Bacillus subtilis. Unphosphorylated and phosphorylated forms of DegU are known to activate target gene transcription in B. subtilis. Although phosphorylated DegU (DegU-P) regulates more than one hundred and twenty genes, the targets of unphosphorylated DegU are unknown, except for comK.The response regulator DegU and its cognate histidine kinase DegS constitute a two-component system in the Gram-positive soil bacterium fla/che (flagella and chemotaxis) operon is positively regulated by unphosphorylated DegU. The effect was most prominent in a strain bearing the functional swrAA gene, a positive regulator of fla/che. Unphosphorylated DegU bound to two regions in the fla/che regulatory region containing an inverted repeat-like sequence that resembles the inverted repeat (IR) in the comK promoter. Mutational analysis revealed that positive regulation of fla/che by SwrAA requires DegU-binding. An analysis of the DegU-P-regulated gene sacB (levansucrase gene) by footprint and mutational analyses revealed that DegU-P bound to a direct repeat (DR) of the DegU-recognition motifs, which has been shown to be functional in vivo, while unphosphorylated DegU did not. These results strongly suggest that the arrangement of the DegU-binding motifs determines whether unphosphorylated DegU or DegU-P binds to the sacB promoter. The hypothesis was confirmed by observing degS-independent expression when the DR in the sacB-lacZ fusion was changed to an IR, suggesting that unphosphorylated DegU regulates the sacB promoter through the newly created IR. This was confirmed by binding of unphosphorylated DegU to the IR in the sacB promoter.We found that the flgB and sacB through its binding to the promoter regions. We demonstrated that DegU-P prefers binding to DR but not to IR in the sacB promoter.This study demonstrated that DegU positively regulates  Bacillus subtilis consists of the response regulator DegU and its cognate histidine kinase DegS. DegU belongs to the NarL family, whose members have a helix-turn-helix structure at their C-terminus [rapG is repressed by RghR [To respond to environmental fluctuations, bacteria employ a large and elaborate family of two-component signaling systems. The classical two-component system consists of a sensor kinase and its cognate response regulator . In respterminus . The Degterminus -9. It haterminus ,10,11. Iterminus . The actterminus . DegU isterminus . RapG ac by RghR .comK, which encodes a master regulator of competence development [comK promoter [comK promoter (GTCATTTA-N7-TAAATATC) by using various mutated comK-lacZ fusions [Unphosphorylated DegU is required for competence development and binds to the promoter region of elopment ,17. Unphpromoter . Previou fusions . AdditioaprE  and sacB (which encodes levansucrase); it also represses wapA, which encodes a cell-wall associated protein [bpr, which encodes bacillopeptidase F, has been reported to be probably dependent on DegU-P [aprE and bpr. Our analysis revealed that an important cis-factor for DegU-dependent aprE expression is a direct repeat (DR) of the downstream half of the DegU-recognized IR in the comK promoter with two-nucleotide spacing (-70 to -52 relative to the transcription start site), [bpr promoter region and DegU-P-dependent expression of bpr [degU32 mutation, which renders DegU-P resistant to dephoshorylation, resulted in a decrease in the expression of the fla/che operon encoding chemotaxis-related proteins and components of the flagella apparatus [Phosphorylated DegU (DegU-P) activates the expression of more than one hundred twenty genes, including  protein -9,20. Inn DegU-P ,21. To dt site), . In addin of bpr . Furtherpparatus ,9,23,24.comK transcription, DegU is regarded as a molecular switch that controls cell fate [Since DegU-P stimulates the transcription of many genes and unphosphorylated DegU is required for ell fate . HoweverflgB, which is the first gene of the 26-kb-long fla/che operon, is subject to direct positive regulation by unphosphorylated DegU through two DegU-binding sequences containing an IR-like sequence. In contrast, footprint analyses of the sacB promoter revealed that DegU-P bound to a DR within the DegU-binding sequence. Since unphosphorylated DegU bound to flgB, the arrangement of the DegU-binding sequence within promoter regions (DR or IR-like sequence) must dictate which form of DegU (phosphorylated or unphosphorylated) binds to them. This hypothesis was confirmed by expression analysis of sacB-lacZ fusions carrying an artificial IR within their DegU-binding sequence.In this paper, we found that flgB (BR1 and BR2). BR1 seems to contain an IR-like sequence (the -95 to -69 region relative to the transcription start site) with long spacing (eleven bases), and BR2 also seems to contain an IR-like sequence (the +119 to +136 region relative to the transcription start site) with two-nucleotide spacing  strain, which is consistent with former results [degU mutation but not the degS mutation would have an effect on fusion expression. As shown in Figure swrAA and swrAA+ backgrounds, the degU mutation decreased the expression of F1D, while the degS mutation had little or no effect. The effect of the degU mutation was more prominent in the swrAA+ background than in the swrAA background. These results indicated that unphosphorylated DegU positively regulates F1D fusion expression. Moreover, the degU32 mutation, which makes DegU-P resistant to dephosphorylation [swrAA and swrAA+ backgrounds, respectively. This observation is in good agreement with former results [To examine the in vivo functions of the BRs, we constructed a F1D fusion carrying both BRs and measured the β-galactosidase activities of the fusion in various genetic backgrounds, including disruption mutation of A Figure . The degechanism . In addiechanism . The tralymerase . In the  results . As unphrylation , reduced results   and tested its expression in various genetic backgrounds  or by deleting BR1 (F2 and F3). These results demonstrated that unphosphorylated DegU binding to BR1 is required for the positive regulation of flgB by DegU, and for the enhancing effect of SwrAA. This result strongly suggested that at least part of the positive function of SwrAA is exercised through DegU. In fact, in the degU strain carrying F1D, F1DM or F1, SwrAA had little effect on the expression of flgB. Surprisingly, the introduction of the degU32 mutation into a strain carrying F1 and swrAA resulted in about a three-fold increase in the expression of F1, which is not consistent with the results of the expression analysis of F1D. This positive effect of degU32 in the swrAA strain was abolished by disrupting or deleting BR1, suggesting that higher cellular concentrations of DegU-P may positively regulate flgB expression through increased binding of DegU-P to BR1. However, this hypothesis is not in agreement with the result shown in Figure To determine the in vivo role of BR1 in the positive regulation of s Figure . As wellflgB. Moreover, the absence of effects of degU and functional swrAA in F1MD was reasonable, because F1MD is equivalent to F2D with respect to distribution of BRs. These results suggested that BR2 is not involved in positive regulation by SwrAA and unphosphorylated DegU. A comparison of the expression patterns of the F1D and F1DM fusions showed that there was essentially no difference except for the epistatic effect of the degU32 mutation. This suggested that BR2 could have some role to play in the negative effect of the degU32 mutation on F1D expression. In the presence of BR2, the degU32 mutation did not exhibit positive regulation of flgB through BR1, while, in the absence of BR2, the mutation resulted in increased fusion expression. Thus, BR2 appears to prevent the positive regulation of flgB by DegU-P through BR1, leading to downregulation of F1D in degU32 cells . A previous analysis found that the transcription start site is seven-base downstream of the newly determined site [sacB was done based on our result hereafter.To characterize the interaction of DegU-P with its targets, we selected eviously . We detened site . This disacB promoter, we performed DNase I footprint analysis  of the putative DegU-binding sequence was only protected on the both strands. In addition, we found that His-tagged unphosphorylated DegU was able to bind to the sacB promoter region in footprint analysis for an unknown reason (see Discussion). In the experiments using His-tagged DegU the region containing the DR was protected from DNase I cleavage  among the mutants tested. In addition, the M1 and M2 mutations disrupting the DR led to significant decreases in sacB-lacZ expression. These results confirmed the functionality of the DR. Furthermore the other detected DegU-binding sites did not have significant in vivo roles, because the introduced mutations to the regions showed marginal effects on transcription of the fusions. We note that the mutation located immediately downstream of the DR (S-M12) reduced fusion expression to some extent. Notably, a previous deletion study had shown that the ability of the degU32 mutation to enhance sacB expression is dependent on the -137 to -90 region of the sacB promoter [To confirm the functions of the detected DegU-binding regions  sucrose . Thus, wr Figure . We intrpromoter ; this reaprE and sacB, whose promoters carry a DR, is positively regulated by DegU-P  have long spacing regions, whereas the DRs of aprE, bpr and sacB have short spacing regions. Thus, we examined whether a long spacer between the two half sites of sacB DR might alter its regulation. The introduction of a long spacer corresponding to that of the comK IR into the DR of sacB (SC-DR) did not affect degS-dependent positive regulation of sacB , and the region appeared to be protected from DNase I cleavage to some extent. The mutational analysis, however, revealed that the sequences do not serve as a functional cis-element for DegU-P . It was reported that disruption of degU decreases the expression of sigD [flgB promoter [We found that unphosphorylated DegU is a positive regulatory factor of the  of sigD , which ipromoter ,38,39. OflgB expression in the non-laboratory strain requires DegS-independent low-level of DegU-P [fla/che operon. To further characterize the DegS-independent phosphorylation of DegU, a study using a DegU mutant lacking the phosphorylation site should be needed.Recently it has been reported that swarming motility and the f DegU-P ,41. ThesflgB and sacB via the BRs containing the IR-like sequences and the DR present in their promoter regions, respectively . The plasmids and oligonucleotides used in this study are listed in Table  enzymes . To cons enzymes . To conseviously . S-del4 E. coli BL21 carrying pET-degS, M15 carrying pRep4 and pDG-His-degU, and BL21 carrying pTYB-degU by previously described methods [E. coli and purification was performed by step-wise elution from a Ni-affinity column with imidazole as described previously [The recombinant His-tagged DegS, His-tagged DegU and chitin-binding domain (CBD)- and intein-fused DegU proteins were induced in  methods . His-tageviously . CBD- aneviously . AliquotPhosphorylation of DegU and the binding of DegU-P to the probe DNA were carried out by incubating intact DegU and His-tagged DegS at 25°C for 30 min (molar ratio 3:1) with a biotinylated DNA probe, i.e., in a buffer containing protein solution (TEDG buffer). The final concentration of the reaction mixture (65 μl) for the footprint analysis was as follows; 10 mM (NH4)2SO4, 1 mM DTT, 0.2% Tween20, 5 mM MgCl2, 31 mM Tris-HCl (pH 7.5), 0.3 mM EDTA, 44 mM KCl, 1 μg poly dI-dC and 1 mM ATP. Immediately at the end of the reaction, 4 units of DNase I  in 7. 