Arabidopsis. It is known that root growth inhibition by ethylene involves auxin, which is partially mediated by the action of the WEAK ETHYLENE INSENSITIVE2/ANTHRANILATE SYNTHASE α1 (WEI2/ASA1), encoding a rate-limiting enzyme in tryptophan (Trp) biosynthesis, from which auxin is derived. However, the molecular mechanism by which ethylene decreases root growth via ASA1 is not understood. Here we report that the ethylene-responsive AP2 transcription factor, ETHYLENE RESPONSE FACTOR1 (ERF1), plays an important role in primary root elongation of Arabidopsis. Using loss- and gain-of-function transgenic lines as well as biochemical analysis, we demonstrate that ERF1 can directly up-regulate ASA1 by binding to its promoter, leading to auxin accumulation and ethylene-induced inhibition of root growth. This discloses one mechanism linking ethylene signaling and auxin biosynthesis in Arabidopsis roots.The gaseous phytohormone ethylene participates in the regulation of root growth and development in  ASA1, encoding ANTHRANILATE SYNTHASE α1, a rate-limiting enzyme in Trp biosynthesis where auxin is derived, by directly binding to its promoter and activating ASA1. Consequently, auxin biosynthesis is promoted, leading to increased auxin accumulation and thus inhibition of primary root elongation. This study unravels a molecular mechanism that bridges ethylene signaling and auxin biosynthesis in primary root elongation.Ethylene is a gaseous phytohormone that plays critical roles in plant development and defense. It is well known that ethylene inhibits primary root elongation through effects on auxin. However, it is not clear how ethylene signal is translated into auxin. In this report, the highly ethylene-responsive transcription factor ETHYLENE RESPONSE FACTOR1 (ERF1) is demonstrated to positively regulate  Phytohormones are central regulators of plant root growth and development. Each root development process is determined by a network of interacting signals to give the final architecture of the root. EthylenS-adenosyl-methionine (SAM) by SAM synthetase. Then a family of 1-aminocyclopropane-1-carboxylic acid (ACC) synthases (ACS) converts SAM to ACC. This reaction is a rate-limiting step and a key regulatory point in ethylene biosynthesis[Arabidopsis genome, there are 12 members in ACS gene family, which display overlapping temporal and spatial expression patterns and are responsive to a variety of biotic and abiotic stresses and hormones, such as auxin[ERF genes and activate their transcription[Ethylene, a gaseous plant hormone, acts as a key regulatory signal during the plant life cycle,11. The synthesis. Finallysynthesis,14. In t as auxin,16. Appa as auxin–21. Once as auxin–24. The  as auxin. Inhibit as auxin. The actscription,28. ThusArabidopsis[ERF1 (AT3G23240) is a downstream component of the ethylene signaling pathway and is directly regulated by EIN3 at the transcriptional level[ERF1 is a key integrator of the jasmonic acid (JA) and ethylene signaling pathways involved in the regulation of defence response genes such as b-CHI and PDF1.2[ERF1 also plays a positive role in abiotic stress responses such as salt, drought, and heat stress[ERF1 further mediates ethylene responses in developmental processes, such as the inhibition of primary root growth and hypocotyl elongation in the dark. This has been confirmed by the production of transgenic plants with constitutively activated ERF1, which displayed phenotypes similar to that are observed in ctr1 mutant, EIN3-overexpressing plants, and wild-type plants treated with ethylene[ERF109 was shown to mediate crosstalk between JA signaling and auxin biosynthesis[ERFs, which contain an AP2 DNA-binding domain, form a plant-specific superfamily of 122 transcriptional factors in abidopsis. ERFs inabidopsis–34, throabidopsis. It was abidopsis. ERF1 , WEI7/ASB1, TAA1, and TAR1, which are regulated by ethylene, also exhibit ethylene-insensitive root growth[YUC genes also play an important role in root responses to ethylene[WEI2 encodes the α-subunit of the enzyme anthranilate synthase in Trp-dependent auxin biosynthesis. Its expression in roots can be induced by ethylene and wei2 mutations cause ethylene-insensitive root growth phenotypes[Arabidopsis roots, ethylene promotes auxin biosynthesis in a ASA1-dependent manner[ASA1 by ethylene is not well understood.Root growth relies on two essential developmental processes: cell division in the root meristem and elongation of cells produced by the root meristem. Root ceyric acid. Many muyric acid,47–50, sot growth,49,51–53ot growth,9. YUC g ethylene. These shenotypes. In Arabnt manner, this isERF1 displayed similar root development phenotype to mutants of ethylene signaling. ERF1 affected auxin accumulation through directly binding to the ASA1 promoter and positively regulating ASA1 expression. Our results indicate that ERF1 plays a pivotal role in the inhibition of ethylene-induced primary root growth in Arabidopsis and acts as the crosstalk node between ethylene and auxin in primary root elongation.Here, we report that ERF1, a downstream AP2 transcription factor in the ethylene signaling pathway, positively regulates auxin biosynthesis during inhibition of ethylene-mediated primary root growth. Transgenic plants with constitutive expression or knockdown of ERF1 in ethylene response, we examined the ethylene-induced expression of ERF1 in 5-day-old wildtype seedlings (Col-0) grown on Murashige and Skoog (MS) medium with 10 μM ACC for 0–12 h. The ERF1 transcript level in whole seedlings was measured by quantitative real-time PCR (qRT-PCR). Consistent with previous studies[ERF1 transcript level was rapidly induced by ACC treatment. ERF1 increased in a biphasic manner showing a 4-fold increase after 0.5 h, and a second plateau increasing by approximately 8-fold at 3 h. Thereafter, the ERF1 transcript levels remained high  activity of transgenic plants carrying an ERF1pro:GUS construct, in which a GUS reporter gene was under the control of the ERF1 promoter . ERF1 was mainly expressed in the maturation zone of the primary root of seedlings  (EIN3ox), respectively. Expression of ERF1 could not be induced by ACC in the ein2-5, ein3-1 and ein3-1eil1 mutants, while it was constitutively expressed in the ctr1-1 and EIN3ox seedlings  were stunted. In contrast, the RNAi lines showed longer primary roots compared to the wildtype controls , the elongation zone (EZ), and the meristem zone (MZ). Root meristem size was measured as the cell number from the quiescent centre (QC) to the first elongated cell in the cortex[ERF1ox lines, whereas it was greater in the DZ of the RNAi lines compared to wildtype . The difference of DR5:GUS expression in these lines were more evident when treated with ACC for 24 h  upstream of its target genes, to regulate downstream gene expression[ASA1 and found one GCC-box at 27 bp upstream of the translational start codon. We sought to test whether this GCC-box could provide a handle for ERF1 to regulate ASA1 directly.Ethylene positively regulates the transcription of longation. This fixpression,40.We anASA1 promoter sequence, we conducted an electrophoretic mobility shift assay (EMSA) with the full-length ERF1 protein fused to a maltose binding protein (MBP-ERF1) that was expressed in E. coli and purified through affinity chromatography. As shown in ASA1 promoter. Moreover, the binding specificity was confirmed by competition with native DNA probes, or probes carrying a mutated GCC-box. Unlabelled native DNA probe was used as a competitor and unlabelled promoter fragment containing the mutant form of the GCC-box motif as a non-competitor. The EMSA results showed that ERF1 specifically binds to the promoter sequence containing a GCC-box of ASA1 promoter in vitro, but not to the DNA probe containing the mutant GCC-box sequence  on MS medium with or without ACC. The asa1 mutants grown on media containing different concentrations of ACC displayed longer primary roots than wildtype seedlings  was less sensitive to ACC compared to wildtype. The mutants with enhanced ethylene signals  showed dramatically shortened primary roots under normal conditions, mimicking wildtype seedlings treated with ACC[ASA1 functions downstream of CTR1[ASA1 transcript levels in these mutants in normal conditions and found that the expression level of ASA1 in ein2-5, ein3-1eil1 and ein3-1 was lower than that of wildtype and conversely higher in ctr1-1 and EIN3ox lines . When ERF1 is overexpressed, the primary root of ERF1ox asa1-1 is significantly longer than that of ERF1ox and a little shorter than Col-0 , yeast-one-hybrid, and chromatin immunoprecipitation (ChIP) experiments and confirmed that ERF1 could directly bind a conserved GCC-box element in the promoter of ASA1 in vitro and in vivo  showed that when ethylene signal pathway was enhanced, the expression of ASA1 was also enhanced. Conversely, if ethylene signal pathway was blocked, the expression of ASA1 was reduced and the induction by ACC was also impaired  medium with 1% sucrose for the indicated days . Seedlings were then transferred to plates supplemented with or without ACC for the indicated days, and the plates were placed vertically in a growth room. ACC (Sigma-Aldrich) was dissolved in water and prepared as a 2 mM stock solution. Arabidopsis thaliana 7-d-old seedlings were transferred to soil and grown to maturity in a growth room. All plants were grown under long-day conditions (16-h light / 8-h dark) at 22–24°C. Every experiment was repeated at least three times.Surface-sterilised Arabidopsis thaliana ecotype Columbia-0 (Col-0) was used. Some plant materials used in this study were previously described: ein2-5[ein3-1[ein3-1 eil1-1[ctr1-1[EIN3-FLAG (iE/qm) (EIN3ox) [ASA1pro:GUS [DR5:GUS[d: ein2-5, ein3-1[-5[ein3-1, ein3-1 -1 eil1-1, ctr1-1[-1[ctr1-1, EIN3-FL(EIN3ox) , ASA1pro1pro:GUS , DR5:GUS [DR5:GUS.ERF1pro:GUS construct, which contained a 3.0-kb promoter sequence with primers  were obtained from transformation of Col-0 with the 35Spro:ERF1 construct, which contains full-length ERF1 (At3g23240). ERF1 knockdown plants were obtained from transformation of Col-0 with the 35Spro:ERF1:RNAi construct. 