Drosophila Hox gene Ultrabithorax(Ubx), for example, is required for specifying the identityof the third thoracic (T3) segment of the adult, which includes the dorsalhaltere, an appendage required for flight, and the ventral T3 leg.Ubx mutants show homeotic transformations of the T3 legtowards the identity of the T2 leg and the haltere towards the wing. All Hoxgenes, including Ubx, encode homeodomain containingtranscription factors, raising the question of what target genesUbx regulates to generate these adult structures. Toaddress this question, we carried out whole genome ChIP-chip studies to identifyall of the Ubx bound regions in the haltere and T3 leg imaginal discs, which arethe precursors to these adult structures. In addition, we used ChIP-chip toidentify the sites bound by the Hox cofactor, Homothorax (Hth). In contrast toprevious ChIP-chip studies carried out in Drosophila embryos,these binding studies reveal that there is a remarkable amount of tissue- andtranscription factor-specific binding. Analyses of the putative target genesbound and regulated by these factors suggest that Ubx regulates many downstreamtranscription factors and developmental pathways in the haltere and T3 leg.Finally, we discovered additional DNA sequence motifs that in some cases arespecific for individual data sets, arguing that Ubx and/or Hth work togetherwith many regionally expressed transcription factors to execute their functions.Together, these data provide the first whole-genome analysis of the bindingsites and target genes regulated by Ubx to specify the morphologies of the adultT3 segment of the fly.The Hox genes are responsible for generating morphological diversity along theanterior-posterior axis during animal development. The Drosophila because of theirrole in anterior (A)-posterior (P) axial patterning, these genes are now known toassign morphological identities along the AP axes in both vertebrates andinvertebrates Hox genes encode an evolutionarily conserved set of homeodomain-containingtranscriptional regulators that play critical roles in the development of allmetazoans. Although first discovered in Drosophila embryos ledto the altered expression of approximately 1500 genes, suggesting that∼10% of all genes in the fly genome could be Hox-regulated Drosophila wing and haltere imaginal discs,which give rise to the dorsal regions of the second (T2) and third (T3) thoracicsegments, respectively, were compared Ultrabithorax (Ubx), which is expressed in one but not the other (the wing).Thus, a comparison between the genes expressed in these two wild type tissues hasthe potential to identify genes that are either activated or repressed by Ubx. Inthis study by Hersh et al., the number of differentially expressedgenes was less than 500, suggesting a much more limited number of targets. However,it is likely that this number is an underestimate, because expression profilingstudies will miss target genes whose expression patterns are altered by Hoxregulation, but are still expressed, on average, at similar levels in both tissues.In addition, any approach that relies on expression profiling cannot distinguishbetween directly and indirectly regulated target genes.To execute their various functions, Hox proteins regulate the transcription of manytypes of target genes, of which only a handful are known thickveins(tkv) and the proteoglycan dally, differ inthe haltere compared to the wing in a manner that restricts the diffusion of Dpp inthe haltere, thus limiting the extent of pathway activation. These observationsunderscore the idea that instead of simply turning genes on or off, Hox genescontribute to the regulation of spatial patterns of gene expression. Although such acandidate approach is good at identifying biologically relevant target genes, theapproach is limited in scope and typically cannot discriminate between direct andindirect regulation.A third method to identify Hox target genes is to take a candidate approach invivoDrosophila and 39 Hox genes in vertebrates,all bind to very similar DNA sequences via identical DNA-contacting residues intheir homeodomains Hox proteins typically bind to degenerate AT-rich DNA sequences Ubx, for example, is notonly expressed in haltere imaginal discs, but is also expressed in the imaginaldiscs that will give rise to the T3 legs. In the absence of Ubx,the identity of the T3 leg is transformed into the identity of the T2 leg;analogously, the identity of the haltere is transformed into that of the wing, alsoa T2 structure pannier, mirror, andvestigial, while the leg discs give rise to ventral regions ofthe fly and express ventral identity genes, such as Sp1,Distalless, and dachshundThe functions that Hox proteins execute, and thus the target genes they regulate,must be dependent on the cellular context. Ubx in all cells, and require this Hox gene for their uniqueidentities, a comparison between the binding sites in these two tissues has thepotential to identify haltere- and leg-specific binding events. Further, by alsoanalyzing Hth binding, which is a surrogate for Exd+Hth binding, we candistinguish between Ubx-Exd-Hth binding events  from Exd-Hth-independent Ubx binding . To our knowledge, these experiments provide one of thefirst in vivo tissue-specific comparisons between genome-wideChIP-chip data sets. In contrast to whole embryo ChIP-chip studies, the data revealboth tissue-specific and non-specific binding by these factors. Together, thesecomparisons lead to a comprehensive view of the target genes being regulated by Ubx,and provide a valuable platform for exploring Hox specificity and the network ofgenes required to generate these two regions of the adult fly.In the work described here, we use chromatin immunoprecipation (ChIP) and wholegenome, high density tiling arrays to identify the sequences bound by Ubx and Hth inboth the T3 leg and haltere imaginal discs . Unlike Ubx is required to establish the fates of the T3 segment of the adult fly, which iscomprised of both dorsal body structures, including the haltere, and ventral bodystructures, including the T3 leg . As a fiThese numbers also indicate that there is a large amount of specificity in Ubxbinding that cannot be accounted for simply by its preference to bind itsmonomer binding site, TAAT[t/g]G. Not only is a small subset of thetotal number of TAAT[t/g]G sites occupied in either tissue (seebelow), the number and type of binding events differ in a tissue-specificmanner. For example, even though there are ∼5-fold fewer Ubx binding eventsin the T3 leg compared to the haltere, ∼16% of these are not observedin the haltere. We return to the question of tissue specificity below.Although only a subset of the binding events identified in these ChIP experimentsis likely to be functional, it is nevertheless of interest to ask how many andwhat types of target genes Ubx is binding to in these two tissues. To explorethe genes targeted by Ubx, each Ubx or Hth ChIP peak was assigned a target genebased on the nearest transcription start site see . For exa−5 for both “DNAbinding” and “imaginal disc development” in the haltere); Ubxtarget genes with only one peak are more enriched for regulators of metabolismand the cell cycle (not shown). Examination of the Ubx-bound genes in moredetail (Supplementary hiiragi (FBgn0015949) and Repressor splicing factor1 (FBgn0011305)) and mitosis (e.g. Sak kinase(FBgn0026371) and Septin 4 (FBgn0259923)). In the Ubx-boundhaltere, not leg data set  there are genes involved in bristlemorphogenesis (e.g. pawn (FBgn0003174) andforked (FBgn0000630)) and cell cycle control (e.g.decapo (FBgn0010316) and Cyclin E(FBgn0010382)). Genes involved in Notch signaling (e.g.fringe (FBgn0011591), numb (FBgn0002973),and Tace (FBgn0039734) are also specifically enriched in thisdata set. The wide range of functions represented by these gene lists suggeststhat Ubx is not only regulating transcription factors, but alsogenes involved in many aspects of the development of the leg and haltere,including terminal differentiation genes.The predominant Gene Ontology (GO) categories for Ubx target genes, in both theleg and the haltere, are those involved in transcription, such as “DNAbinding” and “Transcription”, indicating that Ubx regulatesthe expression of a large number of subordinate transcription factors in bothtissues . Thispi−5; 1Cbx wing discs, whichare transformed towards haltere due to misexpression of Ubx1Cbx wing discs, Ubx+Hth targeted genestend to be involved in imaginal disc development, while Ubx (and not Hth)targeted genes tend to encode transcription factors ,Sex combs reduced (Scr), and thehead-determining Hox genes Deformed (Dfd) andlabial (lab), but not the more posteriorlyexpressed abdominal Hox genes, abdominal-A(abd-A) and Abdominal-B(Abd-B). Ubx is also known to negatively autoregulate itsown transcription lab, proboscipedia(pb), Dfd, Scr,Antp, and Ubx, but not in the vicinity ofabd-A and Abd-B  that is not there in the leg or haltere disc. It is also possiblethat the loss of this binding event in the imaginal discs is due todevelopmental changes in chromatin structure, possibly via Polycomb-mediatedsilencing, play a role in the specificity of Ubx binding to Hox loci.One set of genes that are transcriptionally regulated by Hox proteins is the Hoxgenes, themselves. The general rule from genetic studies is that moreposteriorly expressed Hox genes have the ability to repress more anteriorlyexpressed Hox genes nd Abd-B . These delopment . The facvestigial (vg) encodes a transcription factorthat plays a central role in the development of both dorsal appendages , but is not required for the development of ventral structures suchas the legs. vg has a different expression pattern in thehaltere compared to the wing, suggesting regulation by Ubxvg in the haltere, but none in the T3 leg, which isrequired for wing vein formation and is differentially expressed in the wingcompared to the haltere e T3 leg. Ubx binE2F, which encodes a transcription factorthat positively regulates many genes required for cell cycle progression Finally, we highlight Ubx in the haltere, although it was not determinedif any of this regulation is direct. In thickveins(tkv) and the glypican dally (frizzled2 (fz2), to wg,itself, and notum, which encodes a secreted hydrolase that alsomodulates Dpp and Hedgehog signaling pathways bantam (ban) andexpanded (ex), which is required for cellproliferation and survival Notch(N), mind bomb 1 (mib1),and to fringe (fng) but only in the haltere, suggesting thatthe Notch pathway is being modulated in this tissue ), suggesting that Ubx is repressing these genesrather than activating them. The one exception is the microRNA geneban, which is transcribed uniformly in the distal haltere.In contrast, in the homologous region of the wing ban isrepressed at the dorsoventral and anteroposterior compartment boundaries  aremore likely to be evolutionarily conserved in related species than the bindingsites that are shared between imaginal discs and embryos . BecauseFinally, we note that this overall picture does not change if we analyze sitesbound by both Ubx and Hth: co-bound sites still show a similar degree of tissuespecificity as sites that are bound by only Ubx or only Hth  sequences bound by Ubx (but not Hth) in the haltere, T3 leg, orboth discs , 2) sequences bound byUbx+Hth in the haltere, T3 leg, or both discs , and 3) sequences bound by Hth (but not Ubx) in the haltere, T3leg, or both discs   as well as ctively) . Becausectively) . In multDrosophila Hox PWMs (e.g. Abd-A andScr), while prevalent in all of the Hth, not Ubx datasets, were not identifiedabove a statistically significant threshold of p<0.01 in any of the Ubx, notHth bound regions. It is also of interest that the Ubx monomer PWM was notidentified in the leg-specific, Ubx not Hth set of bound regions, perhapssuggesting that, in the distal leg, Ubx cofactors other than Hth are shiftingthe specificity of Ubx binding away from the canonical TAAT motif. Also asexpected, motifs matching the