5 μl buffer containing 6% glycerol, 10 mM Tris-HCl (pH 7.5), 50 mM NaCl, 5 mM MgCl2, 1 mM DTT and 1 mg bovine serum albumin was added. The reaction mixture was left at room temperature for 5 min and then subjected to phenol extraction and subsequent ethanol precipitation after the addition of stop solution . After the addition of a loading dye, the samples were applied onto a 6% polyacrylamide gel. The probes were prepared by PCR using the oligonucleotide pairs indicated in Additional file lacZ analysis and wrote the paper. All the authors have read and approved the final manuscript.KT carried out protein purification and footprint analysis. MO carried out construction of plasmids and strains, and in vivo Oligonucleotides used for this study. Oligonucleotide sequences used for this study.Click here for file


Our genome-wide analysis shows conclusively that in yeast, relative exchange is a novel consistent feature of coding regions. Outside of replication, each coding region has a characteristic pattern of histone H3 exchange that is either higher or lower than what was expected by its RNAPII transcription rate alone. Histone H3 exchange in coding regions might be a way to add or remove certain histone modifications that are important for transcription elongation. Therefore, our results that gene-specific coding region histone H3 exchange is decoupled from transcription rate might hint at a new epigenetic mechanism of transcription regulation.In eukaryotic organisms, histones are dynamically exchanged independently of DNA replication. Recent reports show that different coding regions differ in their amount of replication-independent histone H3 exchange. The current paradigm is that this histone exchange variability among coding regions is a consequence of transcription rate. Here we put forward the idea that this variability might be also modulated in a gene-specific manner independently of transcription rate. To that end, we study transcription rate–independent replication-independent coding region histone H3 exchange. We term such events  relative exchange, is a reproducible and consistent feature of the entire coding region and cannot be explained by regional effects. Next, we characterize the relations between relative exchange and a variety of histone H3 modifications, as well as the histone chaperone Asf1. Taken together, our analysis shows that gene-specific replication-independent histone H3 exchange in coding regions is mediated independently of transcription rate, thus constituting a new mechanism in epigenetic transcription regulation.During nucleosome disassembly and reassembly, evicted histones are exchanged with newly synthesized histones. Histone exchange occurs in several DNA metabolism processes, including replication, transcription, and repair. Recent reports from several labs show that replication-independent histone H3 exchange in yeast coding regions is tightly correlated with transcription rate. We have computationally shown that histone exchange variability among genes is not only a consequence of transcription rate. Instead, each coding region has a characteristic amount of replication-independent histone exchange, even when excluding the confounding effect of transcription rate. We show that this transcription rate–independent exchange, referred to as  The nucleosome is the basic repeating unit of the chromatin and comprises 147 bp of DNA wrapped around an octamer of histone proteins . Nucleosome disassembly and reassembly is tightly coupled with replication, transcription, DNA repair and heterochromatin silencing e.g., . Under nCoding region replication-independent H3 exchange varies from gene to gene. Recent studies have shown that this variation among coding region is linked to differences in transcription rate Although it is widely accepted that coding region replication-independent H3 exchange differences are a consequence of transcription rate, it is still unknown whether this variability is also controlled independently of transcription rate. In other words, it is not clear whether different coding regions can have substantially different replication-independent H3 exchange even if they have the same transcription rate. This leads us to investigate genome-wide coding region H3 exchange independently of both replication and transcription rate. We address two key questions: First, is there evidence that transcription rate–independent and replication-independent H3 exchange in coding regions is a consistent feature of genes? Second, is there evidence for an active regulation of this feature?total exchange, whereas the calculated transcription rate–independent total exchange is referred to as relative exchange. Positive (negative) relative exchange implies that the total exchange is higher (lower) than what is expected based on transcription rate alone. Importantly, although relative exchange is independent of transcription rate, it is still likely to be influenced by the transcription process. We found that relative exchange varies from gene to gene and is a reproducible feature of genes. Elevated or reduced relative exchange occurs along the entire coding region and not only in a specific part of it. Moreover, relative exchange is a gene-specific property rather than a regional effect. Finally, we revealed that H3K79 trimethylation is depleted in coding regions with hyper relative exchange and enriched in coding regions with hypo relative exchange. Taken together, our data provides evidence that coding regions have a characteristic relative exchange, a new feature of genes. Genes might have either hyper or hypo relative exchange, irrespective of their total exchange or transcription rate. Histone exchange in coding regions might be a way to add or remove certain histone modifications that are important for transcription elongation. Therefore, decoupling replication-independent histone exchange from transcription rate is a process with potential for epigenetic gene regulation.In this study, we analyzed published data sets of replication-independent histone H3 exchange in yeast Asf1 is a histone H3/H4 chaperone that has been implicated in histone H3/H4 exchange during elongation transcription rate is defined as RNAPII density averaged over the coding region. Total exchange is the measured replication-independent histone H3 exchange averaged over the coding region.The present work is focused on the understanding of replication-independent histone H3 exchange in coding regions. For our study, we used published genome-wide measurements of histone H3 exchange and RNAPII densities Relative exchange is the distance of total exchange to a running average of the total exchange along the transcription rate axis (probe's relative exchange), we used measurements of total exchange and RNAPII density in a single probe without averaging over the entire coding region  gene. For comparison, we have computed the difference between relative exchange replicates ]. This observation is particularly significant because of the bias toward both negative and positive differences between similar-transcription genes. Whereas the bias toward positive values can be attributed to the global relationships with transcription rate, this effect cannot explain the bias toward negative values.The observed relative exchange variation among genes might be a consequence of biological or experimental noise. To exclude the latter, we grouped genes into pairs with minimal difference in transcription rate. Such a gene pair is termed −200, Next, we have investigated the reproducibility of relative exchange in different laboratories. Total exchange (together with transcription rate) was measured in two different laboratories To examine whether relative exchange is a general property of genes, we wished to analyze the reproducibility of relative exchange along the entire coding region. To that end, we divided the genes into five subsets according to their relative exchange based on Dion et al. For each subset, we plotted a profile of probe's relative exchange throughout the coding region based on Rufiange et al. . Note thsegmental relative exchange) was calculated using only probes located within this segment (see within genes) tend to be smaller than relative exchange differences between segments from neighboring genes (denoted between genes). In agreement, relative exchange variation between genes is significantly larger than the variation within genes [P<10−200 (F-test for significance of difference between variances)], indicating the consistency of relative exchange along the coding region. Notably, the distribution of relative exchange differences between neighboring genes is similar to the distribution of relative exchange differences between random genes (hyper relative exchange (or hypo relative exchange), based on their total exchange that is relatively higher (or lower) from what can be expected from their transcription rate alone. A genome-wide mapping shows that hyper relative exchange coding regions are scattered throughout the genome , indicating that H3K79me3 is specifically associated with relative exchange. In agreement, Unexpectedly, we found that relative exchange is tightly related to H3K79 trimethylation H3K79me3 . H3K79mePrevious studies show that in coding regions, there is a correlation between H3K79me3 and transcription rate e.g., . We aske−35, Figure S4 in −3). Therefore, our analysis indicates that although both H3K36me3 and H3K36Ac are inversely related, they are associated differentially with relative exchange.Histone H3K36 can be