Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement.E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain  PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5′-ends of these six Northern-supported sRNA candidates were successfully mapped using 5′-RACE analysis.Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that ∼40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel  Escherichia coli K12 MG1655 alone E. coli K-12 Bacillus subtilisStaphylococcus aureusPseudomonas aeruginosaListeria monocytogenesSystematic experimental and computational approaches have led to the identification of ∼92 small RNAs (sRNAs) in Most computational methods, such as QRNA S. aureusSalmonella enterica serovar Typhimurium S. Typhimurium were reported to partake in complex networks for stress adaptation and virulence regulation S. Typhimurium is an important food-borne pathogen that causes a substantial burden of diarrhoeal disease globally. Life-threatening systemic infections can also occur in those with severe co-morbidities, at extremes of age and/or with impaired immune systems.Comparative genomic approaches are restricted to identifying sRNA candidates located within conserved genomic backbone regions common to closely related bacteria E. coli K-12 MG1655 sRNA-specific transcriptional signals as positive training data, for the identification of intergenic sRNAs. Experimentally characterized E. coli sRNA promoters appear to vary slightly in base distribution frequencies when compared to E. coli mRNA promoters  based tool named sRNAscanner, using romoters , though cumulative sum of scores (CSS) values above a nominated threshold. The CSS value is determined by summating three individual matrix-specific sum of scores (SS) values for each candidate TU . sRNAscanner uses pre-computed PWM and the following pre-defined parameters to predict intergenic sRNAs: promoter box 1 SS value (≥2), promoter box 2 SS value (≥2), terminator SS value (≥3), spacer 1 range , spacer 2 range , Unique Hit value (200) and CSS (≥14). The Unique Hit value identifies potential TU from a set of overlapping hits based on the presence of closely located start coordinates mapped within a defined window size which by default is set at 200 bp. sRNAscanner selects the TU with the maximum CSS value from each overlapping set as a unique representative hit for the set. Note: all parameters can be altered by users as required. Predicted TUs are examined for the presence of a putative ribosome binding site and initiation codon; if both signals are identified the TUs are classified as coding for putative mini-proteins sRNAscanner uses as inputs matching complete bacterial genome sequence and protein coding table files in standard FASTA and tab-delimited text formats, respectively, to identify sRNA genes in intergenic regions. The sRNAscanner suite consists of algorithms to perform the following functions: (a) construct PWMs from sRNA-specific transcriptional signals, (b) search complete genome sequences using constructed PWMs to identify ‘orphan’ intergenic promoter and terminator locations, (c) perform coordinate based integration of promoter/terminator signals to define putative intergenic transcriptional units (TU) and (d) select predicted TUs based on x columns for N input sequences each having x residues; N and x can be any positive integer. The program uses multiple sequences of sRNA-specific transcriptional signals in fasta format as input for the construction of alignment matrices. The alignment matrix captures the number of occurrences, i,jn, of letter i at position j across the set of aligned sequences. Subsequently, actual occurrence values were converted into log-odd scores; values that reflect the positional weights of each of the four bases  at each position. Frequency calculations and scoring schemes were adopted from previous algorithms and the positional weights were derived from the alignment matrix itself. A PWM was then derived from the above alignment matrix using the following formula  this expected frequency is 0.25 for each of the four nucleotides, i,jf = i,jn/N is the frequency of the letter i in position j. Importantly, the precise genomic base frequency of the training or test genomes do not have a role in the construction of PWM. The log-odd scores are used for the construction of PWM; the algorithm was implemented using the PWM_create module of the sRNAscanner program. We have used ten promoter boxes and twenty one rho-independent terminators E. coli K-12 sRNA genes as training data to construct PWM1 (promoter box1), PWM2 (promoter box2) and PWM3 (rho-independent terminator) (sRNAscanner computes a PWM of four rows and minator) .E. coli K-12 sRNA genes from the sRNAMap and Rfam datasets (http://srnamap.mbc.nctu.edu.tw/). sRNAscanner was run with an arbitrary minimum SS value of 1 for each of the three matrices to identify potential intergenic TUs which were then compared manually with the known K-12 sRNA genes to identify concordant pairs. Using these criteria and no imposed CSS cut-off, 66 of the 92 known sRNAs were identified as possessing sRNAscanner-detectable potential transcriptional signals  was used for experimental validation. For early stationary phase (ESP) and late stationary phase (LSP) cultures, 25 ml of Luria-Bertani broth was inoculated with a 1/100 overnight culture and grown at 37°C in a shaking incubator (220 rpm) in a 100 ml flask. Optical density at 600 nm (OD600) was monitored. Two ESP cultures  and four LSP cultures  were obtained. Approximately 108 ESP (OD600 = 0.5) cells were treated with mitomycin C (0.5 µg/ml) [SOS], acidic LB (pH 5.4) [Acid] or cold shock (15°C) [Cold] for 30 min to induce an SOS response, acid stress or cold shock conditions, respectively. Abbreviations shown are to describe the eleven growth conditions. Salmonella pathogenicity island 1 (SPI-1) induced cultures [SPI-1] were grown with high salt-containing LB broth (0.3 M NaCl) for 12 hours at 37°C/220 rpm in tightly closed tubes. Salmonella pathogenicity island 2 (SPI-2) induced cultures [SPI-2] were prepared by inoculating 70 ml of SPI-2 medium 600 = 0.3. The above cultures were spun down and the cell pellets mixed with stop mixture  and immediately frozen in liquid nitrogen.Total RNA was prepared from frozen cells using the TRizol (Invitrogen) method and treated with DNase I (Fermentas) as described previously 5′RACE experiments were performed as described by Vogel and Wagner E. coli K-12 MG1655 (NC_000913) genome using pre-defined parameters (see User Guide) and matrices trained with data from ten promoter boxes and twenty one rho-independent terminators E. coli K-12 sRNA genes. To maximize sensitivity at the expense of specificity, we ran this analysis without application of a CSS cutoff. Predicted intergenic sRNA-specific transcriptional units were compared with the 92 reported E. coli K-12 sRNAs available in sRNAmap E. coli K-12 intergenic TUs remained to be experimentally defined or that the matrices and/or the sRNAscanner algorithm lacked specificity. Interestingly, the single uncharacterized hit outlier with a CSS = 19.56 has also been predicted by SIPHT  – specificity (Sp) performance of sRNAscanner at CSS>14  was comparable to that of sRNAPredict2 . Secondly, we compared the performance of the pre-computed training-set-derived PWMs with those of randomly generated ‘equivalent’ matrices and used both sets of matrices to analyse the E. coli K-12 genome sequence. Equivalent random matrices were generated by randomly shuffling entire columns within each matrix (R1 random matrices)  of individual motifs were defined as the number of predictions per nucleotide of the genome. The ratios of OF obtained with the random and rationally-derived original matrices were expected to be inversely proportional to the ratios of matrix specificities vs 152)  detected 24% of the known 2 genome . The set2 genome  suggesteatrices) , the numatrices) , and a catrices) . This ap vs 152) . Use of Enterobacteriaceae, Vibrionaceae, Pseudomonadaceae, Bacillaceae, Clostridiaceae, Chlamydiaceae and Lactobacillaceae were analyzed using sRNAscanner. Intergenic transcriptional unit data derived from sRNAscanner analyses were compared with previously reported sRNAPredict2 results Sn = 31.3%) and 184 (Sn = 32%) known sRNA genes, respectively, across all 13 bacterial genomes investigated (vs 2344), suggesting a higher signal-to-noise ratio for the latter. Similarly, large numbers of novel hits missed by sRNAPredict2 were predicted by sRNAscanner, and vice versa. Indeed, combined use of the two tools may potentially offer a degree of cross-validation. However, sRNAscanner as optimized presently appeared to be more appropriate for the analysis of genomes of Enterobacteriaceae and other medium/low G+C organisms. sRNAscanner sensitivity versus known sRNAs ranged from 51% for Clostridium tetani E88 (28.6% G+C) to 24% for Salmonella Typhi CT18 (51.9% G+C) to 0% for Mycobacterium tuberculosis CDC1551 (65.6% G+C). Detailed lists of known and putative sRNA regions predicted by sRNAscanner in the above genomes are provided as supplementary data files . SscB is needed for normal secretion and function of the SseF T3SS effector, which in turn is required for Salmonella-induced epithelial cell filamentation and bacterial proliferation in macrophages ssaP, which is postulated to code for part of the SPI-2 T3SS translocon apparatus itself stiB, a fimbrial chaperone gene, potentially allowing for sRNA28-based fine-tuning of Sti fimbriae expression Typhimurium outer membrane protein (OMP) profiles in response to envelope stress Recent discoveries of three sRNAscanner identified hits that had originally been classified as novel provide further biological validation of this algorithm; sRNA17, sRNA20 and sRNA29 are now known as E. coli-derived PWMs used in this study performed well with medium and low GC genomes but not with high GC genomes. Consequently, we propose that an organism-targeted approach is likely to lead to significantly enhanced performance characteristics. Importantly the tool developed and the strategy proposed would allow users to generate individualized PWMs based on species-, genus- or family-derived training sets to better identify sRNA genes in selected bacterial organisms. In addition, a reiterative process of PWM optimization and selection of rationally informed cut-offs based on newly discovered and validated sRNAs may allow for progressively higher levels of specificity without excessive loss of sensitivity. Finally, we propose that PWM-based scanning strategies may in time prove to be a powerful way of revealing other cryptic codes not only in DNA but in protein molecules as well.We have developed and implemented a simple PWM-based strategy for the discovery of intergenic sRNA genes. Despite use of a small, single species-derived training set, we have demonstrated the major utility of sRNAscanner to predict large numbers of potential sRNA genes in diverse bacterial species. Undoubtedly, it is vital to further experimentally validate the predictive accuracy of sRNAscanner and other sRNA prediction programmes using Northern blot analysis, ultra-high-density cDNA sequencing Table S1Details of sRNAscanner training dataset.(0.03 MB PDF)Click here for additional data file.Table S2List of known E. coli K-12 MG1655 sRNA TUs identified by sRNAscanner.(0.08 MB PDF)Click here for additional data file.Table S3Oligonucleotides used in this study.(0.02 MB PDF)Click here for additional data file.Table S4Details of known and novel sRNA regions predicted by sRNAscanner in 13 bacterial genomes.(0.02 MB PDF)Click here for additional data file.Table S5Analysis of Virtual Intergenic Genome Sequences (VIGS) and Random Intergenic Genome Sequences (RIGS) derived from the E. coli K-12 genome using sRNAscanner and Glimmer.(0.03 MB PDF)Click here for additional data file.Figure S1Training set-derived PWM1 - PWM3 matrices.(0.04 MB PDF)Click here for additional data file.Figure S2R1 versions of random matrices.(0.03 MB PDF)Click here for additional data file.Figure S3R2 versions of random matrices(0.03 MB PDF)Click here for additional data file.Figure S4R3 versions of random matrices.(0.03 MB PDF)Click here for additional data file.Figure S5TargetRNA-identified putative sRNA-mRNA interactions.(0.07 MB PDF)Click here for additional data file.File S1Details of known and novel sRNAs predicted by sRNAscanner in the 13 genomes analysed.(0.47 MB XLS)Click here for additional data file.File S2Supplementary References.(0.03 MB PDF)Click here for additional data file.


