Helicobacter pylori, the hp1043 gene, also known as hsrA, is one of the transcriptional regulator that is essential for cell viability. This gene could not be deleted, nor the amount of protein modulated, supporting the hypothesis that HP1043 could be involved in the regulation of crucial cellular processes. Even though detailed structural data are available for the HP1043 protein, its targets are still ill-defined. Using Chromatin Immunoprecipitation-sequencing (ChIP-seq), one of the most powerful approaches to characterize protein-DNA interactions in vivo, we were able to identify genome-wide several new HP1043 binding sites. Moreover, in vitro DNA binding assays enabled precise mapping of the HP1043 binding sites on the new targets, revealing the presence of a conserved nucleotide sequence motif. Intriguingly, a significant fraction of the newly identified binding sites overlaps promoter regions controlling the expression of genes involved in translation. Accordingly, when protein translation was blocked, a significant induction of almost all HP1043 target genes was detected. These observations prompted us to propose HP1043 as a key regulator in H. pylori, likely involved in sensing and in coordinating the response to environmental conditions that provoke an arrest of protein synthesis. The essential role of HP1043 in coordinating central cellular processes is discussed.Many bacterial regulatory genes appear to be dispensable, as they can be deleted from the genome without loss of bacterial functionalities. In  Helicobacter pylori is a major pathogen, highly widespread among the human population, and it has been recognized as class I carcinogen by World Health Organization. It is considered the primary cause of severe gastrointestinal diseases such as peptic ulcer, gastric adenocarcinoma and MALT lymphoma1H. pylori genome is the relative scarcity of genes encoding regulators of transcription. To date, only 17 transcriptional regulators have been identified and characterized to different extents80 and the alternative RNA polymerase sigma subunits σ54/σ28, both involved in the transcription of flagellar genes) and 4 transcriptional repressors involved in metal homeostasis (Fur and NikR) or stress response (HrcA and HspR), H. pylori employs several two-component systems. Two-component systems are composed of a histidine-kinase and a response regulator. Upon signal perception, the sensor kinase catalyses its auto-phosphorylation and then transfers the phosphoryl group to a partner response regulator, a specific DNA binding protein that modulates transcription of target genes. Two-component systems are employed by H. pylori to coordinate gene expression in crucial cellular processes like chemotaxis (CheA/CheY)H. pylori genome harbours 2 genes, named hp1043 (HPG27_RS02035) and hp1021 (HPG27_RS02145), encoding for two so-called “orphan response regulators”, as their partner sensor kinases are missing. HP1043, in particular, appears to be essential for cell viability. The hp1043 gene, in fact, could not be deleted unless a second gene copy was integrated into the H. pylori chromosome8In vitro DNA binding studies demonstrated that HP1043 binds its own promoter and the promoter region of tlpB, a gene encoding a methyl-accepting chemotaxis proteincncR1, a small regulatory RNA of H. pylori G27 involved in the opposite modulation of motility and adhesion to host cellshp1043 gene, or even of modulating the amount of HP1043 protein in the cell, has hampered the detailed characterization of its regulatory functionhomeostatic stress regulator) of the bacterial cell and to a role in oxidative stress defence and nitrogen metabolism13porGDAB promoter region, the regulation of other genes by HP1043 was inferred solely by the finding of a putative binding sequence13in vivo and the characterization of selected binding sites in vitro. The combination of recent advances in deep sequencing coupled with well-established Chromatin Immunoprecipitation (ChIP) protocols for bacterial transcription factors, establishes ChIP-seq as one of the most powerful approaches to identify genome-wide all the in vivo binding sites of a regulator of interest. The ChIP-seq analysis here presented allowed us to identify several new in vivo HP1043 binding sites. Moreover, in vitro protein-DNA binding assays enabled precise mapping of the HP1043 binding sites on new targets, whose analysis revealed the presence of a conserved nucleotide sequence motif. Interestingly, a significant fraction of the newly identified motifs overlaps promoters associated to genes involved in the process of translation. Accordingly, a stress signal leading to the arrest of protein synthesis, resulted in a significant induction of almost all HP1043 target genes. These observations prompted us to propose HP1043 (HsrA) as a key regulator in H. pylori, likely involved in sensing and in coordinating the response to environmental conditions that provoke an arrest of protein synthesis.In the present study we report the identification of HP1043 (HsrA) DNA targets hp1043, tlpB and cncR1 genes, have been characterized in details and they are represented in H. pylori, we applied a ChIP-seq approach in vivo. In particular, liquid-grown H. pylori G27 wild type cells were cross-linked, sonicated and HP1043 protein-DNA complexes were immunoprecipitated (IP) with a specific polyclonal α-HP1043 antibody; at the same time a control sample (Input), deriving from sonicated but non-immunoprecipitated DNA, was prepared and used to estimate the background. A minimum of 2 million reads, with optimal mapping performances (>98.5%), for each sample and biological replicate were obtained (H. pylori G27 RefSeq annotation (GCF_000021165.1), and cross-mapped to the transcription start sites and ncRNAs defined in strain 26695To date, the exact positions of only three HP1043 genomic binding targets, located on the promoter regions of obtained  and usedobtained . These pcncR1, previously demonstrated to be bound by this regulatorhp1043 itself, tlpBporGhp1043 and porG were identified as enriched by the peak caller and are present in the full list of 107 peaks identified by Homer2  databaserps16, HPG27_RS06525/rpl36, HPG27_RS05215/rps1, HPG27_RS05705/rpl19, HPG27_RS07170/rpl34) or involved in rRNA and ribosome maturation and assembly (HPG27_RS05770/ybeY endoribonuclease and HPG27_RS06295/frr ribosome recycling factor) were targeted in vivo by HP1043. Strikingly, in the same analysis we identified HP1043 binding sites associated to genes involved in other fundamental cellular processes like energy production and conversion , as well as RNA transcription  this gene is mis-annotated to pseudogene. Overall, from the above data we can infer that HP1043 may represents a key regulator in H. pylori, involved in the control of crucial cellular processes, such as transcription and translation.Among HP1043 target genes, we found the small RNA y Homer2 . These pin vitro binding to a purified recombinant HP1043 protein in DNase I footprinting experiments. Among the 37 putative highly