a target of acetylation, mono-, di- and trimethylation . Recent report shows that H3K36Ac pattern is inversely related to H3K36me2 and H3K36me3 patterns in coding regions, suggesting that H3K36 is an ‘acetyl/methyl switch’ asf1Δ, denoted Asf1-mediated exchange . However, given total exchange, the conditional correlation is insignificant . This can be clearly seen in We next sought to investigate the relations between Asf1-mediated exchange and transcription rate. In agreement with previous reports, we found that Asf1-mediated exchange indeed correlates with transcription rate . The presence of Asf1 at promoters is important for disassembly of H3/H4 upon activation and for reassembly of H3/H4 upon loss of activation  but is not associated with total exchange . To avoid biases related to genes that are not transcribed by RNAPII and global effects on histone H3 exchange, we removed non-coding genes, 25-kbp regions near the telomeres and centromeres, and 1-kbp regions near rRNA, tRNA, ARS and mitochondrial DNA . To calculate relative exchange, we needed the running average curve of total exchange along the RNAPII density. To avoid complications arising from different total exchange along the coding region .In x-axis), total exchange (y-axis) and an additional molecular feature  exchange and an additional molecular feature, were calculated independently of total (relative) exchange and transcript length. This way, the correlation with any exchange measure is calculated only after factoring out the contribution of the other exchange measure and after eliminating potential effects of transcript length. For each coding region, we used the molecular feature value averaged over the coding region. Transcript lengths were computed based on sequencing of cDNA library (Text S1(0.74 MB PDF)Click here for additional data file.Text S2(0.03 MB PDF)Click here for additional data file.Text S3(0.02 MB PDF)Click here for additional data file.Text S4(0.04 MB PDF)Click here for additional data file.Text S5(0.28 MB PDF)Click here for additional data file.


Mycobacterium tuberculosis are apparently required for its growth and resistance in hostile host environments. In such environments, MtrAB has been reported to regulate the expression of the M. tuberculosis replication initiator gene, dnaA. However, the dnaA promoter binding sites and many potential target genes for MtrA have yet to be precisely characterized.The two-component systems of dnaA promoter region was identified for MtrA binding using DNaseI footprinting assays and surface plasmon resonance (SPR) analysis. Approximately 420 target genes potentially regulated by MtrA, including the isoniazid inducible gene iniB, were further characterized from M. tuberculosis and M. smegmatis genomes. When assayed using quantitative real-time PCR (qRT-PCR), many of the target genes demonstrated significant expression changes when the antisense mRNA of the mtrA gene was expressed in M. smegmatis. The recombinant mycobacteria grew in length and were more sensitive to two anti-tuberculosis drugs, isoniazid and streptomycin.In this study, a 7 bp sequence motif in the M. smegmatis.These findings yield critical information about the regulatory mechanisms of the MtrAB two-component system and its role in the drug resistance of  M. tuberculosis is one of the most devastating human pathogens, and its threat to human health has intensified with the emergence of multidrug-resistant tuberculosis (TB) and the worldwide prevalence of co-infection with HIV  ATP , respectively.The 84 bp (S6) and 75 bp (S7) escribed . The ladM. tuberculosis and M. smegmatis and the database of intergenic regions of ORFs (from stop codon to start codon) were constructed. The exact motifs (CACGCCG or CACGAGG) were then used to search for the distribution of the identified 7 bp motifs in the M. tuberculosis H37Rv and the M. smegmatis genomes. The identified target genes are listed (Additional file The regulatory sequences were collected from the complete genomes of 3-AT: 3-amino-1, 2, 4-triazole; EMSA: electrophoretic mobility shift assay; PCR: polymerase chain reaction; qRT-PCR: Quantitative real-time PCR; SA chips: Streptavidin chip; SEM: scanning electron microscopy; SPR: surface plasmon resonance; TCRs: two-component systemsThe authors declare that they have no competing interests.YL and ZGH designed the experiments. YL and JZ performed the experiments. YL HZ and ZGH analyzed the data. ZGH contributed reagents/materials/analysis tools. ZGH and YL wrote the paper. All authors have read and approved the final manuscript.Plasmids and recombinant vectors used in this study. The data present plasmids and recombinant vectors used in this study.Click here for fileSPR assays for the binding of unspecific promoter chip by MtrA. The data present SPR assays for the binding of unspecific promoter chip by MtrA.Click here for fileCompeting SPR assay with the unlabeled DNA fragments for the binding of the promoter chip by MtrA. The data present the competing SPR assay with the unlabeled DNA fragments for the binding of the promoter chip by MtrA.Click here for fileM. tuberculosisPotential target genes for MtrA in . The data provided potential target genes for MtrA in M. tuberculosis.Click here for fileM.smegmatisPotential target genes for MtrA in . The data present Potential target genes for MtrA in M.smegmatis.Click here for fileM. tuberculosis and M. smegmatisHomologous target genes recognized by MtrA in . The data present homologous target genes recognized by MtrA in M. tuberculosis and M. smegmatis.Click here for filePrimers used in this study. The data provided primers used in this study.Click here for fileSequences of the DNA substrates used in this study. The data provided sequences of the DNA substrates used in this study.Click here for filePrimers used for quantitative real time PCR in this study. The data present the primers used for quantitative real time PCR in this study.Click here for fileM. smegmatisClassification and percentage of the target genes containing the 7-bp motif recognized by MtrA in . The data present the categories and percentage of the target genes containing the 7-bp motif recognized by MtrA in M. smegmatis.Click here for fileM. tuberculosisThe data present the categories and percentage of the target genes containing the 7-bp motif recognized by MtrA in . The data present the categories and percentage of the target genes containing the 7-bp motif recognized by MtrA in M. tuberculosis.Click here for file


RPS19) has been linked to two kinds of red cell aplasia, Diamond-Blackfan Anemia (DBA) and Transient Erythroblastopenia in Childhood (TEC). Mutations in RPS19 coding sequences have been found in 25% of DBA patients, but not in TEC patients. It has been suggested that non-coding RPS19 sequence variants contribute to the considerable clinical variability in red cell aplasia. We therefore aimed at identifying non-coding variations associated with DBA or TEC phenotypes.The Ribosomal protein S19 gene locus  that all are located outside of the RPS19 open reading frame, and of which 67.1% are classified as novel. We hypothesize that specific alleles in non-coding regions of RPS19 could alter the binding of regulatory proteins or transcription factors. Therefore, we carried out an extensive analysis to identify transcription factor binding sites (TFBS). A series of putative interaction sites coincide with detected variants. Sixteen of the corresponding transcription factors are of particular interest, as they are housekeeping genes or show a direct link to hematopoiesis, tumorigenesis or leukemia .We targeted a region of 19'980 bp encompassing the RPS19, modify an important interaction between transcription factors with overlapping TFBS or remove an important stimulus for hematopoiesis. We suggest that the detected interactions are of importance for hematopoiesis and could provide new insights into individual response to treatment.Specific alleles at predicted TFBSs may alter the expression of  RPS19) Diamond Blackfan Anemia (DBA) is a congenital pure red cell aplasia (OMIM 205900) typically presenting within the first year of life RPS19 but no structural mutations have been identified so far Transient erythroblastopenia of childhood  is a transient red cell aplasia with clinical similarities to DBA RPS19 mutations have been reported in DBA patients RPS19 mutations display a poorer response Until now, more than 70 RPS19 locus in 77 DBA and 12 TEC patients not carrying a mutation in exons of the RPS19 gene and we provide a catalog of the genetic variations identified. Furthermore, we searched the entire region for putative transcription factor binding sites (TFBS) some of which are presumably altered by the variations identified. We suggest that gene variants at TFBSs influence the expression of RPS19 with a resulting effect on disease pattern and response to treatment. These findings are important to clarify the regulation of RPS19 gene expression and for our understanding of the pathobiology behind DBA.We therefore aimed at identifying non-coding variations that are associated with the DBA or TEC phenotypes. Here, we report on the targeted resequencing of the entire RPS19 and 2 kbp of flanking region . Variations were assigned in all individuals to provide a genetic map of the RPS19 locus. In addition, we analyzed a region upstream of the initial sequencing effort in a subset of patients ; both analyzed regions together comprised of 19'980 bp  is one per 273.7 bp (3.6 variations kbp−1), including one SNP per 293.8 bp (3.4 SNPs kbp−1). This is in contrast to the expected density of one SNP per 1.9 to 2.18 kbp−1 that has been estimated for human chromosome 19 in previous studies We initially targeted a region of 14.7 kbp on human chromosome 19 , encompassing 9'980 bp . We dete9'980 bp . Altoget9'980 bp . All varRPS19 has homologs in eukaryotes and archaebacteria but no eubacterial counterparts RPS19 gene is conserved (i.e. number and order of exons). We obtained and aligned syntenic genomic regions of 200 kb around the orthologous RPS19 gene from 6 species , at one position within the second intron (one detected SNP) and to three sites located in intron 4 (three SNPs). In some cases, the TFBS was independently identified by two different tools/databases  focusing on regions with a high degree of conservation . Our aimRPS19. Our study confirms that the RPS19 promoter region shares typical features with other mammalian ribosomal protein genes . We also detected an accompanying non-consensus CCATT-box 72 to 83 bp upstream of the transcription start site commonly described in public databases (according to EnsEMBL). Three different transcription start sites (TSS) have been described  which differ only in the length of the 5′UTR of the resulting mRNA. We have observed additional 5′UTR variants differing in length between 33 to 467 nucleotides (unpublished data). The observed spread of the TSS together with the absence of a canonical TATA-box classify the RPS19 promoter as “broad type promoter” according to Sandelin and colleagues RPS19 described previously by DaCosta et al.  and Transient Erythroblastopenia of childhood (TEC) Association studies take advantage of the known variations throughout the human genome including SNPs and microsatellites. It has been suggested that the identification of all the potential risk-conferring variations within one disease associated gene is important for appropriate genotype-phenotype correlations RPS19 gene locus. Estimates have approximated the degree of variation for chromosome 19 to 1 SNP in about 2 kbp of sequence RPS19 locus in our patient cohort is significantly higher, with 1 SNP per 294 bp, and provide a catalog of additional variations associated with DBA and TEC. A large proportion of the presented variations consists of “private” SNPs. Interestingly, independent resequencing studies of e.g. the innate immunity genes and the APO gene cluster have also detected an unexpectedly high degree of variation RPS19 seems to play a central role in a large proportion of DBA patients suggest that regulatory networks altered by one or the other SNP may have implications for RPS19 expression.In the present resequencing study, we show a considerable and previously unrecognized variation within the RPS19 expression. Indeed, it has been described that regulatory elements may be situated far away from the actual gene. Mutations in such elements have previously been implicated in human diseases RPS19, but to a different gene within the 1 Mbp region described previously However, our results revealed no clear correlation between any of the identified SNPs and either of the DBA or TEC phenotypes. Linkage analyses have previously indicated co-segregation of the two disorders suggesting they are allelic variants RPS19 could disrupt the binding of regulatory proteins. We aimed to identify new regulatory modulators and carried out a bioinformatics analysis of the locus to identify putative transcription factor binding sites (TFBS). As a result, we obtained a catalog of variations within our patient cohort and we provide a map of putative transcription factor binding sites  deletes the putative binding site for CDC5. Instead, a strong TFBS for GATA-1/2 is created. This suggests a significant change for tissue specific expression. Moreover, the general TFs as well as the Ebox binding site could be pivotal for cellular response to extracellular stimuli and this may explain individual response to treatment or endogenous cytokines Consequently, we hypothesized that mutations in non-coding regions of ng sites . SeveralRPS19 during hematopoiesis, and alterations in the respective TFBS could lead to diminished RPS19 expression. This might render erythroid precursors to be less capable to proliferate, which has been suggested as a mechanism underlying DBA in patients with mutations in the coding sequence of RPS19Six TFBS identified in our study are even more interesting (GATA binding proteins 1 and 2 (GATA-1 and GATA-2); Myeloid zinc finger 1 (MZF-1); Pre-B-cell leukemia homeobox 1 (PBX-1); hematopoietic transcription factor PU.1 (PU.1); Ebox binding site). They belong to factors with a direct link to hematopoiesis Another possible mechanism is that specific alleles in SNPs overlapping with the TFBS for PU.1, GATA-1/2 or PBX-1 might be of importance for the development of hematopoietic stem cells by altering their capacity of self-renewal, expansion and quiescence RPS19 in DBA and TEC patients. Furthermore, we identified a series of transcription factors putatively involved in the regulation of RPS19 expression and implicated in the pathobiology of DBA and TEC. Functional follow-up studies are needed to further investigate the predicted interactions described in this report.In summary, we report here on the considerable individual variation detected in our resequencing study of the disease locus The study was approved by the Regional Ethical Review Board of Uppsala (Diary Number 2006/118). Informed consent of patients or their parents was obtained and has been documented in the patient files by the responsible clinician following routines approved by the Regional Ethics Board and according to Swedish legislation.RPS17, RPS19 or RPS24, respectively (Primer sequences available on request). Most of the patients are sporadic cases, except for 10 of the DBA patients and eight of the TEC patients who previously showed association with this genomic region. All patients were ascertained by hematologists of their country for criteria for DBA or TEC, respectively, and have been described previously We analyzed DNA prepared from peripheral blood of 77 DBA and 12 TEC patients of Caucasian origin. Patients included in the study were excluded by sequencing to carry a structural mutation in http://www.ncbi.nlm.nih.gov/SNP/)). Additional sequence analysis was performed by sequencing standard PCR products (from approximately 2 µg genomic DNA) in both directions on an ABI PRISM® 3700 DNA Analyzer (AppliedBiosystems) according to manufacturer's protocol and using Sequencher® Programme for analysis of the resulting sequences. Primer sequences are listed in supplementary Resequencing was carried out as described  as well as orthologous sequences for six mammalian species in different orders  were retrieved from EnsEMBL (http://www.ensembl.org/Homo_sapiens/) 200 kb of the human genome sequence around the http://www.sanger.ac.uk/Software/formats/GFF/GFF_Spec.shtml.In order to obtain a more informative alignment of the mammalian clade, the multi-species alignment EPO track was downloaded from EnsEMBL containing elements conserved along 29 eutherian mammals and subsequently converted into a *.gff file  as well as 2nd and 4th introns  and the 6 mammalian species already mentioned were subjected to a transcription factor binding sites (TFBS) detection with help of various programs: MotifScanner, MotifLocator, MotifSampler, MATCH and pMATCH.Sequences of the whole human These programs identify over-represented motifs in a sequence data set and annotate putative binding sites consulting libraries of position weight matrices (PWMs). PWMs represent the intrinsic sequence variability of TFBS in the form of a matrix. Each matrix stores the frequency for each nucleotide at every position of the putative motif in order to summarize the alignment information for the TF with a binding site. The libraries of PWMs used in this study were Jaspar MotifScanner uses different orders of markov chains as background model for matching PWMs. A parameter called a ‘prior’ assigns an a priori probability of TFs binding to a distinct sequence. In MotifLocator the ‘prior’ is substituted by a posterior threshold for filtering matching PWMs. The resulting scores, in an absolute scale without log correction, represent the likelihood ratio of a certain PWM match versus a random match. MotifSampler MATCH There are other recognition tools for promoter and regulatory motif analysis available: RSAT (Universite Libre de Bruxelles), TESS (University of Pennsylvania), TSSG/TSSW (Baylor College of Medicine), MatInspector (Genomatix Gmbh), SiteSeer (University of Manchester), AliBaba2 (BioBase Gmbh), FUNSITE (ICG), Footprinter (University of Washington). However, we used the tools best integrated with the libraries used in the study.RPS19 locus containing variations, conserved areas, annotations and TFBS provided within this report were created using the UCSC Genome Browser (http://www.genome.ucsc.edu/) Additionally, several Perl scripts were created to perform data analysis in the suitable tools, parse TFBS annotations into *.gff files and filter overlapping SNPs within selected TFBS. All available annotations of the locus were manually formatted into *.gff files  for visualisation (see supplementary (1.71 MB PDF)Click here for additional data file.Figure S2Gene structures of the orthologous RPS19 loci of the species selected for comparative analysis taken from EnsEMBL .(1.09 MB PDF)Click here for additional data file.Table S1Full list of all detected TFBS overlaying identified variations. a location of the SNP/TFBS with respect to the RPS19 open reading frame b database identifier (dbSNP) for previously described SNPs or number of novel SNP, respectively. We complement this field with a small registry of which TFBSs are created (+) or destroyed (−) in a certain SNP  and the detection score for this binding site if applicable. c SNP alleles, indicating reference and alternative (replace) allele d name of transcription factor or motif recognized, as named in the databases e library, version of the library and identifier to which a TFBS is associated under a PWM database release f motif recognized (5′ to 3′ sequence). Allele positioning marked in bold. Alleles falling exactly adjacent to the end of a motif highlighted in bold and italics. Capital letters highlight important positions for the putative binding strength of a motif. Chromosome, start, end position and strand within the human reference genome  g score for the reference genome allele. MATCH and pMATCH use 1.000 as maximum score between an optimal binding site match and matrix power of detection. TOUCAN detection tools  do not use global maximum or matrix scoring, but the higher the numbers, the better the predicted site. The a priori probability or threshold is stated under the score when any of the TOUCAN tools has been used h score for non-reference allele i program used for detection of a motif. Error minimization criteria stated when applicable j transcript factor contained in any of the multi-conserved sequence (MCS) region of the multi-species alignment. The first value belongs to the Infocon program and the second to the EPO EnsEMBL track. ‘Y’ indicates that the TFBS motif is totally contained in the area (see supplementary (0.16 MB PDF)Click here for additional data file.Table S2Primer sequences for analysis of 5′upstream region.(0.16 MB PDF)Click here for additional data file.Text S1Sequences collected for the bioinformatic analyses (in fasta format).(0.07 MB TXT)Click here for additional data file.Text S2Compressed archive with files used in the study for visualization in the UCSC Genome Browser (in *.gff format)(0.16 MB ZIP)Click here for additional data file.