35S:HA-ERF1 were obtained from transformation of Col-0 plants with a vector comprised of a 35S promoter and HA sequence in front of the full-length ERF1 cDNA. 35Spro:ERF1-GFP transgenic lines were constructed in Col-0 Arabidopsis. ERF1ox transgenic plants used in genetic analysis of crossing ERF1ox with asa1 were obtained from transformation of Col-0 plants with the vector comprised of an estradiol-inducible promoter in front of the full-length ERF1.Plants were obtained from transformation of Col-0 plants with the  primers . ERF1 ovERF1pro:GUS construct, the primer set ERF1pro-F and ERF1pro-R , and introduced into Arabidopsis plants by the floral dip method[To prepare the RF1pro-R , was user pCB2004. To preptor pGWB5. All of formation.These coip method. More thin planta GFP analysis, seedlings were stained in 10 mg/mL propidium iodide for 8 min and washed twice in water. Propidium iodide fluorescence and GFP were imaged under a ZEISS710 confocal laser scanning microscope: 488-nm and 543-nm lines of the laser were used for excitation, and emission was detected at 510 nm and 620 nm, respectively. More than 15 seedlings were examined for each transgenic line, and at least three independent experiments were displayed.Histochemical staining for GUS activity in transgenic plants was conducted as previous described. SeedlinArabidopsis UBQ5. All qRT-PCR experiments were performed at least three biological replicates. The primers used in this study have been listed in Total RNA was extracted with TRIzol reagent (Invitrogen) from seedlings or root samples. cDNA was prepared by TransScript RT kit (Invitrogen) for RNA reverse transcription, and was used for real-time quantitative RT-PCR. All quantitative RT-PCR assays were performed with a SYBR Premix Ex Taq II kit on StepOne real-time PCR system (Applied Biosystems) according to the manufacturer’s instructions. Expression levels of target genes were normalized to The free total IAA content was measured by ELISA as described.ERF1 was amplified with the primers ERF1Y1HF and ERF1Y1HR, and cloned into pAD-GAL4-2.1 (AD vector) to produce the pAD/ERF1 plasmid. Two complementary core 30-bp DNA single strands including GCC-box in the promoter of ASA1, named ASA1Y1HF and ASA1Y1HR, were annealed and cloned into the reporter plasmid via SacI and MluI restriction sites, pHIS2 (BD vector), which contained the nutritional reporter gene, HIS3.Two complementary DNA strands with three copies of the DNA sequence GCC-box, named GCCY1HF and GCCY1HR were annealed and cloned into plasmid pHIS2 through SacI and MluI restriction sites as a positive control. The pAD/ERF1 construct containing the ERF1 cDNA sequence and the pHIS2 reporter construct containing the GCC-box cis-element were co-transformed into Y187 yeast cells. For the negative control, the pAD/ERF1 plasmid and the pHIS2 empty plasmid were co-transformed into Y187 yeast cells. Yeast was cultured in SD/–Trp–Leu medium and then transferred to SD/–Trp–Leu–His medium containing10 mM 3-aminotriazole (Sigma) with dilutions as indicated. The plates were incubated at 30°Cfor 4 d and the results were observed. Growth of yeast cells on the SD/–Trp–Leu–His medium in the presence of 10 mM 3-aminotriazole indicated that the transcription factor could bind this cis-element, and activated relevant gene expression.The procedures of the yeast one-hybrid assay were displayed as described previously with minor modifications. A DNA f35S:HA-ERF1seedlings and Col-0 plants were harvested for ChIP experiments. The procedures were conducted essentially as previously described[ASA1 promoter was amplified by PCR using the following primer pairs: ChIP-ASA1pro F, ChIP-ASA1pro R, and β-tubulin8 was used as negative control. The resultant PCR-products were resolved by electrophoresis on 2% agarose gels. The results displayed above represent at least three independent repeats.One gram each of six-day-old described. The enrTo analyse the binding quantitatively, a qRT-PCR assay was performed on the basis of the procedure described previously. The relERF1 coding sequence was amplified by PCR with primers ERF1EMSAF and ERF1EMSAR, and cloned into the pMAL-C2 vector via EcoRI and XbaI restriction sites, to construct a plasmid for the expression of recombinant MYC2 protein in Escherichia coli.The fragment of the full-length ASA1EMSAF-DIG, ASA1EMSAR, ASA1EMSAF, ASA1EMSA-MuF and ASA1EMSA-MuR. These DNA fragments were annealed with their complementary oligonucleotides, ASA1EMSAF-DIG with ASA1EMSAR for EMSA labelled probe, ASA1EMSAR with ASA1EMSAF for EMSA competitive probe, ASA1EMSA-MuF with ASA1EMSA-MuR for EMSA non-competitive probe. EMSA was performed according to a DIG Gel Shift kit, 2nd Generation (Roche).Five individual synthetic 30-bp single-stranded DNA molecules containing the GCC-box were used, namely S1 Figa-c) Seeds of ERF1pro:GUS were germinated on MS medium and stained for GUS expression. ERF1 expression pattern in 4- (a), 6- (b), and 15-day-old (c) plants. Scale bar, 0.5 cm. (d) Twenty-five-day-old ERF1pro:GUS transgenic seedlings grown in soil. (e) Stem, siliques and flowers of 35-day-old ERF1pro:GUS transgenic lines grown in soil. (f) The cellular localization of ERF1 protein revealed by the 35Spro:ERF1-GFP transgenic plants. The roots of 5-day-old 35Spro:ERF1-GFP transgenic plants were photographed using a ZEISS710 confocal laser scanning microscope. Scale bar = 50 μm.((DOC)Click here for additional data file.S2 Figa) Seeds of the transgenic lines and wildtype were germinated vertically on MS medium for 5 days, and the representative seedlings were photographed. Scale bar, 1 cm. (b) The primary root length of the transgenic lines and wildtype was measured from 4 to 8 days. Data shown are average and SD . (c) The expression level of ERF1 in these materials was tested by qRT-PCR. Values are mean ± SD of three replicates .((DOC)Click here for additional data file.S3 Fig(a) Images of representative 5-d-old etiolated seedlings grown in the MS medium are displayed. Genotypes are as indicated. Scale bar, 1 cm.(b) The primary root length of 5-d-old etiolated seedlings was measured. Data shown are average and SD .(DOC)Click here for additional data file.S4 Figa) The primary root elongation phenotype in the mutants as indicated. Ethylene signalling pathway-related mutants ein2-5, ein3-1, ein3-1eil1, ctr1-1, and wildtype (Col-0) were geminated on MS medium with 0 or 0.8 μM ACC for 5 d. The seeds of EIN3ox (EIN3-FLAG (iE/qm)) were grown on medium containing 1 μM β-estradiol and 0 or 0.8 μM ACC for 5 d, respectively. Scale bar, 1cm. (b-c) ERF1 and ASA1 expression levels in ERF1 knockdown lines  and wildtype seedlings with or without 0.2 μM ACC treatment from 5 to 8 d. Total RNA was extracted from roots. The expression level of ERF1 and ASA1 in these materials was detected by qRT-PCR. Values are mean ± SD of three replicates.((DOC)Click here for additional data file.S5 FigERF1 in 5-day-old Col-0 wildtype, ERF1 knockdown  and overexpression  seedlings in DR5:GUS (a) and ASA1pro:GUS (b) backgrounds was tested by qRT-PCR. Values are mean ± SD of three replicas .The expression level of (DOC)Click here for additional data file.S6 Figa) The root phenotype of 5-day-old Col-0 wildtype and 35S:HA-ERF1. Scale bar, 0.5 cm. (b) The ERF1 expression level in 5-day-old wildtype and 35S:HA-ERF1 seedlings. Values are mean ± SD of three replicas (***P<0.001. Asterisks indicate Student’s t-test significant differences).((DOC)Click here for additional data file.S7 FigRNAi-1, asa1, ein2-5) grown in the presence of 0, 0.2, 0.5, 1, and 10 μM ACC. The response of each genotype to ACC was expressed as the percentage of the root length at a particular concentration of ACC with respect to the average length of root in the absence of ACC. Data shown are average and SD .(a) Images of representative 5-d-old seedlings grown in the presence of 0, 0.2, 0.5, 1, and 10 μM ACC are displayed. Genotypes are as indicated. Scale bar, 1 cm. (b) Relative root length of 5-d-old seedlings Click here for additional data file.S8 Figasa1-1, ERF1ox and ERF1ox asa1-1 seedlings grown on MS medium with either 0 or 1 μM ACC for 5 d. Scale bar, 1 cm. (b-c) Primary root length of Col-0, asa1-1, ERF1ox and ERF1ox asa1-1 seedlings grown on MS medium with either 0 or 1 μM ACC were measured at the fifth days. Data shown are average and SD .(a) The primary root phenotypes of Col-0, (DOC)Click here for additional data file.S1 Table(DOC)Click here for additional data file.


