatin preparation.Mutu cells were diluted to 5×10ChIP methods were optimised for each target using a number of alternative strategies.For ER/EB 2.5 cells ChIP assays were carried out as described previously ChIP assays for EBNA 2 using Mutu cell chromatin were carried out as for ER/EB 2.5 cells using 8 µg (PE2) antibodies. Pol II, Spt5, acetylated Histone H3 and acetylated Histone H4 immunoprecipitations were carried out as described previously 5/ml, harvested after 24 hrs and total RNA extracted using TriReagent (Sigma). RNA samples were purified using the RNeasy kit (Qiagen) and cDNA was then synthesised using the ImProm II reverse transcription system using random oligonucleotides (Promega). For Brd 4 and pTEFb inhibitor experiments, cDNA was prepared from 105 cells using Power SYBR Green Cells-to-CT Kit (Applied Biosystems) according to the manufacturer's instructions.Cells were diluted to 5×10Quantitative PCR (Q-PCR) was performed as described previously SDS-PAGE analysis and immunoblotting was carried out as described previously The type 1 EBV genome used for primer design, transcription start sites and nucleosome predictions is the annotated sequence from the B95-8 strain (NC_007605.1). The type 2 EBV genome used was from the AG876 strain (NC_009334.1).Figure S1Increased serine 2 phosphorylation on the pol II CTD is dependent on EBNA 2 activity. (A) ChIP using anti-EBNA 2 antibodies in ER/EB 2.5 cells cultured in the absence (open bars) or presence of β-estradiol (black bars) shows functional EBNA 2 binding only in the presence of β-estradiol (B) ChIP using anti-phospho serine 2 pol II CTD antibodies detects high-level serine 2 phosphorylation only in the presence of β-estradiol. Results show the mean +/− standard deviation of a minimum of three independent experiments carried out using at least 2 chromatin batches.(PDF)Click here for additional data file.Figure S2The Q promoter drives EBNA-1-only expression in Mutu I cells. (A) ChIP using anti-acetylated histone H3 antibodies and Mutu I (open bars) and Mutu III cell chromatin (black bars). Results show the mean +/− standard deviation of a minimum of three independent experiments carried out using at least 2 chromatin batches. Numbers indicate the 5′ end of the forward primer relative to the Cp transcription start site in the annotated EBV sequence (NC_007605.1). Qp is located at +38800 relative to the Cp transcription start site. (B) PCR amplification of Qp-specific and Cp-specific transcripts. Akata cells (Qp only) and the PER 253 LCL (Cp only) served as positive controls for Qp and Cp usage respectively. The IB4 LCL has a deletion upstream of Cp so is negative for Qp and Cp transcripts. Qp or Cp signals were normalised to 18S rRNA primer signals. (C) Western blot analysis of whole cell lysates of Mutu I and Mutu III cells. Blots were probed with M.S. human serum to detect EBNA 1, PE2 to detect EBNA 2 and re-probed with anti-actin antibodies as a loading control.(PDF)Click here for additional data file.Figure S3DRB treatment of Mutu III and ER/EB 2.5 cells inhibits CTD phosphorylation. (A) ChIP using anti-phospho serine 5 pol II CTD antibodies in Mutu III cells minus (open bars) or plus 500 µM DRB (black bars). (B) ChIP using anti-TBP antibodies in Mutu III cells −/+ DRB. (C) ChIP using anti-phospho serine 2 pol II CTD antibodies in ER/EB 2.5 cells cultured in the presence of β-estradiol and in the absence (open bars) or presence (black bars) of 100 µM DRB. (D) ChIP using anti-pol II antibodies in ER/EB 2.5 cells cultured in the presence of β-estradiol and in the absence or presence of DRB.(PDF)Click here for additional data file.Figure S4Pausing factor recruitment is dependent on the function of EBNA 2. ChIP using anti-Spt5 (DSIF) antibodies in ER/EB 2.5 cells cultured in the absence (open bars) or presence of β-estradiol (black bars) detects significant DSIF recruitment only in the presence of functional EBNA 2.(PDF)Click here for additional data file.Figure S5PolII is not paused at the LMP 2A promoter. Results show the mean +/− standard deviation of four independent pol II ChIP experiments using Mutu I (open bars) and Mutu III cell chromatin (black bars). Percentage input signals, after subtraction of no antibody controls, are expressed for comparison purposes relative to the highest signal obtained using Cp-specific primers.(PDF)Click here for additional data file.Figure S6Low level pol II and elongation factor recruitment at the LMP gene locus. (A) Primers across the LMP locus are as in (PDF)Click here for additional data file.Figure S7Low level pol II and elongation factor recruitment at LMP genes in an LCL. ChIP carried out in an EBV immortalised LCL (PER 253 B95-8 LCL). Primers across the LMP locus are as in (PDF)Click here for additional data file.Figure S8Cp-initiated EBNA 2 and EBNA 1 transcript levels are similar to those of LMP1. Transcript levels from cDNA prepared at the same time from Mutu I, Mutu III, PER 253 B95.8 LCL and PER 142 B95.8 LCL were determined using specific Q-PCR primers to EBNA 2, Cp-initiated EBNA 1 (YUK spliced) and LMP1. Transcript quantities were determined using the absolute quantitation method and a cDNA standard curve and divided by actin quantities as a normalization control. Results show mean +/− standard deviation of Q-PCR duplicates from a representative experiment. Note that EBNA 1 transcripts initiate from Qp in Mutu I cells ( I cells  and are (PDF)Click here for additional data file.Figure S9Brd4 is recruited to LMP gene promoters. Results show the mean +/− standard deviation of a minimum of three independent experiments using Mutu I (open bars) and Mutu III cell chromatin (black bars). LMP gene locus primers are as in (PDF)Click here for additional data file.Figure S10pTEFb is not recruited to OriP at high levels. Results show the mean percentage input signal  +/− standard deviation of two independent experiments using Mutu I (open bars) and Mutu III cell chromatin (black bars). (A) ChIP using anti-Brd4 antibodies. (B) ChIP using anti-CDK9 antibodies (C) ChIP using anti-cyclin T1 antibodies. Cp analysis was carried out with the primer set that gave the highest signal for each transcription factor. Ori P primers are adjacent to the EBNA 1 binding element   .(PDF)Click here for additional data file.Figure S11Nucleosome occupancy prediction analysis at Wp resembles LMP genes. The probability of nucleosome occupancy (P occupancy) at regions proximal to Cp, LMP1p, LMP 2Ap and Wp was predicted using tools available at http://genie.weizmann.ac.il/software/nucleo_prediction.html(PDF)Click here for additional data file.Table S1Real-time PCR primers. a Primer locations are given relative to the start of the relevant mRNA sequence in the annotated EBV sequence (NC_007605.1) or the GAPDH gene. b Numbers relate to the annotated EBV sequence (NC_007605.1). c Primers are located in the W repeat region which contains on average 7.6 repeats of an estimated 3072bp sequence. d Primer sequences obtained from Prof. Paul Lieberman, The Wistar Institute, Philadelphia, USA. e Primer locations for the LMP1 gene located in the reverse orientation are given in parentheses. f The LMP1 polyA is located at 166483 to 166488 so MW 361 is outside of the transcription unit. g Actin primers span exon 3 to 4. Nucleotide positions for chromosome 7 are shown (BC002409).(PDF)Click here for additional data file.