confident HP1043 targets , apparently with different relative affinities (some probes showed protection in the presence of higher amounts of HP1043 protein). Probes bound in vitro with higher relative affinity are reported in isoA, rpoD, rpl36 and of nuoA genes , these two conserved AT-rich motifs. To determine a possible consensus motif recognized by the HP1043 protein, we performed the same analysis using only the regions protected by HP1043 in the in vitro footprint experiments as input sequences. In this way GLAM2 outlined a highly conserved motif, reported in secE, cyt553, cncR1 and nuoA), or shifted towards one side of the protected region . Possibly, this is due to the intrinsic low level of resolution in mapping protein binding sites with DNase I footprinting assays. In fact, some regions of the DNA probes used for these assays naturally lack bands of digestion even in the sample without protein and this introduces a degree of uncertainty in defining the precise boundaries of the binding regions. Hence, to precisely map HP1043 (HsrA) binding sites and to further characterize the connection between the identified consensus motif and the intimate contacts of HP1043 with target DNA, hydroxyl-radical footprinting assays were carried out on selected targets, previously probed by DNase I footprintings . Similar results were obtained upon treatment with chloramphenicol, which blocks ribosomes on mRNA during translationH. pylori wild-type cells exposed for 60 min to tetracycline and from a control culture were used to generate strand-specific cDNA libraries for RNA-sequencing analysis. Overall, differential gene expression outlined 1065 out of the 1592 total number of H. pylori genes to be deregulated upon antibiotic treatment. Of these, 543 genes were upregulated, representing 34% of the H. pylori genes  were included in the analysis and remained essentially unchanged upon antibiotic challenge  increased upon tetracycline treatment, with fold variations ranging from 3- to 7-fold. Moreover, as shown in H. pylori to environmental conditions or signals that promote arrest of protein synthesis.To validate RNA-seq data, the differential mRNA levels of a subset of HP1043 targets were assayed upon tetracycline exposure by qRT-PCR analysis. Three negative controls  in a 9% CO2–91% air atmosphere at 37 °C and 95% humidity in a water-jacketed incubator (Thermo Forma Scientific). Liquid cultures were performed in Brucella broth medium supplemented with 5% FCS in glass flasks. E. coli cells were grown on Luria–Bertani (LB) agar plates or LB liquid broth; when required, ampicillin was added to the medium to achieve a final concentration of 100 μg/ml.Bacterial strains used in this study are listed in et al.DNA manipulations were performed as described by Sambrook E. coli and affinity-purified as previously described2; 1 mM DTT; 0.01% Igepal CA-630; 10% glycerol) for DNA-binding assays or against two changes of 1X PBS  for animal immunization. Protein concentration was determined by Bradford colorimetric assay (Bio-Rad) and the purity of the protein preparations was analysed by SDS-PAGE.Recombinant N-terminal His-tagged HP1043 protein was overexpressed in 4)2SO4 and subsequent resuspension in water. H. pylori G27 wild type cultures were liquid-grown to an OD600 of 1.0, harvested, crosslinked, sonicated and immunoprecipitated as previously described2O, as previously describedAvailable α-HP1043 polyclonal antibody from immunized rabbitsIllumina libraries were prepared, for each biological replicate either from 5 ng of immunoprecipitated-DNA (IPs) or from 5 ng of the Input-DNA following the Illumina TruSeq ChIP-seq DNA sample preparation protocol; then each library was sequenced on a MiSeq Illumina sequencer and 51 bp single stranded reads were produced.H. pylori G27 genome. End-to-end mapping was performed and non-deterministic option was specified to force a single assignment of multi-mapping reads to the best scoring region (if present) or a random attribution in the case of regions with identical scores. High quality reads were then selected requiring: for uniquely mapping reads MAPQ  greater than 30 and alignment score greater than −10 while for multi-mapping reads alignment score was set equal or greater than −10. The quality of ChIP-Seq data was evaluated following ENCODE quality metrics  and the numerical values obtained are provided in https://code.google.com/archive/p/phantompeakqualtools/) cited by ENCODE consortium (http://genome.ucsc.edu/ENCODE/encodeTools.html). Moreover, we obtained average PBC scores. Irreproducible Discovery Rate procedure (IDR v 2.0.2) following ENCODE guidelinesH. pylori G27 RefSeq annotation (GCF_000021165.1). Transcription start sites were derived by blasting the 50 bp before the initiation of transcription found by Sharma et al.Bowtie 2 (v2.2.6)32P]-ATP and T4 polynucleotide kinase and gel purified. Approximately, 15 fmol of labelled probe were used for each footprinting reaction. DNase I footprinting experiments were performed as previously described2 and 1 mM DTT for 90 s at room temperature. Reactions were stopped with 140 μl of STOP buffer , phenol-chloroform extracted and ethanol precipitated. Samples were resuspended in 10 μl of Formamide Loading Buffer , denatured at 100 °C for 3 min, fractionated on a 8 M urea-6% polyacrylamide sequencing gel and auto-radiographed.Genomic regions harbouring the putative HP1043 binding sites identified by ChIP-seq analysis were PCR-amplified with oligonucleotides listed in 0  containing 300 ng of sonicated salmon sperm DNA as non-specific competitor in a final volume of 30 μl for 20 min at room temperature. Partial digestion of the labelled probe was performed by the simultaneous addition of 2 μl each of the following solutions: 2 μl of Fe:EDTA solution (125 mM Fe (NH4)2 (SO4)2; 250 mM EDTA pH 8.0), 2 μl of 0.1 M DTT and 2 μl 1% H2O2. After a 2-min incubation, cutting reaction was stopped with the addition of 25 μl of STOP solution , phenol-chloroform extracted and ethanol precipitated. Samples were resuspended in 6 μl of FLB, denatured at 100 °C for 3 min, fractionated on 8 M urea-8.4% polyacrylamide sequencing gel and auto-radiographed.Genomic regions of interest were 5′-end labelled and purified as described above for DNase I footprintings. Hydroxyl-radical footprinting assays were performed as previously describedH. pylori cultures were liquid-grown until mid-exponential phase (OD600 = 0.8) then treated with 0.5 μg/ml of tetracycline for 60 min. Bacterial cells were harvested and total RNA was extracted with TRI-reagent (Sigma-Aldrich), according to manufacturer’s protocol.TM v2 RNAseq library preparation kit  starting from 50 ng of previously rRNA depleted RNA from each biological replicate and for all the conditions analysed. Then each library was sequenced on a MiSeq Illumina sequencer and 76 bp reads were produced. Bowtie 2 (v2.2.6)H. pylori G27 genome with the same parameters used for ChIP-seq data. High quality reads were selected requiring: for uniquely mapping reads MAPQ  greater than 30 and alignment