In vivo, high levels of both an activated BMP receptor and the BMP effector Mad can inhibit the expression of Wg target genes. Conversely, loss of mad can induce Wg target gene expression. In addition, we find that ectopic expression in vivo of the Wg transcription factor dTcf is able to suppress the inhibitory effect caused by ectopic Mad. In vitro binding studies revealed competition for dTcf binding between Mad and the Wnt effector β-catenin/Armadillo (Arm). Our in vivo genetic analyses and target gene studies support a mechanism consistent with the in vitro binding and competition studies, namely that BMP pathway components can repress Wg target gene expression by influencing the binding of Arm and dTcf.Precisely regulated signal transduction pathways are crucial for the regulation of developmental events and prevention of tumorigenesis. Both the Transforming Growth Factor β (TGFβ)/Bone morphogenetic protein (BMP) and Wnt/Wingless (Wg) pathways play essential roles in organismal patterning and growth, and their deregulation can lead to cancers. We describe a mechanism of interaction between Drosophila Wg and BMP signaling in which Wg target gene expression is antagonized by BMP signaling.  Wnt/Wingless (Wg) and TGFβ/BMP are two distinct families of secreted ligands that employ different signaling components to exert their biological effects. These pathways play essential roles in the growth and patterning of most tissues across species from worms to humans (reviewed in achaete (ac), senseless (sens) and distal-less (dll) organize the D/V boundary of the wing and contribute to adult features such as the wing margin Wg signaling is mediated by the DNA-binding transcription factor, dTcf, and its co-activator β-catenin/Armadillo (Arm) The best-characterized TGFβ ligand in Drosophila is Decapentaplegic (Dpp), a member of the BMP family of ligands  member of the Wnt family organizes the dorsal/ventral (D/V) axis, while BMPs are is required to pattern the anterior/posterior (A/P) axis UAS-Mad with either omb-Gal4   . These idisc see  and is nng pouch  [24], . Covg>dTcf expression. Ectopic expression of dTcf does not lead to a modulation of transcription as members of the Lef/Tcf family of transcription factors are abundantly expressed and bound to DNA and must rely on association with co-factors to activate gene transcription (reviewed in nmo (data not shown). Thus, enhanced levels of dTcf could suppress the negative effects of ectopic Mad on Wg transcriptional output. These observations strengthen our model in which ectopic Mad competes with dTcf and leads to a reduction in Wg signaling output. By expressing even higher levels of dTcf, we effectively were able to titrate the suppressive effects of elevated Mad protein.To test the hypothesis that excess Mad can saturate dTcf in vivo, Wg target gene expression was monitored in wing discs clones ectopically expressing Mad and dTcf. Our prediction would be that Mad inhibits Wg targets by competing with Arm for dTcf binding. Thus, if excess dTcf is provided, it should alleviate the repressive effect of Mad and allow dTcf/Arm-driven transcription to proceed. Ectopic dTcf in flip-out clones showed no change in Sens expression , consistspalt major  To determine if the effect we observed was specific to Wg target genes, we examined the expression of the Mad target gene In this study, we show that Wg-dependent gene expression can be modulated in vivo by elevated BMP signaling due to activated receptor or high levels of Mad. We find that the molecular basis for this effect arises through Mad/dTcf complex formation, which can inhibit the binding of Arm with dTcf and block Wg-dependent gene expression in vitro. We propose that Mad and Arm compete for binding of dTcf, and that ectopic nuclear Mad inhibits Wg signaling through direct binding with dTcf. In support of this model, overexpression of dTcf inhibits Mad-dependent suppression of Wg target gene expression in vivo. Thus elevated Dpp signaling can inhibit Wg signaling both in vitro and in vivo. We also show that loss of BMP signaling can result in elevated Wg target gene expression, suggesting the interaction between the two pathways normally acts to fine-tune the Wg response.mad loss of function studies.Consistent with our findings, Takaesu et al. (2005) describe that expression of a dominant negative human Smad4 construct in Drosophila wings leads to elevated Wg signaling and target gene expression vg>Mad or vg>Med may activate the cell death pathway directly. However, the observation that the notching phenotype can be enhanced by loss of dTcf and rescued by gain of Wg, as seen with the +/−zw3 heterozygous mutant, supports the hypothesis that vg>Mad and vg>Med suppress Wg signaling activities, therefore leading to the terminal consequence: apoptosis in the wing margin. Indeed, reduction of Wg signaling leading to activation of the JNK apoptotic pathway has been elegantly illustrated by Giraldez and Cohen (2003)We and others dpp loss of function mutants and mad somatic clones display ectopic bristles phenotypes on the wing blade, a phenotype indicative of elevated Wg signaling. The relatively weak adult phenotype can be explained if Dpp attenuation of Wg signaling plays a fine-tuning role in a specific developmental time window. Likely the damage caused by loss of dpp and mad can be compensated for by later development events. During early larval development, Dpp and Wg execute their global patterning function in organizing the A/P and D/V axes. In this stage, Dpp signaling antagonizes Wg signaling at the A/P-D/V intersection. In the late 3rd instar larval and pupal stages, Wg functions in wing margin organization, which requires repression of dpp expression rd instar, Dpp expression is suppressed when the A/P and D/V boundaries intersect ; c-myc activation needs Smad4 binding to the Lef/Tcf binding elements 1 (TBE1). Our studies in the fly wing unveil a novel mechanism of interaction of the Smad-Tcf complex, in which it exerts an antagonistic role on Wg target gene expression, both in vivo and in vitro. The antagonism is cis-element independent, as evidenced by the finding that expression of Mad in vitro can inhibit Tcf-response in Topflash assays, where reporter gene expression is controlled solely by Tcf binding sites. These in vitro findings are consistent with the modified levels of targets we observe in the wing, namely Dll, nmo, Sens and Ac. Our biochemical studies suggest a more general molecular mechanism for the Smad-Lef/Tcf interaction in the wing, in which Mad and Arm compete for the binding of dTcf. Excessive Mad can inhibit the association of Arm/dTcf in vitro. The suppression of Wg target genes by ectopic Mad can be rescued by replenishing the dTcf pool (co-expression of dTcf) in vivo. Although a different molecular mechanism is proposed here, the binding domains between Smad and Tcf are conserved between Drosophila and vertebrate. Similar to the vertebrate study Several studies in vertebrate have shown that association between Smad and Lef/Tcf plays significant roles in controlling certain developmental events. In Xenopus, Smad interacts with Lef1 to synergistically activate wg expression during gut development Distinct tissue-specific mechanisms of interaction between Wg and BMP signaling have evolved. For example, in contrast to the mutual repression of Wg and BMP seen in leg discs, elevated Dpp actually induces Pnmo-lacZ (nmo) UAS-lacZ, UAS-Mad, UAS-Med, QDUAS-TkvF678AUAS-Sara  vg-Gal4 (expressed in the wing pouch), omb-Gal4 , T93-Gal4, s10UAS-ArmUAS-dTcf, 3dTcf and UAS-dTcfΔNAy-Gal4.25-UAS-GFP.S65T, d5dpp, hr56dpp, M1-1sggand 10madThe following fly strains were used: mad clones were generated by crossing 1 GFP FRT40A/CyOhsflp.22/Y; M(2)21AB males to 10 FRT40A/In(2LR) Glayw; mad females. Embryos were collected for 24 hours and heat-shocked at 38°C for 90 minutes at 72–96 hours after egg laying. 10mad clones located outside of the endogenous Dll domain were examined and ectopic Dll was observed in 39% (n = 33).Flip-out ectopic expression clones and staining were generated as described in The following antibody dilutions were used: rabbit anti-βgal (1∶2000), rat anti-Dll (1∶500), mouse anti-Dll (1∶400)pCMV-HA-Arm and pCMV-Myc-dTcf were generated by D. Bessette . dTcf constructs were generated in pCMV-Myc. The dTcfΔC1 construct encodes amino acids (a.a.) 1–522 (full length dTcf is 751 a.a.); dTcfΔC encodes a.a. 1–394; dTcfΔHMG encodes a.a. 1–244. Mad constructs were generated from pCMV-T7-Mad HEK293 cells were cultured in 6 well plates and transiently transfected by using Polyfect (Qiagen). The renilla luciferase pRL-CMV served as an internal control. Transfections contained 1 mg of pTOPFLASH reporter, 0.1 mg of pRL-CMV and others as described in


In embryonic stem (ES) cells, bivalent chromatin domains with overlapping repressive (H3 lysine 27 tri-methylation) and activating (H3 lysine 4 tri-methylation) histone modifications mark the promoters of more than 2,000 genes. To gain insight into the structure and function of bivalent domains, we mapped key histone modifications and subunits of Polycomb-repressive complexes 1 and 2 (PRC1 and PRC2) genomewide in human and mouse ES cells by chromatin immunoprecipitation, followed by ultra high-throughput sequencing. We find that bivalent domains can be segregated into two classes—the first occupied by both PRC2 and PRC1 (PRC1-positive) and the second specifically bound by PRC2 (PRC2-only). PRC1-positive bivalent domains appear functionally distinct as they more efficiently retain lysine 27 tri-methylation upon differentiation, show stringent conservation of chromatin state, and associate with an overwhelming number of developmental regulator gene promoters. We also used computational genomics to search for sequence determinants of Polycomb binding. This analysis revealed that the genomewide locations of PRC2 and PRC1 can be largely predicted from the locations, sizes, and underlying motif contents of CpG islands. We propose that large CpG islands depleted of activating motifs confer epigenetic memory by recruiting the full repertoire of Polycomb complexes in pluripotent cells. Polycomb-group (PcG) proteins play essential roles in the epigenetic regulation of gene expression during development. PcG proteins are repressors that catalyze lysine 27 tri-methylation on histone H3. They are antagonized by trithorax-group proteins that catalyze lysine 4 tri-methylation. Recent studies of ES cells revealed a novel chromatin pattern consisting of overlapping lysine 27 and lysine 4 tri-methylation. Genomic regions with these opposing modifications