consensus binding site for Hox-Exd heterodimersAlthough the complete list of discovered motifs is provided in SupplementaryDrosophilaspineless-aristapedia (ss) is a member ofthis gene family and is expressed specifically in ventral, but not dorsal,imaginal discs, including the legs. In fact, ss is onlyexpressed in the distal leg – the Ubx, not Hth domain – so this is apotentially interesting tissue- and region-specific cofactor of Ubx A motif matching the PWM for Sp1, which is a selector gene for the legs inpannier(pan), which encodes a GATA factor that is expressed in asubset of the wing and haltere imaginal discs A motif matching the PWM for Irx2, a member of the Iroquois gene family, wasidentified in the Hth, not Ubx haltere data set. This is of interest because theIro genes are expressed and required in the Hth-expressing domains of the wingand haltere discs Motifs matching PWMs for Lef1, which is a homolog of Pangolin (Pan), atranscription factor in the Wg signaling pathway The general picture that emerges from these analyses is that the different datasets, which reflect tissue-specific binding of Ubx and/or Hth, identify distinctmotifs. In several cases , the proteins that are likely tobind these motifs are expressed in a tissue-specific manner. Thus, the motifsdiscovered by these analyses represent excellent candidates for binding factorsthat collaborate with Ubx and/or Hth in these tissues In summary, we have used genome-wide ChIP-chip approaches to map the bindingsites for two developmentally critical transcription factors in two tissues thatgive rise to very limited subsets of the adult fly. In particular, the haltereand T3 leg imaginal discs only give rise to the ectoderm of the dorsal andventral portions, respectively, of the third thoracic segment. Both discsinclude cells that generate non-appendage portions of the body, as well as twovery distinct appendages, the haltere and the T3 leg. Although the cells withinthese discs have distinct fates , imaginal discs haveno endodermal cells and no or very few mesodermal cells. Thus, compared to thefly embryo, which includes cells from all three germ layers, imaginal discsrepresent a much purer population of cells. Further, the imaginal discs used inthese experiments came from a single time point in development – the endof the third instar larval stage – thus limiting another potential sourceof variability that is more problematic in ChIP experiments carried out withwhole embyos.Ubxonly needs to differentiate the T3 leg from the T2 leg, while in the haltere,Ubx must suppress wing fates and promote haltere fates.Although the most significantly enriched class of target genes encodetranscription factors, we find that Ubx is bound to many genesthat carry out a wide variety of functions. Many of these genes may representthe so-called ‘realizator’ genes conceived by Garcia-Bellido, whichexecute functions that are required for the terminal differentiation of specificcell fates Consistent with this limited amount of cell type diversity, and the fact thatthese tissues give rise to distinct parts of the adult fly, we found that theputative target genes regulated by these transcription factors are different inthese two tissues. This makes sense, given that in the leg Tissue specificity is also reflected in the actual binding sites occupied by Ubxand/or Hth. Not only are the sets of ChIPed fragments different between the T3leg and haltere, the associated DNA sequence motifs are also distinct.Together, these data provide a rich source of information for understanding thecomplete network of genes required to make these two parts of the adult fly, aswell as understanding how these transcription factors exhibit distinct DNAbinding profiles in these two tissues. The suggestion from this work, which mustbe tested experimentally in the future, is that Ubx and/or Hth bindingspecificities are in part determined by the other regionally expressedtranscription factors, some of which are likely to be represented by the motifsdiscovered here.http://intermine.modencode.org/release-22/portal.do?class=Antibody&externalids=UBX1) were used at a final concentration of 1.5 µg/ml for eachimmunoprecipitation. Immunoprecipitated DNA and input DNA were amplified forarray hybridization using the GenomePlex WGA4 Whole Genome Amplifcation Kit(Sigma). The samples were then labeled according to Affymetrix protocols andhybridized on Affymetrix GeneChip Drosophila Tiling 2.0RArrays.Wandering third-instar larvae were dissected and imaginal discs were collected inPBS on ice. Discs were fixed with 1.8% formaldehyde, and crosslinkedchromatin was sonicated to an average size of 500 bp. Chromatin preparation andimmunoprecipitation were performed as described Binding data were processed with both MAT Similar ChIP-chip data sets for Ubx and Hth binding in the haltere were generatedusing different reagents by Steven Russell, Robert White and Siew Woh Choo ofthe University of Cambridge . Comparisons between ourdata and these data reveal a broadly similar datasets, providing additionalvalidation for both data sets. Approximately 3000 (65%) of the Ubxhaltere peaks identified in this study are also associated with Ubx in the ChIPexperiments performed by Choo et al., and 432 (77%) of the Hth halterepeaks are also associated with Hth in the data generated by Choo et al.as this is the most inclusive of the Ubx embryo data. Peak calling for theembryo data was performed using the same criteria as was used for the imaginaldisc data. Imaginal disc ChIP-chip data have been submitted to the GeneExpression Omnibus (GEO) under accession number GSE26793 and all data describedhere will be available through modENCODE.Embryo ChIP-chip was performed as described previously yw. The followinglacZ reporters were used: fj-lacZvn-lacZwg-lacZnotum-lacZ (notumJW), andex-lacZban-Gal4 is an enhancer trap line (P{GawB}NP0016) from theDrosophila Genetic Resource Center. The following antibodies were used forimmunostaining: rabbit anti-βgal (Capell), guinea pig anti-Dll, mouseanti-ct , and mouse anti-Bs/DSRF (MarkusAffolter).Wild-type flies used for ChIP were 1Cbx wing versus wild-typewing comparisons  were analyzed using SignificanceAnalysis of Microarrays (SAM) Gene expression microarray data are from Hersh et al. Target genes were called as the transcription start site (TSS) nearest to thecalled Ubx or Hth binding region. For each peak the nearest by distance, oneither strand, to the center of the peak call is identified. Transcriptsassociated with a given TSS were identified based on Flybase annotation, and thestrand information of the TSS and the relative location of the peak call wereused to determine if the distance measurement is upstream or downstream of theTSS. Strand information was not included in assigning target genes, so a peakcould fall upstream or downstream of the TSS of its assigned target gene. Thevast majority of peaks for both Ubx and Hth fall within 2kb of the assignedgenes TSS (Supplementary For http://cistrome.dfci.harvard.edu/ap/), a Galaxy-based platformfor ChIP-chip analysis de novo motifdiscovery. Selected motifs are represented in Motif analysis was performed using SeqPos/CEAS, which is part of CistromeGenome-wide correlation plot and correlation values  for haltere data generated in this study and the study byChoo et al. Ubx-Ubx comparison is on the left and Hth-Hth comparison on theright. B) Percent of haltere Ubx or Hth peaks called in this study thatoverlap peaks called from data generated by Choo et al. at variousstringencies . (C–E) Haltere disc Ubx and Hth binding profiles generatedin this study and the study by Choo et al. at the following loci: BX-C HOXlocus (C), ANT-C HOX locus (D), bantam (ban) (E), E2F (F), dally (G),frizzled 2 (fz2) (H).(1.83 MB TIF)Click here for additional data file.Figure S2Examples of Ubx- and Hth-bound genes. Ubx and Hth binding profiles in the T3leg and haltere imaginal discs at the following genes: cyclin A (cycA) (A),Distalless (Dll) (B), dally-like protein (dlp) (C), four-jointed (fj) (D),frizzled (fz) (E), hedgehog (hh) (F), patched (ptc) (G), vein (vn) (H),spalt major  (I), and knot (kn) (J). Enhancers near salm and knpreviously shown to be targeted by Ubx are shown as yellow boxes along thechromosomal map. The salm enhancer is not called as targeted by Ubx usingour dual threshold for calling peaks , however(2.00 MB TIF)Click here for additional data file.Figure S3Genome-wide Ubx and Hth correlations. Genome-wide correlation plots  for Ubx leg versus Ubx haltere (A), Hth legversus Hth haltere (B), Hth haltere versus Ubx haltere (C), and Hth legversus Ubx leg (D). Correlation values are indicated within each plot.(1.24 MB TIF)Click here for additional data file.Figure S4Binding site location analysis. A) Percent of Ubx- or Hth-bound regions mapping to the indicated genomic features. Promoter isdefined as −1000bp to the transcription start site; intergenic regionsare those that fall between a promoter and the next upstream gene. B)Comparison of Ubx binding events that are haltere-specific (relative to Ubxbinding in the embryo) and Ubx binding events that are shared between theembryo and the haltere. As in (A), the percent of bound regions mapping tothe indicated genomic feature is represented.(2.27 MB TIF)Click here for additional data file.Figure S5Distribution of Ubx and Hth binding around TSS. Histograms representing thelocation of binding sites around the transcription start sites of calledtarget genes for Ubx in the haltere (A), Ubx in the leg (B), Hth in thehaltere (C), Hth in the leg (D).(1.40 MB TIF)Click here for additional data file.Table S1High confidence Ubx haltere peaks and target genes  All Ubx haltere peaks called in this study that also overlap a top10% Ubx peak  called from the Choo et al. data. Calledtarget gene(s) associated with each peak are also indicated, and enriched GOcategories are represented in the second sheet.(0.81 MB XLS)Click here for additional data file.Table S2High confidence Hth haltere peaks and target genes  All Hth haltere peaks called in this study that also overlap a top10% Hth peak  called from the Choo et al. data. Calledtarget gene(s) associated with each peak are also indicated, and enriched GOcategories are represented in the second sheet.(0.21 MB XLS)Click here for additional data file.Table S3Medium confidence Ubx haltere peaks and target genes  All Ubx haltere peaks called in this study that also overlap a top20% Ubx peak  called from the Choo et al. data. Calledtarget gene(s) associated with each peak are also indicated.(0.36 MB XLS)Click here for additional data file.Table S4Medium confidence Hth haltere peaks and target genes  All Hth haltere peaks called in this study that also overlap a top20% Hth peak  called from the Choo et al. data. Calledtarget gene(s) associated with each peak are also indicated.(0.07 MB XLS)Click here for additional data file.Table S5Called Ubx and Hth target genes (this study). All genes called as Ubx or Hthtargets in either the haltere or the leg. Whether Ubx or Hth targets a genein a given tissue is indicated with a “yes” or “no”in each column.(0.52 MB XLS)Click here for additional data file.Table S6Ubx-responsive genes bound by Ubx or Hth. Ubx-responsive genes see  that are(0.06 MB XLS)Click here for additional data file.Table S7Significant Gene Ontology categories for haltere datasets. Biological processor molecular function GO categories with Bonferroni-correctedp-value<0.01 are represented for the indicated Ubx and/or Hth, halteredatasets.(0.04 MB XLS)Click here for additional data file.Table S8Significant Gene Ontology categories for leg datasets. Biological process ormolecular function GO categories with Bonferroni-corrected p-value<0.01are represented for the indicated Ubx and/or Hth, haltere datasets.(0.03 MB XLS)Click here for additional data file.Table S9Enriched DNA motifs. Top 100 significant DNA motifs (p<0.01) identified bySeqPos for binding categories described in (0.74 MB DOC)Click here for additional data file.