score greater than −15; for multi-mapping reads alignment score was set equal or greater than −15 rRNA depletion, strand specificity and gene coverage were evaluated using BEDTools (v2.20.1*)H. pylori G27 RefSeq annotation (GCF_000021165.1) for at least 50% of their length were considered to produce the raw-counts of each sample. Only rRNAs were excluded as they were depleted during the library preparation procedure. The R package DESeq2 (v1.4.5)Ribosomal RNAs were depleted starting from 1 μg of total RNA from each of the conditions analysed by using the RiboZero Gram negative kit . Strand specific RNA-seq libraries were prepared by using the ScriptSeqRaw data are publicly available at Sequence Reads Archive under accession number BioProject PRJNA327549.Synthesis of cDNA and qRT-PCR analysis were carried out as previously describedHow to cite this article: Pelliciari, S. et al. Insight into the essential role of the Helicobacter pylori HP1043 orphan response regulator: genome-wide identification and characterization of the DNA-binding sites. Sci. Rep.7, 41063; doi: 10.1038/srep41063 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


By characterizing the input function of its representative genes in wild-type and mutant bacteria, we recognize a dual autoregulation motif as main determinant of the response, which is further adjusted by the interplay with other regulators. We show that basic attributes, like its reaction to a wide range of stress or its moderate expression change, are associated with a strong negative autoregulation, while others, like the buffering of metabolic signals or the lack of memory to previous stress, are related to a weak positive autoregulation. With a mathematical model of the input functions, we identify some constraints fixing the molecular attributes of the regulators, and also notice the relevance of the bicystronic architecture harboring the dual autoregulation that is unique in E. coli. The input function emerges then as a tool to disentangle the rationale behind most of the attributes defining the mar phenotype. Overall, the present study supports the value of characterizing input functions to deconstruct the complexity of regulatory architectures in prokaryotic and eukaryotic systems.Many essential bacterial responses present complex transcriptional regulation of gene expression. To what extent can the study of these responses substantiate the logic of their regulation? Here, we show how the input function of the genes constituting the response, i.e., the information of how their transcription rates change as function of the signals acting on the regulators, can serve as a quantitative tool to deconstruct the corresponding regulatory logic. To demonstrate this approach, we consider the multiple antibiotic resistance ( Escherichia coli, a team led by Juan F. Poyatos at the National Center for Biotechnology in Madrid studied the response input function by combining theoretical models and experiments. This function quantifies the rate of transcription of the genes constituting the response with respect to the signals acting on its cognate regulators. By examining how the shape of the function changes in different situations, e.g., when a given regulator is mutated, the team identified the implications for the specificity and dynamics of the response of a dual autoregulation at the core of the control architecture. The use of input functions as quantitative tools allows us to reverse engineer the complex regulations that dictate essential physiological functions in both prokaryotic and eukaryotic cells.Many cellular responses result from the integration of numerous regulatory signals. To deconstruct the regulation of one of these responses, which enables resistance to multiple antibiotics in  In the context of 18 To demonstrate this view, we examined the multiple antibiotic resistance (mar) response in Escherichia coli as a model system of complex regulation.19 The mar control system enables E. coli with intrinsic resistance, which makes it particularly attractive to explore, as well as the fact that homologous systems exist in other pathogenic bacteria.20 Specifically, the cis-regulatory region linked to the expression of the core agents of the response, the genes marR, marA and marB, combines several signals that correspond to global (CRP) and local  transcription factors.21 We introduced a mathematical model that integrates the known molecular details of the response to calculate the input functions with respect to its associated signals. Changes on the input functions as a consequence of certain mutations help explain the role of the regulators involved, and the rationale behind many of the properties of the system. Notably, the dual autoregulation being part of this combinatorial control turns out to be a core regulatory architecture with implications for the specificity and dynamics of the response.We propose here to utilize input functions in a complementary manner, by interpreting them as dissecting tools to reverse engineer the complexity of combinatorial regulation. How these functions change as we disturb the activity of the associated regulators would uncover the underlying principles behind a particular regulatory logic Fig. .18 To demar operon  and global (CRP) transcription factors, both presenting operator sequences in the cis-regulatory region21 . The model incorporates known features like the strong repression of MarR,22 the weak action of MarA,24 and the independent binding between MarA and MarR, but see ref. 30 The theoretical results were combined with experimental measurements of promoter activity with the use of a YFP reporter under the control of the marRAB promoter30 .We integrated this information in a quantitative model of the response to anticipate the shape of the input function of the wild-type (WT), and several relevant mutants, as function of salicylate . The validation of the theory confirmed in this way the model assumptions, and emphasized both the relevance of Rob, and the important interplay between MarA and Rob in the generation of the mar response.Figure mar system remains, nonetheless, uncertain.32 The input function of a Δrob strain  and output (Rout) dynamic range, which account for the “steepness” of the expression of the operon with respect to the dosage of the input signal , and its response fold change, respectively.33 For the WT strain , we found Rin ≈ 31 and Rout ≈ 9, while in case of a Δrob strain we obtained Rin ≈ 21 and Rout ≈ 11 (Methods). The relative similarity of these values is confirmed by a model simulation that revealed equivalent numbers (Rin ≈ 28 and Rout ≈ 7 for the WT and Δrob systems), and agrees with previous experimental estimations of the activity of the regulators of the system.25 Presence, or absence, of Rob does not alter then the shape of the input function. This is clearly seen in Fig. rob systems is indeed observed after normalization .Beyond the previous characterization, the regulatory role of Rob in the ain Fig.  appearedRout, by approximating the equilibrium