were termed “bivalent domains” and proposed to silence developmental regulators while keeping them “poised” for alternate fates. However, our understanding of PcG regulation and bivalent domains remains limited. For instance, bivalent domains affect over 2,000 promoters with diverse functions, which suggests that they may function in diverse cellular processes. Moreover, the mechanisms that underlie the targeting of PcG complexes to specific genomic regions remain completely unknown. To gain insight into these issues, we used ultra high-throughput sequencing to map PcG complexes and related modifications genomewide in human and mouse ES cells. The data identify two classes of bivalent domains with distinct regulatory properties. They also reveal striking relationships between genome sequence and chromatin state that suggest a prominent role for the DNA sequence in dictating the genomewide localization of PcG complexes and, consequently, bivalent domains in ES cells. Increasing evidence suggests that Polycomb- (PcG) and trithorax-group (trxG) proteins and associated histone modifications are critical for the plasticity of the pluripotent state, for the dynamic changes in gene expression that accompany ES cell differentiation, and for subsequent maintenance of lineage-specific gene expression programs PcG proteins are transcriptional repressors that function by modulating chromatin structure Interplay between PcG complexes and modified histones has been proposed to mediate stable transcriptional repression Several groups have combined chromatin immunoprecipitation (ChIP) with microarrays to examine the genomic localizations of individual PcG subunits Concurrent studies of histone methylation in ES cells led to the unexpected finding that virtually all sites of PcG activity not only carry the repressive H3K27me3 modification, but are also strongly enriched for the activating, trxG-associated H3 lysine 4 tri-methylation (H3K4me3) mark Drosophila, PcG complexes are recruited to DNA elements termed Polycomb response elements (PREs). However, mammalian equivalents of these elements have yet to be identified Despite this progress, our understanding of PcG regulation and bivalent domains remains limited. In the current study we sought to address two outstanding issues. The first relates to whether all bivalent domains have the same regulatory structure. The recent observation that human and mouse ES cells show overlapping H3K27me3 and H3K4me3 at over 2000 promoters, only a portion of which have developmental functions, suggests that bivalent domains may reflect multiple, distinct regulatory entities We addressed these outstanding issues through genomewide analysis of PcG complex localization in mouse and human ES cells. We used the newly developed ‘ChIP-Seq’ method, which leverages ultra high-throughput sequencing to generate uniquely comprehensive maps of protein-DNA interactions The data reveal two classes of bivalent domains with distinct regulatory properties. The first class corresponds to bivalent domains with both PRC2 and PRC1. These ‘PRC1-positive’ bivalent domains show striking evolutionary conservation, correspond to large H3K27me3 regions in ES cells that are significantly more likely to retain H3K27me3 upon differentiation, and account for a vast majority of implicated developmental regulator genes. By contrast, PRC1-negative bivalent domains, which are exclusively bound by PRC2, are weakly conserved, poorly retain H3K27me3, and largely correspond to membrane proteins or genes with unknown functions. Remarkably, computational genomic analysis of the ChIP-Seq data suggests a simple genomic code in which the locations, sizes and motif contents of CpG islands may predict the genomewide localizations of PRC2, PRC1 and bivalent domains in ES cells. Based on these data, we propose a model in which large CpG islands depleted of activating transcription factor motifs confer epigenetic memory elements through mammalian development by recruiting PRC2 and PRC1 during early embryogenesis.http://www.broad.mit.edu/seq_platform/chip/.To gain insight into the structure, function and conservation of bivalent chromatin, we used ChIP-Seq to acquire genomewide maps of PcG complex components and related histone modifications in ES cells . ChromatThe availability of genomewide data for mouse and human ES cells acquired using identical antibodies and methodologies provides an opportunity to study the conservation of chromatin state in pluripotent cells. We systematically compared chromatin state at 13,200 orthologous promoters, identifying striking similarities at orthologous genomic loci , and S4.In both mouse and human ES cells, roughly three-quarters of gene promoters are marked by H3K4me3. There is strong correspondence between species as >94% of promoters with H3K4me3 in mouse also carry H3K4me3 in human. Roughly one fifth of H3K4me3 promoters also carry H3K27me3, and thus are bivalent  . There iThe promoters of Fgf2, Fgfr3, Activin A, Lefty1 and Lefty2 are bivalent in mouse ES cells but show active ‘H3K4me3 only’ states in human . This isConversely, the chromatin maps reveal developmental regulators that are bivalent only in human ES cells, and these may also relate to known physiologic differences between the models . ExampleThus, comparative analysis of human and mouse ES cells suggests extensive conservation of the pluripotent chromatin state while also illuminating divergent chromatin regulation associated with signaling pathways and transcriptional programs known to vary between the studied cell models see also . The strphi = 0.77). There is also overwhelming overlap with bivalent promoters: nearly all Suz12 and Ezh2 targets have bivalent histone markings and, conversely, 78% of bivalent promoters have Ezh2 or Suz12  Ring1B targets correspond to bivalent, PRC2-bound genomic regions. However, just 39% of bivalent promoters are enriched for Ring1B . This ocThus, ChIP-Seq analysis suggests that while PRC2 is ubiquitously present at bivalent promoters, PRC1 occupies only a distinct subset. Since PRC2 and PRC1 have generally been described at common genes and loci First, we used ChIP and qPCR to exclude the possibility that the absence of Ring1B at a subset of bivalent promoters reflected a lack of sensitivity of the ChIP-Seq data. This analysis confirmed that Ring1B-negative bivalent promoters also do not show any enrichment by qPCR .Next, to rule out antibody-related bias, we used bioChIP to purify Ring1B-bound chromatin from transgenic ES cells carrying a fusion between Ring1B and biotin ligase recognition peptide . Ring1B-Third, to test whether the existence of Ring1B-positive and negative bivalent domains is a conserved phenomenon, we examined Ring1B occupancy in human ES cells by ChIP-Seq. We again found that Ring1B occupies only a subset of bivalent domains. The locations of PRC1 show remarkable cross-species conservation: 60% of Ring1B-positive promoters in human are also Ring1B-positive in mouse .Finally, to confirm that Ring1B status is reflective of PRC1 status, we studied the localization of a distinct PRC1 component, Bmi1. Using an epitope tagged construct in ES cells, we showed that Bmi1 specifically localizes to Ring1B-positive bivalent domains . This suThe identification of a distinct set of bivalent promoters targeted by Ring1B prompted us to investigate the functional significance of PRC1 occupancy. We made several striking observations relevant to chromatin regulation, epigenetic memory, development and differentiation:−10), and is not explained by baseline expression levels as bivalent promoters show very low activity, regardless of PRC1 status.We first considered whether physical targets of PRC1, as defined above, are also regulated by the complex. Since Ring1B and Ring1A are functionally redundant, we employed a conditional Ring1A/B double-knockout ES cell system in which Ring1B depletion is induced by addition of 4-hydroxy tamoxifen (OHT) Several factors could contribute to de-repression of this smaller set of PRC1-negative bivalent promoters. The changes may reflect indirect effects as expression is measured after 2 days of OHT treatment. Also, the Ring1 knockout experiment and the location analyses were done in different ES lines, and this could be the basis of some of the discrepancy. Nonetheless, the fact that the PRC1-positive set shows a significantly greater response indicates that PRC1 occupancy correlates with functional repression. As a control, we examined expression changes associated with PRC2 loss. We found that PRC1-positive and PRC1-negative bivalent promoters are de-repressed to roughly equal extents in ES cells lacking the PRC2 component Eed  [13].−10; Next, we asked whether the patterns of histone modification vary between the two sets of bivalent domains. We observed two significant trends. First, PRC1-positive bivalent domains are associated with much larger regions of H3K27me3 than PRC1-negative bivalent domains (median size of 3.2 kb versus 1.0 kb). The large size is consistent with a proposed role for H3K27me3 in PRC1 recruitment −20), including members of the Hox, Sox, Pax and Pou domain families, or cell signaling and morphogenesis molecules, such as Wnts and Fgfs (−10). Remarkably, despite the strong correlation of PcG proteins with developmental TFs, this PRC1-negative (PRC2-only) subset of bivalent domains shows statistically significant depletion of TF genes relative to the genome average .Next, we examined the gene targets associated with the different classes of bivalent promoters. The PRC1-positive set contains a dramatic enrichment of genes encoding TFs   derived from the same ES cell line −10) . Many PRWe conclude that two distinct sets of bivalent domains can be defined based on PcG complex occupancy in ES cells. Bivalent domains that carry both PRC2 and PRC1 are larger, more conserved and more efficiently retained through differentiation. They account for the vast majority of implicated developmental regulators. By contrast, bivalent domains occupied by PRC2 only are poorly maintained, correspond to distinct non-developmental gene sets, and thus may reflect alternate regulatory processes.We next studied the chromatin maps to gain insight into another fundamental unanswered question – namely, the mechanisms that underlie the initial recruitment of PcG complexes and the formation of bivalent domains in ES cells. The extensive epigenetic reprogramming that precedes the pluripotent state suggests that elements in the genomic sequence itself must play central roles in this process To identify sequence elements that could contribute to PcG recruitment, we