RRD1 gene is required. To further investigate this connection, we performed genome-wide location analysis of RNA polymerase II (RNAPII) and Rrd1 in response to rapamycin and found that Rrd1 colocalizes with RNAPII on actively transcribed genes and that both are recruited to rapamycin responsive genes. Strikingly, when Rrd1 is lacking, RNAPII remains inappropriately associated to ribosomal genes and fails to be recruited to rapamycin responsive genes. This occurs independently of TATA box binding protein recruitment but involves the modulation of the phosphorylation status of RNAPII CTD by Rrd1. Further, we demonstrate that Rrd1 is also involved in various other transcriptional stress responses besides rapamycin. We propose that Rrd1 is a novel transcription elongation factor that fine-tunes the transcriptional stress response of RNAPII.Rapamycin is an anticancer agent and immunosuppressant that acts by inhibiting the TOR signaling pathway. In yeast, rapamycin mediates a profound transcriptional response for which the  Saccharomyces cerevisiae, TOR1 and TOR2 encode serine/threonine kinases that form the core of the rapamycin sensitive (TORC1) and the rapamycin insensitive (TORC2) complex, respectively Rapamycin is an immunosuppressant and an anticancer molecule that acts through inhibition of the TOR (target of rapamycin) signaling pathway S.cerevisae have been a useful tool to identify novel factors that are required to mediate an efficient response to rapamycin Rrd1Δ mutants exhibit multiple phenotypes including sensitivity to the carcinogen 4-nitroquinoline-1-oxide and UVA radiation, and, most prominently, extreme resistance to rapamycin in vitroin vivo role for this activity has not yet been described, and subsequent studies revealed that PTPA as well as Rrd1 are required for PP2A substrate specificity, complex formation and the reactivation of inactive PP2A complexes MEP2 was activated independently of Rrd1, suggesting that this latter factor has an additional role in the response to rapamycin CPA2 and PYC1) and down-regulated  following rapamycin exposure showed an altered transcription pattern in rrd1Δ mutants in vivo and in vitro, which we ascribed to the peptidyl prolyl isomerase activity acting on the C-terminal domain (CTD) of RNAPII 2PTS5PS7 heptad sequence which are differentially phosphorylated on Ser2, Ser5 and Ser7 The exact mechanisms of these regulatory circuits are not fully understood but genome wide deletion screens in rrd1Δ deletion affected RNAPII occupancy on a large set of rapamycin responsive genes. This was independent of TATA binding protein (TBP) recruitment to the promoter, suggesting that Rrd1 acts downstream of PIC formation during transcriptional initiation and elongation. The observation that Rrd1 modulated Ser5 and Ser2 phosphorylation of the RNAPII CTD further supported a role for Rrd1 in elongation. Finally, we demonstrate that Rrd1 is required to regulate gene expression in response to a variety of environmental stresses, thus establishing Rrd1 as a new elongation factor required for effective transcriptional responses to environmental challenges.In this study, we analyzed how Rrd1 regulates transcription by RNAPII. We mapped Rrd1 and RNAPII occupancy using ChIP-chip analysis in the presence and the absence of rapamycin. We found that Rrd1 colocalized with RNAPII on actively transcribed genes under both conditions. Furthermore, rrd1Δ mutant cells HSP104 and PUT4, were significantly enriched for RNAPII in the wild-type strain, but this association was reduced in the rrd1Δ mutant  in response to rapamycin Δ mutant . Examinahe rrd1Δ . Localizdeletion . These dHSP104 and PUT4, depleted on those of RPL32 and RPS2, and remained constant on ACT1, in response to rapamycin . Taken together, this data suggest that Rrd1 and RNAPII co-localize on the coding region of most of the actively transcribed genes, even after massive transcriptional changes such as the ones caused by rapamycin treatment.To further confirm that Rrd1 changes in occupancy correlate with RNAPII in response to rapamycin, we plotted the difference in RNAPII occupancy between untreated and rapamycin-treated cells versus the difference in Rrd1 occupancy between these two conditions . This anrrd1Δ mutant. To compare RNAPII levels from the wild-type and rrd1Δ cells, we plotted RNAPII occupancy in wild-type and rrd1Δ cells, both in absence and presence of rapamycin (2 = 0.87) in untreated cells, they were less correlated in response to rapamycin (R2 = 0.75), suggesting that Rrd1 is required to regulate RNAPII occupancy in response to rapamycin as observed at individual genes in rrd1Δ mutant on the six clusters from rrd1Δ mutant failed to reach levels as low as in wild type cells (cluster W1 and W6). On the other hand, on upregulated genes (cluster W3 and W4) RNAPII also failed to reach wild-type levels in the rrd1Δ mutant. We note that RNAPII occupancy was similar for the untreated RNAPII levels (full line) as well as for clusters that were not affected by rapamycin (cluster W2 and W5). Taken together, this data clearly indicates that Rrd1 is required for the optimal up and down regulation of RNAPII levels during rapamycin response.Since Rrd1 co-localizes with RNAPII on actively transcribed genes and Rrd1 was previously shown to be required to modulate the expression of a few rapamycin-responsive genes apamycin . WhereasSPT15 using ChIP-chip. If, upon rapamycin treatment, Rrd1 affects steps upstream of PIC formation, TBP occupancy would be affected in rrd1Δ cells. In the other scenario, whereby Rrd1 was to affect a step downstream from PIC formation (such as elongtion for example), TBP levels  on genes from clusters W1–W6  encoded by rs W1–W6 . Interesrrd1Δ cells on these clusters . Interestingly, Rrd1 is required for both mechanisms by optimizing TBP depletion (S2 and S3) and RNAPII levels (S1 and S4). This suggests that Rrd1 has an influence on TBP binding, probably by affecting the signaling cascade upstream of PIC assembly We next checked if the regulation of the rapamycin-downregulated genes by Rrd1 is TBP-independant. Preliminary analysis of the downregulated genes (clusters W1 and W6) suggested that these are heterogeneous with respect to TBP binding . We therclusters . As predE.coli β- galactosidase ORF and is thought to cause RNAPII stalling/pausing downstream of the promoter during elongation rrd1Δ mutant and dst1Δ mutant strains and measured LacZ expression  within the pression . Similar mutants . Strikindst1Δ and spt4Δ, are known to be sensitive to 6-azauracil (6-AU), an inhibitor of the IMP dehydrogenase, which decreases GTP pools and thereby causes transcriptional arrest rrd1Δ mutants were sensitive to 6-AU, although not as sensitive as dst1Δ cells . This suggests that Rrd1 influences the phosphorylation status of RNAPII in response to rapamycin. We next mapped Ser5-P and Ser2-P from wild-type and rrd1Δ mutant on clusters W1 to W6 . However, when treated with rapamycin (dashed line) the patterns of both phosphorylations were altered in the rrd1Δ mutant in each cluster, leading to an increase in the 3′ region of the genes. On down regulated genes, Ser5-P largely remained at the 3′end in the mutant as compared to wild-type for cluster W1 and increased for cluster W6 . The CTD is highly phosphorylated on Ser5 at the promoter and in the early elongation phase, and is progressively dephosphorylated as transcription progresses. In contrast, Ser2 phosphorylation gradually increases throughout the ORF, reaching maximum levels at the 3′ end of the gene W1 to W6 . We founuster W6 . In the uster W6 . Taken trrd1Δ mutants exhibit multiple phenotypes including resistance to caffeine, but sensitivity towards vanadate, 4-NQO and calcium 2O2) and sodium arsenite (NaAs) and found that the rrd1Δ mutant was indeed sensitive to these agents  or downregulated  in response to environmental stresses as well as control genes which are not significantly altered under these conditions  rrd1Δ yeast for each condition  We have previously shown that Rrd1 is localized to the chromatin, interacts with RNAPII and directly isomerizes in vivo and in vitro it's CTD in response to rapamycin (ii) Here we have shown that overexpression of RRD1 suppresses the elongation defect of DST1 (TFIIS) deletion in an artificial elongation assay (ARTAR). Dst1 is crucial to transcribe this ARTAR as it is required to restart paused RNAPII RRD1 increased dosage rescues the dst1Δ deficiency, we presume that this might be via its function during elongation. (iii)Rrd1Δ mutants displayed hypersensitivity against the agent 6-AU, a phenotype which is common for elongation factors although it was not as sensitive as dst1Δ mutants, possibly because Rrd1 affects elongation at only a subset of genes whereas Dst1 acts globally (iv)rrd1Δ mutants displayed an altered phosphorylation pattern for Ser5-P and Ser2-P on most genes , but sensitivity to oxidative stress (because the cells fail to arrest and adapt to high ROS conditions). In accordance with this, we show that Rrd1 is required to adequately induce gene expression on a subset of stress responsive genes upon various stress conditions . The RRD1 and SPT15 genes were endogenously tagged as previously described 600 of 0.6–0.8 before crosslinking with 1% formaldehyde for 30 min.All strains used in this study were from the BY4741 background (7 pan-mouse IgG DynaBeads (Invitrogen) per sample. RNAPII ChIPs were done using 2 µL of 8WG16 antibody coupled to 2×107 panmouse IgG DynaBeads per sample. For Ser2-P and Ser5-P ChIP, we used 100 µL of rat serum (3E8 and 3E10 respectively) coupled to 2×107 protein G DynaBeads per sample ChIP experiments were performed as per The microarrays used for location analysis were purchased from Agilent Technologies  and contain a total of 44,290 Tm-adjusted 60-mer probes covering the entire genome for an average density of one probe every 275 bp (±100 bp) within the probed regions (4X44K). Myc-tag ChIPs were hybridized against ChIPs from isogenic strains that did not contain the tag as controls. RNAPII ChIPs were hybridized against a sample derived from 400 ng of input (non-immunoprecipitated) DNA.Quantitative real-time PCR analysis was performed using the ABI 7000 machine (Applied biosciences) and Sybr green PCR mastermix (Applied biosystems). The % IP ORF/% IP no ORF ratio was determined using the relative efficiency method and calculated as in Lloyd et al. http://genome.ucsc.edu/). All