expression of MarR with the mathematical model . Rob clearly scales the equilibrium. A modified model that featured instead a strong action of MarA does not show the effect . We examined the consequences of this genetic architecture by contrasting the natural system with a hypothetical one in which the repressor is encoded in a different transcriptional unit. A model describing this variant system predicts a lower input dynamic range (Rin = 9) than the natural one . This agrees with earlier work, which associates the occurrence of negative autoregulation to more linear input function curves, i.e., lower steepness of the response.36It is also noteworthy how the sensor of the system (MarR) is encoded in the same operon than the actuator MarA  regulators of the n14 Fig. . In partrob strain. We observed a sizeable influence of cAMP as predicted . Because the activation by MarA is weak, we considered Rob as potential underlying element. Simulations of two-dimensional input functions with and without Rob supported the assumption , this could represent a limitation in the oxidation of MarR proteins and thus in the full transcription of the operon. We examine this potential restriction next.That salicylate releases the repression of the operon by the inactivation of MarR was a result discussed for two decadesn38 Fig. . As the 40 Using this value and the oxidation curve of MarR by Cu2+ . Consequently, a higher number of molecules of MarR at maximal induction would further hinder activation of the response. We propose that the inefficient translation rate of MarR40 might be beneficial to successfully release repression by reducing MarR concentration, given the restrictions of copper signaling.At maximal salicylate induction, one finds ~ 1000 molecules of MarR per cell.42 To evaluate this effect, we mimicked the strength of the remaining repression with the use of different salicylate doses. As predicted, we found that the lower the dose  the faster the dynamics . Simulations performed with the mathematical model verified the experimental data on dynamics  . The similarity between distributions corroborates the lack of memory, an effect similarly obtained in a WT strain . This behavior is correctly described with a stochastic version of the model .To further inspect any possible presence of bistability, we showed the corresponding distributions of the single-cell expression of YFP in a population  switches from repressor to activator action was considered , or higher ultrasensitivity, i.e., more nonlinearity, to trigger the response under a very narrow signal range . In addition, the rise-time to reach the induced steady state was faster in the natural circuit  helps us unravel the role of different regulators on the transcription rate of the marRAB operon, and underlines the implications of its dual autoregulatory architecture.49 The function is well predicted by a mathematical model that assumes independent binding between the activator (MarA) and repressor of the system (MarR). Investigation of the regulatory role of one of the additional regulations, i.e., Rob, reveals two important features: (1) Rob scales the expression of the system, a result that is made possible by the weak strength of the actuator protein MarA, and (2) the strengthening of the response by Rob also sets apart the operon from metabolic signals coupled to CRP, other of the transcription factors involved. The activation by CRP might consequently be interpreted, in this cis-regulatory context, as an evolutionary contingency related to a more general program of regulation.14Here, we put forward the use of the input function of a response system as a quantitative tool to deconstruct its regulation. We validate this approach by examining the 2+ in response to salicylate oxidizes MarR preventing its binding to DNA.38 Because the intracellular concentration of this cation is bounded (to avoid toxicity), copper balance imposes maximal repressor abundance for which the promoter can be derepressed at saturating signal levels, a constraint providing a rationale for the limited translation rate observed for MarR.40Moreover, copper signaling has been recently documented as the mechanism connecting the signal  to the sensor element (MarR). The accumulation of intracellular Cu43 Regulatory logic does not necessarily determine function and the biochemical parameters matter, as we find in this example. The particular genetic implementation of the logic also matters. We identified alternative designs of the dual autogenous control in E. coli that consists on dual regulators rather than polycistrons (carrying both repressor and activator). Dual regulators switch between repression and activation due to the presence of its cognate inducer . Which are the differences between these two implementations? To explore this question, we apply the input function again as a quantitative tool to examine a hypothetical mar system in which oxidized MarR turns into an activator . The more compact genetic design exhibits a smaller input range , a wider output range , and a slower response (with no speedup at high dosages). The result exemplifies how the response associated to a given regulatory logic is certainly influenced by its genetic design.46The weak strength of the positive autoregulation (through MarA) also causes the absence of memory in the system, a property that one expects since the transcriptional circuit includes a positive feedback.37 We identified an underlying principle in which the presence of dual autoregulation in a complex transcriptional control system can very much delineate the shape of this function. An additional positive regulation limits the interference of other general signals on the promoter by scaling the response. Our study contributes in this way to the glossary of regulatory structures and their input functions that needs to be quantified in both prokaryotic and eukaryotic systems.More broadly, this work supports a view of the input function of a gene as a dissecting tool that can make clear the logic of its regulation.mar core network. It is based on E. coli K-12 MG1655. The strain contains a chromosomal copy of the gene coding for a yellow fluorescent protein (YFP) under the control of the marRAB promoter, and the gene coding for a cyan fluorescent protein (CFP) expressed with a constitutive promoter as a control.50 Four more strains called TC01, DB01, IE02, and TC02 were constructed by deleting fundamental genes of the system, with the application of the knockout protocol on the strain IE01.51 TC01 is a ∆marA strain, DB01 a ∆marB strain, IE02 a ∆rob strain, and TC02 a ∆marA∆rob strain.A strain called IE01 carrying two fluorescent reporters was engineered to measure the activity of the WT 4, 0.1 mM CaCl2, 0.4% glucose, 0.05% casamino acids, 0.05% vitamin B1) was used to grow cells during characterization experiments. To induce the marRAB promoter, different concentrations of salicylate and cAMP (Sigma Aldrich) were used. Note that glucose inhibits the production of internal cAMP.Medium LB was always used for