applied computational sequence analysis and the new ChIP-Seq data. We focused initially on Ezh2, reasoning that this catalytic PRC2 subunit would most closely reflect the initial recruitment mechanisms. Bivalent domains and PcG target sites have been shown previously to correlate with CG-rich DNA; for example, ∼50% of Suz12 binding sites in human ES cells correspond to CpG islands When we examined the small minority (12%) of Ezh2 binding sites that do not correspond to an annotated CpG island, we found that three-quarters of these sites overlap highly CG-rich sequences that just fall short of the defined threshold for CpG islands see . IncludiStill, only a minority of CpG islands carries Ezh2 or H3K27me3 in ES cells – that is, are PRC2-positive. Most are enriched for H3K4me3 only and are PRC2-negative . We thusWe considered CpG island length, CG density and the frequency of all possible dinucleotides  as potenWe also compared the conservation properties of these CpG island sets. Mammalian genomes contain ∼200 large regions characterized by striking enrichment for highly conserved non-coding elements D. melanogaster, PcG recruitment is mediated by combinations of motifs recognized by specific TFs Because the distinction between PRC2-positive and PRC2-negative CpG islands is not explained by simple sequence composition, we next considered more complex sequence motifs. In We used the MAST algorithm Drosophila. However, most of the corresponding TFs are not actually expressed in ES cells, but rather are expressed in differentiated cells. These include developmental regulators induced along specific differentiation pathways, such as MyoD (myogenesis), Lmo2 (hematopoiesis), Brachyury  and Pou6F1 (neurogenesis) We initially focused on the motifs associated with Ezh2-positive CpG islands as these could potentially mediate PRC2 recruitment. Although the enrichment ratios were relatively low, it is conceivable that combinations of factors might be required, as in However, three of the factors identified in the Ezh2-positive islands are expressed in ES cells, and these cases are illustrative . The mosabsence of motifs capable of conferring transcriptional activity.Next, we turned to examine motifs enriched in the Ezh2-negative CpG islands. We were immediately struck that these motifs are recognized by several well-characterized classes of transcriptional activators that are highly expressed in ES cells . Some ofDrosophila, which are present at 6 to 27% of experimentally-determined PcG binding sites A simple count of the motif occurrences within a CpG island allows accurate prediction of roughly two-thirds of Ezh2 binding sites see . This coIn sum, we find that PRC2-positive CpG islands are characterized by an over-representation of repressor motifs and a strong depletion of transcriptional activator motifs. While it is possible that the implicated repressors directly mediate PRC2 recruitment, each has been well-studied and linked to distinct biological processes. Rather, we favor the view that the paucity of activating motifs and, to a lesser extent, the presence of repressive motifs dictate a transcriptionally inactive state in ES cells that is permissive to PRC2 binding. We suggest that CpG islands play a central role in PRC2 recruitment and, in the absence of transcriptional activity, assume a bivalent chromatin state by ‘default’ in ES cells see .Lastly, we considered whether PRC1 association can also be predicted from genome sequence. PRC1 occupies roughly half of all PRC2 sites in ES cells, and is essentially never observed in the absence of this second PcG complex. We collated and compared two sets of Ezh2-positive CpG islands, one with Ring1B (n = 1036) and the other without Ring1B (n = 981) see . We founRather, the best predictor appears to be the length of CG-rich DNA. PRC1-positive CpG islands are roughly twice as large as those that carry only PRC2 . They arWe have applied ChIP-Seq and computational genomic analysis to study the genomewide distributions of key histone modifications and PcG subunits in mouse and human ES cells, thereby gaining insight into the structure, function and establishment of bivalent domains.The ChIP-Seq data reveal two distinct sets of bivalent domains in ES cells. One set, defined based on co-occupancy by both PRC1 and PRC2, shows special epigenetic properties, including higher evolutionary conservation of chromatin state and robust retention of repressive chromatin through differentiation. This set is exquisitely enriched for developmental targets in that over one third of the corresponding genes encode TFs, morphogens or cytokines. In striking contrast, a second set of bivalent domains, occupied by PRC2 only, is actually under-represented for TF genes relative to the genome average, and shows weak conservation and retention of the PcG-associated chromatin marks. We suggest that the complete repertoire of PcG machinery is needed for full functionality of bivalent domains and associated chromatin in the epigenetic regulation of key developmental genes.The data also suggest a potential model for understanding the initial recruitment of PcG complexes for the coordinated establishment of bivalent chromatin. In particular, we find that PRC2 association in ES cells is entirely restricted to sequences with high CpG content, the vast majority being annotated CpG islands. The status of a given CpG island – whether it carries PRC2 and bivalent H3K4me3/H3K27me3 chromatin or only H3K4me3 – correlates with underlying motif content. CpG islands with PRC2 show a striking depletion of transcriptional activator motifs and a modest enrichment of repressor motifs. Thus, PRC2 appears to localize to CpG islands that are transcriptionally silent in ES cells because they lack activating DNA sequence motifs.CpG islands have been extensively correlated with trxG complexes and H3K4me3; recruitment of the former likely involves CXXC proteins with affinity for un-methylated CpG dinucleotides Large PRC2-positive CpG islands tend to also carry PRC1. The expansive regions of H3K27me3 associated with these islands may contribute to PRC1 recruitment via chromodomain proteins The tight correspondence between DNA sequence and PcG localization may have implications for important cellular processes, such as development and epigenetic reprogramming. Induced pluripotent stem (iPS) cells and ES cells exhibit nearly identical chromatin patterns, including the locations of bivalent domains What then might be the purpose of an initial chromatin state fully encoded by genetic sequence and an associated transcriptional program? Based on existing evidence, we suggest that PcG complexes and associated chromatin buffer the pluripotent ground state by reinforcing the repression of factors that induce differentiation. The initial chromatin architecture also appears poised for the dynamic expression changes that accompany differentiation and for the subsequent engagement of epigenetic controls to maintain lineage-specific transcriptional programs. Our analysis suggests that such epigenetic functions mainly apply to large bivalent CpG islands that also carry PRC1. It remains to be seen whether small PRC1-negative bivalent domains have distinct regulatory functions or are simply byproducts of the mechanisms that have evolved for establishment of the former.Further studies are needed to determine the precise DNA elements and protein interactions that mediate PcG recruitment. As discussed above, the proposed central role for CG-rich sequences implies the involvement of CXXC domains or other proteins that recognize CG dinucleotides. However, several factors complicate the interpretation of our genomic findings. In particular, CpG islands are at least partly a consequence of reduced CpG deamination rates in regions that lack DNA methylation in the germ line Finally, it should be emphasized that our findings on the relationships among PRC2 and PRC1 and the sequences that underlie their genomic localizations pertain specifically to ES cells. PcG complexes show remarkable tissue-specificities in terms of their expression levels, stoichiometry and localization 2Mouse v6.5  ES cells were cultured on fibroblast feeders in DMEM (Sigma) with 15% fetal bovine serum (Hyclone), GlutaMax (Invitrogen), MEM non-essential amino acids (Invitrogen), pen/strep (Invitrogen), ESGRO (Chemicon) and 2-mercaptoethanol (Sigma), incubating at 37°C, 5% COhttp:/www.WiCell.org. Briefly, the human ES cells were cultivated on irradiated MEFs (strain DR4) in Knockout DMEM (Invitrogen) containing 10% Knockout Serum Replacement (Invitrogen), 10% Plasmanate , GlutaMax (2 mM), pen/strep, MEM non-essential amino acids (0.1 mM), 10 ng/ml β-FGF (Invitrogen) and 2-mercaptoethanol. Cells were incubated at 37°C, 5% CO2. MEF-free ES cells were used for analysis. MEF-free culture was prepared in the following manner: First, MEFs were depleted at the time of trypsin passaging through brief transfer (thirty minutes) of hES cells onto gelatin-coated plates. MEF-subtracted ES cells were then propagated on plates coated with Matrigel (Invitrogen). ES cells grown on Matrigel were supported with the aforementioned human ES cell medium that had first been conditioned on MEFs for 24 hours. Fresh β-FGF was added to the conditioned medium immediately prior to use.Human H9  ES cells were cultured as described 5′-GGAATTCCACCATGGACTACAAAGACCATGACGGTGATTATAAAGATCATGATATCGACTACAAGGACG-3′, 5′- GCTCTAGAGCACCAGATGAAGTTGCTGATGACCCATTTAGTGATGATTTT-3′). This was cloned into the pLox vector (pPGK-loxP-neoEGFP) and incorporated into Ainv15 mouse ES cells using a cre recombinase expression vector as previously described Doxycyclin-inducible Flag-Bmi1 transgenic ES cell line was generated by PCR amplifying a 1× flag tagged Bmi1 ORF (Addgene) with primers that incorporate a 3× flag tag as well as EcoRI and XbaI restriction enzyme sites , on ice for twenty minutes, passed through a 16G needle 20 times and centrifuged to collect nuclei BioChIP assays were carried out using transgenic Ring1B-Biotin ligase recognition peptide ES cells (above). Nuclei were isolated, lysed and sonicated as described above. Dynabeads M-280 Streptavidin (Invitrogen 112.05D) were used to capture biotinylated Ring1B-DNA complex. Beads were washed with a 2% SDS buffer and a high salt buffer , in addition to the regular washes. Elution and cross-link reversal were done simultaneously by incubating Dynabeads in 300 mM NaCl at 65°C overnight Antibodies used in this study include anti-H3K4me3 (Abcam ab8580), anti-H3K27me3 (Upstate 07-449), anti-H3K36me3 (Abcam ab9050), anti-Ezh2 (Active Motif 39103), anti-Suz12 (Abcam ab12073), anti-Ring1B −), and double-stranded Illumina Adapters were ligated to the ends of the ChIP fragments. Adapter-ligated ChIP DNA fragments between 275 bp to 700 bp were gel-purified and subjected to 18 cycles of PCR. Prepared libraries were quantified using PicoGreen and sequenced on the Illumina Genome Analyzer per standard operating procedures.Library preparation and ultra high-throughput sequencing were carried out as described http://www.broad.mit.edu/seq_platform/chip/.Sequence reads (36 bases) from each ChIP experiment were compiled, post-processed and aligned to the appropriate reference genome using a general purpose computational pipeline as described previously We used a Hidden Markov Model (HMM) to demarcate chromosomal segments likely to be enriched for a given chromatin modification or PcG protein We defined 17760 mouse and 18522 human promoters for 17442 and 17383 genes, respectively, as the sequences between −0.5 kb and +2.0 kb of the annotated transcription start site, using the mouse mm8 and human hg18 genome builds. Transcripts were defined for these genes as the range from transcription start to end ftp://ftp.ncbi.nih.gov/pub/HomoloGene/). Genes with multiple start sites were excluded from this analysis. Promoters were associated with CpG states as described previously For conservation analyses of human and mouse promoter states, we used NCBI HomoloGene (build 58) gene clusters to assign orthologous human promoters and transcripts to the 17442 mouse promoters and transcripts, yielding a set of 13200 orthologous promoters and 13625 orthologous transcripts for which human and mouse chromatin state could be compared . Read mapping to the mouse genome and analysis of promoter state were performed as described above.http://fokker.wi.mit.edu/primer3/input.htm). Real-time PCR assays were carried out on ABI 7000 or 7500 detection systems. We used Quantitect SYBR green PCR mix (Qiagen) with 0.1 ng ChIP or 0.1 ng un-enriched input DNA (WCE) as template. Log2 enrichment was calculated from geometric means obtained from three independent ChIP experiments, each evaluated by duplicate PCR assays. Background was subtracted by normalizing over negative genomic control.PCR primer pairs were designed to amplify designated genomic regions using Primer3  ES cells  and Eed KO ES cells (Eed −/− and control Eed+/+ ES) http://www.broad.mit.edu/cancer/software/genepattern). CEL files were processed with RMA, quantile normalization and background correction Gene expression data for Ring1A/B-dKO (http://david.abcc.ncifcrf.gov/home.jsp). P-values were adjusted for multiple hypothesis testing using Bonferroni correction.Gene ontology (GO) functional annotation for the Ring1B positive and negative sets was done using DAVID analysis tool  overlap with CG-rich sequences. CpG island coordinates were obtained from the UCSC Genome Browser We next classified CpG islands according to their chromatin state . This was done by computing the median ChIP-Seq read density across each defined CpG island, and setting thresholds using a null background model of randomized reads. For these analyses we excluded CpG islands that fall within unalignable regions, typically due to low complexity sequence, and thus could not be evaluated by ChIP-Seq . To maximize discriminatory power, we excluded intermediate CpG islands with sub-threshold Ezh2 signal.We computed median values and distributions for length, CG density and observed-to-expected ratio for the different CpG island sets, and also evaluated nucleotide content by calculating the frequencies of all 16 dinucleotide combinations. Conservation scores were determined for each CpG island by aligning the regions between mouse and rat, and performing a dinucleotides level comparison of the conservation between the two species. Both CpG and non-CpG dinucleotides were conserved at slightly higher levels in the Ezh2-bound CpG islands .We next screened the CpG island sets for TF motif occurrences. 668 position weight matrices (PWMs) were obtained from the Jaspar  contained >8 ‘Ezh2-positive’ motifs or (ii) contained >4 ‘Ezh2-positive’ motifs and <2 ‘Ezh2-negative’ motifs. Ezh2 status in human was predicted using the motifs identified in mouse but with the following metric: a CpG island was predicted to be Ezh2-positive if it contained >15 ‘Ezh2-positive’ motifs and <2 ‘Ezh2-negative’ motifs.In order to quantify Ring1B presence in CpG islands, we considered the distribution of ChIP-Seq reads in control regions. We specifically used all alignable, H3K4me3-only CpG islands as our null hypothesis background model. The distribution of Ring1B ChIP-Seq read densities across these islands was calculated and a threshold was set to minimize the false positive detection rate. We then calculated Ring1B ChIP-Seq read density in sliding 200 bp windows in all Ezh2-positive CpG islands, with a CpG island assigned the maximum enrichment in any of its 200 bp windows. For maximum discriminatory power, we excluded 20% of CpG islands with sub-threshold Ring1B signal. Ring1B status was predicted using the length of CpG-richness in PRC2-positive CpG islands. Islands were predicted to be Ring1B-positive if they were either >1200 bp or within 2 kb of another CpG island.Figure S1Comparison of chromatin states in mouse and human ES cells. (A) Conservation of H3K4me3 for 13,200 transcription start sites between human and mouse. Dashed lines indicate cutoff thresholds used to binarize the data for further analysis. Genes that carry H3K4me3 are likely to be conserved (upper right quadrant), as are those that are not marked (lower left quadrant). Less than 12% of genes are differentially methylated between human and mouse (upper left and lower right quadrants). (B) Conservation of H3K27me3 for the same regions used in (A). Most genes in both mouse and human are not marked with H3K27me3 (bottom left quadrant). Only slightly more than half the genes that carry H3K27me3 in mouse do so in human also. (upper and lower right quadrant). (C) H3K4me3 vs. H3K27me3 plotted for 17,760 mouse genes reveal three prominent marks in ESC: H3K4me3 only, (lower right quadrant), H3K4me3+H3K27me3/bivalent (upper right quadrant) and “no mark” (lower left quadrant). Very few genes are marked with H3K27me3 only (upper left quadrant).(3.85 MB PDF)Click here for additional data file.Figure S2Quantitative PCR enrichment for Ezh2 ChIP, Ring1B bioChIP and Flag-Bmi1 ChIP. (A) Plot shows Log2 ChIP-qPCR enrichment of Ezh2 in mouse v6.5 ES cells at bivalent gene promoters. Included are promoters classified as PRC2-bound (orange) or PRC2-unbound (yellow) by ChIP-Seq. (B) Plot shows Log2 enrichment of Ring1B bioChIP-qPCR in transgenic mouse ES cells expressing biotin-tagged Ring1B (mES*) at bivalent promoters classified by ChIP-Seq as PRC1-bound (purple) or PRC1-unbound (blue). H3K4me3 only genes are green. (C) Plot shows fold enrichment of Flag ChIP-qPCR in transgenic mouse ES cells expressing Flag-tagged Bmi1 (mEŜ) at bivalent promoters classified by ChIP-Seq as PRC1-bound (purple) or PRC1-unbound (blue).(0.31 MB PDF)Click here for additional data file.Figure S3Chromatin states of species-specific factors from ES cell Pathways. Divergent chromatin states of species-specific factors in transcription and signaling pathways observed in mouse and human ES cells reflect known distinctive biological functions between the two pluripotency models.(0.28 MB PDF)Click here for additional data file.Figure S4Expression analysis in PRC2 wild-type (WT) and knock-out (KO) mouse ES cells. Expression changes for all genes, Ring1B-positive bivalent and Ring1B-negative bivalent genes in PRC2 knock-out (Eed−/−) mouse ES cells.(0.15 MB PDF)Click here for additional data file.Figure S5Analysis of the CG-richness of HMM-defined intervals of H3K4me3, H3K27me3, H3K36me3, H3K9me3, H3K20me3, and Ezh2. (A) The fraction of intervals that either directly overlap or are within 500 bp of a CpG island. (B) The maximum CpG observed-to-expected ratio in any 200 bp window within the interval. The dashed line marks 0.6, one of the criteria used to define a CpG island.(0.21 MB PDF)Click here for additional data file.Figure S6Comparison of Ezh2-positive and Ezh2-negative CpG islands. No marked difference was observed in CpG observed-to-expected ratio (A), percent CpG (B), or percent GC (C), whereas Ezh2-positive CpG islands tend to be longer .(0.22 MB PDF)Click here for additional data file.Figure S7Conservation of Ezh2-bound and Ezh2-unbound dinucleotides between rat and mouse. Aligning regions in rat (rn4) for both classes of CpG island were identified, and a dinucleotide level comparison was performed on the conservation between the two species. Both non-CpG (A) and CpG (B) dinucleotides were conserved at slightly higher levels in the Ezh2-bound CpG islands than in those islands that did not bind Ezh2.(0.70 MB PDF)Click here for additional data file.Figure S8Motif clusters and their respective enrichment p-values for Ezh2-positive and Ezh2-negative CpG islands. The top ranking motifs  for Ezh2-negative (A) and positive (B) CpG islands. The motifs were clustered and collapsed to reduce redundancy.(0.49 MB PDF)Click here for additional data file.Figure S9Length of CpG islands in Ring1B-positive and Ring1B-negative bivalent promoters. Ring1B-positive bivalent CpG islands are larger than bivalent CpG islands that are only bound by PRC2.(0.12 MB PDF)Click here for additional data file.Table S1List of ChIP-Seq datasets showing numbers of aligned reads.(0.28 MB PDF)Click here for additional data file.Table S2Chromatin states of analyzed promoters in mES cells.(3.72 MB XLS)Click here for additional data file.Table S3Chromatin states of analyzed promoters in hES cells (Microsoft Excel file).(2.81 MB XLS)Click here for additional data file.Table S4Comparison of chromatin states of analyzed promoters between mES and hES cells.(1.69 MB XLS)Click here for additional data file.Table S5PCR primers used for Ezh2, Ring1B and Flag-Bmi1 ChIP-qPCR in mouse ES cells.(0.61 MB PDF)Click here for additional data file.Text S1Supporting information on the specificity of antibodies. Western blots using mouse ES cell protein extracts demonstrate the specificity of anti-Ring1B and anti-Ezh2 (Active Motif 39103), antibodies used in this study. *Indicates the expected molecular weight. Previous publications that demonstrate the specificity of the antibodies used are listed.(1.83 MB PDF)Click here for additional data file.Text S2Relevant references for transcription factors (TFs) that correspond to implicated motifs and are active in ES cells.(0.63 MB PDF)Click here for additional data file.