How gene positioning to the nuclear periphery regulates transcription remains largely unclear. By cell imaging, we have previously observed the differential compartmentalization of transcription factors and histone modifications at the nuclear periphery in mouse C2C12 myoblasts. Here, we aim to identify DNA sequences associated with the nuclear lamina (NL) and examine this compartmentalization at the genome-wide level.We have integrated high throughput DNA sequencing into the DNA adenine methyltransferase identification (DamID) assay, and have identified ~15, 000 sequencing-based Lamina-Associated Domains (sLADs) in mouse 3T3 fibroblasts and C2C12 myoblasts. These genomic regions range from a few kb to over 1 Mb and cover ~30% of the genome, and are spatially proximal to the NL. Active histone modifications such as H3K4me2/3, H3K9Ac and H3K36me3 are distributed predominantly out of sLADs, consistent with observations from cell imaging that they are localized away from the nuclear periphery. Genomic regions around transcription start sites of expressed sLAD genes display reduced association with the NL; additionally, expressed sLAD genes possess lower levels of active histone modifications than expressed non-sLAD genes.Our work has shown that genomic regions associated with the NL are characterized by the paucity of active histone modifications in mammalian cells, and has revealed novel connections between subnuclear gene positioning, histone modifications and gene expression. The nuclear periphery is a unique subcompartment of the nucleus that consists of the inner nuclear membrane (INM) and its associated proteins, nuclear lamina (NL) and chromatin ,2. A numInteractions of chromatin with the NL have been studied by microscopic approaches , and altMyoD is located [MyoD promoter. Additionally, we noticed that H3K4me3 — the histone modification labeling active promoters — was localized away from the nuclear periphery, and that recognition of H3K4me3 appeared to be required for the subnuclear localization of TAF3 [Cellular imaging and genomic analyses have revealed substantial compartmentalization of the nucleus, and have suggested that this compartmentalization functions in gene regulation -19. Gene located . This di of TAF3 . Another of TAF3 . Thus, i of TAF3  may reveIn this paper, we have generated the initial maps of genome-NL interactions in mouse 3T3 fibroblasts and C2C12 myoblasts by DamID assay coupled to high throughput DNA sequencing. We have identified sequencing-based LADs (sLADs) that cover ~30% of the genome and represent the genomic regions with significant NL association. In 3T3 fibroblasts, the sLADs largely overlap with the LADs reported previously , and ranE. coli DNA adenine methyltransferase (Dam) and expressed the fusion protein in mouse cells. In eukaryotic cells that lack endogenous Dam proteins, the tethered Dam is capable of methylating adenines in GATC sequences that are in close spatial proximity [The published genome-NL interaction maps were constructed using DamID assay followed by analysis on genome tiling arrays ,15  , we compThird, we compared the genomic distributions of sLADs and the published LADs in MEFs . The vasBy DNA fluorescence in situ hybridization (FISH), we validated the spatial proximities of sLADs relative to the NL. First, we examined the localizations of several gene-containing regions in sLADs and non-sLADs in C2C12 myoblasts. We defined sLAD genes as those with more than 60% sLAD coverage over the gene body, i.e. from TSS to transcription end site (TES)  than non-sLAD genes . In MEF cells, sLADs largely overlap with the LADs previously identified using the DNA microarray approach . CompariWe have noticed that among sLAD genes, NL association is decreased in TSS proximal regions and that the decrease in NL association appears to correlate with gene expression. Highly sensitive, direct measurements of transcription are needed in order to define more precisely the relationship between gene expression and NL association. The reduced NL association in TSS proximal regions can be due to their increased spatial separations from the NL, or their increased chromatin accessibilities, and it is likely that both scenarios may contribute to this observed phenomenon. It is also intriguing to speculate that the reduced NL association in TSS proximal regions may provide a local environment amenable for recruiting transcription machineries and transcription initiation.We have performed parallel examinations on the nuclear compartmentalization of several key histone modifications in C2C12 myoblasts by cellular imaging and a combined analysis of ChIP-Seq and Dam-Seq data. Histone modifications (such as H3K27me3) that are localized throughout the nucleoplasm including the nuclear periphery are distributed both within and outside of sLADs with similar densities. In contrast, active histone modifications that are largely located away from the nuclear periphery are also predominantly distributed outside of sLADs Figure . These hBy coupling next generation sequencing to the DamID assay, we have identified sLADs that are genomic regions with significant association to the NL, ranging from a few kb to over 1 Mb and covering about 30% of the mouse genome. Single cell imaging and genomic analyses have provided consistent evidence on the paucity of several active histone modifications at the nuclear periphery both at the microscopic and the molecular levels in C2C12 myoblasts. Comparing sLAD genes and non-sLAD genes has revealed distinct histone modification levels and NL association that correlate with gene expression states. Our work may give clues on how gene positioning to the nuclear periphery (via NL association) affects transcription regulation in mammalian cells.2.NIH-3T3 mouse fibroblasts were obtained from ATCC (CRL-1658) and were grown in DMEM + 10% newborn calf serum. C2C12 mouse myoblasts were grown in DMEM + 10% fetal bovine serum. Both cells were grown at a 37°C incubator with 5% CODamID was performed using the lentivirus transduction protocol as previously described . Brieflyhttp://ycga.yale.edu/sequencing/Illumina/protocols.aspx) with reagents ordered from NEB. The sequencing was performed by YCGA using Illumina’s HiSeq 2000 sequencing system. The sequencing reads and the alignments (see the next section) have been deposited in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) with accession number GSE41583.To prepare for NGS, the above PCR products were purified and digested by DpnII again to remove DamID adaptors, and fragmented by NEBNext® dsDNA Fragmentase (New England Biolabs). Because the methyl PCR products are a smear of 200~2000 bp and the appropriate fragment size for sequencing is ~200 bp, we experimentally determined the appropriate time of fragmentation. For each DNA sample, we performed six reactions, each fragmenting 1μg DNA