overnight cultures. Minimal medium M9  were grown overnight in LB medium supplemented with 0.4% glucose at 37 °C and 170 rpm. Cultures were then diluted 1:200 in M9 minimal medium, and were grown for 2 h at the same conditions. Cultures were then used to load the wells of the microplate  with salicylate and cAMP when appropriate. The microplate was assayed in a fluorometer Victor × 2 (Perkin Elmer) measuring absorbance (600 nm), yellow fluorescence , and cyan fluorescence  for 4 h at 37 °C with shaking.In case of pre-induction, cultures inoculated from single colonies  were grown overnight in LB medium supplemented with 0.4% glucose and 5 mM salicylate at 37 °C and 170 rpm. Cultures were then diluted 1:200 in M9 minimal medium with 5 mM salicylate, and were grown for 5 h at the same conditions. Cells were pelleted by centrifuging 10 min at 13,000 rpm, and resuspended in fresh M9 minimal medium. Cultures were then used to load the microplate as described above.52 Data were analyzed with Matlab (MathWorks). The means and standard deviations of three replicates were computed. See The normalized fluorescence of YFP was calculated as the ratio of fluorescence and absorbance, having subtracted background values. The growth rate of cells was calculated as the slope of the linear regression between the log of absorbance and time. Promoter activity in steady state was calculated by multiplying the normalized fluorescence and the growth rate.Cultures inoculated from single colonies  were grown overnight in LB medium with and without salicylate (5 mM) at 37 °C and 170 rpm (to have uninduced and pre-induced cultures). Cultures were subsequently diluted 1:200 in M9 minimal medium with different concentrations of salicylate, and were grown for 4 h at the same conditions. Cultures were then transferred to Eppendorf tubes (1 ml). The fluorescence distribution of each sample was obtained with a flow cytometer MACSQuant VYB (Miltenyi Biotec) measuring yellow fluorescence . Using delimited forward and side scatter ranges with the commercial software installed in the machine gated events. To calculate the normalized fluorescence, the background value was subtracted, and the resulting values were rescaled according to the mathematical model. The medians and standard deviations of the distributions were computed.Cultures inoculated from single colonies  were grown overnight in LB medium at 37 °C and 170 rpm. Cultures were then diluted 1:200 in M9 minimal medium, and were grown for 2 h at the same conditions. Cells were pelleted by centrifuging 2 min at 13,000 rpm, and resuspended in TE buffer . Cells were broken with phenol-chloroform and vortexing thoroughly, recovering the RNA in the aqueous phase. Samples were then passed through a silica-based, DNA-clean column (Zymo), and were eluted in TE buffer with application of DNase I (Fermentas). Total RNA eluted was quantified in a NanoDrop.marA and 16S rRNA were taken from previous work.54 Reactions in triplicate were carried out using a Step One Plus Real-Time PCR System (Applied Biosystems). The thermal cycling program for amplification was 5 min at 42 °C, 10 s at 95 °C, and 40 cycles of 5 s at 95 °C and 34 s at 60 °C (Shuttle PCR), followed by default melting curves.One-step SYBR PrimerScript RT-PCR Kit II (Takara) was used for detection, following the Kit protocol for preparing the reaction volumes. 16S ribosomal RNA (rRNA) was used as housekeeping gene to normalize RNA quantity in each reaction. The primer sequences for amplifying Rout is the ratio between the maximal and minimal promoter activities . Rin is the ratio between the salicylate doses at which the system shows 90% and 10% of its maximal activity . Fittings shown in Supplementary Fig. Rout and Rin associated to experimental data (errors derived by bootstrapping). Model simulations were used to perform theoretical predictions of Rout and Rin.mar core network (see x) and MarR (y) normalized concentrations act as model variables, while time is given as number of cell cycles . For simplicity, we did not consider the marginal regulatory effect of MarB  and cAMP (c). It reads dx/dt = βPmar - δx and dy/dt = Pmar−y, where Pmar is promoter activity, β the ratio between the translation rates of MarA and MarR , and δ the degradation rate of MarA . By knowing that the marRAB promoter is activated by MarA, Rob, and CRP:cAMP (acting as monomers), and repressed by MarR (acting as dimer), promoter activity reads Pmar = P0 [1 + ρ (κx + κz + c)] / [(1 + κx + κz + c) (1 + y02)], where P0 is the basal production rate (P0 = 12), ρ the activation fold of the system (ρ = 10), and κ the ratio between the effective dissociation rates of MarR and MarA . In the limit, we have y02 » 1 and κx « 1, which served to solve the model analytically (see below). In steady state, the concentration of MarA is proportional to the concentration of MarR (x∞ = βy∞/δ).We briefly describe here the basic features of the model describing the dynamic response of the y0 represents the concentration of non-oxidized MarR38). Here, we wrote y0 = y [1 + α (S/θ)ν] / [1 + (S/θ)ν], where α, θ and ν are three parameters that model the action of salicylate , neglecting the influence of MarA.24 The normalized concentration of cAMP is c = C/KC, where C is the external cAMP concentration (in mM) and KC = 5 mM. The model was adapted to account for the dynamics of other regulatory architectures. In case of ∆marA, we set x = 0; and in case of ∆rob, z = 0. Note that the model does not account for MarB, dynamics of both WT and ∆marB systems are consequently the same.The concentration of Rob was considered constant (yeq ≈ (P0ρ)1/3 in the absence of salicylate, and yeq ≈ (P0ρ/α2)1/3 in the presence of a saturating dose. And for the Δrob system, it reads yeq ≈ P01/3 or yeq ≈ (P0/α2)1/3 in the absence or presence of salicylate, respectively.We can approximate the equilibrium expression of MarR to obtain analytical expressions . For the56 For that, we considered a scenario of quasi-steady state . Then, on the dy/dt equation, a Gaussian white noise with constant amplitude (q) was added. Here, q2 was assumed proportional to yeq (q2 = 0.11 yeq), the stationary solution of the system (dyeq/dt = 0). To solve this stochastic differential equation in steady state, we used the Fokker–Planck equation,57 leading to the probability distribution of y.A Langevin approach was followed to model the stochastic response of the system.mar core network in which the oxidized MarR  acts as an activator, we just took one differential equation, dy/dt = Pmar−y. Now, promoter activity reads Pmar = P0 [1 + ρ (y−y0)2]/[1 + (y−y0)2 + y02], since we considered competitive binding (34 Further biochemical assumptions, like the asymmetry of binding affinities, were also examined in Supplementary Fig. To model the dynamic response of the hypothetical  binding . The comSupplementary Table S1Supplementary Table S2SupplementarySupplementary Figure S1Supplementary Figure S2Supplementary Figure S3Supplementary Figure S4Supplementary Figure S5Supplementary Figure S6Supplementary Figure S7