Candida albicans are two such heritable cell types, each thought to be adapted to unique niches within their human host. To systematically investigate their differences, we performed strand-specific, massively-parallel sequencing of RNA from C. albicans white and opaque cells. With these data we first annotated the C. albicans transcriptome, finding hundreds of novel differentially-expressed transcripts. Using the new annotation, we compared differences in transcript abundance between the two cell types with the genomic regions bound by a master regulator of the white-opaque switch (Wor1). We found that the revised transcriptional landscape considerably alters our understanding of the circuit governing differentiation. In particular, we can now resolve the poor concordance between binding of a master regulator and the differential expression of adjacent genes, a discrepancy observed in several other studies of cell differentiation. More than one third of the Wor1-bound differentially-expressed transcripts were previously unannotated, which explains the formerly puzzling presence of Wor1 at these positions along the genome. Many of these newly identified Wor1-regulated genes are non-coding and transcribed antisense to coding transcripts. We also find that 5′ and 3′ UTRs of mRNAs in the circuit are unusually long and that 5′ UTRs often differ in length between cell-types, suggesting UTRs encode important regulatory information and that use of alternative promoters is widespread. Further analysis revealed that the revised Wor1 circuit bears several striking similarities to the Oct4 circuit that specifies the pluripotency of mammalian embryonic stem cells. Additional characteristics shared with the Oct4 circuit suggest a set of general hallmarks characteristic of heritable differentiation states in eukaryotes.The differentiation of cells into distinct cell types, each of which is heritable for many generations, underlies many biological phenomena. White and opaque cells of the fungal pathogen  Candida albicans are two heritable cell-types, each thought to be adapted to unique niches within their human host. Here we examine the differences between these two cell-types by sequencing their RNA contents and subsequently reconstructing and comparing their gene expression profiles. We know that the transcription factor Wor1 plays a central role in mediating these expression differences. As with many other transcriptional regulators, however, a major unresolved issue is the apparent discordance between the genomic locations to which Wor1 binds and whether neighboring genes are differentially expressed. Here we resolve this discordance, showing that hundreds of Wor1 binding sites, previously without apparent function, actually flank differentially-expressed genes that were undiscovered, or not measured accurately, before. Additionally, we find that transcripts regulated by Wor1 have many unusual properties, several of which we also observe for transcripts regulated during the development of mammalian embryonic stem cells, suggesting they may be general hallmarks of cell differentiation.The differentiation of cells into distinct cell-types, each of which is “remembered” for many generations, underlies the development of both healthy and cancerous tissues. Such differentiation, however, is not restricted to multi-cellular organisms: “white” and “opaque” cells of the unicellular fungal pathogen  Candida albicans, the most prevalent human fungal pathogen. This eukaryote forms two distinctive types of cells, white and opaque, that differ strikingly in their appearance 4 generations. The low rate of switching makes it easy to obtain large populations of homogeneous cells of each type. Furthermore, it is relatively straightforward to manipulate the genes of C. albicans, which has allowed dissection of both the regulation underlying the switch and the functions of downstream genes that are ultimately responsible for conferring the specific attributes of each cell type How differentiated cell types are epigenetically maintained through repeated cell division is a topic of intensive study White Opaque Regulator 1 (Wor1), forms interlocking feedback loops with two other transcription regulators (Czf1 and Wor2). The three regulators are up-regulated in opaque cells compared to white cells and together are responsible for the establishment and maintenance of the opaque cell type C. albicans, we note that discordance between binding (determined by ChIP) and regulation  has frequently been observed in the circuits of a broad range of organisms A master regulator of the white-opaque switch, C. albicans transcriptome. This revealed that thousands of transcripts overlap another transcript on the opposite strand, demonstrating widespread presence of anti-sense transcription in this yeast, as in the model yeast Saccharomyces cerevisiaeTo better resolve the relationship between the binding of a master regulator of differentiation and differential expression of its direct targets between cell types, we performed massively-parallel strand-specific sequencing of RNA from white and opaque cells. Applying several novel algorithms to the resulting dataset and merging these results with the existing ORF-based gene annotation, we first annotated the Our analysis of the Wor1 circuit revealed several unusual properties. For example, the targets of Wor1 have abnormally long upstream intergenic regions and un-translated regions (UTRs). We show here that many of these long UTRs are cell-type-specific  and thus may function to bring additional layers of regulation to the differentiation circuit. A meta-analysis of the Oct4 circuit Candida albicans genome (build Ca21) and a database of previously annotated splice junctions  fraction of RNA extracted from replicate white and opaque cell cultures , expectiunctions . An overC. albicans, which until now has had a gene annotation based primarily on computationally-predicted open reading frame (ORF) sequence boundaries and generally not informed by experimental data. We first developed a general computational approach , which are based on gapped read alignments and clusters of sequence coverage, respectively , leading to an estimated 9% increase in the number of coding transcripts. Many of these ORFs may have been missed in previous annotations due to their short length (91% are shorter than 100 amino acids) and, in some cases, due to lack of conservation in other species. It is likely that some of the ORFs defined here by our arbitrary length cutoff are not translated into protein. On the whole though, the number of putative ORFs at least 40 amino acids long found in novel transcripts (N = 561) is significantly higher than expected by chance  covering 13% more of the genome (76.1% versus 63.6%) than the old annotation. We estimate that roughly 1,048 of these transcripts are non-coding because they do not contain a canonical ORF that is at least 120 nucleotides long , which increases the number of non-coding RNAs (ncRNAs) annotated in Saccharomyces cerevisiae. Finally, 50% of transcripts in the new annotation overlapped transcripts from the opposite strand by at least 1 bp and 31% did so across more than 10% of their length, indicating that, as in other eukaryotes C. albicans. This observation underscores the importance of sequencing RNA in a strand-specific manner. Overall, the new transcript annotation described here represents a dramatic revision from previous annotations that microarrays were designed to assess. Using this new annotation we revisited the differences in gene expression between white and opaque cells.In the new transcript annotation 5′ and 3′ UTRs of median length 99 and 136 bases were defined for 5,465 and 5,768 transcripts, respectively. These estimates are longer than estimates of 5′ and 3′ UTR length based on tiling arrays . Unexpectedly, the lower WOR1 expression in white cells is associated with increased expression on the strand opposite this long UTR, suggesting an alternative internal antisense promoter is active and may be repressing WOR1 expression in white cells.We determined which of the 7,823 newly defined transcripts were differentially expressed between white and opaque cell types by employing a likelihood ratio test nscripts . As expewitching . As predC. albicans. We hybridized the same samples used for RNA sequencing , a provocative observation we can not yet entirely explain, but which suggests an important role for non-coding transcripts and short proteins in the white-opaque circuit. In any case, this observation emphasizes the importance of “hypothesis-free” approaches to measuring gene expression. The remaining differentially-expressed transcripts, not recognized as such by microarray (N = 376), may be explained by the documented, improved sensitivity and dynamic range of RNA-Seq The 1,306 differentially expressed transcripts found here represent a 3-fold increase in the number observed by microarray NTAR_364 is a particularly informative example of a differentially expressed novel transcript that overlaps another transcript on the opposite strand . These results suggest that some, but not all, anti-sense transcripts act to repress the steady-state abundance of their sense counterpart. Despite the lack of perfect anti-correlation, there are several transcript pairs that, like the STE4-NTAR_364 pair mentioned, are considerably