A comparative genomics study maps nucleosomes across the entire genomes of 12 fungal species, identifying multiple distinct mechanisms linking changes in chromatin architecture to evolution of gene regulation. Hemiascomycota yeast species. We found substantial conservation of global and functional chromatin organization in all species, including prominent nucleosome-free regions (NFRs) at gene promoters, and distinct chromatin architecture in growth and stress genes. Chromatin organization has also substantially diverged in both global quantitative features, such as spacing between adjacent nucleosomes, and in functional groups of genes. Expression levels, intrinsic anti-nucleosomal sequences, and trans-acting chromatin modifiers all play important, complementary, and evolvable roles in determining NFRs. We identify five mechanisms that couple chromatin organization to evolution of gene regulation and have contributed to the evolution of respiro-fermentation and other key systems, including (1) compensatory evolution of alternative modifiers associated with conserved chromatin organization, (2) a gradual transition from constitutive to trans-regulated NFRs, (3) a loss of intrinsic anti-nucleosomal sequences accompanying changes in chromatin organization and gene expression, (4) re-positioning of motifs from NFRs to nucleosome-occluded regions, and (5) the expanded use of NFRs by paralogous activator-repressor pairs. Our study sheds light on the molecular basis of chromatin organization, and on the role of chromatin organization in the evolution of gene regulation.Chromatin organization plays a major role in gene regulation and can affect the function and evolution of new transcriptional programs. However, it can be difficult to decipher the basis of changes in chromatin organization and their functional effect on gene expression. Here, we present a large-scale comparative genomic analysis of the relationship between chromatin organization and gene expression, by measuring mRNA abundance and nucleosome positions genome-wide in 12  Divergence in gene regulation plays a major role in organismal evolution. Evidence suggests that changes in the packaging of eukaryotic genomes into chromatin can underlie the evolution of divergent gene expression patterns. Here, we explore the role of chromatin structure in regulatory evolution by whole-genome measurements of nucleosome positions and mRNA levels in 12 yeast species spanning ∼250 million years of evolution. We find several distinct ways in which changes in chromatin structure are associated with changes in gene expression. These include changes in promoter accessibility, changes in promoter chromatin architecture, and changes in the accessibility of specific transcription factor binding sites. In many cases, changes in chromatin architecture are coupled to physiological diversity, including the evolution of a respiration- or fermentation-based lifestyle, mating behavior, salt tolerance, and broad aspects of genomic structure. Together, our data will provide a rich resource for future investigations into the interplay between chromatin structure, gene regulation, and evolution. Regulatory differences affecting gene expression can play a major role in species evolution Hemiascomycota yeasts  chromatin remodelers S. cerevisiae and S. paradoxus (Last Common Ancestor (LCA) ∼2 million years ago (MYA)) are due to linked cis polymorphisms predicted to affect nucleosome occupancy C. albicans and S. cerevisiae (LCA ∼200 MYA) were associated with a change in nucleosome organization C. albicans is accompanied by enrichment for the PolyA-like “RGE” binding site in the mRP gene promoters S. cerevisiae. Finally, a recent study S. cerevisiae and S. pombe (LCA ∼300M–1 BYa), finding changes in global nucleosome spacing and in the apparent sequences that intrinsically contribute to nucleosome positioning in vivo.Recent studies in yeast suggest a broad role for chromatin organization in regulatory evolution. Most regulatory divergence between closely related While these examples are intriguing, they are limited in their phylogenetic coverage (a pair of species) or their functional scope (one regulon). Thus, we understand little about the evolutionary interplay between gene expression, regulatory sequence elements, and chromatin organization. How does chromatin organization change over evolutionary time scales? Are the mechanisms underlying chromatin packaging of functional gene modules conserved? If not, how do they evolve and what is the role of different factors in this divergence? Are changes in chromatin organization related to changes in gene regulation? Can phylogenetic comparisons shed light on the distinct mechanisms that help establish chromatin organization?Hemiascomycota yeast species, spanning over 250 million years of evolution  regulatory divergence can also occur by re-positioning of binding sites relative to nucleosome positions or by expanding the use of accessible sites by paralogous TFs. These mechanisms played a role in the evolution of respiro-fermentation, as well as in the evolution of regulation of other key regulons at different phylogenetic points, including mating, meiosis, RNA polymerase subunits, proteasomal, and splicing genes. Together, they uncover novel insights into the general roles for chromatin in regulating genomic access and in the evolution of regulatory programs, and provide a rich resource for future investigation.Our analysis uncovers several major principles that govern the evolutionary and functional relationship between chromatin organization and gene regulation in this phylogeny. (1) While qualitative features of chromatin organization are conserved in all species, quantitative features such as nucleosome packing, NFR length, and NFR to ATG distance have substantially diverged; (2) promoter chromatin organization and gene expression levels of “growth” and “stress” genes follow distinct patterns, and this dichotomy is conserved in all species; (3) evolutionary divergence in gene expression is often accompanied by transition of chromatin organization from a “growth” to a “stress” pattern; (4) changes in transcription levels, gain/loss of anti-nucleosomal sequences, and gain/loss of binding sites for “general regulatory factors” (GRFs) all play substantial and complementary roles in divergence of chromatin organization; (5) the loss of anti-nucleosomal sequences and parallel gain of binding sites for GRFs drive shifts from intrinsic to Hemiascomycota species  functionally, averaged across all genes in a functional category; and (3) locally, at a single gene.Aligning nucleosome reads to each genome and averaging over all genes showed remarkably similar profiles in all species studied , S2, S3.+1), and increasing nucleosome fuzziness over the body of genes , have a more open chromatin organization on average We next explored possible functional implications of chromatin organization in specific sets of genes with related function. Prior studies in S. cerevisiae across species  We identify a range of additional conserved patterns of chromatin architecture associated with other specific functions, which were not previously reported. For example, a number of gene sets  have conserved long NFR to ATG distances , but havS. cerevisiae and C. albicans for genes involved in respiratory metabolism On this backdrop of conservation, we find that coordinated changes have occurred in chromatin organization of specific functional gene sets, consistent with major phenotypic changes. Most notably, respiration and mitochondrial genes have switched from a “growth”-like chromatin pattern in pre-WGD species (where they are highly expressed) to a more “stress”-like pattern post-WGD  and S6. Yarrowia lipolytica spliceosome genes are associated with long and deep NFRs, but in all other species they are enriched for short and shallow NFRs  are packaged with wide and deep NFRs, while many poorly expressed stress genes have shorter, occupied NFRs , S6. BetS. cerevisiae, where the distinctive chromatin organization of growth and stress genes was largely maintained even after genetically inactivating RNA Pol II However, transcription level is insufficient to solely explain the NFR occupancy measured across the 12 species. Globally, expression level alone explains only 1.7%–13.1% of the variation in NFR occupancy in each of the 12 species  tracts S. cerevisiaeY. lipolytica are associated with a greater length and number of PolyA sequences at these genes  tracts. Within each species, Poly(dA:dT) sequences are enriched upstream of many highly expressed, nucleosome-depleted, “growth” gene sets, consistent with previous observations in se genes . Convers species .Nevertheless, intrinsic anti-nucleosomal sequences explain only 8.6%–25.7% of the global variation in NFR occupancy within a given species . Even whD. hansenii, C. albicans, and Y. lipolytica, where they are packaged with long (but not deep) NFRs, despite no enrichment for Poly(dA:dT) tracts (see below). In these and other cases, even when we consider expression levels, much of the depletion in NFRs remained unexplained . This loss of predictive power by Abf1 and Reb1 sites at increasing phylogenetic distance led us to hypothesize that other GRFs, with distinct binding specificity, are active in these species.Notably, the Abf1 and Reb1 sites explain little of the variation in cis-elements, we therefore searched for short sequence elements that are depleted of nucleosomes in vivo but not in vitro S. cerevisiae GRFs such as Reb1  but not in post-WGD species (except C. glabrata) (K. lactis)  . Convers lactis) . This copolytica , the earD. hansenii promoters than at promoters of any other species, whereas Cbf1-like and Rsc3/30-like sites are strongly nucleosome-depleted in D. hansenii  sequences, suggesting a global mode of compensatory evolution. Most notably, A7/T7 is less nucleosome-depleted at hansenii . This trhansenii , a sequeFR width , consistWe next hypothesized that the identified GRFs are important for the observed chromatin organization in functional gene sets across species. To test this hypothesis, we assessed the enrichments of GRF motifs in the NFRs of each gene set across the 12 species .S. cerevisiae and S. kluyverii  tracts) were enriched in a gene set across multiple species, strongly indicating a conserved regulatory mechanism. For example, the Abf1 site is enriched in RNA polymerase genes across the clade spanning luyverii . Howeverluyverii , S14, weCandida clade to an Abf1-dependent mechanism in later lineages, with additional contribution from Reb1 and Rsc3/30 sites, as these GRFs gained dominance in specific species and clades (upregulated (rather than downregulated) during stress responses Instead, we found a number of cases where a single gene set has a conserved chromatin architecture but is associated with distinct GRF sites in different species, consistent with changes in the global GRF repertoire. This is most notable in proteasome genes, which are uniformly associated with wide/deep NFRs but are depleted of Poly(dA:dT) tracts . The estd clades  and S15EY. lipolytica and the species of the Candida clade, with both Poly(dA:dT) and the site for the Abf1 GRF in species from S. kluyveryi to S. bayanus, and only with Abf1 in the clade spanning S. mikatae, S. paradoxus, and S. cerevisiae  tracts were lost from the genes' promoters, leading to a complete switch from a constitutively programmed to a regulated NFRs. This compensatory evolution is consistent with patterns observed for TF binding sites in functional regulons Could promoters evolve from having constitutively programmed NFRs to regulated ones? To test this, we searched for gene sets where chromatin organization is conserved, while the underlying anti-nucleosomal sequences have diverged in a phylogenetically coherent pattern. We found that genes encoding RNA polymerase subunits exhibit deep NFRs across most of the phylogeny . These grevisiae and S15D.Y. lipolytica, C. albicans, and D. hansenii, and shorter NFRs in subsequently divergent species  and C. albicans (where peroxisomes play a key role in virulence).In some cases, the gain or loss of binding sites for GRFs can contribute to divergence in chromatin organization, coupled to phenotypic changes. Most notably, peroxisomal genes are associated with wider NFRs in  species  and S15FS. cerevisiae most functional TF binding motifs are found in NFRs Even when NFR positions and their underlying