Dot1  is the only known enzyme responsible for histone H3 lysine 79 methylation (H3K79me) and is evolutionarily conserved in most eukaryotes. Yeast Dot1p lacks a SET domain and does not methylate free histones and thus may have different actions with respect to other histone methyltransferases. Here we show that Dot1p displays histone chaperone activity and regulates nucleosome dynamics via histone exchange in yeast. We show that a methylation-independent function of Dot1p is required for the cryptic transcription within transcribed regions seen following disruption of the Set2–Rpd3S pathway. Dot1p can assemble core histones to nucleosomes and facilitate ATP-dependent chromatin-remodeling activity through its nucleosome-binding domain, in vitro. Global analysis indicates that Dot1p appears to be particularly important for histone exchange and chromatin accessibility on the transcribed regions of long-length genes. Our findings collectively suggest that Dot1p-mediated histone chaperone activity controls nucleosome dynamics in transcribed regions. Dot1 is an evolutionarily conserved enzyme, responsible for histone H3K79 methylation in most eukaryotes. Here authors show that, in yeast, Dot1p has histone chaperone activity and regulates nucleosome dynamics via histone exchange in transcribed regions. The absence of the Set2–Rpd3S pathway makes chromatin more accessible to histone chaperones such as Asf1p, which results in the active assembly of histones and consequently an increases in histone exchange. These findings show that the balance of histone exchange at transcribed regions is crucial for genomic stability10. Previous studies have shown that H3K79me is enriched in the transcribed regions of genes11, suggesting that Dot1p may function in transcription elongation. However, the function of Dot1p in transcription is still unclear. Although the involvement of Dot1p in transcription is not fully understood in yeast, some studies have shown that its mammalian ortholog, Dot1L, is involved in transcription and is essential for embryonic development in mammals13. Human Dot1L (Dot1-Like histone methyltransferase) has been shown to bind RNAPII through its C-terminal domain and regulate the expression of actively transcribed genes14. Moreover, human Dot1L reportedly contributes to RNAPII-mediated elongation as part of the super elongation complex, which is involved in oncogenic transcriptional activation in mixed-lineage leukemia (MLL)20. Collectively, the existing evidence suggests that Dot1p is an evolutionarily well-conserved chromatin factor that can contribute to euchromatin through yet-unknown functions.The Dot1p-mediated histone mark, histone H3 lysine 79 methylation (H3K79me), is also enriched in transcribed regions, suggesting that this modification and/or Dot1p may contribute to regulating chromatin dynamics. Dot1p is reported to be the sole nucleosomal H3K79 methyltransferase and is well conserved from yeast to humans. It catalyzes the mono-, di-, and trimethylations of H3K79; these stable histone modifications undergo slow exchange through a nonprocessive (distributive) mechanism in which Dot1p detaches from the core histone after each round of methylationIn this study, we demonstrate that yeast Dot1p participates in transcription regulation in a manner independent of its H3K79 methyltransferase activity. We used Set2p-depleted cells to assess the function of Dot1p in an environment of increased cryptic transcription and histone exchange. Based on the findings from our genome-wide, genetic, and biochemical analyses, we propose that Dot1p regulates the accessibility of chromatin through its histone-chaperoning and nucleosome-binding activities, and is involved in histone exchange.22. We hypothesized that an acetylation-mediated change in the charge of the H4 tail  would modulate its interaction with Dot1p. To investigate the involvement of H4 acetylation in H3K79me deposition, we measured global H3K79me levels by western blot analysis in yeast strains in which each respective lysine residue had been mutated to alanine. Interestingly, the H4K16A mutation significantly decreased the global level of H3K79me3, whereas the other mutations had no apparent effect  . Unlike other histone methyltransferases, Dot1p is known to introduce multiple methyl groups via a nonprocessive (distributive) mechanism10. Thus, we hypothesized that the decrease of H3K79me1/3 level in sas2Δ mutant cells could reflect a reduction in the contact between Dot1p and chromatin. Supporting this idea, regions with high H3K79me1 and low H3K79me3 levels in wild-type cells showed even lower H3K79me1 enrichment in sas2Δ cells. Based on the nonprocessive activity of Dot1p, we inferred that H4K16ac could regulate the binding preference of Dot1p to transcribed regions . We also observed that H4K16ac was broadly distributed on the transcribed regions of wild-type cells  analysis, we were unable to reproducibly identify regions of Dot1p occupancy, perhaps because Dot1p is expressed at a relatively low level and is distributed throughout the chromatin.To examine the nature of this crosstalk, we used chromatin immunoprecipitation sequencing (ChIP-seq) to analyze the genome-wide distributions of both modifications in wild-type and ons Fig. . Averageons Fig. . A genomons Fig. . Calculalls Fig. , bottom,ons Fig. . Concordons Fig. , H3K79mells Fig. . These fsas2Δ cells, we hypothesized that Dot1p specifically recognizes H4K16ac. To investigate the preferential binding of Dot1p to H4K16ac, we performed peptide pull-down assays using biotin-labeled N-terminal histone H4. Indeed, Dot1p repeatedly showed a binding preference for the H4K16ac peptide , while there was no change in mRNA levels in sas2Δ and dot1Δ mutants compared to wild type . Analysis of log2 fold changes revealed that this gene set was significantly suppressed in dot1Δset2Δ cells compared to set2Δ cells  showed decreased expression in dot1Δset2Δ versus set2Δ mutants28, we confirmed deletion of DOT1 suppressed cryptic transcription . We found that the two gene groups did not overlap significantly 10, and the non-methylatable histone mutant, H3K79R. Surprisingly, neither H3K79R nor Dot1(G401R) repressed mRNA expression in the set2Δ background set2Δ, and H3K79Rset2Δ cells . From these data, we conclude that Dot1p regulates cryptic transcription independently of its methyltransferase activity.The most well-known biochemical function of Dot1p is the methylation of histone H3K79und Fig. . We examPCA1 and FLO8. We found that the relative RNA ratio was decreased in set2Δdot1Δ cells compared to set2Δ cells, and no significant decrease was observed in Dot1(G401R)set2Δ cells compared to set2Δ cells 29 showed repression similar to that of dot1Δset2Δ cells  over transcribed gene regions, whereas set2Δdot1Δ cells exhibited lower H4ac levels across these regions relative to the levels found in set2Δ cells  and POL2 (6.7 kb), we found that H3 was increased in set2Δ cells relative to wild-type cells, and that the loss of DOT1 suppressed the increase of H3 in the set2Δ background . Furthermore, we found that the levels of H3 in dot1Δ and Dot1(101–140Δ) cells were lower than those in wild-type cells. Next, we analyzed the average log2 fold change values of H3 levels on transcribed regions for each mutant versus the wild type. We found that the H3 level was slightly higher in set2Δ cells compared to wild-type cells, whereas the level in dot1Δset2Δ cells was lower than that in set2Δ cells  mutants, which showed suppression of the cryptic transcription triggered by the absence of SET2 -dependent remodeling in vitro and in vivo38. Since our results suggest that Dot1p is likely to be a histone chaperone that is involved in histone exchange and cryptic transcription, we hypothesized that Dot1p may stimulate ATP-dependent chromatin remodelers in vitro. To test this hypothesis, we performed nucleosome-sliding assays using Chd1p 39 and 601 positioned mononucleosomes in the presence of wild-type or mutant Dot1p , Dot1(G401R) showed no defect in the ability to stimulate chromatin remodeling  are known to have both nucleosome assembly and disassembly activitiest1p Fig. . We founDot1101–10Δ, Dot1. Notably, the histone turnover of dot1Δ cells was more dramatically decreased (blue color) relative to that in wild-type cells when we considered genes with long transcribed regions  versus short (0.5–1 kb) genes in dot1Δ cells . This result reflects the average plot shown in the graph of Fig. dot1Δ mutant cells for genes of 1–2 kb in length (n = 2266); thus, it seems that histone exchange decreases with the distance from the TSS, at least in this case. For genes of 0.5–1 kb in length, the plot showed that the histone exchange of the transcribed region was slightly increased in dot1Δ cells (n = 1600). Therefore, we conclude that Dot1p contributes to histone exchange in a gene length-dependent manner  along the gene, and compared these values between the gene length groups. As shown in Fig. We hypothesized that strong RNAPII-mediated histone exchange near the promoter could affect the gene length-related difference in the histone exchange rate of 2 difference ratio of histone H3 in dot1Δ versus wild-type cells in terms of gene length showed that the levels of histone H3 were decreased in long genes. Together, these data show that the effect of Dot1p on histone exchange is stronger for long genes, which have a higher frequency of contact between Dot1p and chromatin. Moreover, this result is consistent with the widely accepted notion that histone exchange is a major factor in chromatin dynamics.To assess whether Dot1p affects the histone exchange rate on long genes (>2 kb), we examined the levels of H3K79me3 and H3K79me1 by gene length Fig. . Althougdot1Δ cells. We performed MNase-seq to assess the effect on nucleosome positioning and nucleosome occupancy and ATAC-seq, a technique with a similar rationale to that of DNase-seq, to assess the effect on chromatin accessibility. However, the analysis of MNase-seq showed that there were no significant change in nucleosome phasing  analyses using the relevant deletion mutants. If Dot1p regulates the dynamics of chromatin structure, changes in nucleosome positioning or chromatin accessibility may be observed in 43. Since the loss of H4K16ac is well known to trigger the formation of condensed chromatin46, we first confirmed the change of chromatin accessibility in sas2Δ cells. As expected, accessibility was significantly and globally decreased in sas2Δ cells compared to wild-type cells , whereas set2Δ cells displayed increased accessibility (log2 fold change >0).Next, nucleosome accessibility was measured by mapping the fragment less than 100 bp among the read counts of ATAC-seqons Fig. . As showSaccharomyces cerevisiae (n = 6692), we separated the genes of each mutant based on their increased or decreased accessibility compared to wild-type cells . About 50% of the total genes showed decreased accessibility in dot1Δ cells (n = 3287) and such difference between sas2Δ and dot1Δ cells is not due to a change in increased accessibility (n = 340), but rather the increased steady genes (n = 3065). In set2Δ cells, 2580 genes showed increased accessibility; although this accounted for less than 50% of the total gene set, we note that accessibility was increased in most of the genes if we excluded the steady genes (n = 3467). These results are important as they suggest that: (1) the H4K16ac–Dot1p pathway functions to increase chromatin accessibility; and (2) Dot1p modulates nucleosome accessibility, perhaps via nucleosome assembly activity at the transcribed regions of genes. Therefore, we envision that Sas2p and its downstream partner, Dot1p, regulate nucleosome accessibility during transcription, and that Dot1p further uses its histone-chaperoning and histone methyltransferase activities to assemble epigenetic information on chromatin, thereby modulating later processes, such as DNA replication, recombination, and repair.From all the annotated genes of lls Fig. . Our staDOT1 suppresses histone exchange on the transcribed regions and down-regulates cryptic transcripts in set2Δ cells. Third, Dot1p has a nucleosome assembly activity in vitro. Fourth, Dot1p is able to stimulate the nucleosome-sliding activities of ATP-dependent chromatin remodelers, such as Chd1p. Fifth, the functions of Dot1p in nucleosome assembly and histone exchange are independent of histone H3K79me. Finally, Dot1p itself can affect global chromatin dynamics by altering nucleosome accessibility at the transcribed regions of genes.The findings of this study indicate that the histone H3K79 methyltransferase, Dot1p, is a histone chaperone and plays a pivotal role in the histone exchange that balances nucleosome dynamics during transcription Fig. . We pres4. These findings suggest that long genes are more sensitive to alteration of chromatin dynamics and to the functions of Dot1p. In addition, histone turnover and histone chaperone activities has been shown to be important in the context of DNA damage situation49. We thus propose that Dot1p has dual methylation-dependent and methylation-independent functions. The methylation-independent function of Dot1p involves the facilitation of histone turnover, which balances chromatin dynamics and consequently helps the cell to maintain chromatin stability. The methylation-independent function of Dot1p includes its conventional H3K79me methyltransferase activity. Given that H3K79me is broadly involved in the DNA damage response, it may be described as a ‘contingency platform’ for a DNA damage situation50 especially in longer genes, adding another layer of chromatin protection.We found that the effects of Dot1p on nucleosome assembly and histone exchange were more pronounced in long genes (>2 kb), reminiscent of the higher histone exchange in long genes in Set2p-depleted cells51, rather than the activity of an as yet unidentified H3K79 demethylase. Moreover, loss of Rtt109p, a histone H3K56 acetyltransferase that is involved in the exchange of newly synthesized histones, was reported to increase the ratio of H3K79me3/H3K79me1 near