differentially-expressed in opposite directions  antisense transcripts, (2) isolated transcripts that encode proteins, and (3) isolated non-coding transcripts. We discuss these three classes in turn. We found 213 novel transcripts that overlap another transcript on the opposite strand across at least one third of their length. e strand . The gens coding . Our exps coding . Sense–arections , which sNTAR_1179.2, which with 287-fold higher abundance in opaque cells is the third most differentially-expressed transcript genome-wide. Using a combination of BLAST and PSI-BLAST against fungal genomes and eukaryotic protein sequence databases, we identified 46 members of this family  experiments WOR1 gene has a 2 kb long 5′ UTR and about 6 kb of Wor1-bound intergenic DNA upstream of it . For several of these transcripts, such as ORF19.2049 (EFG1 (PPS1 (not shown) and ORF19.7060  is associated with strong down-regulation of the bound transcript in opaque cells. The non-coding transcript NTAR_913 provides a clear example of this phenomenon . Genome-Candida cell types: Oct4 expression is required to maintain the pluripotent ES cell type Candida are shared with the Oct4 circuit, we performed a meta-analysis of publicly-available data, including ChIP-Seq-based Oct4 binding data Candida. Intergenic regions bound by Oct4 are 33% longer than expected by chance  and are 2-fold longer than expected if they also flank a transcript that is differentially expressed during differentiation  cells C. albicans  and the differentiated transcriptomes it specifies. We found substantial (>100%) improvement of the concordance between Wor1 binding and the differential expression of nearby transcripts compared with our earlier analysis . This neThe Wor1-regulated mRNAs show unusually large 5′ and 3′ UTRs , suggestMany mRNAs exhibit different 5′ UTRs in white and opaque cells , indicatC. albicans to those determined in other species reveals some important differences and similarities. With the new strand-specific data presented here we were able to systematically examine changes in the expression of sense and antisense transcripts. The high frequency of antisense transcripts combined with the weak anti-correlated expression of transcripts in sense-antisense pairs . Samples (5 ml) were collected by centrifugation , and pellets frozen in liquid nitrogen. Total RNA was extracted from frozen pellets as described White cells of mating type To construct libraries suitable for SOLiD System sequencing , each poTemplated beads were generated for sequencing using standard manufacturers' protocols. Beads from the first pair of white and opaque libraries (“Replicate #1”) were deposited onto a full slide with 8 other barcoded libraries not presented here. Beads from the second pair of white and opaque libraries (“Replicate #2”) were deposited onto two quadrants of a slide each. Massively parallel ligation sequencing was carried out to 50 bases using Life Technologies SOLiD System V3 and following the manufacturer's instructions.For microarray analysis, we used aliquots of the same total RNA samples used to generate the WT libraries . Aminoallyl-labeled cDNAs were synthesized using 5 µg of total RNA in 50 µl reverse transcription reactions with 250U SuperScript III Reverse Transcriptase (Invitrogen), as described previously Candida albicans genome http://solidsoftwaretools.com/gf/project/transcriptome/). An overview of the alignment strategy is presented in http://www.ncbi.nlm.nih.gov/geo) and are accessible through accession number GSE21291.Whole transcriptome reads were aligned to a modified version of the Assembly 21 release of the Known and novel splice junctions were identified by looking for sets of read sequences whose alignments share a gap  with the same genomic start and end coordinates. We determined empirically that by requiring at least 5 such reads, and considering only deletions of at least 50 nucleotides, we captured, and thus validated, 85% of the 421 known junctions, while also predicting 158 novel junctions or deletions. False positives were filtered from this set by requiring matches to splice motifs and by removing deletions caused by obvious artifacts , yielding 45 new introns in total. The details of this method are provided elsewhere in Mitrovich et al. (In preparation) C. albicans genome. The approach is described in depth in the manual for Life Technologies' Novel Transcribed Region (NTR) finder . Briefly, a window of specified size is scanned base-by-base across the genome, average sequence coverage is calculated within each window, and windows with average coverage greater than a specified cutoff are marked. A set of contiguous marked regions in the genome is then joined and trimmed from each end to better fit the coverage profile, forming a putative TAR (pTAR). We used the NTR finder to perform TAR-finding on the combined dataset of all four sequence libraries presented in this work. TAR-finding was performed with many different parameter sets  and it was determined that a window size of 125 and minimum average coverage of 20 were optimal for reproducing the previously annotated TARs (aTARs), with the expectation that the pTARs would be slightly larger than the aTARs because the existing annotations were ORF-based only and thus did not include UTR definitions (http://www.candidagenome.org/) and the exons defined therein were used as our aTARs.A TAR is a region of the strand-specific genome exhibiting a cluster of sequence coverage, most often representing the presence of an exon. We employed a sliding window approach to identify such clusters on each strand of the initions . Other pIn merging the pTARs with aTARs to define a new transcript annotation, we found that in addition to this optimal pTAR set , a more fragmented pTAR set produced from a smaller window size  was also helpful (see below).We also experimented with Hidden Markov Model (HMM) approaches to finding pTARs (not shown), but found that the models we trained did not perform better than the simpler sliding window approach taken here. In fact, they tended to perform much worse, which may simply reflect that we did not find the best way of modeling the segmentation problem.de novo transcript annotation Rather than providing a completely We performed 10,000 rounds of simulation to determine whether the 561 nTARs containing an ORF of length 40 amino acids or longer was more than expected by chance. In each round, 1,443 regions with the same size distribution as the 1,443 nTARs were chosen randomly in a strand-specific fashion from regions of the genome not covered by ORFs in the previous annotation . The median number of ORFs found per round was 453. 561 or more ORFs were not found in any round of the simulation .−4 and an absolute fold-change greater than or equal to 2 defined a set of 1306 differentially expressed transcripts using the new transcript annotation and a set of 824 using the old annotation. RPKM, fold-change estimates, P-values and FDRs for each transcript can be found in For each transcript model (in either the new or old annotation), reads that uniquely aligned to the genome within its exons or across its splice junctions were counted. One pseudo-count was added to this sum and the resulting modified raw transcript count was converted to a normalized measurement of abundance by normalizing for transcript length and total number of uniquely aligned reads in the sample  −4 and required an absolute fold-change greater than or equal to 2. This defined a set of 512 differentially expressed transcripts.Microarray data were normalized and differentially expressed transcripts were identified using limma v2.16.5 2 enrichment values of the Wor1 ChIP-Chip tiling arrays followed by the application of an algorithm to detect local regions of maximal enrichment (i.e. binding peaks), which are assigned a P-value using permutation testing. Note that this algorithm is not based on deconvolution of binding events using shearing profiles – in the case of the Wor1 ChIP-chip data, the binding peaks are atypically broad and varied, and thus tend to confound deconvolution-based algorithms. Peak extraction was applied independently to the normalized ChIP-Chip data derived from antibodies targeting the N- and C-terminus of Wor1 wor1ΔΔ controls), though the amount of sampling was increased 10-fold from default to improve significance estimates. The minimum value for peak inclusion/consideration was set to 0.25. All other settings were kept at their default values. It was subsequently determined that the union of Wor1-bound regions defined independently from the N- and C-terminal datasets gave the best concordance with microarray-based and RNA-seq-based gene expression measurements of differential expression. Thus, the 504 Wor1-bound regions used throughout this work result from taking the union of Wor1-bound regions generated from the N- and C-terminal ChIP-Chip datasets.Wor1-bound regions were identified as peaks of binding enrichment in the Wor1 ChIP-Chip data using the “Extract peaks from Data Set(s)” utility of MochiView v1.311 For the purposes of comparing Wor1 binding to differential expression, Wor1-bound regions were associated with nearby divergently transcribed transcripts as depicted in For the purposes of calculating the distribution of intergenic lengths “in the Wor1 circuit” a slightly different approach was taken to associate Wor1-bound regions