GAL2 and MET17 loci, pre-existing cohesin is pushed downstream along the DNA in response to transcriptional gene activation, apparently without need for intermittent dissociation or reloading. We observe translocation intermediates and find that the distribution of most chromosomal cohesin is shaped by transcription. Our observations support a model in which cohesin is able to slide laterally along chromosomes while maintaining topological contact with DNA. In this way, stable cohesin binding to DNA and enduring sister chromatid cohesion become compatible with simultaneous underlying chromosomal activities, including but maybe not limited to transcription.The ring-shaped cohesin complex is thought to topologically hold sister chromatids together from their synthesis in S phase until chromosome segregation in mitosis. How cohesin stably binds to chromosomes for extended periods, without impeding other chromosomal processes that also require access to the DNA, is poorly understood. Budding yeast cohesin is loaded onto DNA by the Scc2–Scc4 cohesin loader at centromeres and promoters of active genes, from where cohesin translocates to more permanent places of residence at transcription termination sites. Here we show that, at the  The cA crucial moment for cohesin comes at the time of DNA replication. Two newly replicated sister chromatids emerge from the replication fork, which cohesin will hold together until mitosis. To ensure stable sister chromatid cohesion from S phase until mitosis, the cohesin ring is modified during DNA replication. The replication fork-associated acetyltransferase Eco1 acetylates cohesin rings on their Smc3 subunit, which stabilizes cohesin's grip on chromosomes –12. ThisIn budding yeast, acetylated cohesin remains chromosome-bound for longer than could be directly measured, at least for an hour . In humaWhile cohesin is stably bound to chromosomes to provide sister chromatid cohesion, other forms of chromosomal metabolism must continue unabated underneath. Much of the budding yeast genome consists of genes that are frequently transcribed. Almost the entirety of the human genome is also transcribed, at least occasionally ,23. In aWe have previously observed that transcriptional activation of budding yeast genes can lead to a striking repositioning of cohesin along chromosomes, with cohesin apparently moving downstream of active genes to accumulate at convergent transcriptional termination sites . LateralGiven the importance of stable sister chromatid cohesion, we have revisited the nature of cohesin translocation along budding yeast chromosome arms in response to transcriptional changes. We study two model loci where transcriptional induction leads to downstream translocation of cohesin. Cohesin behaviour is observed by chromatin immunoprecipitation (ChIP) followed by analysis on high-resolution oligonucleotide tiling arrays. We find that, following gene activation, cohesin can move downstream of genes without requirement for known dissociation or reloading factors and we observe a translocation intermediate, consistent with cohesin sliding along genes while retaining topological association. At another locus, close to a centromere, transcriptional activation leads to the apparent loss of cohesin from chromosomes. Together, our results support a model in which, at least along chromosome arms, cohesin retains topological contact with DNA and thereby sister chromatid cohesion, while sliding laterally to accommodate requirements of transcription and probably other forms of chromosomal metabolism.2.2.1.GAL2 locus on the right arm of budding yeast chromosome 12 . F. FGAL2 l of GAL2 a. The reL2 locus b.FigureGAL2 locus and a new cohesin binding site is established downstream, where cohesin is newly loaded. Alternatively, cohesin that topologically encircles DNA could be pushed downstream along chromosomes, without dissociation and therefore without need for reloading (c). To distinguish between these two possibilities, we first asked whether the Scc2–Scc4 cohesin loader is required for cohesin translocation. In the reloading model, this is likely to be the case. We therefore repeated the GAL2 induction experiment in strains in which the cohesin loader can be inactivated owing to a temperature-sensitive scc2–4 or scc4–4 mutation [scc2–4 and scc4–4 alleles, before galactose addition. Following either Scc2 or Scc4 inactivation, GAL2 induction still caused cohesin translocation, but the extent of both cohesin loss from the GAL2 locus and its downstream accumulation were reduced (d). This suggests that, while not absolutely required, the cohesin loader facilitates cohesin repositioning at the GAL2 locus.The following experiments were designed to explore two possible explanations for cohesin translocation. It could be that cohesin is removed from chromosomes following transcriptional activation of the eloading c. To dismutation . Once ce reduced d. This sGAL2 locus, following Scc2–Scc4 inactivation, could be for two reasons. Scc2–Scc4-dependent cohesin loading might augment a less efficient, loading-independent translocation reaction. This scenario would explain the reduced downstream cohesin peak, but would not explain why clearance of the GAL2 locus is also less efficient in the absence of the cohesin loader. Alternatively, Scc2–Scc4 inactivation might hamper transcriptional activation of the GAL2 locus, which could explain both reduced cohesin loss from the GAL2 locus as well as reduced downstream accumulation. To investigate this possibility, we quantified GAL2 induction by real-time PCR analysis of transcript levels. This revealed that GAL2 induction was markedly reduced in scc2–4 mutant cells, compared with the wild-type control (e). While we do not know how the cohesin loader promotes GAL2 gene induction, we recently reported on its role in promoter nucleosome eviction, which might pertain to reduced gene activation [GAL2 locus remains to be further explored.The cohesin translocation defect at the  control e. While tivation . Attenua2.2.a). If cohesin translocation involves ATP hydrolysis-dependent reloading, then downstream accumulation of Smc1R58A/Smc3R58A cohesin is expected to be reduced or delayed. To analyse whether this was the case, we performed the following experiment. Wild-type and Smc1R58A/Smc3R58A cells were synchronized in raffinose-containing medium by α-factor block and release. Cell cycle progression was halted by hydroxyurea (HU) treatment in early S phase for 2 h, to allow Smc1R58A/Smc3R58A cohesin to accumulate on chromosomes. Cultures were now released into nocodazole-containing medium, and the cohesin distribution in the G2/M-arrested cells was assessed at the GAL2 locus before and after induction by galactose addition. Expression of budding yeast galactose-inducible genes becomes detectable around 8–12 min following galactose addition to the growth medium [GAL2 locus was largely complete by this time. Notably, translocation of Smc1R58A/Smc3R58A cohesin was similarly fast and efficient (figure 2b). Thus, while causing a marked delay in loading onto chromosomes, Smc1R58A/Smc3R58A cohesin was fully proficient in transcription-induced downstream relocation. This suggests that renewed ATP hydrolysis, and therefore renewed cohesin loading onto chromosomes, are unlikely to be part of the translocation mechanism.Given the complications with the interpretation of the above experiment, we used a complementary approach to test the contribution of new cohesin loading to transcription-induced translocation. Cohesin loading onto DNA requires hydrolysis of ATP, bound to the Smc1 and Smc3 head domains ,31,32. Mfigure 2h medium . To sens2.3.a). Following DNA replication, acetylation turns cohesin resistant to Wapl, though Wapl retains an influence over at least a fraction of cohesin [wpl1Δ strain, deleted for the gene encoding Wapl. Following synchronization, cells were again arrested in G2/M by nocodazole treatment and then GAL2 gene expression was induced by galactose addition. Cohesin translocation occurred in an indistinguishable fashion in both wild-type and wpl1Δ cells (c). This observation again suggests that cohesin does not dissociate and reload during the translocation process.We took an additional approach to test whether a dissociation and reloading cycle forms part of cohesin translocation, by asking whether the dissociation activity of cohesin's Wapl subunit is involved in the process. Before DNA replication, cohesin associates with chromosomes in a dynamic fashion, undergoing cycles of loading and Wapl-mediated dissociation a. Follow cohesin ,16,18,301Δ cells c. This o2.4.scc2–4 allele were synchronized by α-factor block and release and arrested in G2/M by nocodazole treatment under permissive conditions. Endogenous cohesin, containing a Pk epitope-tagged Scc1 subunit for detection, is bound to chromosomes under these conditions. The cohesin loader was now inactivated by temperature shift. After this, we induced synthesis of an HA epitope-tagged Scc1 subunit, under control of the galactose-inducible GAL1 promoter, for 1 h. The differentially epitope-tagged Scc1 assembles into functional new cohesin complexes [a). Now, we induced cohesin translocation and analysed the composition of the downstream cohesin pool.Another way to assess whether cohesin translocates by lateral movement, or by dissociation and reloading, is to determine the composition of the newly formed downstream cohesin pool. This pool could consist of previously chromosome-bound cohesin, as expected following lateral movement. Alternatively, it could include cohesin that was not previously chromosome-bound. To distinguish between these possibilities, we devised the following experiment. As before, cells containing the temperature-sensitive omplexes , so thatMET17, whose transcription is activated by amino acid starvation [MET17 gene, while the downstream, convergently transcribed ACO1 gene is cohesin free (b). Thirty minutes after transfer to synthetic medium lacking amino acids, endogenous cohesin translocated downstream. MET17 was now free of cohesin, whereas the ACO1 locus was partly covered. This confirms our earlier results that cohesin translocation can occur independently of the Scc2–Scc4 cohesin loader. We next quantitatively analysed the composition of the new cohesin peak by ChIP followed by quantitative real-time PCR, using seven primer pairs across the MET17–ACO1 locus. This analysis confirmed downstream translocation of endogenous Scc1-Pk-containing cohesin, whereas we did not detect HA epitope-tagged Scc1 from soluble cohesin above background levels at any of the locations, either before or after MET17 induction (c). This suggests that the ‘new’ cohesin peak consists largely, if not exclusively, of previously chromosome-bound cohesin. The closest possible origin of cohesin in the new peak is those cohesin molecules that previously occupied the upstream location and appear to have moved downstream along the chromosome following transcriptional activation.As we used galactose to induce Scc1-HA synthesis, we used another translocation model locus, arvation ,28. In rsin free b. Thirtynduction c. This sSCC1 locus is tagged with the same epitope (d). We furthermore confirmed that, under these conditions, both HA and Pk epitope-tagged cohesin is retrieved with similar efficiency by ChIP (e). This validates the quantitative comparison between ectopic and endogenous cohesin following MET17 induction and supports the conclusion that little if any ectopic cohesin is incorporated into a newly established cohesin peak following transcriptional induction.As a control for the above experiment, we confirmed that ectopic Scc1-HA, following 1 h of induction, reached levels comparable to those in a strain in which the endogenous  epitope d. We fur by ChIP e. This v2.5.GAL2 locus (a). We again used synchronized cells that were arrested in G2/M by nocodazole treatment. To enhance the time resolution of our observations, we lowered the temperature of the culture to 16°C before GAL2 induction by galactose addition. Samples were now collected every 3 min and processed for ChIP analysis. Cohesin along the GAL2 locus decreased sequentially, starting 6 min after galactose addition at the 5′ end of the gene, over time progressing towards the 3′ end (b). At 9–12 min after galactose addition, a transient intermediate peak formed that continued to move further downstream to reach its final position 15 min after induction. These observations are consistent with a sliding movement of cohesin along chromosomes, being pushed downstream while retaining topological contact with DNA.Our above results suggest that pre-existing chromosomal cohesin is repositioned during cohesin translocation. To observe the process at a greater time resolution, we returned to the L2 locus a. We agae 3′ end b. At 9–12.6.UTP20 gene, the 13th longest gene in the budding yeast genome. UTP20 lies close to the centromere on chromosome 2, within the cohesin-enriched region that surrounds budding