mechanisms are largely conserved, they can play an important role in regulatory divergence. Nucleosomes are generally inhibitory to TF binding S. cerevisiae; S. cerevisiae  that either mobilize or destabilize the nucleosomes covering STE12 sites at pheromone-response genes might enable mating in this species. Similarly, motifs for UME6, a major regulator of meiosis genes in S. cerevisiaeC. glabrata  understand the determinants of chromatin organization and function using comparative genomics and (2) characterize the role of chromatin organization in the evolution of gene regulation.S. pombe Poly(dA:dT) plays only a minor role in nucleosome exclusion in vivo What establishes the nucleosomal organization of a genome? While it has been argued that intrinsic DNA sequence can almost fully explain nucleosome organization S. cerevisiae (10.6%). Similarly, expression levels alone can only account for 1.7%–13.1% of the variation in each species. Even when combining both the expression and intrinsic models we can only explain 13%–29% of the variation within any single species. (2) Although changes in intrinsic sequences and expression levels can explain changes in chromatin across species for some gene sets  and in specific gene sets , GRFs alone can explain 5.2%–15.1% of the variation in nucleosome organization (in species where their orthologs are present), and 3.7%–12.6% of the residual variation after considering the contribution from expression and Poly(dA:dT). Taken together, this analysis points to a complex interplay between the different factors that control nucleosome positions, allows us to assess their contributions, and recognizes the plastic and evolvable nature of all the determinants.Our analysis provides several lines of evidence that expression levels, intrinsic anti-nucleosomal sequences, and binding sites for GRFs that may recruit chromatin modifiers all play a role in establishing promoter chromatin architecture, and that the balance between these three contributors changes in evolution and between functional groups of genes. (1) We show that a sequence-based model based on in vitro depletion alone Our study also discovers an intricate and intimate relationship between conservation and divergence of chromatin organization and evolution of gene regulation. At one extreme, we found a broad functional dichotomy in chromatin organization between “growth” and “stress” genes, which is largely conserved. At the other extreme, we found that chromatin organization has diverged at a major evolutionary scale, as has happened during the evolution of respiro-fermentation, and at other points of phylogenetic and phenotypic divergence.trans-factors can expand the regulatory behavior of conserved NFRs and binding sites  gain or loss of intrinsic (PolyA) sequences can open or close NFRs  [32]; 2; (3) NFRng sites .The evolution of the respiro-fermentative lifestyle following the WGD required a major reprogramming of the yeast transcriptional network and involved all of the mechanisms we describe. The shift thus included loss of intrinsic Poly(dA:dT) anti-nucleosomal sequences in some functional modules , and the loss or switch of putative GRF sequences in others . Furthermore, sites for certain respiratory TFs  have re-positioned out of NFRs, and those for glucose repression TFs have re-positioned into NFRs . In yet other cases, the WGD has resulted in the retention of paralogous activator-repressor pairs that control several modules in carbohydrate metabolism. Notably, each of these mechanisms has acted also at other phylogenetic points, suggesting that they point to general principles, and emphasizing the utility of the WGD as a model to study regulatory evolution.http://www.broadinstitute.org/regev/evolfungi/) and a host of analytical approaches with broad applicability. Future studies can use our resource and methods to decipher more detailed models of the relationship between sequence elements, trans-factors, and gene expression, as well as on the evolution of regulatory systems. Finally, our comprehensive study in the emerging field of comparative functional genomics demonstrates how to combine the power of functional assays with extensive phylogenetic scope, to shed light both on mechanistic and evolutionary principles.Our work provides a general framework for the study of chromatin organization, function, and evolution. This includes a comprehensive genomics resource , Peptone (1%), Dextrose (2%), SC Amino Acid mix (Sunrise Science) 2 grams per liter, Adenine 100 mg/L, Tryptophan 100 mg/L, and Uracil 100 mg/L. This in-house recipe was designed to mitigate differences in growth rates between species.600 = 0.5, WPA biowave CO 8000 Density Meter). Before formaldehyde fixation, 50 ml of the culture were transferred to a 50 ml conical and spun down immediately. The isolated cell pellets were then placed in liquid nitrogen, stored at −80°C, and were later archived in RNA later for future RNA extraction. Nucleosomal DNA isolation was carried out as previously described Overnight cultures for each species were grown in 450 ml of media at 220 RPM in a New Brunswick Scientific air-shaker at 30°C until reaching mid log-phase (ODR>.97 in each of 4 independent hybridizations), indicating that the shearing procedure is reproducible and unbiased. Total RNA samples were labeled with Cy3 (cyanine fluorescent dyes) and genomic DNA samples were labeled with Cy5 using a modification of the protocol developed by Joe Derisi (UCSF) and Rosetta Inpharmatics  that can be obtained at www.microarrays.org.Total RNA was isolated using the RNeasy Midi or Mini Kits (Qiagen) according to the provided instructions for mechanical lysis. Samples were quality controlled with the RNA 6000 Nano ll kit for the Bioanalyzer 2100 (Agilent). Genomic DNA was isolated using Genomic-tip 500/G (Qiagen) using the provided protocol for yeast. DNA samples were sheared using Covaris sonicator to 500–1000 bp fragments, as verified using DNA 7500 and DNA 12000 kit for the Bioanalyzer 2100 (Agilent). Independently sheared samples labeled with Cy3 and Cy5 were highly correlated  or a custom 8×15 K format for all other species . After hybridization and washing per Agilent's instructions, arrays were scanned using an Agilent scanner and analyzed with Agilent's feature extraction software version 10.5.1.1. For each probe, the median signal intensities were background subtracted for both channels and combined by taking the log2 of the Cy3 to Cy5 ratio. To estimate the absolute expression values for each gene, we took the median of the log2 ratios across all probes. The experiments were highly reproducible; most biological replicates correlated at R = 0.99 and replicates with R<0.95 were removed. Different biological replicates were combined using quantile normalization to estimate the absolute expression level per gene per species.Between three and four biological replicates of Cy3-labeled RNA samples were mixed with a reference Cy5 labeled genomic DNA sample and hybridized on two-color Agilent 55- or 60-mer oligo-arrays. We used the 4×44 K format for the We used BLAT To infer the location of nucleosomes from the data, we used a Parzen window approach similar to that previously described S. cerevisiaeWe define 5′ and 3′ NFRs as the linker DNA of “significant length” closest to the 5′ and 3′ end of each gene, respectively. To find NFRs, we first created a nucleosome call landscape for each genome, normalized for sequencing depth in the same manner as the nucleosome occupancy maps (above). NFR boundaries were often obscured by very low occupancy nucleosome calls. We therefore removed all nucleosome calls with occupancy less than 40% of the average nucleosome occupancy from the map. We searched for 5′ or 3′ NFRs within 1,000 bases upstream/downstream of the 5′ or 3′ end of each gene, truncated when neighboring ORFs overlapped this region. We then defined an NFR as the linker DNA longer than 60 bp closest to the 5′ or 3′ end of each gene. If no linker longer than 60 bp was found in this search, we defined the NFR as the first linker from the 5′ or 3′ end. Our method was highly predictive of transcription start sites (TSSs) in C. glabrata and S. castellii look similar when aligned by the +1 nucleosomes (+1Nuc) but very different when aligned by ATG (unpublished data). A previous study C. glabrata NFRs are contained within 200 bp, while 90% are contained within 500 bp). To avoid such pitfalls and analyze nucleosome organization consistently in all species we aligned the data by +1Nuc, which is consistent with alignment by TSS.Since 5′NFR-ATG distances vary substantially between species, an analysis of nucleosome organization that relies on alignment by ATG can be misleading. For example, the average nucleosome organization of S. cerevisiae we used functional gene sets from several sources: KEGG www.broad.mit.edu/regev/orthogroups.For K-SK is defined as the maximum absolute difference between the cumulative distribution functions (CDFs) of the two samples. We estimated the P value, K-SP, for the statistical significance of this difference as follows:The chromatin features used in our analysis are listed and defined in P values to K-S scores, K-SS, where K-SS = ±log10(K-SP) if the difference realizing the statistic K-SK is positive/negative, respectively. To account for multiple hypotheses testing, we only considered K-SP as significant if it was below the P value threshold for a False Discovery Rate of 5% trans factor motif affinity scores in NFRs, and comparison in expression of sites located in NFRs versus sites located in nucleosomes.For further analysis, we converted To subtract the effect of expression on observed chromatin features, we used robust Lowess smoothing. We smoothed the scatter data of each chromatin feature versus expression level using a Lowess linear fit and a smoothing window set to 10% of the span of expression level values. We assigned zero weight to outliers, defined as data more than six standard deviations from the mean. To remove the effect of expression, the Lowess fit was subtracted from its corresponding chromatin feature value. K-S functional enrichments for the Lowess subtracted chromatin features were calculated as described above.We assessed transcriptional activity by absolute RNA expression. We assessed intrinsic anti-nucleosomal sequence by Poly(dA:dT) strength in NFRs, since it explains the vast majority of the intrinsic sequence information and generalizes to all species in an unbiased manner. Other models of intrinsic sequence contribution i from the Lowess fitting line based on each of the three determinants. Then the variance of the residuals σ2R = var({Fi−Zi}) was compared to the variance of the original data σ2D = var({Zi}). The percent of variance explained is defined as (1−(σ2R/σ2D))*100. To find the percent variance explained by all determinants we first fit NFR occupancy versus one determinant, then iteratively took the residual, and fit it against the next determinant. For the figures, we first fit expression, then fit the successive residual versus Poly(dA:dT) tracts, and then fit the residual versus Abf1 and Reb1 motif affinity scores. Changing the order of the successive fits did not significantly reduce the total percent variance explained.To compute the percent of variance explained by the robust Lowess fit, each NFR occupancy was assigned a “fitted” value FS. cerevisiae DNA-binding proteins as previously defined P value <0.05 as significant. Since a few motifs had thousands of instances for this cutoff, we also limited the number of promoters with significant sites to the top 1,000. The upper bound was chosen to exceed the maximal number of promoters bound  by any TF in S. cerevisiae, as measured by ChIP-chip Promoter sequences for each gene were defined as 1,000 bases upstream, truncated when neighboring ORFs overlapped with this region. We collected a library of Position Weight Matrices (PWMs) for several hundred +1Nuc, 5′NFR, −1Nuc, −2Nuc, and NFR2—the linker between Nuc1− and Nuc2−) if their centers overlapped with the defined regions. In addition, sites were also split into two categories: Linkers (5′NFR and NFR2) and Nucs . We assigned the expression level of each gene to each site in the upstream promoter of that gene. We used a two-sample K-S test (as described above) to quantify the difference in expression levels between sites in Linkers versus Nucs.All motif instances were binned into five regions  against the mean