with 1 μl Fragmentase, incubated them at 37°C for 25 to 50 minutes at an interval of 5 minutes and pooled all for purification. The fragmented DNA was further subject to end repair, addition of “A” bases, ligation of NGS adaptors, gel selection of 300 bp fragments and PCR enrichment according to the protocol provided by Yale Center for Genome Analysis  as the fifth percentile of the combined RPKM values of LmnB1 and Dam, then computed the scaled log2 seq ratio as log2 ((LmnB1 RPKM + p)/(Dam RPKM + p)). The ChIP-seq peaks were assigned to the same set of 2 kb windows based on the peak center and then used to depict their distribution relative to sLADs  on May http://www.genomics.agilent.com/files/Manual/G3300-90001_FE_Plugin_A1.pdf), the data from an array probe contained a number of GeneSpring flags, each of which had three levels: Absent (A), Marginal (M) or Present (P). We identified a transcript (detected by a probe) as present only when all flags were shown as P. A gene was considered as expressed only when the transcripts were present in all three replicates of arrays. Custom Perl scripts were written to implement the task. The parsed expression states were used in Figure For a subset of 29235 genes (19269 genes), expression data of myoblasts are available from GEO database under the accession number GSE19968 . We downImmunostaining of histone modifications, DNA immuno-FISH and imaging analyses were carried out in C2C12 myoblasts as described previously . AntibodNL: Nuclear lamina; DamID: DNA adenine methyltransferase identification; sLAD: Sequencing-based lamina-associated domain; INM: Inner nuclear membrane; LAD: Lamina-associated domain; Pol II: RNA polymerase II; TSS: Transcription start site; NGS: Next generation sequencing; MEF: Mouse embryonic fibroblast; Dam: DNA adenine methyltransferase; FISH: Fluorescence in situ hybridization; TES: Transcription end site; FDR: False discovery rate; RPKM: Reads per kilobase per million mapped readsThe authors declare that they have no competing interests.FW and JY designed experiments. FW performed DamID assay, analyzed sequencing data and performed all the bioinformatic analyses. JY performed cell culture, lentivirus infection, FISH, immunostaining and imaging analysis. FW and JY wrote the paper. All authors read and approved the final manuscript.Table S2. Genomic coordinates of identified sLAD regions and non-sLAD regions in myoblasts. Table S3. The list of sLAD genes and non-sLAD genes in MEFs and myoblasts. Table S4. Comparison of myoblast and MEF gene expression states within common sLADs and cell type-specific sLADs.Genomic coordinates of identified sLAD regions and non-sLAD regions in MEFs. Click here for fileFigure S2. Genome browser tracks displaying representative genomic regions that are identified as sLADs but not LADs (A-C) or LADs but not sLADs  in 3T3 fibroblasts. Figure S3. Dam and LmnB1 read densities across genic regions in myoblasts. Figure S4. Distributions of histone modifications and Pol II across genic regions in myoblasts. Figure S5. A 6 Mb segment in chromosome 1 shows that the differential sLAD regions between MEFs and myoblasts include genes. Figure S6. Gene enrichment analysis of sLAD genes.The DamID maps of mouse chromosome 1–19 and X in MEFs and myoblasts. Click here for fileComparisons of sLADs and sLAD Genes between MEFs and Myoblasts.Click here for file


We employed bioinformatic analysis to find overrepresentation of noise-minimizing operon organization compared with randomized controls. Among constitutively expressed physically interacting protein pairs, higher coupling frequencies appeared at lower expression levels, where noise effects are expected to be dominant. Our results thereby suggest an important role for gene expression noise, in many cases interacting with an ultrasensitive switch, in maintaining or selecting for operons in bacterial chromosomes.Bacterial chromosomes are organized into polycistronic cotranscribed operons, but the evolutionary pressures maintaining them are unclear. We hypothesized that operons alter gene expression noise characteristics, resulting in selection for or against maintaining operons depending on network architecture. Mathematical models for 6 functional classes of network modules showed that three classes exhibited decreased noise and 3 exhibited increased noise with same-operon cotranscription of interacting proteins. Noise reduction was often associated with a decreased chance of reaching an ultrasensitive threshold. Stochastic simulations of the  E. coli has operons more frequently than random if operons reduce noise for the type of interaction various gene pairs have, but not otherwise. Our study thus provides an example of how the architecture of post-translational networks affects bacterial evolution.In some species, most notably bacteria, chromosomal genes are arranged into clusters called operons. In operons, the process of transcription is physically coupled: a single pass of the RNA polymerase enzyme reading that region of the chromosome simultaneously produces messenger RNA encoding multiple proteins. So far, we do not have a satisfying explanation for what evolutionary forces have maintained operons on bacterial chromosomes. We hypothesized that different types of interactions between operon-coded proteins affect how strongly operons are selected for between two genes. The proposed mechanism for this effect is that operons correlate gene expression noise, changing how it manifests in the post-translational network depending on the type of protein interaction. Mathematical models demonstrate that operons reduce noise for some types of interactions but not others. We found that operon-dependent noise reduction has an underlying dependence on surprisingly high sensitivity of the network to the ratio of proteins from each gene. Databases of genetic information show that  The organization of genes into operons is a prominent feature of bacterial chromosomes Arising from stochasticity of individual biochemical reactions and low copy numbers of reactants per cell, intrinsic noise plays a central role in network dynamics The effects of intrinsic noise on operon maintenance are not well characterized, but covariance between protein levels arising from intrinsic noise depends on transcriptional coupling (co-expression from an operon) of the corresponding genes lac operon. To test our predictions bioinformatically, we classified naturally occurring interacting pairs of E. coli proteins by their type of interaction and analyzed the effect of chromosomal distance between pairs of genes with interacting products. Finally, we used single-cell protein copy number data to determine differences in operon frequencies at high and low expression levels in E. coli for physically interacting protein pairs.In this study we assessed these effects for different types of