yeast centromeres [UTP20 promoter with the GAL2 promoter, so that we could switch UTP20 expression off and on at will. The UTP20 gene product is essential, with a role in 18S rRNA maturation [GAL2-UTP20 repression, cells continued to proliferate for approximately three divisions without notable adverse effect, before proliferation slowed and eventually terminated. Therefore, cultures were grown in galactose-containing medium to maintain UTP20 expression, then shifted to medium containing raffinose just before cell synchronization by α-factor block and release. After release, cells were arrested in G2/M by nocodazole treatment, and the temperature reduced to 16°C. Galactose was added, and the cohesin distribution observed in 3 min intervals. Starting at 6 min after galactose addition, cohesin levels at the 5′ end of UTP20 declined, after which cohesin loss spread in the 5′ to 3′ direction over time (c). Unlike at the GAL2 locus, abundant cohesin was present downstream of UTP20 already at the start of the experiment, and we did not observe further cohesin accumulation there. Instead, it appeared as if cohesin was sequentially cleared from the UTP20 locus in the direction of transcription. Cohesin might be dislodged from chromosomes as the consequence of transcription at this locus. Alternatively, we cannot exclude that cohesin was in fact pushed downstream, but entered a more dynamic cohesin pool in the vicinity of the centromere that might have obscured the arrival of new cohesin. In either case, our findings confirm that cohesin is cleared from genes by transcription. It also reconciles our results with previous observations that, at certain loci, cohesin is apparently lost from chromosomes following transcriptional activation [With an aim to improve the spatial resolution when visualizing cohesin translocation, we engineered a synthetic inducible locus, making use of the 7.5 kb long tromeres . We replturation . Followiver time c. Unliketivation .2.7.GAL2 locus, respectively (a). The GAL2 locus itself remained unreplicated in the arrest. When we induced GAL2 transcription by galactose addition in HU-arrested cells, we observed cohesin translocation in a similar manner to what is seen in G2/M arrested cells (b). Thus, cohesin is able to move along chromosomes both on unreplicated and replicated DNA. This suggests that the mode of cohesin binding to DNA that endows its ability to translocate, most likely topological DNA embrace, is gained already before S phase and remains unaltered during the establishment of sister chromatid cohesion.So far, all our cohesin translocation experiments were performed in G2/M arrested cells in which cohesin is engaged in holding sister chromatids together. We wondered whether the ability to slide along chromosomes is a feature that cohesin gains during the establishment of sister chromatid cohesion, or whether chromosome-bound cohesin can do so already before DNA replication. To address this, we arrested cells in early S phase using HU treatment. We used a strain engineered to take up bromodeoxyuridine (BrdU) and performed ChIP against BrdU to assess which parts of the genome were replicated in this arrest. This analysis revealed BrdU incorporation at regions surrounding the early origins ars1211 and ars1213, 60 kb upstream and 80 kb downstream of the ectively a. The GAed cells b. Thus, 2.8.rat1-1 mutation in the Rat1 exonuclease, required for efficient transcriptional termination in budding yeast. At a non-permissive temperature for the rat1-1 allele, RNA polymerase II progresses past normal termination points at an increased frequency [a). When we compared the cohesin association pattern between wild-type and rat1-1 cells in G2/M-arrested cells, we noted that cohesin peaks were broader and less well defined in rat1-1 mutant cells already at the permissive temperature (b). Following shift to the restrictive temperature, numerous cohesin peaks disappeared, exemplified at the strongly expressed RPL40A gene, where the downstream cohesin peak was lost as the consequence of compromised transcriptional termination (b). Genome-wide, of a total of 981 prominent cohesin peaks that we counted, 167 disappeared following rat1-1 inactivation. Additional examples from different chromosomes are shown in the electronic supplementary material, figure S1.Scc2–Scc4 cohesin loader binding sites are found in the promoters of strongly expressed genes. Cohesin can be transiently detected at these loading sites in G1 before the cohesin distribution becomes dominated by its more permanent places of residence at convergent transcriptional termination sites ,24,30. Trequency ,37 . These observations are consistent with the possibility that cohesin moves downstream along genes, until reaching places where transcription terminates. This further suggests that much of chromosomal cohesin reaches its final distribution by moving from promoter loading sites towards places of convergent transcriptional termination.At the AX2 gene c. These 2.9.a). While loop hopping should not lead to translocation intermediates of the sort that we observed along the GAL2 locus (b), we nevertheless wanted to investigate whether gene looping affects the chromosomal cohesin pattern. We took advantage of the TFIIB mutant sua7-1, which is defective in gene looping [sua7-1 cells, with most of the cohesin concentrated at convergent transcriptional termination sites (figure 7b). This demonstrates that gene looping is not required for cohesin to reach terminators. Despite a largely identical cohesin pattern, we observed small differences in sua7-1 cells, e.g. a minor new cohesin peak at ‘58 kb’ along chromosome 6 (figure 7b). We do not currently know whether this difference is due to the absence of gene looping, or whether it is caused by another difference in transcriptional dynamics in sua7-1 cells [Gene loops have been proposed to bring gene promoters and terminators into spatial proximity in budding yeast . An altefigure 7L2 locus b, we nev looping . The coh-1 cells .Figure 2.10.GAL2 locus such that it can be transcribed by the bacteriophage T7 RNA polymerase [a). The T7 RNA polymerase is a 99 kDa monomeric protein, much smaller than the eukaryotic RNA polymerase II complex. For this purpose, we replaced the endogenous GAL2 promoter by a T7 promoter consensus sequence and expressed the T7 RNA polymerase, fused to a nuclear localization signal, under control of the yeast housekeeping ADH1 promoter. The cohesin distribution in cells harbouring the T7 promoter-GAL2 locus, but lacking T7 RNA polymerase, was indistinguishable from what is observed in wild-type cells (b). Addition of T7 RNA polymerase resulted in striking clearance of the GAL2 locus and downstream sequences, with hardly any cohesin detectable up to the SMC4-CSF1 convergence site 15 kb further along the chromosome. This suggests that transcription by the T7 RNA polymerase efficiently removes cohesin.We finally wanted to address what aspect of transcription makes cohesin slide along chromosomes. It could be that a particular component of the RNA polymerase II machinery contacts cohesin, or simply that the sheer overall size of the greater than 2 MDa polymerase holocomplex pushes cohesin . Alternalymerase ,42 . We now added isopropyl β-d-1-thiogalactopyranoside (IPTG) to the culture to relieve LacI repression and induce T7 RNA polymerase transcription. Over the course of 2 h, this led to successive clearance of the GAL2 locus with concomitant accumulation of cohesin at the terminator of the opposing EMP70 gene (c). After overnight growth in medium containing IPTG, cohesin was also detected further downstream at the RAX2 terminator (d). The transcriptional strength of the induced T7 promoter in the presence of LacI and IPTG was lower than if LacI was altogether absent (e), which could be the reasons for the less complete clearance of cohesin downstream of GAL2. Together, these results suggest that transcription even by the relatively small T7 RNA polymerase leads to downstream translocation of cohesin along chromosomes towards places where transcription converges. While we cannot exclude direct interactions between cohesin and cellular RNA polymerases, a driving force that is sufficient for cohesin movement is provided by a feature in common with T7 RNA polymerase, possibly the RNA transcript that emerges from the polymerase.We next wanted to establish whether T7 RNA polymerase pushes cohesin along chromosomes, similar to yeast RNA polymerase II, or displaces cohesin from chromosomes. Therefore, we rendered T7 transcription inducible by co-expressing the cription c–e. We nP70 gene c. After rminator d. The trr absent e, which 3.In this study, we analyse the nature of the striking cohesin pattern changes along chromosomes that result from transcriptional alterations. We find that factors with known roles in dynamic cohesin loading and turnover on DNA, such as the Scc2–Scc4 cohesin loader, the cohesin ATPase or Wapl, have little or no impact on cohesin movement in response to transcriptional changes. While we cannot exclude that an as-yet-unidentified mechanism unloads and re-loads cohesin during transcription, we did not find evidence that cohesin repositioning involves recruitment of new cohesin. Rather, pre-existing cohesin appears to slide along the chromosome to reach new places of residence, defined by transcription termination. These observations support a model in which cohesin that is topologically bound to DNA is able to slide laterally to adapt to the needs of other aspects of chromosome biology including, but probably not limited to, transcription.MET17 locus, translocation is more obvious in this study than previously seen. The greater resolution of our oligonucleotide tiling microarrays, compared with a relatively sparse probe distribution [A previous study concluded that cohesin is displaced from chromosomes by transcription . While aribution , could bribution . For theribution ,28,30, wCohesin is topologically loaded onto DNA by the cohesin loader and holds together sister chromatids by topological embrace ,43. If cDrosophila, cohesin and its loader colocalize with each other, both at promoters of expressed genes and along their gene bodies [Drosophila cohesin and its loader engage in a somewhat tighter complex, compared with what is observed in other organisms. In this way, cohesin and its loader could stay associated during and after the loading reaction while cohesin moves along the gene body.In most organisms that have been studied, cohesin loader binding sites are distinct from where much of the cohesin accumulates. This includes fission yeast and mammals –46. In me bodies . Such a Do SMC complexes other than the cohesin complex also slide along chromosomes? In analogy to cohesin, the budding yeast condensin complex has been demonstrated to bind chromosomes by topological embrace . While m4.4.1.Saccharomyces cerevisiae W303 background, apart from the rat1-1 mutant strain and its control, which were of FY23 background. The strain genotype details can be found in the electronic supplementary material, table S1. Cells were cultured in rich YP medium containing 2% glucose, 2% raffinose or 2% raffinose + 2% galactose as the carbon source at 25°C, or at the indicated respective restrictive temperatures for the various temperature-sensitive alleles. For amino acid starvation, cells were filtered, washed and transferred to synthetic complete SC medium lacking amino acids [−1 nocodazole in the growth medium, respectively. Cell cycle progression and cell synchrony were in all cases confirmed by FACS analysis of cellular DNA content.All yeast strains were of the no acids . Cell sy4.2.S. cerevisiae Tiling 1.0R arrays. Presented is the genome-normalized ratio of the hybridization signals of the chromatin immunoprecipitate over the input DNA. Each bar in the bar graphs represents the average of 25 oligonucleotide probes within neighbouring 125 bp windows. For the line graphs, a smoothed moving average of 200–500 bp is shown. The microarray data are available from the GEO database under the accession number GSE80464. The quantitative analysis of cohesin binding to individual loci was performed as described, using previously described qPCR primer pairs [GAL2 locus, listed in the electronic supplementary material, table S2.At the indicated times, aliquots of the cultures were harvested and processed for ChIP of the cohesin complex as described . Antiboder pairs , as well4.3.Cells were collected by centrifugation and total RNA was extracted using RNeasy reagents (Qiagen), following the manufacturer's instructions. Double-stranded cDNA (dscDNA) was synthesized from 10 µg of RNA using SuperScript II reverse transcriptase (Invitrogen), using random primers. Quantitative analysis of cDNA levels was then performed using a SYBR Green real-time PCR master mix (Life Technologies) and a ViiA 7 real-time PCR system (Thermo Fisher). The sequences of the primer pairs used are listed in electronic supplementary material, table S3.