data analyses described here were done using data from protein-coding genes longer than 500 bp. Median Z-score values for promoter and gene coding sequences were calculated and used in our clustering analyses. Promoters are defined as the shortest of either 250 bp or half the intergenic region  relative to the reference gene's 5′ boundary. Self-organizing map (SOM) clustering was done with the Cluster software The data was normalized and replicates were combined using a weighted average method as described previously funcassociate 2.0. For this, an association file for the entire gene set was generated and used for all analyses GO analyses on clusters were performed with 2 was calculated.Regression analysis was plotted using excel, where the x-axis and y-axis contained the whole data set of the average ORF enrichments of the indicated ChIP. A trend line was plotted and the R2O2 survival curves were performed as described previously 2O2 (Bio basic Inc) in 50 mM KPO4 (pH 7.5) buffer for one hour. Cells were then scored for colony formation after three days at 30 C on YPD agar.The HACR3-LacZ fusion was transformed into strains and exponentially growing cells were treated with 1 mM NaAs (Sigma), aliquots were taken and the β-gal assay was performed as described in Spottest with NaAs and 6-azauracil (Sigma) analysis was performed as described previously 600 of 0.6–0.8. Cells were then treated with the indicated drugs for 30 minutes . For heat shock, cells were spun down and resuspended in preheated media at 37°C for 30 min. mRNA extraction was performed with the RNeasy extraction kit (Qiagen), following user guide lines.Overnight cultures were sub-cultured and grown to an ODPrimer design of the set of 10 genes .For Q-PCR, survival curves and lacZ expression analysis at least three independent experiments were performed and the standard deviation and For GeXP analysis the percentage CV was calculated for each replicate. Only % CV ≤20% was taken for analysis. Values>2 SD were excluded before final calculation.Table S1A list of all primers used for the GeXP and Q-PCR analysis.(TIF)Click here for additional data file.File S1GO analysis of (XLS)Click here for additional data file.Figure S1A) A complement to B) A complement of ((TIF)Click here for additional data file.Figure S2A) Contains the mapping analysis of Ser5-P and Ser2-P of rapamycin unaffected genes (cluster W2 and W5). (B) Mapping of Ser5-P and Ser2-P on clusters S1–S4 from ((TIF)Click here for additional data file.Figure S3Comparison of GeXP mRNA expression and RNAPII median average ChIP data of the corresponding gene for untreated and rapamycin treated conditions. Both are expressed in log2 ratio.(TIF)Click here for additional data file.


Successful infection of herpes simplex virus is dependent upon chromatin modulation by the cellular coactivator host cell factor-1 (HCF-1). This review focuses on the multiple chromatin modulation components associated with HCF-1 and the chromatin-related dynamics mediated by this coactivator that lead to the initiation of herpes simplex virus (HSV) immediate early gene expression.  HCF-1host cell factor-1FHL2four and a half limb domain protein-2GABPGA-binding proteinOct-1octamer binding protein-1VZVvaricella zoster virusASF1anti-silencing function protein 1PCAFhistone acetyltransferase KAT2BSWI/SNFswitch/sucrose non-fermentableISWIImitation SWITBPtata-binding proteinMedmediatorMLLhistone-lysine N-methyltransferase MLL1LSD1lysine-specific demethylase 1JMJD2Jumonji domain-containing protein 2PHF8PHD finger protein 8CHD8chromodomain helicase DNA binding protein 8NURFnucleosome remodeling factorTHAPthanatos-associated domain-containing apoptosis-associated proteinPGCperoxisome proliferator-activated receptor gamma coactivatorPRCperoxisome proliferator-activated receptor gamma coactivator PGC-1-related coactivatorInfection of a cell with herpes simplex virus (HSV) results in a complex and dynamic interplay between host and pathogen on numerous levels. The progression of successful infection leading to the production of progeny virus is dependent upon both circumventing host suppression mechanisms and utilization of host machinery to express viral immediate early (IE) genes and thus establish the initiation of infection. This review focuses on the viral and cellular factors that govern the expression of IE genes and specifically, the more recently recognized impact of chromatin and chromatin modulation machinery in determining initial events leading to IE gene expression.The historical perspective of HSV viral IE gene expression is defined by the identification of IE regulatory elements and a complex set of transcription factors that cooperatively promote the expression of these genes , Top. OnIn addition to the enhancer core complex, the regulatory domains of each IE gene contains binding sites for the ETS protein GABP and Kruppel/Sp1 factors that synergistically amplify viral IE gene expression ,8. HowevWhile initially identified as a cellular component required for the stable assembly of the viral enhancer core complex and a determinant of VP16-mediated IE gene activation, the coactivator HCF-1 is an essential factor for IE gene expression. Depletion of HCF-1 results in abrogation of IE gene expression in contrast to the impacts of depleting other factors such as the enhancer core protein Oct-1 ,12. One In addition to those that have been demonstrated to regulate viral IE gene expression, the critical nature of HCF-1 in the regulation of cellular programs has been demonstrated by its interaction and regulation of multiple DNA binding factors and coactivators ,17,18,19Importantly, the dependence of IE gene expression on HCF-1 suggested that the protein must mediate critical rate-limiting stage(s) in transcription.Studies that led to the elucidation of the role(s) of HCF-1 in governing chromatin modulation of alpha-herpesvirus (HSV and VZV) IE genes came from two intersecting directions; (i) the demonstration of the impacts of host-cell assembled chromatin on infecting viral genomes and (ii) the association of HCF-1 with multiple chromatin modulation components.The first important clue to the regulation of HSV by chromatin was the observation that a virus encoding a mutant VP16 that lacked the protein’s activation domain exhibited accumulation of nucleosomes on the viral IE gene promoters . This waet al., this issue). In addition, (i) histone chaperones were required for efficient HSV IE gene expression (HIRA) [Subsequently, however, it was clearly demonstrated that the viral genome was rapidly assembled into nucleosomal arrays mimicking host cell chromatin ,36,37 (sn (HIRA)  and subsn (HIRA) , and (iin (HIRA) ,41,42. Wn (HIRA) . Rather,n (HIRA) . et al., this issue). These data suggested that CLOCK might be important for acetylation of nucleosomes associated with the viral genome, although direct histone acetylation remains to be demonstrated. Studies to identify acetyltransferases that might regulate viral chromatin concluded that p300, CBP, PCAF and GCN5 did not appear to play a significant role . HoweverIn addition to the investigation of host cell acetyltransferases that may regulate viral gene expression, the viral IE protein ICP0 has been implicated in promoting histone acetylation and preventing deacetylation, leading to a decreased level of stable nucleosomes on the viral genome ,48,49,50i.e., histone H3K9-methylation) were prevalent, likely as a result of cellular responses to the invading pathogen [Most importantly, the combined studies clearly pointed to the requirement for various histone/ chromatin modulation machinery for efficient viral IE gene expression and progression of infection. With respect to the characteristics of the chromatin associated with the viral genome at the onset of infection, several important studies demonstrated that marks characteristic of repressive chromatin  depletion of Setd1A resulted in impaired HSV gene expression ; (ii) SeInvestigations into the components that might play roles in reversing repressive chromatin marks associated with the viral genome revealed two sets of interdependent H3K9 demethylases . StrikinIn addition to the Setd1A/MLL1 H3K4 methyltransferases and the H3K9 demethylases LSD1/JMJD2(s), HCF-1 has been identified as a component of or has been associated with multiple chromatin modulation components. Whether these complexes/components are involved in the regulation of alpha-herpesviral chromatin has not yet been determined. It should be noted that the global state of HSV chromatin during various stages of infection is relatively uncharacterized and therefore, future studies may ultimately provide linkages between multiple HCF-1 complexes and modulation of HSV chromatin. Given the important nature of this coactivator for viral expression and the interplay of histone modification, recognition, and remodeling components that must occur during IE gene transcription, there is the potential that insights into control of viral chromatin may come from the studies of HCF-1 chromatin complexes. Therefore, relationships between HCF-1 and various chromatin components are shown in In addition to the demonstrated roles of JMJD2s/LSD1 in removing repressive H3K9-methylation coupled with Setd1A/MLL1 in promoting H3K4-methylation of IE promoter-associated nucleosomes, HCF-1 is a component of complexes containing multiple members of the MLL family  ,58,63,64With respect to cell cycle, HCF-1 interacts with E2F1 in conjunction with either MLL1 or Setd1A and the demethylase PHF8, resulting in H4K20-demethylation, H3K4-methylation, and cell-cycle-dependent transcription (G1/S transition) ,22. Three sets of acetyltransferases have been associated with HCF-1. The MOF/NSL complex cofractionates with the HCF-1/MLL1 complex and the chromatin remodeling factor chromodomain helicase DNA binding protein 8 (CHD8) ,66,67,68In addition to multiple acetyltransferase complexes, HCF-1 is also a component of the repressive Sin3A complex containing the histone deacetylases HDAC1/2 . This coHCF-1 directly interacts with the histone H3/H4 chaperones Asf1a and Asf1b . AlthougFor HSV, HCF-1/Asf1b complexes are linked to viral replication factors, providing a unique mechanism for nucleosome reorganization coupled to viral DNA synthesis . In contTwo HCF-1-associated remodeling activities are components of multifunctional complexes. As noted above, the HCF-1/MLL1/MOF-NSL methyltransferase/acetyltransferase