posttranslational interaction between gene products. In several types of interactions, the noise difference between cotranscribed and uncoupled configurations was amplified by the existence of a zero-order ultrasensitive switch A and B (ABσ). Using the linear noise approximation  mRNAτ and proteinτ are the characteristic timescales of mRNA and protein decay. Increased covariance of cotranscribed genes is preserved regardless of whether the translation processes are coupled  gene regulators from the same operon results in increased CV in the regulated gene as compared to the uncoupled regulator configuration . Here, wAB; Consistent with a previous study A (A*) that can be of either sign depending on parameter values, whereas the unmodified form (A) has consistently lower CV in the cotranscribed configuration  between cotranscribed and uncoupled forms of each network module. For each molecular species denoted by j, we calculated the noise difference between cotranscribed ; if it is negative, the uncoupled configuration has lower CV2. A more complete analysis for each module is presented in The stochastic simulation approach  gives deLinear metabolic pathway: We find a lower bound on the difference in metabolic intermediate:APH and BPH are the logarithmic gains (sensitivities) of product P to proteins A and B, respectively. Because Redundant metabolic step: We find a difference in the productAsH, BsH, and ssH are the logarithmic gains of substrate flux ratio  to concentration of mRNA. For realistic parameter values at which protein modification flux significantly exceeds the degradation flux, Covalent modification module: We haven flux,  . In this2 between two configurations is proportional to the value of covariance To summarize, we found that differences in noise between cotranscribed and uncoupled configurations in stochastic simulations are qualitatively consistent with the analytical approach. Notably, in all the cases the magnitude of the differences in CV+V/−V>1 in +V and −V are proportional to their enzyme levels, the numerator and denominator of the ratio fluctuate together when both enzymes are in the same operon. Therefore transcriptional coupling lowers noise in the flux ratio and making it unlikely to cross the threshold +V/−V = 1. When the enzymes are uncoupled, simulations show more variability in the +V/−V ratio, allowing the ratio to cross the threshold with consequent large spikes in metabolic intermediate. Thus, the ultrasensitive switch amplifies noise differences already present between cotranscribed and uncoupled configurations.Timecourse simulations predict that uncorrelated fluctuations of the two enzymes in a linear metabolic pathway result in large bursts of metabolic intermediate . This suDifferences in noise between cotranscribed and uncoupled configurations of all of the non-redundant modules can be amplified by ultrasensitive switches in a similar manner . The metlac operon model that is based on a previous deterministic model d-galactose+β-d-glucose , intermediate induction , and excess inducer with maximal lac operon induction . Timecourses suggest that transcriptional coupling between lacY and lacZ (wild-type situation) eliminates the large fluctuations in allolactose  in time . These findings hold even after we divide each class into essential and nonessential genes using data from Taniguchi et alProteins in the linear metabolic pathway, physical interaction, and covalent modification modules appear in the same operon significantly more frequently than do randomized controls p<<10−6; . On the To separate the specific effect of noise from that of other factors affecting selection for operons, such as proportional coregulation, we considered whether the tendency toward operon membership of posttranslationally interacting protein pairs is related to gene expression levels E. coliWe used a dataset of average single-cell mRNA and protein copy numbers in p<0.01) and mRNA (bootstrap test p<0.05) copy numbers. This suggests that evolutionary selection against decorrelation  if they are not cotranscribed , but witE. coli .A] and [B] and those concentrations are less likely to simultaneously fluctuate upward simultaneously.The simple physical protein interaction module in our study  may resuA) compared with the uncoupled configuration. Noise effects of transcriptional coupling may therefore be important in covalent modification systems where the unmodified form of the protein is capable of interacting with other systems  across a typical bacterial length of ∼1 µm is much faster than the expected time lag between translation of two proteins given typical ribosomal speeds of 12–21 AA/s Gene pairs encoding constitutive physically interacting proteins are significantly more likely to be in the same operon if their expression levels are low . This trThe opposite trend would be expected if proportional expression of mean concentrations or other mechanisms are the primary selective pressure on operon maintenance. In general, genes with high expression levels may operate under greater evolutionary pressure than genes with low expression levels in E. coli, may also explain why no redundant enzymes are in the same operon. Similarly, different growth conditions may result in different regulators affecting downstream expression of the same genes. Further work is necessary to distinguish the noise reduction hypothesis more decisively from differential gene regulation as a selective force in redundant pairs; differential regulation may be physiologically important in some cases and not in others.Partial functional redundancy of proteins allows one protein to compensate for a downward fluctuation in concentration of the other protein, thereby reducing noise with uncorrelated protein fluctuations . TherefoImprovement of dynamic performance of simple networks arising from cotranscription of interacting genes from the same operon raises the question of why operons are rare in eukaryotes. Eukaryotic cell volumes are much higher than prokaryotes, likely lowering the effect of intrinsic noise relative to the dominant effect of extrinsic noise E. coli correlate with reduction of biochemical noise. Nevertheless, it would be interesting to explore operon coupling frequencies in bacterial stress response systems known to favor population-level heterogeneity, such as stress responses in B. subtilis; the amplification of noise by underlying ultrasensitive switches in non-redundant network modules may be a potential mechanism of population-level heterogeneity.We have developed a theory predicting that operon membership can increase or decrease noise in different types of protein interactions. Bioinformatic analysis finds that naturally occurring operon patterns in The existence of implicit ultrasensitive switches also underscores the idea that dramatic non-linearities are likely present in