Chromatin compactness has been considered a major determinant of gene activity and has been associated with specific chromatin modifications in studies on a few individual genetic loci. At the same time, genome-wide patterns of open and closed chromatin have been understudied, and are at present largely predicted from chromatin modification and gene expression data. However the universal applicability of such predictions is not self-evident, and requires experimental verification.We developed and implemented a high-throughput analysis for general chromatin sensitivity to DNase I which provides a comprehensive epigenomic assessment in a single assay. Contiguous domains of open and closed chromatin were identified by computational analysis of the data, and correlated to other genome annotations including predicted chromatin “states”, individual chromatin modifications, nuclear lamina interactions, and gene expression. While showing that the widely trusted predictions of chromatin structure are correct in the majority of cases, we detected diverse “exceptions” from the conventional rules. We found a profound paucity of chromatin modifications in a major fraction of closed chromatin, and identified a number of loci where chromatin configuration is opposite to that expected from modification and gene expression patterns. Further, we observed that chromatin of large introns tends to be closed even when the genes are expressed, and that a significant proportion of active genes including their promoters are located in closed chromatin.These findings reveal limitations of the existing predictive models, indicate novel mechanisms of epigenetic regulation, and provide important insights into genome organization and function.The online version of this article (doi:10.1186/1471-2164-15-988) contains supplementary material, which is available to authorized users. Chromatin compactness is the key feature of chromatin that reflects its accessibility to transcription machinery. Tightly packed closed chromatin is considered a hallmark of gene silencing, and chromatin opening precedes lineage-specific gene expression thus providing an excellent indicator of cell fate commitment , 2. HoweDrosophila genome and on previous genome-wide analyses of chromatin marks in Drosophila Schneider-2 (S2) cell line, we have created the map of chromatin compactness in these cells and cross-referenced it to known patterns of chromatin modifications and gene expression.Here, we sought clarification on two important topics in chromatin biology: first, whether there is a straightforward and immutable association between chromatin compactness and certain chromatin modifications or combinations thereof, and second – what is the relation between chromatin compactness and gene expression. Toward this objective we designed and validated a high throughput assay for general chromatin sensitivity to DNase I (GCSDI) as a powerful approach to determine chromatin compactness across genome. Previous low-throughput studies have established the power of GCSDI to detect domains of both open and closed chromatin in specific genomic loci –8. This To measure GCSDI across genome we combined a brief DNase I treatment of permeabilized cells with random amplification of the DNase I-nicked genomic DNA, followed with analysis of sequence representation in amplified material by a high-throughput method. DNase I preferentially nicks DNA in open chromatin, rendering these regions inefficient template for amplification and thus predisposing them for under-representation in amplified material. The difference in representation between known open and closed chromatin loci can be reliably detected by the GCSDI assay after treatment with diverse amounts of DNase I, and thus is a reliable analysis outcome that is not overly sensitive to DNase I treatment conditions  were hybridized with tiling Affymetrix microarrays, signal intensities for each probe were averaged within the experimental groups and fold differences between the groups were calculated. Positive log2 values were assigned to the sequences underrepresented in DNase I-treated sample (open) and negative values - to the sequences overrepresented in DNase I-treated sample (closed). The identified open and closed chromatin regions were extensive and contiguous, consistent with previous low-throughput studies –8 and inTwo segmentation models of chromatin compactness were created using the genome-wide GCSDI profiles. (i) We used a sliding window algorithm to identify transition points and to segment genome into contiguous series of open or closed chromatin domains, with the mean size of 15 kb and ranging up to 500 kb .We also analyzed distribution of individual modifications which have been traditionally linked to certain chromatin structure predictions, and intriguingly found that while open chromatin was associated with numerous abundant chromatin modifications, closed chromatin was largely unmodified. This was true in whole-genome analysis Figure  and alsoDrosophila genome [Taking into account that morphologically dense heterochromatin is often situated at nuclear periphery, we proposed that a significant proportion of closed chromatin is included in lamina-associated domains (LADs). Indeed, comparison of our GCSDI analysis with the LAD map of a genome  revealeda genome  these fiWhile intergenic spacers were mostly closed or neutral, actively expressed genes were predominantly open and silent genes were generally closed across the genome   and open chromatin in silent gene promoters (one-third in 2CM and 17% in 3CM). We first sought to rule out the trivial explanations such as frequent presence of alternative inactive promoters in active genes, as well as imprecision of chromatin analysis or incorrect selection of the promoter regions. In these cases, even though a promoter may appear in “odd” configuration, the chromatin structure of the gene body would match its expression status. We found just the opposite - the chromatin configuration of the gene body followed that of the promoter Figure A indicattau was signIntriguingly, we also found that active genes with closed promoters showed lower transcript levels than conventionally expected active gene with open promoters, indicating that chromatin compactness may serve to modulate active gene expression. The mechanisms underlying this type of regulation warrant further inquiry, as do the other unexpected trends identified in our study, such as the general paucity of known chromatin marks positively identifying closed chromatin and the tendency of large introns to stay in closed configuration even when the genes are expressed. We expect that the novel analysis of epigenomic regulation with a straightforward and sensitive assay described here will contribute an empirical approach supplementing predictive chromatin structure assessments, thereby advancing both basic and biomedical research in chromatin biology.6 S2 cells were permeabilized with 0.05% NP40 and resuspended in DNase I Buffer . Part of each sample was set aside and later used as non-digested control. DNase I (Promega) digestion was performed at 37°C for 10 or 15 minutes with diverse amounts of the enzyme to optimize the procedure ; for further analysis cells were treated with 0.5U DNase I for 10 minutes. 20 ng of DNA purified from the treated cells using the DNeasy Blood & Tissue Kit (Qiagen) were used as a template for whole genome amplification. The library preparation step using GenomePlex WGA2 Kit was followed by amplification with the GenomePlex WGA 3 Reamplification Kit (Sigma). dUTP was incorporated at the amplification step to enable the probe fragmentation procedure according to Affymetrix recommendations. The amplification product was purified with the Wizard SV Gel and PCR Clean-Up System (Promega), fragmented and labeled using the GeneChip WT Double-Stranded DNA Labeling Kit (Affymetrix), and hybridized with GeneChip Drosophila Tiling 2.0R Array following the manufacturer’s instructions.DNase I treatment of cells was performed as previously described  with minRaw data from microarray CEL files were normalized using CisGenome  and log2To generate two-configuration model (2CM), probes with log2 differential signal within 1 standard deviation of the mean were discarded and the signals for the remaining probes were capped at 1 for positive and -1 for negative. A sliding window was used to determine transition points between open (positive) and closed (negative) segments. The difference (d) between the mean log2 differential signals of flanking regions was calculated for every probe. The cutoff d values were established by analyzing 100 permutations of probes, with the requirement that real genome data is significantly different from permuted models (p < 0.05). A series of significant d values and flanking region sizes (n) were tested to determine the model most discriminating between real genome and random permutation. The results presented here are based on analysis using n = 48 and d = 0.8, which identified 2244 transition probes in the real dataset and only 72 on average in permutated controls.To generate three-configuration model (3CM), all probes were analyzed for presence of differential signal peaks using an HMM algorithm built in CisGenome, with a posterior probability greater than 0.5. An FDR value of 0.1 was used to filter the detected peaks which were further consolidated into domains as following: two adjacent peaks are joined if the distance between them is less that threshold value , and if they are of the same sign (either both positive or both negative). Otherwise, the segment between the peaks is assigned neutral state. 2CM and 3CM domain coordinates in BDGP5 genome annotation are provided as tables in Additional files http://www.modencode.org) [http://compbio.med.harvard.edu/flychromatin/data.html[LAD coordinates  were dowode.org) ; specifidata.html. Coordindata.html. All datWe downloaded short-read (Illumina) sequences for 5 massively parallel mRNA sequencing experiments on S2 cells from two GEO datasets (GSM390063 and GSM390064), aligned these reads to the Drosophila reference genome (BDGP5) using TopHat , and calactin and Letm1 representing open chromatin, and Crtp and Yu representing closed chromatin [Additional file 1: Figure S1: Evaluation of the amplified DNA from DNase I-treated and untreated cells. Low amplification bias evident by the absence of discrete bands in agarose gel (A), and selective depletion of open chromatin in sample from cells treated with DNase I under diverse conditions . Four genome regions with known chromatin compactness were analyzed: hromatin .  and in more detail for the lower size ends of distribution histograms (left panels). (JPEG 59 KB)D. melanogaster. (JPEG 44 KB)Additional file 3: Figure S3: Distribution of open (green), neutral (yellow), and closed (red) chromatin domains detected by 3CM analysis on chromosomes of Additional file 4: Figure S4: Representation of detected open ,neutral, and closed chromatin domains in genome and their association with predicted chromatin states. Proportions of open and closed chromatin detected by 3CM are shown for individual chromosomes (A) and for the genome regions predicted as 9 chromatin states [Additional file 5: Figure S5: Contributions of the predicted chromatin states to open, neutral, and closed chromatin detected by 3CM. Results are shown for the whole genome, and separately for major autosomes and chromosomes X and 4. (JPEG 49 KB)Additional file 6: Figure S6: Enrichment and depletion of chromatin modifications in open, neutral, and closed chromatin. Heat maps show percent proportions of regions enriched with (red) or depleted of (blue) particular chromatin modifications in open and closed chromatin domains detected by 2CM and 3CM. Data are cumulative for the entire genome, euchromatin of major autosomes and chromosome X, the entire chromosome 4, and combined pericentromeric heterochromatin of major autosomes and chromosome X. (JPEG 104 KB)Additional file 7: Figure S7: Link between lamina-associated domains (LADs)  and closAdditional file 8: Figure S8: Relationship between open and closed chromatin and gene structure. Proportions of open, closed, and neutral chromatin detected by 3CM are shown for intergenic spacers and active or silent genes (A) and for structural elements of active and silent gene (B). Analysis of relationship between chromatin structure and the size of gene (C) and intron (D) shows that proportion of open chromatin diminishes as the gene and intron size increases for both active and silent genes. (E), Distribution of open and closed chromatin along large (>10 kbp) active gene introns. (JPEG 58 KB)GCSDI domains 2CM.(XLSX 64 KB)Additional file 9:GCSDI domains 3CM.(XLSX 645 KB)Additional file 10:


Developmental processes in cells require a series of complex steps. Often only a single master regulator activates genes in these different steps. This poses several challenges: some targets need to be ordered temporally, while co-functional targets may need to be synchronized in both time and expression level. Here we study in single cells the dynamic activation patterns of early meiosis genes in budding yeast, targets of the meiosis master regulator Ime1. We quantify the individual roles of the promoter and protein levels in expression pattern control, as well as the roles of individual promoter elements. We find a consistent expression pattern difference between a non-cofunctional pair of genes, and a highly synchronized activation of a co-functional pair. We show that dynamic control leading to these patterns is distributed between promoter, gene and external regions. Through specific reciprocal changes to the promoters of pairs of genes, we show that different genes can use different promoter elements to reach near identical activation patterns. Many processes which involve a major change in the state of the cell  are governed by a master regulator in the form of a single transcription factor that activates dozens or even hundreds of the genes needed to carry out the process –4. Such et. al. [et. al. [et. al. [et. al. [There is yet limited knowledge on what mechanisms and genomic elements contribute to differential timing and stoichiometry of jointly regulated genes. Previous works have shown the existence of regulated target timing in temporal processes. Differential timing has been shown in bacteria in multiple regulator cascades ,6 and wiet. al.  showed t[et. al. , studied[et. al.  found th[et. al.  found thHere, we use a single cell comparative approach to study the questions of relative target timing and stoichiometry. We use early meiosis in yeast as a model for a system with a master regulator. Ime1, the master regulator of early meiosis, is controlled by multiple starvation signals –15. ThroWe find that the co-functional pair displays a tight synchrony with a small offset at the single cell level, while the non co-functional pair is differentially timed and un-synchronized. We show that timing and stoichiometry control are regulated through different parts of the gene, including ORF, promoter and regions outside the promoter, and that different genes can attain very similar expression profiles through different regulatory elements.All strains are from the SK1 background . TransfoGene swap and promoter-gene swap strains were created as follows: for gene swap, the ORF-XFP-resistance-marker segment of Rec8 (Dmc1) was PCR-amplified from a protein-fusion strain using primers with 40 bp tails homologous to the regions upstream of Dmc1 (Rec8) gene start and downstream of its stop codon. This linear segment was then transformed to a wild type strain, replacing the wild type Dmc1 ORF. For promoter-gene swap, a similar procedure was carried, but amplifying the region starting from the promoter . In that case the 40bp homology in the 5’ primer matched the promoter start of the opposite gene.Specific motif changes were made by cloning of the target ORF from the tagged yeast strain, starting at a position immediately after the region to be changed, and then extending this construct by a forward primer containing the required change at a length of ~40 bp and a complementary portion of ~20 bp, and a reverse primer from the previous reaction.All Dmc1 promoters with motif changes were tagged with Venus, and mated with a native Dmc1 strain tagged with Cerulean. All Rec8 promoters with motif changes were tagged with Cerulean, and mated with a native Rec8 strain tagged with Venus. Mei5 motif change was done similarly.2 and 5mM MnCl2 in sterile double distilled water.Microfluidic chambers were made from Poly-dimethylsiloxane (PDMS) polymer using SYLGARD 184 silicone elastomer kit  and a 1:10 curing agent ratio. Monomer base and curing agent mix was poured onto a photoresist mold and then incubated overnight at 65°c, forming simple channels each with one input and output. The PDMS was then reversibly attached to a cover glass slide by pre-treatment with a hand-held plasma generator. In order for cells to adhere to the bottom of the glass, the channels were then filled with a 2mg/ml Concanavalin A solution supplemented with 5mM CaClCells were picked from single colonies and grown overnight before each experiment in YPD . The cells were then washed twice with SPM medium  at 2500 rpm for 1 minute. Washed cells were then inserted into the channels to form a single cell layer. SPM medium was pumped into each channel at a constant rate of 0.5 ml/hour. Images were taken using a Nikon TiE fluorescent microscope at 100X or 60X magnification. In each experiment 10 positions along the channel containing a total of 300–1000 cells were chosen. These positions were imaged every 15 minutes using the relevant fluorescent emission and detection filters .The captured images were analyzed using custom written code  in Matlahttp://genome.ucsc.edu/) and from GermOnline (http://www.germonline.org/index.html). Expression data during meiosis was obtained from Brar et al [et al [et al [et. al. [et. al. [et. al. [et. al. .Genetic sequence data was obtained from the UCSC genome browser . We followed single cells after induction of meiosis in a diploid strain in which the Dmc1 protein is fused to YFP and Rec8 protein is fused to CFP . Rec8 anWe next quantified the coordination in protein levels of the gene pairs. The protein products of a pair of genes that are involved in the same function may play their role at a certain stoichiometry . In such a case, it may be beneficial to regulate not only the relative timing of those genes but also their relative stoichiometry. We therefore wanted to check whether the co-functional pair of proteins (Dmc1 and Mei5) has a tighter stoichiometry at the protein level than the non co-functional pair (Dmc1 and Rec8). We note that tight stoichiometry does not automatically follow from coordinated timing: theoretically, a pair of proteins can be highly synchronized in their timing (as are Dmc1 and Mei5), yet have high cell-to-cell variability in their relative abundance. Examining the log ratio of the two signals over time, we find that the protein level ratio between Mei5 and Dmc1 stabilizes on a constant value within a few hours from onset, with a tighter distribution over time, while the ratio between Dmc1 and Rec8 changes over time . This diOverall, the two pairs defined by these 3 genes show distinct relations in their protein levels, where one pair is differentially timed and divergent in relative abundance, and the other more synchronized and tightly coordinated in stoichiometry.We next looked for the regulation level at which the activation and de-activation timing are controlled. Since we measure nuclear protein levels, this regulation could occur at the level of transcription, translation, protein localization, mRNA or protein degradation or any combination of the above. To study the role of promoter activity in the protein expression pattern, we tagged the promoters of Rec8, Dmc1 and Mei5 with fluorescent proteins (replacing the endogenous ORF with NLS-CFP or NLS-YFP). The results again showed a consistent difference between Rec8 and Dmc1, with a smaller temporal offset compared to the protein level . The DmcOur results suggest that there is a major contribution to the regulation of timing of our test gene pairs at the transcriptional level. If specific promoter features regulate timing and expression level similarly in many targets of the Ime1 master regulator, we expect to identify a correlation between such features and the expression properties . We therWe then attempted to narrow down the specific promoter elements that dominate the temporal behavior of Rec8, Dmc1 and Mei5 . We decicis-effect of the altered allele to the wild type in individual cells, where the trans environment (including Ime1 protein concentration) is identical. This was important, as the cell-to-cell variability between Ime1 profiles and target promoter activation was high [To test the effect of individual sequence changes in the various promoters, we created strains with one WT allele and one altered allele of that gene. This allowed the accurate comparison of the was high .The perturbation of the MBF binding site located on the Rec8 promoter resulted in a marked reduction of Rec8 promoter activity, compared to the WT Rec8 promoter . This reThe lack of significant contribution of individual promoter element changes to the timing regulation stands in contrast to our observation that the timing is highly reflected in the promoter activity level. This suggests that the timing of these genes is controlled by other promoter elements, by a combined effect of several promoter elements, by elements outside the promoter or by a combination of the above. Moreover, the timing difference between the promoters  is smallWe now compare the 6 alleles in terms of their relative timing and maximal protein expression level during the 7 hours period starting from the onset time. To this end, we integrate the relative timing and abundance information from single experiments into one comparative view. First, we note that behavior of the alleles clusters according to the ORF identity, as the ORF provides the dominant contribution to both timing and maximal protein level. Second, the promoter of Dmc1 leads to delayed activation and increased maximal level in two different contexts (RRD→RDD and DRR→DDR). Therefore its contribution to timing and level is consistent with that of the Dmc1 ORF. Finally, the external regions at the Rec8 locus contribute to higher expression (RDD→DDD and RRR→DRR), but their effect on activation time depends on the promoter or gene context: the external regions of the Dmc1 locus contribute to earlier activation of a Rec8 gene (RRR→DRR), but not of Dmc1 (RDD→DDD).S. cerevisiae cells containing either protein or promoter tagging of pairs of Ime1 target genes. Our results show that these target