log2 normalized nucleosome occupancy over the corresponding promoters (y). To estimate the significance of the difference of the two vectors (x-y), we used the paired Wilcoxon signed rank test that assigns a k or more elements intersect subsets of n and m members at random in a superset of size N  we summed over the right tail of a hypergeometric distribution:To estimate the probability that P values, we estimated the significance of k overlaps between n genes with sites in their upstream promoter and m genes within a GO category, for a species with N genes.Using the hypergeometric L by a position specific scoring matrix (PSSM) P, or the probability distribution 1, …, SL)PB, a motif's affinity score for an NFR sequence S, by summing the contributions of 1, …, SL)/BPbp, subtracted the mean, and divided by the standard deviation. Hence, the global nucleosome occupancy data for each species is approximately normal with zero mean and unit variance. We also used the same procedure for processing published in vitro data in vivo depletion score as the mean −log2 normalized nucleosome occupancy across all instances and all instances of the reverse complement. We also defined the depletion score relative to in vitro as power 2 of the difference between the in vivo depletion scores in each species and the in vitro depletion scores in S. cerevisiae  tract with k allowed mismatches, k = 1, …,20. The depletion score increases linearly with L for tracts with different k, confirming that a linear fit is appropriate  tracts in each species and determine their nucleosome repelling strength we used an approach similar to a previously described one ropriate .L. We define the fold depletion score of all tracts of length L as power 2 of the depletion score. We then quantized all Poly(dA:dT) tract fold depletion scores to the highest fold depletion level exceeding 2, 4, 8, 16, and 32. For example, a tract with a depletion score of 3.5 is 23.5 = 11.3-fold depleted in nucleosomes relative to average, and would be assigned a fold depletion score of 8. We next iterated over all Poly(dA:dT) tracts with mismatches k = 0, …,20, replacing overlapping tracts only if the tract with more mismatches had a higher quantized fold depletion score.To aggregate all non-overlapping Poly(dA:dT) tracts within a given genome, we first quantized the strengths for each Data have been submitted to GEO, accession #GSE21960.http://www.broadinstitute.org/regev/evolfungi/Supplementary website Figure S1Isolation of mononucleosomal DNA from 12 species. Shown are MNase titrations from which mononucleosomal DNA (red box) was isolated for construction of deep sequencing libraries.(2.22 MB PDF)Click here for additional data file.Figure S25′ alignment of nucleosome data for 12 species. Sequencing reads were extended to a length of 100 bp. Data for all annotated genes were extracted and aligned by +1Nuc, and average profiles over all genes are shown for each species.(0.60 MB PDF)Click here for additional data file.Figure S33′ alignment of nucleosome data. Data shown as in +NNuc.(0.61 MB PDF)Click here for additional data file.Figure S4Non-redundant set of chromatin features. After calling nucleosomes from S. cerevisiae data, 56 chromatin features were estimated at all gene promoters. Shown is the correlation matrix between all features in S. cerevisiae. The features used in this study are highlighted in red.(0.52 MB PDF)Click here for additional data file.Figure S5Two scenarios for changes in NFR-ATG distance. (A) Canonical promoter architecture in S. cerevisiae –transcriptional start site (TSS) is typically found at ∼13 nt 3′ to the upstream border of +1Nuc. (B) 5′NFR to ATG distance (5′NFR-ATGD) varies in other species without annotated TSSs. For example, NFR-ATG distance is shorter in D. hansenii than in S. cerevisiae  5′ UTRs are the same length and the TSS is situated within the NFR. Several lines of evidence support the latter possibility (D), including the conservation of 5′UTR length distribution in a small number of measured cases in S. cerevisiae and C. albicansS. pombe and S. cerevisiae+1Nuc+1Nuc varies substantially between Hemiascomycota species. This would affect TSS-exposure rates and pre-initiation complex geometry and has unknown consequences for basic gene regulatory mechanisms revisiae . Dependi(0.27 MB PDF)Click here for additional data file.Figure S6Conservation and variation in chromatin structure of functional gene sets. (A) Global overview of chromatin behavior within functional gene sets. K-S scores , indicating that these genes likely play species-specific stress-related roles. (B) Gene sets associated with increase in NFR occupancy in post-WGD species were identified and are shown as in panel A.S scores  were calS scores . Gene se(0.61 MB PNG)Click here for additional data file.Figure S73′ NFR enrichments. K-S enrichments for 3′ chromatin parameters were calculated for all gene sets as in +NNuc occupancy or tight 3′ CDS spacing) downstream of ribosomal protein genes. Additional annotations associated with long 3′ NFRs are noted on the right.(6.80 MB PDF)Click here for additional data file.Figure S8Chromatin feature enrichment for gene sets is robust to gene orientation. K-S enrichments for chromatin parameters were calculated for all gene sets as in (0.60 MB PNG)Click here for additional data file.Figure S9Relationship between RNA level, antinucleosomal tracts, GRF sites, and chromatin structure. (A–D) Gene-by-gene comparisons of NFR depth to mRNA levels or Poly(dA:dT) signal. Shown are plots of NFR depth (y-axis) versus mRNA level  or Poly(dA:dT) score at the NFR  for each gene (blue dot) in the S. cerevisiae  or C. albicans  genome. Also shown is a 50-gene running window average for each panel (red). (E) Variation in NFR depth explained by each determinant and their combination. Shown are the % variation in NFR depth  explained in each species by each determinant alone  and two combinations . (F) The residual contribution of polyA and GRF sites. Shown are the % variation in NFR depth  explained in each species by mRNA expression alone (blue bars), the subsequent residual variation explained by polyA (red bars) and the residual variation (after mRNA and polyA) explained by the binding sites for the GRFs Abf1 and Reb1 (green).(2.97 MB PDF)Click here for additional data file.Figure S10Relationship between RNA level and chromatin structure. The extent of variation in a given chromatin parameter which is explained by RNA abundance was calculated Click here for additional data file.Figure S11Relationship between Poly(dA:dT) tracts and nucleosome depletion varies between species.  Shown are plots of nucleosome depletion  versus length of Poly(dA:dT) tract (x-axis) for Poly(dA:dT) tracts with no mismatches (A) or 2 mismatches (B). (C) Species differ in the number of antinucleosomal PolyA tracts. Shown are the number of anti-nucleosomal Poly(dA:dT) tracts with a strength score greater than 4 in the NFRs of each species. (D) Median NFR width (per species) is correlated (r = 0.77) with number of anti-nucleosomal Poly(dA:dT) tracts in the NFR. Shown are the number of anti-nucleosomal Poly(dA:dT) tracts with a strength score greater than 2 in the NFRs in each species versus that species' average NFR length.(0.37 MB PDF)Click here for additional data file.Figure S12The calculated effect Poly(dA:dT) tracts on nucleosome depletion for gene sets is independent of the dataset used. We calculated the extent of nucleosome depletion over various lengths of Poly(dA:dT) using either in vitro nucleosome reconstitution data (4.39 MB PDF)Click here for additional data file.Figure S13Analysis of anti-nucleosomal 6-mers. (A) The extent of nucleosome depletion over all 6-mers was calculated as in S. cerevisiae is plotted against that in S. mikatae. Few differences are observed. (C) As in (B), but for S. cerevisiae versus K. lactis. CGCG-containing Rsc3/30-like motifs (green) are more nucleosome-depleted in S. cerevisiae than in K. lactis, whereas the Cbf1 motif CACGTG and related motifs (blue) are more nucleosome-depleted in K. lactis than in S. cerevisiae. This is consistent with the loss of the Rsc3/30 ortholog in K. lactis(0.72 MB PDF)Click here for additional data file.Figure S14Species-specific usage of GRF-related motifs. Shown are nucleosome depletion scores over the indicated sequences for all in vivo data reported here, and for two published in vitro reconstitution datasets (blue) in S. cerevisiaeC. albicans(0.32 MB PDF)Click here for additional data file.Figure S15Evolution of anti-nucleosomal programming at specific gene sets. Enrichment of Poly(dA:dT) tracts (A8) or motifs for various GRFs was calculated for the indicated gene sets. Enrichments are shown for high (red) or low (green) expression levels, high (yellow) or low (blue) 5′NFR occupancy or length, and enrichment (pink) or depletion (blue) of A8 or GRF motifs for each gene set. K-S P value saturation levels are indicated to the right of each panel.(0.46 MB PDF)Click here for additional data file.Figure S16Re-positioning of TF motifs relative to NFRs. (A) Location of TF binding sites relative to NFRs in all 12 species  As in (A) and S. cerevisiae, likely due to variation in TF binding site affinities. Much of the variation in the overall fraction of motifs in NFRs can also be ascribed to variation in NFR length across species—C. glabrata, for example, exhibits unusually long NFRs Click here for additional data file.Figure S17Divergence of activity of NFR-localized binding sites. Variation in TF activity across the 12 species. Data shown as in (1.26 MB PDF)Click here for additional data file.Table S1Chromatin features. Definitions of chromatin parameters used throughout the text.(0.03 MB XLS)Click here for additional data file.Table S2Gene set enrichments for mRNA abundance. For each species (columns) in this study, we display the K-S score (-S score  for mRNA(0.26 MB XLS)Click here for additional data file.Table S3Phylogenetic variation in global chromatin features. For each species, we computed the median of the global distribution of each of the 56 estimated chromatin features. Features are displayed as rows, species as columns.(0.04 MB XLS)Click here for additional data file.Table S4Gene set enrichments for 5′ chromatin features. For each species (columns) in this study, we display the K-S score (5DNFR-ATG′ (5′tss_pred), 5′NFR length (5′nfrLen), 5′NFR occupancy (5′nfrOcup), Nuc+1 to 5′NFR occupancy (plus1_to_5′nfrOcup), Nuc+1 occupancy (plus1Ocup), Fuzziness of Nuc+1 (plus1Fuzzy), width of Nuc+1 (plus1Width), median adjacent spacing for Nuc+1 to Nuc+4 (plus1SpacingDown3), Nuc−1 to 5′NFR occupancy (minus1_to_5′nfrOcup), Nuc−1 occupancy (minus1Ocup), Fuzziness of Nuc−1 (minus1Fuzzy), width of Nuc−1 (minus1Width), and median adjacent spacing for Nuc−1 to Nuc−4 (minus1SpacingUp3). GO, KEGG, MIPS, BioCyc categories with 10 or more species with valid enrichments are displayed.-S score  for enri(2.44 MB XLS)Click here for additional data file.Table S5Gene set enrichments for 3′ chromatin features. For each species (columns) in this study, we display the K-S score (+N to 3′NFR occupancy (plusN_to_3′nfrOcup), Nuc+N occupancy (plusNOcup), fuzziness of Nuc+N (plusNFuzzy), width of Nuc+N (plusNWidth), median adjacent spacing for Nuc+N−3 to Nuc+N plusNSpacingDown3, Nuc+N+1 to 3′NFR occupancy (plusN+1_to_3′nfrOcup), Nuc+N+1 occupancy (plusN+1Ocup), fuzziness of Nuc+N+1 (plusN+1Fuzzy), width of Nuc+N+1 (plusN+1Width), and median adjacent spacing for Nuc+N+1 to Nuc+N+4 plusN+1SpacingUp3. GO, KEGG, MIPS, BioCyc categories with 10 or more species with valid enrichments are displayed.-S score  for enri(0.92 MB XLS)Click here for additional data file.Table S6Nucleosome depletion over k-mers. Shown are the negative depletion scores Click here for additional data file.Table S7Distribution of Poly(dA:dT) tract lengths in all species. Shown are the number of genomic occurrences of Poly(dA:dT) tracks of length L (rows) for all species (columns) in this study.(0.04 MB XLS)Click here for additional data file.Table S8Anti-nucleosomal sequence enrichments versus gene sets. For each species (columns) in this study, we display the K-S score  in vitro strength, Poly(dA:dT) in vivo strength, PolyA8, RGE, ABF1, CBF1, REB1, RSC30, PolyG8, and C. albicans-specific k-mer CACGAC. GO, KEGG, MIPS, BioCyc categories with 10 or more species with valid enrichments are displayed.-S score  enrichme(1.95 MB XLS)Click here for additional data file.