many simple protein interaction networks. Our results suggest that ultrasensitive switches are likely undetectable in the wild-type configurations of well-adapted systems as a result of selection against them, but may be present in conditions with lower selective pressure, or recent evolutionary events. These switches nevertheless have important implications for genome evolution. Their effects, and the mechanisms for avoiding them, may in turn shape larger biochemical networks by changing global noise properties, and will be an important factor in designing synthetic networks.http://engineering.ucsb.edu/~cse/StochKit/) tau-leaping routine for 10,000 runs of each model . Initial model construction and test runs were done with Copasi (www.copasi.org). Simulations with an extrinsic noise representation were done in Copasi as detailed below. All models were represented with elementary reaction steps; in models involving gene regulation, we defined a promoter variable as always present at one copy per cell.Symbolic manipulations and data analysis were performed in Mathematica 7.0 and 8.0 . We predicted intrinsic noise characteristics with stochastic simulations at stationary state using the StochKit  and when transcription is coupled, proteins may be cotranslated (single ribosome binding site for both) or translationally uncoupled (two ribosome binding sites). These three cases represent simplified extremes; intermediate translational linkage  is possible but was not further considered here. degk corresponds to the value expected from a dilution rate for typical E. coli doubling every half hour.i = 1 with proteins A and B in the same operon, and i = 2 with proteins A and B in separate operons. Then we solved the fluctuation-dissipation matrix equation at steady state (Mσ+σMT+ΩN = 0) for σ, where M is the Jacobian of the (macroscopic) system, Ω is cell volume, and N is the diffusion matrix i and j taking values corresponding to molecular species , we follow the methods of AAσ is the variance, or the square of the standard deviation.The basis of noise differences between networks with proteins in the same operon and those with proteins in separate operons is the covariance between the expressed proteins. We used LNA to analytically characterize noise and covariance as follows. For the mean values of copy numbers (denoted by angular brackets):To analytically approximate noise of physiologically relevant variables in the simple network modules , we madeE. coli K12 MG1655 as described below. To determine the randomized control, we needed to account for potential biases resulting from dataset size and other features of chromosome organization that we were not attempting to test. For instance, if we randomly assigned genes to extant operons in E. coli across the entire chromosome, the less frequently occurring modules would have much less same-operon membership than the modules with larger numbers of members, and would not be a useful control. We chose to randomize the genes extracted from the pairs within each module to set the random control for each class. Thus, for a list of gene pairsg intoBioinformatic analyses used pairs of interacting genes extracted from databases of E. coli metabolic network was extracted from Kyoto Encyclopedia of Genes and Genomes .The es KEGG; . We partes KEGG;  and 0 tombership , we elimfredundant = 0/114. The resulting randomized distribution had mean μr = 0.0047; standard deviation σr = 0.0015 (p<<10−6 against this distribution). Enzymes catalyzing steps after a metabolic branch point were determined on the basis of common substrates in KEGG .We extracted pairs of enzymes catalyzing the same metabolic step from the database to assess the trend in redundant metabolic steps. Here, c = 0.0047, σc = 0.0047, and p<<10−6, the true fraction is likely greater than the randomized control.Extracting examples of this interaction type from databases is difficult because of the complex nature of the relationship, which requires interaction with subsequent modification. We used the MultiFun Gene Ontology from EcoCyc E. coli gene regulation network from RegulonDB gf = 2/350 = 0.0057. Here, the random distribution has μrg = 0.099, σrg = 0.016, and p<<10−6.We used the fp = 0.35. In the randomized distribution, μp = 0.0030, σp = 0.00073, and p<<10−6.For physical protein interactions we used a functional interaction dataset et alWe extracted single-cell mRNA expression data (RNAseq) and protein copy number data from Taniguchi Figure S1cat2k. The distributions at low expression level are highly skewed, giving misleading CV values that do not accurately reflect the higher variability in the uncoupled architecture. B, K. Product of metabolic pathway. Parameter: cat2k. C, L. Product of redundant metabolic step. Parameter: cat1k and cat2k varied simultaneously. D, M. Substrate of a metabolic branch point. Some cases have lower CV in the co-transcribed model because the distribution is bimodal, but the uncoupled model predicts lower actual variability than the co-transcribed model in all cases. Parameter: cat1k and cat2k varied simultaneously. E, N. Protein product of multiple gene regulator network. Parameter: dk. F, O. Monomer of physical protein interaction module. Parameter: bk. G, P. Heterodimer of physical protein interaction module. Parameter: bk. H, Q. Unmodified protein of covalent modification network. Parameter: pk. I, R. Modified protein of covalent modification network. Parameter: pk The scale for each variable was set by the largest absolute value. Expression levels (protein copy number/gene/cell) for multiple gene regulator module: L: 1 M: 53 H: 529. For all others: L: 53 M: 529 H: 5285.Conservation of noise relationships in network modules across expression level and parameter variations. Percentage noise difference is given by (PNG)Click here for additional data file.Figure S2A of a heterodimer in response to different relative levels of A and B monomers. Physically interacting proteins produced asynchronously cross an ultrasensitive threshold, which is avoided by cotranscription from the same operon. D. Response of unmodified protein A in the covalent modification module to changes in the ratio of A to B. Unmodified protein A undergoes large spikes corresponding to crossing an ultrasensitive threshold when uncoupled.Non-redundant two-gene modules undergo an ultrasensitive switch dependent on production and degradation fluxes. Black lines are the mean-field steady state response while orange and blue lines trace timecourses from individual stochastic simulation trajectories. A. Intermediate in the linear metabolic pathway repeated from (PNG)Click here for additional data file.Figure S3lac operon system at three inducer concentrations. A. Simulated time courses of permease, allolactose and product. At all three concentrations, the transcriptionally uncoupled form of the