complex also contains CHD8, which recognizes histone H3K4-me2/3 . While CInterestingly, CHD8 is also a component of a second HCF-1 associated remodeling complex, NURF, which is recruited by the upstream binding factor (USF) along with HCF-1/Setd1A as components of chromatin boundary elements that prevent the spread of heterochromatin ,83. In aO-linked N-acetylglucosamine transferase, the sole enzyme responsible for O-GlcNAc modification of proteins at S/T residues, is a component of multiple HCF-1 chromatin-related complexes. Its activity is required in the HCF-1/MLL5 complex where O-GlcNAc modification of MLL5 significantly enhances H3K4-methyltransferase activity . SimilarMore recently, OGT has been recognized as a contributor to the histone code by modification of histones including histone H2BS112, thus promoting H2BK120-monoubiquination and enhanced H3K4-methylation ,86. OGT, in complex with HCF-1/Setd1A, is also targeted to CpG islands by the recently identified ten-eleven translocation (TET) proteins ,88,89,90BRCA1-associated protein-1 is a deubiquitinase (DUB) with multiple targets including histone H2AK119-Ub, a repressive mark linked with histone H3K27-methylation, DNA-methylation, and histone H1 association ,92. InteThe roles of this protein and its DUB activity in association with HCF-1 may be significantly more complex. In addition to modulating the repressive H2A-Ub, BAP1 has multiple protein substrates, including HCF-1, OGT, and perhaps, YY1 ,96,97. BIt is now clear that epigenetic regulation of HSV infection is an important contributor to the determination of the outcome of infection and represents a supra-regulatory overlay beyond the contributions of individual DNA binding transcription factors ,100,101.The recognition of important elements of chromatin-mediated control of viral infection coupled with the rapidly advancing development of specific chromatin modulation machinery inhibitors ,106,107 As a “proof of principle”, these compounds were tested for their ability to reduce an HSV primary infection in the mouse model system ,61. TreaHCF-1 chromatin modulation complexes play a dominant role in mediating lytic viral gene expression upon infection. Importantly, several lines of evidence also suggest that HCF-1 is an important component of the regulation that mediates the initiation of viral reactivation from latency.et al., this issue). These studies were fundamental in establishing the potential control of latency-reactivation cycles by epigenetic mechanisms.HSV latency is established in neurons of sensory ganglia where the virus persists in a quiescent state in the absence of detectable viral IE gene expression  Figure , Middle.With respect to HCF-1, the protein is uniquely concentrated in the cytoplasm of sensory neurons but rapidly is transported to the nucleus of these cells in response to signals that promote viral reactivation from latency ,122,123.Recently recognized, the role of chromatin modulation of alpha-herpesvirus infection represents an intricate and complex regulatory overlay. Intrinsically linked to viral-host interactions, chromatin presents a dynamic of repression/subversion of infection by the host cell. This is countered by mechanisms employed by the infecting virus to interface with the cellular chromatin machinery to promote viral gene expression and replication. A key player in this dynamic is the cellular chromatin regulator HCF-1 associated with multiple chromatin modulation components that the virus recruits at an early stage to promote lytic infection. Many stages of chromatin modulation and the enzymology that mediates it remain to be delineated for a true mechanistic view of this important aspect of viral infection. Understanding this dynamic and the key components could significantly increase the avenues for the development of novel antivirals with distinct advantages over present therapies.


The packaging of eukaryotic DNA into nucleosomal arrays permits cells to tightly regulate and fine-tune gene expression. The ordered disassembly and reassembly of these nucleosomes allows RNA polymerase II (RNAPII) conditional access to the underlying DNA sequences. Disruption of nucleosome reassembly following RNAPII passage results in spurious transcription initiation events, leading to the production of non-coding RNA (ncRNA). We review the molecular mechanisms involved in the suppression of these cryptic initiation events and discuss the role played by ncRNAs in regulating gene expression. Transcription of genomic regions in eukaryotes is a complex phenomenon generating a variety of RNAs, only a subset of which is derived from protein coding genes (mRNAs). The non-coding transcriptome includes numerous RNA species involved in the regulation of translation (tRNAs and rRNAs), but more recent studies have indicated the presence of several types of RNA molecules that have the potential to regulate gene expression ,2. NewlyEukaryotic organisms use the ordered packaging of genomes into chromatin as a means of regulating gene expression . IndFinally, the stability of RNA molecules is a consequence of the balance between transcriptional output and the degradation mechanisms that ensure RNA destruction. Recent publications have investigated different RNA degradation pathways that play a role in the production of ncRNA and provide one means of classifying different types of ncRNAs ,22-24.Nucleosomal stability varies based on the genomic location, and cells exploit these differences to regulate DNA based processes. Access to nucleosomal DNA is also facilitated by the action of chromatin remodeling factors, which are in turn aided by post-translational modifications present on histones . These hS. cerevisiae as an example of this organization [Technological advances in the field of genomics (ChIP-chip and ChIP-Seq) merged with well-established biochemical tools (MNase digestion) have generated precise nucleosome maps in several organisms -32. Despnization ,26,27,33A vast majority of nucleosomes in yeast (ca. 80%) are highly positioned, suggesting that there is very little variability in these positions in a cell population . The mosTranscription initiation usually occurs from the NDRs at both ends of the genes Figure A. Apart SSU72 leads to increased levels of divergent ncRNA [Gene looping between the promoter and terminator regions is one way to ensure directionality. Association of the polyadenylation complex factor Ssu72 with both the 5’ and 3’ ends of genes mediates gene looping and results in the reengagement of RNAPII, thereby ensuring directional expression of mRNAs. In contrast, loss of nt ncRNA .One of the factors regulating transcription initiation from NDRs is the chromatin remodeler Imitation switch 2 (Isw2) that mobilizes nucleosomes to reduce NDR size ,38. LossThe maintenance of chromatin organization throughout the genome is therefore key to preventing aberrant transcription initiation. The cell engages different co-transcriptional mechanisms to maintain chromatin integrity over transcribed genes. In the following sections, we shall discuss the details of these mechanisms.in vitro may occur upon loss of a single histone H2A-H2B dimer, leaving a hexameric nucleosomal complex behind [in vivo studies have shown a continuous exchange of the H2A-H2B dimers over the coding regions [The nucleosome serves as a strong impediment to RNAPII progression during transcription elongation. Passage of elongating RNAPII through a nucleosome x behind . In conj regions . However regions . This su regions . Convers regions ,47. The  regions ,14.RNAPII employs several protein complexes that aid transcription in a stage-specific manner . ReversiPhosphorylation of the Ser2 residue in the CTD heptad repeats by yeast Ctk1 a few hundred base pairs from the start site to the 3’ end of genes recruits the Set2 lysine methyltransferase (KMT) through its Set2-Rpb1 interaction (SRI) domain . Set2 taEAF3 or RCO1, two components of the Rpd3S histone deacetylase complex, also results in a similar phenotype [A key observation upon loss of Set2-mediated H3 K36 methylation in yeast is the hyperacetylation of histones over transcribed regions, particularly towards the 3’ end of genes Figure C. Deletihenotype ,52. The henotype ,54. Intehenotype ,55. Addihenotype ,57, therSET2 deletion mutant significantly increased the enrichment of preacetylated histones over the coding region  to the coding regions through the PWWP domain of its Ioc4 subunit  through a PHD finger present within the Set3 subunit. Recruitment of Set3C subsequently results in the deacetylation of nucleosomes over the 5’ coding regions by its Hos2 and Hst1 deacetylase subunits  higher eukaryotes. Examples of regulatory ncRNAs in yeast include PHO84[SRG1-SER3[IME1[IME4[, FLO11[GAL1[Clearly cells invest a lot of effort to keep ncRNA expression in check. This suggests that some ncRNAs may serve as regulatory molecules under specific growth conditions. Indeed, regulatory roles for a number of ncRNAs have been identified and act through several different mechanisms. Most fundamentally, ncRNAs can act either omosomes . Trans rude PHO84,83 as weSRG1-SER3, IME1[86IME1[IME4,88, FLO1LO11[GAL1,90.PHO84 antisense transcript is unusual for yeast as it affects sense transcription of the PHO84 gene both in cis as well as in trans using two different mechanisms [PHO84 expression in trans requires increased production of its antisense transcript, which in turn is dependent on Set1-mediated H3 K4 trimethylation levels [PHO84 in cis depends on another mechanism of ncRNA-mediated repression and involves histone deacetylation by KDAC Hda1 in response to antisense transcription [IME1. Meiosis in yeast is a tightly regulated developmental program that occurs in diploid cells alone. The control of the sporulation transcription program rests with the transcription factor Ime1. Upon sensing starvation, diploid yeast cells begin transcribing IME1. However, in haploid yeast cells IME1 is repressed by the Rme1 transcription factor, such that starvation conditions do not result in a lethal cell division. van Werven et al. identified a stable unannotated transcript (SUT), IRT1 (IME1 regulatory transcript 1) that is regulated by Rme1  and KDM1 demethylase in order to localize to and silence the HOXD locus in humans [Drosophila roX RNAs work in combination with the Male Specific Lethal (MSL) complex to up-regulate expression of X-chromosome linked genes in trans during