genes can be more tightly synchronized and settle on a set relative stoichiometry (Dmc1 and Mei5) or have a relative delay with variable lag between one another, leading to a change over time in relative abundance (Dmc1 and Rec8). The Dmc1 and Mei5 pair showed tight synchronization and no temporal offset at the promoter level, only gaining an offset at the protein level. In contrast, the Rec8 and Dmc1 pair showed temporal offset and high variability both at the protein and the promoter levels.We have studied the degree of differential or synchronous timing and stoichiometry in single cells in two examples of gene pairs controlled by the same transcription factor. We used live imaging of single diploid Our bioinformatic analysis of Ume6 targets does not show a correlation of any of the promoter parameters tested with expression timing, and only a few distance parameters showed some correlation to maximal expression level. This suggests that the shape of gene activation patterns may result from a combination of several control elements, or the utilization of different elements in each promoter. Consistently, Dmc1 and Mei5, which have a very tightly synchronized protein level pattern, have two very different promoter sequences and gene properties (the ORF of Dmc1 is three times longer and contains an intron). Removing one of the Ume6 binding sites from pMei5 or adding such a binding site to pDmc1 caused a decrease or increase (respectively) in promoter activity, but had little effect on activation timing. This suggests that Dmc1 and Mei5 have co-evolved to increase their synchrony, each using a different combination of specific promoter and gene elements. The similar reduction in Rec8 promoter activity upon removal of the nucleosome-covered MBF binding site indicates that MBF acts as a co-activator for this gene, and probably only binds to its binding site once the nucleosome occluding it has been removed, increasing Rec8's transcription rate. The effect on timing, however, is small. This outcome is similar to the effect of the second Ume6 binding site for Mei5: in both cases, the additional binding site serves mostly to increase maximal expression levels, with little effect on timing, consistent with previous results [Dissecting the contributions of ORF, promoter and external regions to the timing and maximal level of Rec8 and Dmc1 lead to several insights. First, it is the ORF that contributes mostly to the timing and maximal working level in these two genes. This may be mediated through the stability of the mRNA, protein or both, as increased stability in either level can lead to slower accumulation as well as higher maximal expression level. The contribution of the promoter region to these properties is secondary, but it is consistent with the ORF contribution: both promoter and ORF contribute independently to later activation and higher maximal level in Dmc1. Second, the contribution of the external regions, providing the chromosomal context in which the gene is located, depends on the gene (or promoter) and varies between the two genes.Together, our results show that yeast can attain both remarkable synchronization and differential timing between protein pairs controlled by the same regulator, using very different mechanisms and cis-elements for each gene. The genes tested in this work are activated by a regulator that interacts with the DNA through a co-factor (Ume6). It would be interesting to examine target genes of a different type of regulator, one that binds directly to the DNA, and see whether it produces different dynamical effects on its targets.S1 Fig50 statistic is designed to overcome the effect of this artifact.Normalized CFP and YFP time series from a single Mei5-CFP, Dmc1-YFP cell entering meiosis. Four snapshots are shown from the first 3 hours after switch to meiotic conditions. YFP has a better signal to background ratio than CFP, most apparent in the early part of the series, where the signals are weak. As a result, the early ramp-up part in the CFP channel is below detection level. The T(EPS)Click here for additional data file.S2 Fig Promoter architecture and normalized mRNA expression profiles of Ime1 target genes. The genes are aligned by ATG and sorted by expression peak time (a) or by maximal expression level (b). Ume6 binding site—red; TATA box—blue; TrSS—green; measured nucleosomes—gray.  Distributions of TATA box (c) or Ume6 binding site (d) distance to ATG for different expression level groups (as marked in (b)). Triangles show th 95% confidence interval for the median estimator. In highly expressed genes the TATA box is closer to the ATG.(EPS)Click here for additional data file.S3 Fig50 and tracks of log(YFP/CFP) over time  are shown. (a) pDmc1 vs. pDmc1 (left), pDmc1 vs. pDmc1(UME6bsSwap) (center), pDmc1 vs. pDmc1+UME6bs (right). (b) pMei5 vs. pMei5 (left), pMei5 vs. pMei5-UME6bs (right). (c) pRec8 vs. pRec8 (left), pRec8 vs. pRec8-MBFbs (right).Timing and stoichiometry measures for strains having one alteration in the promoter of Dmc1, Rec8 or Mei5, compared to the wild type promoter. For each strain, scatter plots of T(EPS)Click here for additional data file.S1 MoviePart of a single position time-lapse movie showing Rec8-CFP, Dmc1-YFP cells entering meiosis. Left: Rec8-CFP signal. Right: Dmc1-YFP signal. Center: merged CFP, YFP and DIC channels. Time interval between acquisitions: 15 minutes.(AVI)Click here for additional data file.


Within just a few years, the new methods for high-throughput next-generation sequencing have generated completely novel insights into the heritability and pathophysiology of human disease. In this review, we wish to highlight the benefits of the current state-of-the-art sequencing technologies for genetic and epigenetic research. We illustrate how these technologies help to constantly improve our understanding of genetic mechanisms in biological systems and summarize the progress made so far. This can be exemplified by the case of heritable heart muscle diseases, so-called cardiomyopathies. Here, next-generation sequencing is able to identify novel disease genes, and first clinical applications demonstrate the successful translation of this technology into personalized patient care. Every cell in our organism is a highly dynamic biological system that must continuously respond and adapt to multiple intrinsic and extrinsic factors. In this ever-changing system, the genome of the cell is its only constant and master plan for its behavior. Hence, if we want to understand the very complex molecular networks of cells in health and disease, we need to enlighten the secrets of the genome itself.Genetic research has begun with fascinating investigations on simple phenotypic characteristics, with Gregor Mendel discovering the basics of inheritance in 1856 without even knowing the molecular composition of the genome. While he studied the color of pea seeds, his rules haven’t changed and are applicable to many diseases today. By now, over 4,000 Mendelian diseases have been recognized, with new ones discovered every year. Additionally, the era of genome-wide association studies has led to new paradigms, discovering the role of non-coding and intergenic variants and their contribution to highly prevalent, complex diseases ,3. It seIt is evident that the genome is far more complex than previously thought. While the understanding of its coding regions has considerably advanced, 99% of the non-coding sequence is still challenging researchers from different disciplines to finally unravel all of the functions of the genome. Additionally, genetic variation across different individuals and populations is higher than estimated, and the transition from common to rare variants is fluid, making interpretation of their functional relevance difficult; structural changes, such as insertions, deletions or copy number variations, are far more frequent than previously thought. For instance, the Database of Genomic Variants (DGV) lists about 60,000 CNVs, 850 inversions and 30,000 insertion/deletions identified in healthy individuals .The sequencing of the genome has laid the groundwork for many investigations that improved our knowledge on the biology and molecular principles, not only of Mendelian disorders, but of human disease in general. However, at the same time, it has raised a vast amount of new questions. For example, the completion of the human genome project revealed that there are just around 20,000 protein-coding genes, a surprisingly low number in comparison to the complexity of the human organism ,5. It haThe advances of sequencing technologies have successfully contributed in elucidating the function of the human genome. NGS technologies have gained the capacity to sequence gigabases of DNA in a high-throughput and highly efficient manner that has not been possible using traditional Sanger sequencing. While Sanger is based on gel separation of chain-terminated fragments from enzymatic synthesis ,7, most et al. introduced one of the first techniques for single-molecule sequencing [Novel platforms of the third generation are under development, which are based on real-time sequencing of the DNA templates without prior amplification . Braslavquencing , and fluquencing ,13,14. Tde novo assembly, variant detection/filtering and annotation [et al. [The massive data produced by current NGS systems presents a significant challenge for data storage and analysis. A number of computational tools and databases have been newly developed to handle base calling, alignment of sequence reads to a reference, notation ,16,17,18 [et al. ,20,21.So far, several diseases and syndromes have been dissected by NGS approaches. Now, the systematic detection and annotation of the complete genome, as well as correct interpretation of its variations, transcription start and polyadenylation sites, exon-intron structures, splice variants and regulatory sequences is required to advance our understanding of biology. The recently published ENCODE project helped to systematically map the regions of gene transcription, transcription factor binding and chromatin modifications, assigning functional properties to 80% of the whole genome.In the following paragraphs, we want to provide an overview on different NGS applications, starting with genomic sequencing .DNA sequencing by NGS can be applied within different applications, such as partial-exome (PES), whole-exome (WES) or whole-genome sequencing (WGS). The broad range of applications opens new and more affordable possibilities to study numerous cellular processes at the single-base resolution. However, both WES and WGS produce massive amounts of data, which presents significant challenges for data storage, distribution, analysis and interpretation. In the nearer future, this will remain one of the main bottlenecks of all described approaches.SHROOM3 for heterotaxy [ANGPTL3 in cases of familial combined hypolipidemia [et al. used exome sequencing in conjunction with genetic mapping to identify a mutation in the mitochondrial ribosomal protein, MRPL3, in a family with mitochondrial cardiomyopathy [et al. to bypass missing sequencing depth to reveal a deletion in BAG3 to be causative for familial dilated cardiomyopathy [Partial and whole-exome sequencing is a relatively cost-efficient method to detect genome-wide variations in exons and adjacent splice-sites. Considering that Mendelian diseases typically affect the protein-coding regions of