system induces higher noise in allolactose (metabolic intermediate) concentration, but not product  or protein (permease). B. Correlations between permease and β-galactosidase in cotranscribed and uncoupled configurations. Throughout the range of induction the system demonstrates a consistent, significant reduction of correlation between permease and β-galactosidase in the uncoupled form of the system.Predicted dynamics of the (PNG)Click here for additional data file.Figure S4Effects of extrinsic noise on transcriptional coupling dynamics for metabolic modules. A. Linear metabolic pathway. B. Redundant metabolic step. Translational and transcriptional rate constants were randomly selected from uniform distributions to mimic global extrinsic noise. The resulting transcriptionally uncoupled protein distributions show a slight correlation between the proteins A and B (r = 0.293 top panel simulations and 0.388 in the bottom simulations). Metabolite noise differences between co-translated and transcriptionally uncoupled architectures are qualitatively unchanged from simulations that do not simulate extrinsic noise, with lower intermediate CV in the linear metabolic pathway (A) and higher product CV in the redundant metabolic step (B) in the cotranscribed configuration.(PNG)Click here for additional data file.Figure S5E. coli K12 MG1655. A. Gene pair locus distances in linear metabolic interactions are not distinguishable from randomized distances. B. A subset of gene pair locus distances in physical protein interactions have a distinct bias toward close chromosomal proximity. A distance randomization procedure Click here for additional data file.Table S1Predicted level of decorrelation between proteins that are uncoupled, cotranscribed, or cotranslated, with or without transcriptional bursting.(PDF)Click here for additional data file.Table S2f.Essential and nonessential subsets of gene pairs both have significantly high fractions of same-operon pairs (PDF)Click here for additional data file.Table S3Mathematical models used for linear noise approximation of five simple network motifs in the form (PDF)Click here for additional data file.Table S4Reactions for two genes expressed from the same and separate operons.(PDF)Click here for additional data file.Table S5Post-translational interactions in the linear and redundant metabolic step models.(PDF)Click here for additional data file.Table S6Post-translational interactions in the covalent modification and physical interaction models.(PDF)Click here for additional data file.Table S7lac operon model.Detailed (PDF)Click here for additional data file.Table S8lac operon simulations.Parameter values for detailed (PDF)Click here for additional data file.Text S1Decorrelation, extrinsic noise and expression levels.(PDF)Click here for additional data file.Text S2Analytical approach confirms predicted effects of cotranscription on intrinsic noise.(PDF)Click here for additional data file.Text S3Analytical determination of ultrasensitive thresholds.(PDF)Click here for additional data file.Text S4Chromosomal proximity of genes does not explain frequencies of metabolic or physical interaction operons.(PDF)Click here for additional data file.


We show that snippets from this sequence, at 100 base pairs or longer, drive gene expression in vitro in a number of mammalian cells, and are thus candidates for use in protein production. We further show that expression is driven by the general transcription factors TFIIB and TFIID, both being ubiquitously present across cell types, which results in less tissue- and species-specific regulation compared to the viral promoter SV40. We lastly found that the strength of a promoter can be tuned up and down by modulating the counts of GC and CpGs in localized regions. These results constitute a “proof-of-concept” for custom-designing promoters that are suitable for biotechnological and medical applications.The choice of promoter is a critical step in optimizing the efficiency and stability of recombinant protein production in mammalian cell lines. Artificial promoters that provide stable expression across cell lines and can be designed to the desired strength constitute an alternative to the use of viral promoters. Here, we show how the nucleotide characteristics of highly active human promoters can be modelled via the genome-wide frequency distribution of short motifs: by overlapping motifs that occur  Artificially engineered promoter sequences have the potential for use in industrial and biotechnological applications, such as recombinant protein production of biopharmaceuticals. Some human proteins require mammalian cell lines for proper production, with e.g. the Chinese hamster ovary (CHO) cell line being a widely used system for EPO, Interferon-β, Factor VIII, IX, etc The promoter is the genomic region around the transcription start site (TSS) of a gene, and acts as an essential component in gene regulation and transcription, its role being to interface with transcription factors (TFs) through protein-DNA binding. The TFs anchor the pre-initiation complex (PIC), specifying the exact point of initiation, and recruit RNA polymerase (Pol) II to start transcription SSRCGCC’ General transcription factors (GTFs), organized in complexes TFIIA, TFIIB, TFIID, TFIIE, TFIIF, TFIIH and TFIIJ, form a special class of TFs, in that they are ubiquitously present and both necessary and sufficient to enable Pol II transcription at significant levels, making these proteins desirable candidates as drivers of expression of artificial promoters. Only TFIIB and TFIID have been shown to exhibit sequence preference: the TATA-Binding Protein  is most well characterized and binds to the TATA-Box, thereby establishing the TSS 25–30 base pairs downstream of its location. However, only about 10% of human promoters rely on a TATA-Box nucleotide composition, rather than sequence motifs, where we define the term “nucleotide composition” as the frequency patterns of mono-nucleotides, di-nucleotides, tri-nucleotides etc. This composition varies over the human genome on a large scale, recognizable as isochores in vitro in mammalian cells, as well as how expression levels depend on highly localized features in these sequences.Designing artificial sequences that attract TFIIB and TFIID requires determining the features that capture the interactions between these proteins and the DNA. Generally, a complicating factor is the lack of a one-to-one relationship between TFs and exact instances of binding motifs, so that neither motif consensus of short sequences nor Position Weight Matrices (PWM) are ideal representations of binding sites in vitro expression. Here, we first recapitulate how highly active promoters (mostly associated with both housekeeping and strong tissue-specific genes) differ from the genome-wide distribution in GC and CpG, as well as in other