with nearby transcripts than described above. In this case, Wor1-bound regions that fall within intergenic regions were associated with all divergent transcripts within 1 kb and intergenic regions that associated with one or more such transcripts were determined to be “in the Wor1 circuit”. This approach avoids the problem of length correction required under the null model that binding sites are distributed randomly throughout the genome . Similarly, to avoid length bias when determining the distribution of 5′ and 3′ UTR lengths “in the Wor1 circuit”, we only considered Wor1-bound regions that resided in the intergenic space immediately upstream of the transcript, thereby avoiding the possibility that random binding to the longer UTRs themselves would drive artificial UTR length discrepancies.2 test of independence comparing the observed, normalized UTR counts to the expected counts in the two cell types. The expected count for each CDS region in each cell type was calculated by redistributing the total reads counted across cell types for the corresponding UTR in a fashion proportional to the fold-change calculated for the CDS. To ensure accurate fold-change estimates for the CDS regions, only transcripts with a CDS that had at least 50 reads aligned in at least one cell type were considered. By also requiring a minimum 2-fold absolute difference in fold-change values for the UTR and CDS regions and a χ2 P-value less than 10−5, we identified 145 transcripts with putative UTR length changes (Putative cases of UTR length change between cell types were isolated by comparing changes in UTR expression to changes in coding sequence (CDS) expression between the cell types. We first calculated differential expression (in white versus opaque cells) independently for the 5′ un-translated, coding, and 3′ un-translated regions of each coding transcript. The number of reads aligned within each region of a transcript was counted in the merged set of alignments from each cell type  and a single pseudocount was added. The counts for the opaque cell type, whose dataset had 4% more uniquely aligned reads overall, were normalized by the ratio of uniquely aligned reads in the datasets of the two cell types . Fold-changes were calculated for each transcript region by dividing the normalized count in opaque by the count in white cells. We then scanned for UTRs whose expression changed more or less than their corresponding coding sequence, as determined by a χ changes .−4) up- or down-regulated across the 18 profiling experiments (median fold-change of at least 1.5) to be differentially expressed between cell types. Mouse transcript annotations were downloaded from the UCSC Genome Browser (http://genome.ucsc.edu/) and are based on alignments of RefSeq transcripts to assembly mm8 of the mouse genome sequence The analysis of transcript features in the Oct4 circuit was performed on publicly available data. Lists of Oct4-bound regions in mouse ES cells determined independently by Chen et al. Figure S1RNA-Seq library workflow. The protocol used to prepare total RNA for SOLiD System sequencing is diagrammed here. This approach achieves strand-specificity by employing end-specific ligation of sequencing adapters to RNA, prior to the cDNA synthesis step. The P1 sequencing adapter is an RNA/DNA complex that contains a 6 bp 5′ single-strand DNA overhang allowing it to hybridize only to the 5′ end of an RNA fragment and, likewise, the P2 adapter will hybridize only to the 3′ end. The ligase used is engineered specifically to prefer the types of double-stranded substrates produced by these hybridizations, effectively making proper hybridization a prerequisite for efficient ligation. Thus, when cDNA is sequenced off the P1 adapter we can determine the genomic strand from which the RNA originated. Also, because RNA is fragmented prior to cDNA synthesis, the protocol is less biased with respect to the positional origin of fragments within transcripts.(0.12 MB TIF)Click here for additional data file.Figure S2http://solidsoftwaretools.com/gf/project/transcriptome/). Briefly, each 50 base read was broken into two pieces  and each piece was mapped independently and contiguously to the Candida albicans genome (Ca21) and a database of annotated splice junction sequences. During this mapping phase we allowed up to three mismatches and removed reads that align to more than 100 locations. The mapping of each read piece was extended along the mapped genomic region using colors  from the rest of the read until a maximal score was reached (+1 for a match and −2 for a mismatch). In cases where the read pieces aligned to the same genomic location, the results from the two halves were merged. Reads that did not align “fully”  or uniquely after merger were passed through to the rescue phase. During rescue a read is re-aligned to the region extending 2 kb downstream of each position to which a read piece was contiguously mapped, this time allowing a single insertion in the read of up to 5 bases or deletion of up to 2 kb relative to the reference. This process is especially helpful for identifying novel splice junctions. Only reads that were aligned both uniquely and “fully” were subsequently used to generate counts for annotated exons, transcripts, and genes, as well as genomic coverage plots (WIG files) that were displayed in the MochiView Genome Browser Short read sequence alignment algorithm. RNA sequencing reads were analyzed using Life Technologies Whole Transcriptome software tools ((0.13 MB TIF)Click here for additional data file.Figure S3Putative transcriptionally active region (pTAR) finder method and results. In the pTAR finding method a window of specified size is scanned base-by-base across the genome, average sequence coverage is calculated within each window, and windows with average coverage greater than a specified cutoff are marked. A set of contiguous marked regions in the genome is then joined and trimmed from each end to better fit the coverage profile, forming a putative TAR (pTAR). TAR finding was performed with many different parameter sets  and the resulting pTAR sets were compared to annotated TARs (aTARs) from the previous ORF-based transcript annotation. (A) The fraction of aTARs that were “recovered” in the pTAR set for various window size (represented as series with different colors) and minimum average coverage (represented as the points within each series) values. “Recovered” aTARs must overlap a pTAR by at least 90%. (B) The average fraction of each aTAR that overlaps a pTAR across different pTAR sets. (C) The average fraction of each pTAR that overlaps an aTAR across different pTAR sets. Based on these plots, it was determined that a window size of 125 and minimum average coverage of 20 are optimal for reproducing the aTARs (panel A), with the expectation that the pTARs would be slightly larger than the aTARs  because the existing annotations were ORF-based only and therefore did not include UTR definitions.(0.26 MB TIF)Click here for additional data file.Figure S4Algorithm for merging putative pTARs (pTARs) with previously annotated TARs (aTARs). (A) The rules used to merge the pTARs and aTARs to form the new transcript annotation are depicted. For example, scenario ‘a’ is the “ideal” scenario in which a single RNA-Seq-based pTAR overlaps a single ORF-based aTAR, with the pTAR's coordinates extending past aTAR's coordinates on both the 5′ and 3′ ends, defining the un-translated regions (UTRs) of the transcript. The number of times each scenario was observed is listed in parentheses. For transcripts found to contain one or more splice junctions see , the int(0.85 MB TIF)Click here for additional data file.Figure S5Reproducibility of fold-changes across biological replicates. The abundance of each transcript as estimated by RPKM  from the sequencing of two independently grown (A) white and (B) opaque cell cultures.(0.34 MB TIF)Click here for additional data file.Figure S6C. albicans and C. dubliniensis. (D) Neighbor-joining tree of the 46 NTAR_1176 homologs. Clusters A, B, and C are shaded green, yellow, and blue, respectively.Three clusters of novel Candida-specific ORFs are strongly up-regulated in opaque cells. (A) Expression and Wor1 binding at cluster B of NTAR_1176 homologs on chromosome R (“chrR”). (B) Expression and Wor1 binding at cluster C of NTAR_1176 homologs on chrR. (C) Multiple sequence alignment of all 46 NTAR_1176 homologs found by PSI-BLAST in (1.23 MB TIF)Click here for additional data file.Figure S7Associating Wor1-bound regions with putatively regulated overlapping and nearby transcripts. Transcripts associated (shaded orange) and not associated (shaded white) with a flanking Wor1-bound region (shaded red) are indicated. Arrows indicate the inferred direction of transcription for each TAR.(0.15 MB TIF)Click here for additional data file.Table S1New transcript annotation.(2.74 MB TXT)Click here for additional data file.Table S2Hand-edited version of the new transcript annotation. Same as (2.74 MB TXT)Click here for additional data file.Table S3Results of the differential expression (white versus opaque) analysis.(0.87 MB TXT)Click here for additional data file.Table S4Results of the differential UTR length (white versus opaque) analysis.(1.04 MB TXT)Click here for additional data file.Table S5Final new transcript annotation. Same as (2.73 MB TXT)Click here for additional data file.