the genome, exome sequencing is particularly relevant to discover such rare-variants. The advances made so far in NGS technologies makes WES, with an average cost of approximately 700–1,000 US$ per sample, a widely applicable tool for discovering rare alleles underlying Mendelian phenotypes and complex traits ,23. To dterotaxy ,30 and Aipidemia  or novelipidemia ,33,34. Imyopathy . A combimyopathy . Althougmyopathy , it is lAlterations in regulatory sequences and non-coding regions account for a significant proportion of genetic susceptibility to common and complex diseases. WGS holds the great advantage that it enables the grasping of variations, not only in the protein coding genes, but it also assesses the large non-coding parts of the genome. Meanwhile, WGS is only approximately five- to ten-fold more expensive than exome sequencing, and costs are expected to further decrease dramatically in the next few years .et al., for instance, identified by WGS a family with a recessive form of Charcot-Marie-Tooth disease, a clinically relevant heterozygous mutation in the SH3TC2 gene [in silico, in vitro and in vivo model systems with WGS may hold the key to facilitate the interpretation of genomic variation and allow a more accurate prediction of the clinical impact of coding, as well as non-protein-coding variations.Especially in cancer research, it was recognized early on that it is important to target all types of somatic/germ-line genetic alterations, including nucleotide substitution, small insertions and deletions, CNVs and chromosomal rearrangements, also of non-coding regions . HoweverTC2 gene . DespiteTC2 gene ,42. HencThe DNA in multicellular organisms contains the same genetic information in every cell (with the exception of gametes or neoplastic cells); the transcriptome of different cells, however, largely varies depending on the cell type, its function and temporal state. The transcriptome describes the complete set of all RNA molecules in a cell, in the sum determined by the genes that are actively expressed and the RNAs that underlay active or passive degradation processes. The exploration of complex cellular processes, like gene expression, alternative splicing, allele-specific expression and RNA editing, are still challenging. Next-generation sequencing techniques allow analyzing RNA-levels, RNA-editing and isoform-analysis in a single, unbiased experiment. Furthermore, rRNA, tRNA and other non-coding RNAs  can be investigated.During mRNA maturation, pre-mRNAs undergo a sequence of chemical and structural changes, such as capping, editing, splicing and polyadenylation. Alternative splicing is one main step in RNA maturation, and transcriptomic diversification is a tissue-specific and developmentally strictly regulated process ,44. ReguAll of the described steps need to be well controlled ,49,50, aUntil now, microarrays are still the most commonly used technique for measuring gene expression, allowing high throughput analysis of thousands of target genes in parallel. Nevertheless, microarrays have some considerable drawbacks, such as problems with unspecific hybridization and limited dynamic range. Also, they cannot be easily used to detect splice-events or previously unknown transcripts .de novo reconstruction of transcriptomes from RNA-seq data, such as Trinity, are promising approaches that allow assembly of full-length transcripts, even without a complete reference sequence [Next-generation sequencing protocols for RNAs, often referred to as RNA-seq, provide direct access to the transcript levels and sequences without the prior knowledge about the targets to be analyzed. RNA-seq is an unbiased, rapid, precise and, meanwhile, not too expensive method to quantify the expression of genes and to detect tissue-specific transcript isoforms, even without a reference genome or predesigned probes . Dependisequence , particusequence .et al. used RNA-seq to generate a map of the transcriptional landscape of 69 lymphoblastoid cell lines derived from unrelated Nigerian individuals who had been completely genotyped by the HapMap consortium. Thus, by using this genotype data, they identified over 1,000 genes at which genetic variation influences expression levels or splicing. They also demonstrate that eQTLs near genes mostly act by a mechanism involving allele-specific expression and that variation that influences the inclusion of an exon is enriched within or near the consensus splice sites [In recent years, RNA-seq was successfully applied to dissect gene dysregulation that contributes to the pathogenesis of cardiovascular diseases, cancer, Chronic Obstructive Pulmonary Disease or Type 2 Diabetes, in some cases directly or indirectly caused by genetic variants in proteins involved in different steps during RNA processing . Hence, ce sites .Various studies using RNA-seq have shown an improvement of assessing alternative splicing and detection of novel transcripts in comparison to splicing-arrays. RNA-seq was used on several human tissues and cell lines uncovering a larger number of alternative splicing events in humans than previously thought ,73. IsofTranscriptome diversity is further increased by RNA editing, which results in a different product than that encoded by the DNA template. RNA editing is a process of site-specific modification of the mRNA sequence. This process involves deamination of adenosines into inosines, which are read as guanines. The substitution of adenosine to inosine is catalyzed by members of the double-stranded RNA-specific Adenosine Deaminase enzymes (ADAR). Since RNA editing can lead to the formation of an altered protein if editing results in a codon exchange, this process may be an essential post-transcriptional mechanism for expanding the proteomic diversity . AlteredFor a long time, only a handful of editing sites within coding sequences have been well characterized . HoweverIn recent years, sequencing technologies have revealed that at least 90% of the genome is actively transcribed, giving rise to thousands of non-coding transcripts ,93,94. Iet al. revealed that more than 100 lncRNAs were differentially expressed in hypertrophic mouse hearts [ANRIL and MIAT ncRNAs, which was linked to the pathogenesis of CAD and myocardial infarction [Different classes of small and large non-coding RNAs (ncRNAs) have been shown to regulate gene expression at the transcriptional level through a direct interaction with the transcriptional machinery. This results in either transcriptional activation or transcriptional repression determined by physiological and developmental processes. Some ncRNA are strongly linked to epigenetic regulation influencing chromatin-remodeling complexes, chromatin architecture, post-transcriptional processing and translation ,101,102.e hearts , suggestfarction ,114,115.farction ,117. Thefarction ,119,120,farction ,122,123.farction ,128,129.Transcriptional regulation is frequently controlled epigenetically by mechanisms that do not directly depend on the underlying DNA sequence . Hence, Epigenetic modifications of the genome are now known to be involved in many cellular processes, such as embryonic development, transcription, chromatin structuring, X chromosome inactivation, genomic imprinting and chromosome stability . Hence, et al. found different methylation patterns between end-stage heart failure and control human hearts in CGIs within gene promoters and gene bodies. Moreover, the observed decreased gene promoter methylation that correlated with upregulated transcripts, but not vice versa. By a genome-wide approach, we could recently identify alterations in cardiac DNA methylation that are associated with human dilated cardiomyopathy. By consecutive fine-mapping and biological validation in independent cohorts and in vivo characterization in zebrafish, it was underlined in this study that epigenetic modifications of distinct pathways and modifiers are functionally involved in the pathogenesis of DCM [To date, most of the epigenetic studies have focused on aberrant DNA methylation patterns, particularly in embryonic development and cancer biology ,133,134.s of DCM .et al. comprehensively summarizes studies that demonstrated that single nucleotide polymorphisms are assumed to be associated with altered DNA methylation and chromatin accessibility, implicating that genomic variants can modify epigenetic patterns [A recently published review by Leung patterns ,141. HenUnlike genome-wide variation data, which is included and steadily updated in a wealth of databases, such as the Human Genome Project, 1,000 Genomes Project or HapMap, whole-epigenome data only started to be systematically identified and catalogue. The now increasing amount of methylation data for many tissues, pathological conditions and species are deposited, for instance, in the NGSmethDB  or ENCODet al. demonstrated, for instance, the applicability of MethylCap-seq, which combines precipitation of methylated DNA by the recombinant methyl-CpG binding domain of MBD2 protein with NGS, to dissect genome-wide DNA methylation profiles of the Cisplatin-sensitive ovarian cancer cell line [Bisulfite sequencing or MeDIP-Seq (methylated DNA immunoprecipitation coupled with sequencing) are used to capture global DNA methylation changes, while ChIP-Seq is applied to identify histone modifications and to analyze transcription factor binding sites ,145. So ell line . MeDIP hell line ,156,157,ell line .de novo motif discovery. Robertson et al. and Euskirchen et al., for instance, catalogued binding sites of the transcription factors STAT1 and NRSF in human cells by ChIP-seq and highlighted the excellent resolution and low necessity of extensive replicates for the method [Histone modifications, chromatin-remodeling factors and binding sites for transcription factors are today mostly analyzed using chromatin immunoprecipitation sequencing in combination with NGS (ChIP-seq). As compared to previously used ChIP-chip approaches, which are also based on chromatin immunoprecipitation, but on a microarray platform ,160, ChIe method ,162.et al., for instance, demonstrated the clinical applicability of NGS in cancer diagnostics using target region capture and NGS to detect germ-line mutations in 21 tumor suppressor genes in genomic DNA from women with primary ovarian, peritoneal or fallopian tube carcinoma. Other publications analyzed the versatility of NGS for clinical applications for diseases, such as retinitis pigmentosa, inflammatory bowel disease, neurofibromatosis, Charcot-Marie-tooth neuropathy, Kabuki syndrome and others [The introduction of NGS technologies has tremendously changed the landscape of genomic research. As described above, NGS has led to important discoveries in biomedical research and has already been implemented in clinical diagnostics, too. Early studies reported such a successful translation of NGS into clinical workflows using whole-genome, whole-exome and enrichment-based sequencing approaches for different diseases. Especially in oncology, NGS-based diagnostic testing has already achieved a broader clinical impact. Walsh d others ,165,166.d others . ImportaIn biomarker discovery, NGS is already applied in many screening studies, e.g., for miRNA or epigenomic signatures. It is foreseeable that NGS technologies can also be applied in clinical biomarker assays, especially when complex, maybe temporal or multivariate biomarker signatures are used to increase diagnostic performance . At the