di-nucleotides. We then exploit these “un-genomic” features by devising a measure that tracks the genome-wide frequency of each short motif: the less common genome-wide, the more likely it is to reflect properties of a promoter. We subsequently overlap a set of uncommon motifs to build “promoter-like” contiguous sequence, which allows for editing existing promoters, as well as to construct entirely artificial ones that work in a number of mammalian cells. We last show that TFIIB and TFIID bind to these promoters, which is reflected in their stability of expression level across multiple cell lines.Highly active promoters exhibit nucleotide patterns that are different from the majority of the genome Trinity . The resulting assembly consists of 38 Mb of sequence, residing in 27,000 disjoint transcripts. We eliminated non-full-length transcript assemblies of less prominently expressed genes by requiring sequences to contain open reading frames of 500 bp or more, aligned the remaining sequences to the human genome, and selected only transcripts with the 5′ end falling within 50 bp of an annotated TSS To capture a set of highly active promoters, we sequenced the mRNA of the most highly expressed genes in human cerebellum tissue. We constructed two cDNA libraries, one normalized and one un-normalized library (both filtered for poly-A tails), and sequenced both libraries on one lane of Illumina each, yielding a total of 2 billion base pairs in 71 bp long reads. We then assembled the reads from both libraries into contiguous transcripts using the transcriptome assembly program While only 127 (7.3%) of the 1,746 promoter sequences contain one or more instances of a TATA-Box in the correct orientation, the sequences are clearly distinct from the genome-wide average by their high G/C content (66% vs. 40% genome-wide) as well as the average frequency of CpGs (9% vs. 1% genome-wide). α score”, see α yields a potential spatial resolution of tens of nucleotides. annotated TSSWe defined a measure that tracks with G/C and CpG content see , incorpoα score for each possible 12-mer, including those not present in the human genome. We selected sequences from two quintiles: (a) the top 5% represent the most “un-genomic” (or promoter-like) 12-mers; and (b) the percentile between 45–50%, which contains 12-mers with di-, tri-nucleotide etc. frequencies close to the genomic median, representing more “normal” (or non-promoter-like) sequences. The 12-mers from each set were then independently assembled into “concatomers”  and non-promoter-like (for down-regulation) artificial constructs. As test case, we chose the promoter upstream of the TSS of the X-linked gene cancer/testis antigen 1A (CTAG1A), which exhibited strong in-vitro activity in human cell line HEK293. The CTAG1A promoter region contains three distinct regions of elevated α scores  with size-matched, but of higher α score, snippets from the promoter-like concatomer.We tested whether we could modulate in-vitro promoter activity in HEK293 cells relative to the original sequence, as does replacement with “non-promoter” sequences with α scores of approximately half the original (“hCTAG1A-replace”). Replacement with “promoter-like” sequences with an α score roughly twice that of the original (“hCTAG1A-UP”) increases activity beyond that of the original sequence. This indicates that highly localized changes in sequence composition can drive up- and down-regulation of in-vitro gene expression.in-vitro expression, we pulled sequences from the promoter-like concatomer using different criteria and of different lengths  in four mammalian cell lines: CHO (hamster ovary); P19 (mouse embryo); Vero (monkey kidney); and HEK293 (human kidney). Promoter strength of most constructs was comparable to, or exceeded activity of the SV40 core promoter, which is a routinely used viral promoter for recombinant protein expression in mammalian cell lines (Spodoptera frugiperda) for any of the constructs, suggesting fundamental differences in promoter mechanisms between insects and mammals .To create entirely artificial promoter constructs for ll lines . NotablyAll constructs contain at least one instance of a TATA-Box. In addition to two TATA-Boxes, ArS232 contains one perfect, and 12 imperfect  instances of BRE, and one Inr. To examine to what extent the TATA-box is needed to drive expression, we constructed three variations : (i) remD values, and that these readings are thus somewhat more difficult to interpret than the TFIIB binding results.The general transcription factors TFIIB and TBP bind to the artificial promoter constructs: we monitored real-time binding of TFIIB to constructs ArS110, ArS300, ArS201 and ArS232 through measurement of quantitative protein kinetics . Figure in vitro transcription depending of the abundance of the binding proteins that drive the promoter.Artificially engineered promoter sequences have potential for use in industrial, biotechnological and medical applications involving recombinant protein production and gene therapy, since they can be designed to have different activities and be adapted to the specific requirements (strong or weak expression). The method proposed here yields constructs that appear less variable and species specifically regulated than the viral promoter SV40, a feature that would increase stability across cell types and conditions. By extension, it should be possible to build artificial test beds to determine the behavior of known binding sites, and subsequently design promoters that are targeted and regulated by specific transcription factors. Preliminary results already show promise that this is, in fact, the case, and future experiments will help expand our “vocabulary” of promoter elements, and to predict their effect on in vivo expression, in presence of additional factors, such as methylation, degradation by miRNAs etc. While it might not be possible to accurately predict the behavior of artificially designed promoters in living organisms in the immediate short term, modifying short sub-sequences to adjust relative expression levels should be. In addition, emerging fields such as Synthetic Biology We do not address here how these results translate to This work might also be relevant for the study of expression regulation on a more general level: our findings suggest that different sequences can respond to transcription factors in very similar ways, even though they share no nucleotide sequence similarity; this would provide an explanation as to why promoters are generally not conserved across species over their entire length, but exhibit a pattern of conservation peaks and troughs α score measures the “un-genomicness” of short (12 nucleotides long) sequences, taking into account the genome-wide frequencies of di-nucleotides, tri-nucleotides etc. within the sequence. Let N denote the number of k-mers  in the genome, and kiϕ the genome wide occurrence count of the k-mer starting at position i (using zero-based counting) in the 12 base pair (bp) long sequence, then the score kα iskThe For existing promoter templates as well as the CTAG1A promoter, we computed the scores for all overlapping 12 bp long sequences within, and assigned the score to the base at position 6.