Pepc) promoter in maize is controlled by light and is independent of other stimuli or gene activity. Acetylation of upstream promoter regions responds to a set of other stimuli which include the nutrient availability of the plant. Here, we have extended these studies by analysing histone acetylation during the diurnal and circadian rhythm of the plant.Acetylation of promoter nucleosomes is tightly correlated and mechanistically linked to gene activity. However, transcription is not necessary for promoter acetylation. It seems, therefore, that external and endogenous stimuli control histone acetylation and by this contribute to gene regulation. Photosynthetic genes in plants are excellent models with which to study the connection between stimuli and chromatin modifications because these genes are strongly expressed and regulated by multiple stimuli that are easily manipulated. We have previously shown that acetylation of specific histone lysine residues on the photosynthetic phosphoenolpyruvate carboxylase (Pepc promoter activity during the day is controlled by the circadian oscillator as it is maintained under constant illumination for at least 3 days. During this period, light-induced changes in histone acetylation are completely removed from the core promoter, although the light stimulus is continuously applied. However, acetylation of most sites on upstream promoter elements follows the circadian rhythm.We show that histone acetylation of individual lysine residues is removed from the core promoter before the end of the illumination period which is an indication that light is not the only factor influencing core promoter acetylation. Deacetylation is accompanied by a decrease in gene activity. Pharmacological inhibition of histone deacetylation is not sufficient to prevent transcriptional repression, indicating that deacetylation is not controlling diurnal gene regulation. Variation of the Our results suggest a central role of upstream promoter acetylation in the quantitative regulation of gene expression in this model gene. Induced core promoter acetylation is dispensable for the highest gene expression in the diurnal and circadian rhythm. Arabidopsis mutants with defects in histone acetyltransferases revealed a requirement of histone acetylation for light-activated gene transcription [et al. suggested that H3K9 acetylation in Arabidopsis is required for the binding of RNA Polymerase II to promoters of light-regulated genes [Acetylation of lysines on the N-terminal tails of histones shows a very high degree of correlation with gene transcription in genome-wide analyses of microbes and mammals . Althougcription . Guo et ed genes . A tighted genes .Two models have been suggested for the function of histone acetylation in gene expression: On the one hand, acetylation neutralizes the positive charge of lysine side chains and, by this, reduces the electrostatic interactions with the negatively charged DNA backbone . On thePepc) gene in maize is an excellent model for the analysis of signal integration on promoters because it is expressed at very high levels and is strongly regulated on the transcriptional level by multiple stimuli. The gene is exclusively active in the mesophyll cells of leaves but inactive in the directly adjacent bundle sheath cells or in other tissues, such as roots [Pepc promoter shows low basal activity in leaves - most promoter acetylation sites are already acetylated at this stage. Light specifically induces acetylation of H4K5 and H3K9 on the core promoter. This is accompanied by the induction of transcription, although transcription is not necessary for acetylation. The situation is complicated under conditions where metabolic stimuli act on the promoter. Low nitrogen levels or high leaf sugar contents are sufficient to efficiently suppress promoter activity. However, histone modifications on the core promoter region, which is proximal to the transcription initiation site and which contains all binding sites for the basal transcription machinery, do not respond to these stimuli. Instead, metabolic stimuli control the acetylation of a distal promoter element that is more than 1000 bp upstream of the transcription initiation site. Here, all tested acetylation sites are affected to a similar degree and a good quantitative correlation between the upstream promoter acetylation and the transcription rates can be observed. Again, acetylation levels are not a consequence of transcription levels, but can also be modulated when transcription is inhibited by pharmacological treatments. Interestingly, histone methylations are not affected by any of these stimuli. They are exclusively controlled by a tissue-specific factor that induces high trimethylation of H3K4 in leaf mesophyll cells. Moreover, H3K4 methylation is restricted to the core promoter and is absent from the upstream promoter [The phosphoenolpyruvate carboxylase  and for the acetylation of lysine 5 and 16 on histone H4 at two typical positions of the upstream and core promoter, respectively, are shown in Figure Actin-1 reference gene.The maize k period . The diun-1 gene  and muchuch-like ). We verActin-1 . Figure Pepc promoter activity and mRNA accumulation through two daily cycles where dark periods were replaced by constant light . However, in the circadian rhythm we observed the highest tylation . For enhtylation . In suchtylation . Both de Figures  and 4. Tone code . HoweverPepc transcription during the diurnal cycle. However, TSA treatments did not affect the diurnal transcription rhythm, albeit inducing strong acetylation of all lysine residues investigated in this study  was cultivated in growth chambers with a 16-h photoperiod and a day/night temperature regime of 25°/20°C. The plants were illuminated with Osram Superstar HQI-T 400W/DH lamps at a photon flux density of 120-180 μmol m-2 s-1. Seedlings were grown in soil  for 10-12 days. Re-etiolated plants were derived from illuminated plants that were subjected to darkness for 72 h. For constant light experiments, the dark period was replaced by continuous illumination with constant temperature.Maize  or 15 h (16 hai) in tap water containing 30 μM trichostatin A . To avoid nitrogen depletion, 0.275 μM trans-Zeatin  and 16 mM KNOre added ,31. Mocket al. [et al. [As described previously, 6 g of foliar leaf material were harvested and crosslinked ,14. ChIPet al.  with mod [et al. . ChromatRNA isolation and reverse transcription were performed as described . As a diQuantitative PCR was performed on an ABI PRISM 7300 (Applied Biosystems) using SYBR Green fluorescence  for detection. Oligonucleotide sequences and conditions are given in Additional File Pepc: phosphoenolpyruvate; PCR: polymerase chain reaction; TSA: Trichostatin A.ChIP: chromatin immunoprecipitation; hai: hours after illumination; hnRNA: heterogeneous nuclear RNA; The authors declare that they have no competing interests.IH, SO, BD and CP conceived of and designed the experiments. IH, SO, BD and MN performed the experiments. IH, SO, BD and CP analysed the data. IH and CP wrote the paper. All authors read and approved the final manuscript.Pepc) promoter histone acetylationDiurnal pattern of phosphoenolpyruvate carboxylase (. Histone acetylation in leaves 4 h after illumination (dark green lines), 16 h after illumination (light green lines) or re-etiolated leaves (orange lines), respectively. Values are H3K9, H3K14, H3K18, H3K23, H3K27, H4K5 and H4K16 histone acetylation levels at nine positions on the Pepc promoter. Data are standardized for acetylation levels on the Actin-1 promoter. For better orientation, the 1.0 level is emphasized by a black line. H3C = chromatin precipitated with an antibody to an invariant epitope on histone H3. Data points are based on four independent experiments. Vertical lines indicate standard errors.Click here for fileComparison of nucleosome densities as determined by histone H3 and histone H4 occupancy. Nucleosome density in leaves 4 h after illumination (dark green lines), 16 h after illumination (light green lines), or re-etiolated leaves (orange lines), respectively. H3C = chromatin precipitated with an antibody to an invariant epitope on histone H3; H4C = chromatin precipitated with an antibody to an invariant epitope on histone H4. Exemplarily, data for the phosphoenolpyruvate carboxylase (Pepc) core promoter and the Actin-1 promoter are given. Data points are based on four independent experiments. Vertical lines indicate standard errors.Click here for filePepc) promoter histone acetylationCircadian pattern of phosphoenolpyruvate carboxylase (. Histone acetylation under constant illumination (free-running conditions). After a normal 16 h light period, illumination was extended for an additional 40 h without any dark period or temperature shift. Values are H3K9, H3K14, H3K18, H3K23, H3K27, H4K5 and H4K16 histone acetylation levels at nine positions on the Pepc promoter. The different lines represent acetylation levels at different time points during constant illumination. Green lines represent time points where high transcription levels were detected (solid line = 4 h after illumination (hai), dotted line = 24 hai, dashed line = 44 hai). Orange lines represent time points where low transcription levels were detected . Data are standardized for acetylation levels on the Actin-1 promoter. For better orientation, the 1.0 level is emphasized by a black line. H3C = chromatin precipitated with an antibody to an invariant epitope on histone H3. Data points are based on four independent experiments. Vertical lines indicate standard errors.Click here for fileOligonucleotides and polymerase chain reaction conditions.Click here for file