dosage compensation in male flies [rn ncRNA . Alternativation . In contn humans -95. Drosle flies .MALAT1 ncRNA with splicing factors [erbAα, correlate with the amount of overlapping antisense transcripts [Non-coding RNAs can also influence mRNA processing, such as alternative splicing, for example, through the direct interaction of the  factors . Furthernscripts . In thisnscripts ,99. H3 KAirn: Antisense to Igf2r RNA non-coding; Asf: Anti-silencing factor; ChIP-Chip: Chromatin immunoprecipation on microarray; ChIP-seq: Chromatin immunoprecipitation sequencing; CTD: C-terminal domain; Ctk: C-terminal kinase; CUT: Cryptic unstable transcripts; Chd: Chromodomain-helicase-DNA binding; DNA: Deoxyribonucleic acid; Eaf: Essential SAS2-related Acetyltransferase 1 (ESA1) associated factor; FACT: Facilitates chromatin transcription; GAL: Galactose metabolism; H2A: Histone 2A; H2B: Histone 2B; H3: Histone 3; H4: Histone 4; Hda: Histone deacetylase; Hos,: Hda One Similar; HOTAIR: HOX antisense intergenic RNA; HOTTIP: HOXA distal tip transcript antisense RNA; HOX: Homeobox; Hst: Homolog of SIR2; IME: Inducer of meiosis; Isw: Imitation Switch; K: Lysine; KDAC: Lysine deacetylases; KDM: Lysine demethylase 1; KMT: Lysine methyl transferase; MALAT1: Metastasis-associated lung adenocarcinoma transcript 1; Me: Methylated; me3: Trimethylated; MLL: Mixed-lineage leukemia; MNase: Micrococcal nuclease; MSL: Male Specific Lethal; NDR: Nucleosome depleted region; Poly(dA:dT): Polymer of deoxy-adenylic and deoxy-thymidylic acids; PHD: Plant-Homeodomain; PHO: Phosphate metabolism; PRC2: Polycomb repressive complex 2; PWWP: Proline-tryptophan-tryptophan-proline motif; Rme1: Regulator of Meiosis 1; RNA: Ribonucleic acid; RNAPII: RNA Polymerase II; RNA-seq: Ribonucleic acid sequencing; mRNA: Messenger ribonucleic acid; ncRNA: Non-coding RNA; rRNA: Ribosomal ribonucleic acid; tRNA: Transfer ribonucleic acid; roX: RNA on the X; Rpb: RNA Polymerase B; Rpd3S: Reduced potassium dependency 3 (Rpd3) containing complex Small; RSC: Remodels Structure of Chromatin; Ser: Serine; SER: Serine requiring; Set: Suv39H E(z), Trithorax domain containing; Set3C: Set3-containing complex; Sin: Switch independent; Spt: Suppressor of Ty1 transposon; SRG: SER3 regulatory gene; SRI: Set2-Rpb1 interaction domain; SSU72: Suppressor of SUa7 gene 2; SUT: Stable unannotated transcript; TSS: Transcription start site; Ume: Unscheduled meiotic gene expression.The authors declare that they have no competing interests.This work is supported by NIH grant R01GM047867 to JLW and the Stowers Institute for Medical Research.


S. cerevisiae and ∼1800 E. coli promoters using fluorescent reporters. We show that in both organisms 60–90% of promoters change their expression between conditions by a constant global scaling factor that depends only on the conditions and not on the promoter’s identity. Quantifying such global effects allows precise characterization of specific regulation—promoters deviating from the global scale line. These are organized into few functionally related groups that also adhere to scale lines and preserve their relative activities across conditions. Thus, only several scaling factors suffice to accurately describe genome-wide expression profiles across conditions. We present a parameter-free passive resource allocation model that quantitatively accounts for the global scaling factors. It suggests that many changes in expression across conditions result from global effects and not specific regulation, and provides means for quantitative interpretation of expression profiles.Most genes change expression levels across conditions, but it is unclear which of these changes represents specific regulation and what determines their quantitative degree. Here, we accurately measured activities of ∼900  Quantitative characterization of gene expression is a fundamental yet complex challenge. One of the major challenges stems from the dynamic nature of gene expression, whereby every gene can change its expression value across conditions. Although genome-wide analyses of expression are among the most commonly used methods in modern biology , most stTraditionally, gene expression research has focused on isolated genes and has generally shown that the transcriptional response is highly and specifically regulated. For example, upon exposure to lactose, bacteria respond by transcribing lactose-assimilating genes . More reInitial attempts to bridge the gap between specific regulation and the wide spread changes observed in the data suggested that specific responses actually encompass more genes than initially appreciated . More reA second question invoked by the large magnitude of expression changes across conditions regards the combinatorics and complexity of gene expression programs. Considering all genes in all possible conditions, there is practically an infinite range of possible expression patterns that cells can reach. For example, yeast has ∼6000 potential transcriptional degrees of freedom as each of its ∼6000 promoters can potentially attain a different rate of transcription under each growth condition. It is intriguing to consider the degree to which this complexity is realized in cells. Indeed, genome-wide surveys of expression suggest that not all expression patterns are possible, as functionally related genes tend to be co-regulated . HoweverS. cerevisiae and ∼1800 E. coli promoters in 10 and 9 environmental conditions, respectively, using libraries of fluorescent reporters  categories (http://www.yeastgenome.org/), promoter architectures (OPN/DPN)  , and traOPN/DPN) . In addiOPN/DPN)  and thusOPN/DPN) . We genoOPN/DPN) , enabledOPN/DPN) , hereaftactivity . These sThe use of fluorescent reporters for measuring expression is a well-established approach , with seR=0.99 and R=0.98, R=0.72–0.81 and R=0.57–0.74 respectively, similar to the correlations between these data sets; To make sure that our synthetic fluorescence measurements are representative of the true promoter activity (of the native gene in its native genomic location), we performed several analyses to gauge the integrity and accuracy of the system. We compared the promoter activities with quantitative real-time PCR measurements of 18 selected strains under two growth conditions, and confirmed that YFP levels are an accurate proxy for the corresponding mRNA levels , depending on the pair of conditions compared. (B) We termed a promoter as behaving according to the global scale line if it deviated from it by less than three experimental standard deviations . We found that between any two conditions, 60–90% of genes (depending on the pair of conditions compared) change expression by the same factor to within our relatively precise observation capacity. (C) Limiting the analysis to promoters that are moderately active in at least one of the two conditions being compared , while the specific response to amino-acid starvation is enriched for amino-acid metabolism . Notably, we found that partitioning our set of promoters into six well-separated clusters , promoter types (divergent/unique), and transcription regulation strategies (TFIID/SAGA-dominated) . It also includes promoters of six TFs, and four of these  are major known regulators of respiration . If our clustering truly captures the variation in the underlying data, then we would expect these predictions to match the measured activities. Indeed, using only 10 promoters, we obtained highly accurate predictions, whereby in every condition we can explain over 85% of the variance of the activities of at least 98% of the promoters . These rFinally, we validated that our results were not determined by growth-related groups of genes, such as protein synthesis which are overrepresented in our library, by excluding these promoters from the data set and repeating the analysis . We obtained very similar clustering results with 95% of the genes preserving their cluster identities , indicatOur results present a first attempt to quantitatively analyze the transcriptional response of yeast across conditions in terms of proportional scaling. In addition to partitioning the genes into groups which are co-regulated, as is the usual practice in the field, here we show that individual members within these groups scale together and preserve proportionality. We find that the ∼900 yeast promoters that we measured do not span the entire possible space of expression values, but rather adhere to a small number of scaling lines. This allows us to accurately describe the activity levels of the majority of promoters across conditions using only a handful of numbers representing the scaling factors of each of the different clusters. Moreover, we provide a first estimation for the relative magnitude of global and specific responses, showing that the former can account for much of the expression changes across many conditions. Together, our results suggest that gene expression profiles across different environmental conditions may exhibit less degrees of freedom and more structure than previously appreciated. These findings could probably be largely extended to hold not only for promoter activities, but also for mRNA and protein levels based on the analysis of existing data sets .Next, we asked what mechanisms can account for the quantitative values of the global scaling factors. Since enrichment analysis of promoter types, architectures, and regulation strategies did not provide mechanistic insights , we examA to condition B, S, will be given by:Many genes were shown to change their expression in accordance with the growth rate , and we τ[A] and τ[B] represent the doubling time in conditions A and B, respectively. A possible mechanism for implementing such regulation is discussed in where Gspe, and the globally responding genes, Gglo, where the exact partition is determined by the varying magnitudes of the specific response required in each condition  and different ribosomal subunits have been suggested to perform a similar task , YFP fluorescence, and RFP fluorescence. Measurements of each plate at every growth condition were repeated 1–6 times. The growth media are outlined in Cells were inoculated from frozen stocks into synthetic complete dextrose (SCD)  and grown at 30°C for 48 h, reaching complete saturation. Cells were then diluted 1:36 in fresh medium to a total volume of 180 μl and were grown at 30°C for at least 16 h in 96-well plates while being measured. Measurements were