α score, and extended the boundaries of each until its composite α score became negative, which yields a 2% false positive rate on the promoter template set  were designed as described in the Constructs ArS 50, 110, 201, 232 and 300  were selected from the 160,000 nucleotide (nt) long promoter-like concatomer. For identification and spacing of TATA Boxes, we used a promoter prediction tool trained on fruit fly E. coli and purification using NucleoSpin Extrakt II  all plasmids were sequenced to confirm the original sequence. In case of CHO dhfr-, HEK293 and P19, 4×10∧6 cells were transfected with 10 µg of the firefly luciferase plasmids and co-transfected with 1 µg of the Renilla luciferase reporter vector pRL-SV40  as an internal standard using Amaxa's Nucleofector Kit V  according to the manufacturer's instructions. 3×10∧5 VERO and MDCK cells were transfected with 1 µg DNA of the firefly luciferase constructs and co-transfected with 0,25 µg pRL-SV40 using Dreamfect Gold and CombiMag . 3×10∧6 Sf9 cells were transfected with 1 µg of the firefly luciferase constructs using Cellfectin II  according to the manufacturer's instructions. Instead of the SV40 promoter the baculovirus derived immediate early promoter OplE2, which is active in insect cells was used for the positive control. Luciferase expression was measured 48 h post transfection on a Synergy 2 microplate reader  with the Gen5 software using the Dual-Glo luciferase assay system . To normalize transfection efficiency, promoter activities are expressed as the ratio of firefly and Renilla luciferase activity. The pGL3-Promoter plasmid , containing the SV40 promoter served as positive control. The promoter activity of this viral promoter was set to 100%. All other measurements refer to this value within the same cell-line. The promoterless pGL3-Basic Vector  was used as a negative control.All inserts have been assembled either by oligo synthesis  followed by annealing and PCR or by gene synthesis  and cloning into the reporter vector pGL3 Basic  upstream of a firefly luciferase gene. After propagation in on and koff) and affinities (KD) were calculated using the Octet Data Analysis Software Version 6.3.For the identification of sequence-specific DNA binding of the transcription factors TFIIB and TBP the binding kinetics were measured by biolayer interferometry on an Octet QK instrument (ForteBio Inc.), which provides continuous real-time display of biomolecular interactions. Streptavidin biosensors were loaded with biotinylated DNA fragments (25 µg/ml) of the promoter constructs ArS110, ArS201, ArS232 and ArS300, or with the promoter constructs ArS232, ArS232 dT1, ArS232 dT2 and ArS232 dT12, generated by PCR amplification using 5′ biotinylated primer (Sigma-Aldrich). Binding was conducted in 1× Kinetics Buffer (ForteBio Inc.) with a protein concentration of 285 nM for TBP  and 270 nM for TFIIB . Kinetic parameters (k>ArS 50ACGCACGCGGTATAAACGCGCGACCTATTCGCGACCGTATAGCGACCGGA>ArS 110CTACGCCGCGTAAATATCGCGCGCTAACGGTGCGCGTTAAAACGCCGACGCGTCATAAAGCGCCGGCGTATAAGCGCGCCGTACGTCGTCGAACCACGTTAGTCCGGACC>ArS 201AACGGTGCGCGTTAAAACGGCCGACGCGTCATAACCGCGACTCGTCGACGCAGCGCCGGCGTATAAGCGCGCCGTACGTCAACCGTCGACGTTAGTCCGACGATCGCGGCGTCTATACGCCGCGTCAATCGCGCGCGGTTCAACGTCGCGCTACGGGCGCGTATAAGTCGCGCGTATGGACCGCGTACGTCCTACGAGCGT>ArS 232TCGACGCGCGTATAACACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGCCCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCGCCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGCGTAACGGTGCGCGTTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGC>ArS 300AACGGTGCGCGTTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGCAGCGCCGGCGTATAAGCGCGCCGTACGTCAACCGTCGACGTTAGTCCGACGATCGCGGCGTCTATACGCCGCGTCAATCGCGCGCGGTTCAACGTCGCGCTACGGGCGCGTATAAGTCGCGCGGTTAATACGCGCGGTGTACGCGGATGCCGGGGTCGCGTATAATCGGCGCGTATACCTCGCGCGTATACGCGGCGTATTACGGCCGCGTATAATTCGCGCGTATGGACCGCGTACGTCCTACGAGCGT>CTAG1A_originalCGGAGCACGTGACCGGTTCTCACCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCAGCCGGCTGTCCGCCGGACCCCTGTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGGACTGTGCGGCCATCGTCCCGGACCCTGCCTGCCCTGTCCGTCCTTGGGGGAGCGCCCAGGACAGACfCCCGGGGGGCAGGCCTCTAfACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGGTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCC>CTAG1A_replaceTTACCTAAAACAGCCCAAAAGAGCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCCCACCACCACCTCCACCACCACTGTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGCCAAAGAAGCCCAAAAAGGCCCAGGAAACCCAAACTTTCCGTCCTTGGGGGAGCGCCCAGGACAGACCCCGGGGGGCAGGCCTCTAACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGGTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCC>CTAG1A_deltaTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCCCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGTCCGTCCTTGGGGGAGCGCCCAGGACAGACCCCGGGGGGCAGGCCTCTAACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGG>CTAG1A_upCGTTTGACGGACGCCGTTCGCAGTCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCCGGAGCACGTGACCGGTTCTCACTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGATCGCGCAGCGATCGACGCCGGATCAACGCGATACGGTCCGTCCTTGGGGGAGCGCCCAGGACAGACCCCGGGGGGCAGGCCTCTAACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGGTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCCTATAACACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGCTCGACGCGCG232  60TCGACGCGCG-----CACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGC 55dT1 TATAACACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGCTCGACGCGCGdT2  60TCGACGCGCG-----CACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGC 55dT12 ********** *********************************************CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 120232 CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 115dT1 CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 120dT2 CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 115dT12 ************************************************************CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 180232 CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 175dT1 CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 180dT2 CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 175dT12 ************************************************************TTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGCGTAACGGTGCGCG232  232TTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGCGTAACGGTGCGCGdT1  227GTAACGGTGCGCG------CGCCGACGCGTCATAACCGCGACTCGTCGACGC 226dT2 GTAACGGTGCGCG------CGCCGACGCGTCATAACCGCGACTCGTCGACGC 221dT12 ************* *********************************