carried out every 20 min using a robotic system (Tecan Freedom EVO) with a plate reader (Tecan Infinite F500). Each measurement included optical density  of replicate measurements. Mean values across expression bins were used to estimate the CV of any promoter activity level, by linear interpolation . The CV v(p), of each promoter p was projected to the global scale line. Denoting the difference between the vector and its projection by d(p), the variance explained by the clustering was calculated as 1−variance(d(p))/variance(v(p)). To obtain a P-value for the explained variance, for each comparison between conditions X and Y, we randomized the promoter activities of condition Y and quantified the variance explained by the original global scaling line, as described above. This was repeated 1000 times for each pairwise comparison.For all conditions, the global scaling factor represents the best robust fit to the data . For eacx, y) and  denote its activity level and standard deviation in conditions x and y, respectively. Denoting the scaling factor between the respective conditions by a, then promoters were defined as part of the global trend if y or x. (B) We restricted each pairwise comparison to promoters with an activity of >0.1 in both conditions. For such values, we found the average CV to remain constant at 0.05  We analyzed all data points above detection and estimated their probability to behave according to the global trend. For each promoter, let  changing between conditions according to the global scaling factor.Eighteen representative strains belonging to cluster 1  or cluster 6  were inoculated from frozen stocks into SCD  and grown at 30°C for 48 h, reaching complete saturation. Cells were then diluted 1:36 in fresh medium and pelleted at mid-exponential phase. RNA was extracted using the EPICENTER Yeast MasterPURE RNA extraction kit, and cDNA was created using random hexamers (sigma). Quantitative PCR was performed by RT–PCR  using a ready-mix kit . For each strain, measurements were performed in two sets of triplicates, measuring both YFP and RFP mRNA. Reported values are of mean YFP/RFP from nine replicates derived from three independent experiments .P-value threshold of 10−3. For TF analysis, we examined the distribution of known TF promoters (http://www.yeastgenome.org/). P-values were computed according to the HG distribution and corrected for multiple hypothesis testing using FDR correction =1−cosx and y are vectors of promoter activity levels in a given condition and O is the origin). The clustering was repeated 100 times with different random starting points and the clustering that minimized the sum of distances from the centers was chosen. The number of clusters, K, was determined as the largest K for which the distance between any two centers is at least 0.05 , was projected to the center of the corresponding cluster. Denoting the difference between the vector and its projection by d(p), the variance explained by the clustering was calculated as 1−variance(d(p))/variance(v(p)).For each promoter Y from measurements of several other conditions x1,…,xm. First, the number of clusters k for all promoters under the measured m conditions was determined using above criterion. Then, the promoters were clustered by the k-means algorithm using the cosine metric. Denote the centers of the clusters by c1,…,ck. A small number of representative promoters were chosen as the training set, and their promoter activity levels under the new condition Y were used for the prediction task. For each cluster t, an extended center m+1 was calculated from the representative promoters that belong to cluster t. The activity level of a promoter under the new condition Y was predicted to be x is the vector of activity levels for that promoter. The representative promoters were chosen such that an equal number of promoters were chosen from each cluster, which are closest (by the cosine metric) to the centers c1,…,ck of the relevant clusters . Overnight cultures were diluted 1:33 into M9 medium followed by a second 1:15 dilution into flat bottom microwell plate (nunc) containing one of the growth media (The library of reporter strains, each bearing a low-copy plasmid with one of ding GFP  was prevding GFP . This lith media  in a finS. cerevisiae.Promoter activity was calculated by the rate of GFP production per OD unit, as described above for yeast  for the Identification of representative promoters for predictions was done iteratively. At each iteration, we calculated the best linear sum of the representative promoter, which predicted the experimental data, and added an additional representative promoter, which contribute the most to predict the experimental data.Supplementary Figures S1–19Supplementary Tables S1–S10


Drosophila Trithorax. In Drosophila, Trithorax complexes transmit the memory of active genes to daughter cells through interactions with Trithorax Response Elements (TREs). However, despite their functional importance, nothing is known about sequence features that may act as TREs in mammalian genomic DNA.Mixed Lineage Leukemia 1 (MLL1) is a mammalian ortholog of the HOX loci, the morphemes are spread across CpG islands and in some cases tail into the surrounding shores and shelves of the islands. By analyzing results of chromatin immunoprecipitation assays, we found a connection between morpheme occurrences, CpG islands, and chromatin segments reported to be associated with MLL1. Furthermore, we found a correspondence of reported MLL1-driven “bookmarked” regions in chromatin to frequent occurrences of MLL1 morphemes in CpG islands.By analyzing results of reported DNA binding assays, we identified several CpG rich motifs as potential MLL1 binding units (defined as morphemes). We find that these morphemes are dispersed within a relatively large collection of human promoter sequences and appear densely packed near transcription start sites of protein-coding genes. Genome wide analyses localized frequent morpheme occurrences to CpG islands. In the human Our results implicate the MLL1 morphemes in sequence-features that define the mammalian TREs and provide a novel function for CpG islands. Apparently, our findings offer the first evidence for existence of potential TREs in mammalian genomic DNA and the first evidence for a connection between CpG islands and gene-bookmarking by MLL1 to transmit the memory of highly active genes during mitosis. Our results further suggest a role for overlapping morphemes in producing closely packed and multiple MLL1 binding events in genomic DNA so that MLL1 molecules could interact and reside simultaneously on extended potential transcriptional maintenance elements in human chromosomes to transmit the memory of highly active genes during mitosis. The DNA in human chromosomes is relatively long . In addiGene repression and activation are regulated by proteins that interact with DNA, by enzymes that modify the core histones in nucleosomes and by proteins that bind modified residues in histones . Core-hiMLL1 gene through its involvement in chromosome translocations that cause acute leukemia [Earlier studies discovered the leukemia ,19. Tranleukemia . The norleukemia . The CXXleukemia ,23. A sileukemia -26, swapleukemia . Colony leukemia . Furtherleukemia .Drosophila, two groups of proteins support heritable memory systems that maintain the transcriptional state of target genes [Drosophila, related DNA sequence elements are thought to contribute to the recruitment of both TrxG and PcG complexes to chromatin [Evidence supports central roles for native forms of MLL1 in mechanisms that preserve “the memory” of highly active genes during cell division and at specific stages in embryonic development -31. In Det genes ,33. Tritet genes . Polycomet genes ,33. In Dhromatin . Mammalihromatin  and refehromatin ,35. The hromatin . EZH2 ishromatin ,35. Intehromatin .EZH2 gene cause autosomal dominant Weaver syndrome characterized by generalized overgrowth, advanced bone age, marked macrocephaly, hypertelorism, and characteristic facial features [De novo mutations in the MLL1 gene cause Wiedemann-Steiner syndrome [Mll1 knockout mice support a central role for MLL1 in regulating developmental pathways [Mll1 heterozygous (+/-) mice displayed retarded growth, haematopoietic abnormalities, and bidirectional homeotic transformations of the axial skeleton as well as sternal malformations [Mll1 deficiency (-/-) was embryonic lethal [Mll1 was required for maintaining gene expression early in embryogenesis [Syndromic manifestations support the opposing functions that MLL1 and EZH2 play in embryonic development. Mutations in the features ,38. De nsyndrome -41. Sympsyndrome -41. Mutapathways -30. Mll1rmations . Mll1 dec lethal . In miceogenesis , necessaogenesis ,42.Protein networks that include MLL1 drive coordinated patterns of gene expression . Earlier studies deduced that MLL1 exclusively regulated the expression of homeotic genes and proper segmental identity in mammals ,42. HoweTo uncover sequence motifs that may selectively interact with MLL1, we analyzed sequences of 19 cloned inserts that the MT-domain in MLL1 selected in DNA binding assays . In 16 iEncouraged by results of preliminary promoter analyses, we asked whether the cloned inserts obtained from SELEX assays included sequence-elements that may correspond to MLL1 recognition sites. To approach this question, we separated motifs consisting of CGNCG and CGNNCG according to nucleotides that appeared at N position. We uncovered several motifs, which we refer to equivalently as MLL1 binding sites, binding units, or morphemes Figure B. ExamplResults of promoter analyses prompted examination of a sequence pattern that appeared frequently at the 5′ boundary of human POLII genes . This paIn toto, results of complementary analyses support a role for MLL1 morphemes in promoter-associated functions.Therefore, we reanalyzed the POLII promoter set for morpheme occurrences Figure . For morCGCG) produced from two overlapping MLL1 morphemes: CGGCG and CGCG  is produced from overlap of CGGGCG and CGGGCG. Thus, results of MLL-knockout experiments provide support for a role for MLL1 morphemes in an in vivo context.Results of another study provide evidence for functionality of MLL1 morphemes ith MLL1 . Gene-knith MLL1 . We finds Figure B. Isolata priori one could suspect that they may appear frequently in human genomic DNA just by chance: once every 256 bps for a 4-nucleotide motif; once every 1024 bps for a 5-nucleotide motif; once every 4094 bps for a 6-nucleotide motif. To examine this issue, we counted morpheme occurrences in total human genomic DNA. We find that morpheme frequencies in genomic DNA are relatively low. For example: CGCG (4 bps) occurs once per 53,977 bps; CGACG/CGTCG (5 bps) occurs once per 210,681 bps; and CGCGCG (6 bps) occurs once per 1,546,669 bps.The finding that the MLL1 morphemes are CpG-rich raises the question of whether they are localized in CGIs in order to recruit MLL1 to chromatin. However, since the morphemes are relatively short -50 (detailed in methods section).To evaluate more rigorously a possible connection between MLL1 morphemes and CGIs, we followed a previously described statistical model . The staTo further assess a possible association of MLL1 morphemes with CGIs, we examined individual human chromosomes and total genomic DNA for morpheme occurrences. The analysis compared expected frequencies for random occurrences to observed morpheme frequencies in CGIs. We found that morpheme-occurrences in CGIs exceeded the values expected for random distribution in each human chromosome and in total genomic DNA  occurred 188,320 times while CGTCG/ CGACG occurred 38,647 times. In CGIs, a 6 bp morpheme (CGCCCG/ CGGGCG) occurred 62,702 times while CGTGCG /CGCACG occurred 18,184 times. Overall, the observed trend is consistent with a possible connection between MLL1 morphemes and CGIs since a high G + C content is a hallmark of sequences localized in CpG islands [Additionally, we performed statistical evaluations of CpG-rich motifs that did not appear in results of SELEX assays. The analysis revealed that the non-motifs also are associated with CGIs. However, except for CGGCCG, the overall frequencies of non-motifs in CGIs were much lower than those observed for MLL1 morphemes  does not have a CpG island at their TSS. Group II (about ~ 60%) has a single CpG island at their TSS. Group III (about ~ 10%) has two or more CpG islands in the vicinity of their TSSs . GeneralSCN1A), many CpGs but not a CGI. Figure Promoter classifications  led us tPOLR1B transcripts  is within a CGI that includes the coding region of HOXD1 , occurrences of morpheme overlaps in CGIs are even rarer events than those obtained for isolated morphemes. We found that morpheme overlaps creating long sequences appeared infrequently in human genomic DNA. We noticed that morpheme overlaps could be produced from a repeated DNA sequence element. Examples include CGG repeats associated with genetic abnormalities. (CGG)FMR1 locus in which CGG expansion causes mental retardation [FMR1 gene [FMR1 transcription increases, displaying a positive correlation between repeat number and levels of FMR1 transcripts [Notable examples include the ardation . This exMR1 gene . In normMR1 gene . All allMR1 gene . Remarkanscripts . Additionscripts . Thus, rHOX loci , HOXC8; and CGGCGGCGGCGGCG, HOXD10.Several overlapping MLL1 morphemes are dispersed across the human  Figures  and 11. HOXA13 and HOXD13 coding sequences producing tracts of alanines. Amplification of DNA sequences in a HOXD13 exon causes Syndactyly, fusion of digits in fingers [In some cases, overlapping morphemes are localized in coding regions producing repeated amino acid residues in a polypeptide chain. Notable examples include morpheme overlaps in  fingers . It is t fingers . However fingers .Drosophila trithorax [Statistical criteria indicate that morpheme overlaps are rare events in genomic DNA, raising the question of whether occurrences of overlapping morphemes could play a role in cellular functions regulated by MLL1. In this context, we noted that a previous study found that the SET domain in MLL1 self-associated to form homo-oligomeric complexes . This asrithorax .In leukemogenic MLL1 fusion proteins, the SET domain is deleted and replaced with over 40 different translocation partners . Invariavia the SET domain [via the SET domain. Cooperative DNA binding, via self-association, often increases the DNA binding specificity of a protein. We imagine that overlapping MLL1 morphemes may facilitate MLL1 self-association linking MLL1 molecules to reside cooperatively on DNA sequence elements (TREs) that maintain cellular memory during development. Our data indicate that such TREs also could function in gene-bookmarking to preserve the memory of highly active genes during mitosis. Also, one could imagine that overlapping morpheme occurrences may facilitate localized propagation of MLL1 binding to DNA to maintain a nucleosome-free region and, thus, an open chromatin configuration.Since normal forms of MLL1 self-associate T domain  to produT domain , it seemT domain , associaT domain . This meT domain . PropagaAnnotation of the human genome has involved numerous experimental and computational strategies to identify and describe DNA sequences that are important to cellular functions. However, despite cutting-edge advances, we lack a complete understanding of the function of CpG islands, which were discovered some time ago ,5. ResulOur findings also may explain why CGIs often extend to include promoter, exonic, and intronic sequences of genes. By binding CGIs, MLL1 might preserve and maintain an open chromatin configuration to regulate gene expression and to facilitate rapid gene activation upon mitotic exit. Association of MLL1 with CGIs agrees with a global role for MLL1 in regulation of transcription .Drosophila[Apparently, our findings provide the first evidence for the existence of potential TREs in mammalian genomic DNA and the first evidence for a connection between CGIs and gene-bookmarking by MLL1 to transmit the memory of highly active chromatin states during cell-division. Because of the strong connection of TREs and PREs in rosophila, we specrosophila and our We identified the MLL1 binding units by analyzing results of reported SELEX- and PCR-based assays. These assays were conducted to determine the DNA binding properties of the MLL1 MT-domain . In our To count genomic occurrences of MLL1 morphemes, we downloaded nucleotide sequences of CGIs and human chromosomes from the human genome browser at UCSC . A Perl To localize genomic positions of MLL1 morphemes, we retrieved genomic DNA (Hg19) from the Genome Browser at UCSC . SequencTo analyze promoter sequences of POLII genes, we obtained the accession number of human cDNAs from the UCSC database . HeatherAfterwards, we followed previously described methods ,50,86 toFor statistical evaluation, we followed a previously described approach . BrieflyG length of total genomic DNA, LE total length of CGIs, and LF length of regions that do not correspond to CGIs. Thus, LF = LG - LEWe chose the following notations: Lw1, …, wM), consisting of MLL1 morphemes to identify elements that matched sequences in LE and LGSubsequently, we created a motif table  in LE, LF , and LGFor 1 ≤ i ≤ M, we denote by EE is significantly shorter than LG as an approximation we assume |LE| < |LF| ≈ |LG|Since LQuantities of interest are total counts normalized with respect to length of analyzed sequences:i ≈ gi and fi ≈ p(wi)We made two additional justifiable approximations: fG| is very large, within the margin of error, fi approximates the probability of occurrence of morpheme wi in genomic DNA.Since |Lth so that we could assign statistical significance to cases in which ei > αfi (or ei > αgi). Evaluations require comparing empirical data to a reference model. For reference, we chose a probabilistic model assuming that the genome is generated by a memoryless or Markov source. In this model, ei and fi become random variables.As previously , we aimei = p(wi) is a constant. Subsequently, we determine whether for a given β, the event ei > αfi is statistically significant provided that the probability of ei > αfi is smaller than β. That is, P(ei > αfi) < β . We set β = 10-50 to compute the αth threshold.As detailed above, we simplified the analysis by assuming that fFrom , we knewi does not deviate more than When E2) denotes the normal distribution with mean μ and variance σ2Where, N is a complicated function of δ that depends on moment generation functions . To compi > (1 + δ)LEp(wi)) < β which is clearly within the large deviations domain.That equation translates into P. However, numerical computations of the large deviation function I(δ) are rather cumbersome. Therefore, we followed approximations, noting that a good bound was needed only for the large deviation probability. Ignoring overlapping morphemes, Ele, Ref. ):To be rigorous and take into account overlapping morphemes, we must somewhat relax equation (2). Referring to Azuma’s inequality (cf. Ref. ), we obtth = 1 + δFrom equation (2) and (3), we obtain the following estimate for threshold αCGIs: CpG islands; H3K4: Lysine 4 on histone H3; PcG: Polycomb group; POLII: RNA polymerase II; PRC1: Polycomb repressive complexes 1; PRC2: Polycomb repressive complexes 2; PREs: Polycomb response elements; TREs: Trithorax response elements; TSSs: Transcription start sites; TrxG: Trithorax group; UCSC: University of California Santa Cruz.The authors declare that they have no competing interests.MB designed experiments, performed analyses, and wrote the manuscript. PW wrote programs for creating the database and performed statistical evaluations. EN analyzed results of SELEX assays and identified the MLL1 morphemes. NZ mapped the position of MLL1 morphemes in human genomic DNA. JX improved the programs used in mapping studies. RP and ZG analyzed a listing of human protein-coding-genes downloaded from the human genome browser at UCSC. MF created a database of 9-mers for studies of promoter regions of human genes. BF contributed to statistical evaluations. DW wrote programs to map the position of MLL1 morphemes in human chromosomes. All authors read and approved the final manuscript.Occurrences of CpG-rich motifs in promoter regions of human protein-coding genes. Full magenta-circles correspond to CGNNCG, blue-circles to CGNCG, and empty red-circles to CGCG. Motif frequencies are shown as the function of nucleotide positions in promoter sequences, numbered with respect to TSSs.Click here for fileFrequency of morphemes and non-motifs in promoter sequences of POLII genes.Click here for fileCounts of expected and observed morpheme occurrences in CpG islands.Click here for fileCounts of expected and observed non-motif occurrences in CpG islands.Click here for file