promoters52. These results suggest that the assembly, deposition, and exchange of new histones prominently reduce H3K79me to maintain its chromatin concentration53. Consistent with this hypothesis, we herein show that Dot1p acts as a chaperone for histone exchange, consequently maintaining the level of H3K79me.The correlation between relative histone exchange (replication-independent histone turnover) and H3K79me3 level has been reported, such that genes with high turnover display low H3K79me3 levels. This indicates histone exchange is likely to be an alternative means to reduce H3K79me55. Furthermore, our findings may contribute to the development of future therapeutic interventions since a number of diseases, including MLL, have been associated with functional alterations of DOT1L57.Taken together, our results show that the histone methyltransferase, Dot1p, functions as a histone chaperone to directly regulate chromatin dynamics. Since histone exchange is also important for transcription elongation and enhancer functions in mammals, our result may provide a paradigm for understanding the functions of the human ortholog, DOT1L, in transcription regulation600 reached 0.8.The yeast strains used in this study are listed in Supplementary Table 600 of 0.8. The cells were suspended in 20% TCA before undergoing cell lysis with glass bead beating. Pellets were collected and washed twice with 5% TCA. The pellets with 5% TCA solution were incubated on ice for 30 min. After removing supernatant, pellets were suspended in SDS sample buffer  containing bromophenol blue (1 mg ml−1). The protein samples were then subjected to western blotting. The protein samples were resolved by 15% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) gel and transferred to nitrocellulose membrane at constant voltage 10 V for 1 h. After rinsing the membranes in Ponceau S solution to check transfer quality, the membranes went through blocking reaction with tris-buffered saline and Tween-20 (TBS-T) buffer  containing 5% of skim milk for 1 h. The membranes were then incubated in TBS-T buffer containing 5% skim milk with the relevant primary antibodies  precipitation. Cells were grown in 50 ml YP medium containing 2% glucose, and harvested at ODDot1p was expressed from pET21a-TEV-MBP and purified. The MBP tag was removed by TEV cleavage reaction. The biotinylated N-terminal H4 tail (amino acids 1–21) fragment was bound to streptavidin-coated Dynabeads M280  in coupling buffer , 5% glycerol, 0.03% NP-40) at 4 °C for overnight. Purified Dot1 and the bead-bound H4 tail peptides were mixed in peptide-binding buffer  and incubated at 4 °C for 1 h. After washing five times with wash buffer (peptide-binding buffer containing 200 mM NaCl), the samples were analyzed by SDS-PAGE and subjected to western blotting.S-transferase (GST) fusion proteins were expressed in Escherichia coli  at 18 °C for 10 h and were purified using glutathione superflow resin (Clontech). Then, 5 μg of GST fusion proteins and 10 μg of histone dimer, tetramer, and octamer were mixed in 200 μl of PDB buffer  and incubated at 4 °C for 2 h. After washing resins with PDB buffer three times, resins were boiled in SDS sample buffer for 5 min. The protein samples were resolved by SDS-PAGE gel, and analyzed by Coomasie blue staining. The uncropped scans of coomasie blue stained SDS-PAGE gel is shown in Supplementary Fig. The glutathione KD) were measured by a BLItz system . The streptavidin biosensor dip was soaked in peptide-binding buffer for 10 min before measurement steps. We used peptide-binding buffer as the BLItz assay buffer, both to soak the streptavidin biosensor dip and to perform between-step washes. Biolayer interferometry assays consisted of five steps: initial base line (30 s), loading, base line (30 s), association, and dissociation. Biotinylated H4 (1–20) peptides were immobilized on streptavidin biosensor dip. For the loading step, protein concentrations were adjusted to yield a signal intensity in the range of 1 to 2 nm, thereby ensuring that the sensors were not saturated. Times and protein concentrations for the association and dissociation steps were as indicated in the figures and legends. Control values, measured using empty (no protein loaded) sensors, were subtracted from experimental values before data processing. The analytic program was set to the global mode to a 1:1 binding model by the BLItz Pro program, from which the equilibrium constant (KD) and association (Ka) and dissociation (Kd) rate constants were calculated.The equilibrium dissociation constants  was added to 100 ml of medium and incubated for 2 h at 30 °C. In the case of the histone exchange strain, cells were grown in YP medium containing 2% raffinose. At OD600 0.5, cells were arrested in G1 phase by adding 20 µl of alpha factor (10 mg ml−1) to 100 ml of medium, and incubated the mixture for 4 h at 30 °C. Galactose was added , and the cells were incubated for 1 h, and then cross-linked with 1% formaldehyde. To remove RNA and to decrosslink proteins, the Immunoprecipitates was treated with 50 μg of RNase A for 1 h at 37 °C, and proteinase K (Sigma) for 2 h at 55 °C. After an overnight incubation at 65 °C, immunoprecipitates were recovered, and the DNA was purified using a Qiagen PCR purification kit. ChIP-seq libraries for genome-wide sequencing were prepared using a NEXTflex ChIP-Seq kit . The prepared libraries were sequenced on an Illumima HiSeq2500 using the single-end method (50 bp reads) The ChIP-seq reads were mapped onto the sacCer3 reference genome using the Bowtie2 align program, and data normalization analysis was performed using the MACS2 peak calling program. Log2 values between mutant and wild-type cells were calculated using bamCompare in the Deeptools, a data analysis program for high-throughput sequencing. The read counts and RPKM values were identified using the bedtools and bwtool utility program. RPKM values were calculated as: RPKM = (number of reads mapped to a gene × 1E + 09)/ . The scatter plots and R2 values showing the duplicated ChIP-seq sample variation were included in Supplementary Figs. Yeast strains were grown in YP medium containing 2% glucose (YPD). Cultures were incubated at 30 °C until the OD58, which should (in principle) be constant in all ChIP samples. Before the cell lysis step of the ChIP process, we mixed two types of formaldehyde-fixed cells . We then subjected this mixture to the ChIP-seq library preparation process. For computational analysis, the normalized ratio of IP/input (EIP) was calculated as:For quantitative analysis of our H4K16ac and H3 ChIP-seq data, we normalized the mutant/wild-type IP ratio with respect to a spike-in controlN) for IPmutant: IPWT was calculated as:The normalization factor  was used as ‘-scaleFactors N:1’ for normalization of the mutant over wild type.We used the normalization option ‘59. The scatter plots and R2 values showing the sample variation of the mRNA-seq duplicates were included in Supplementary Fig. Total RNA was prepared by the hot-phenol method. mRNA isolation and mRNA-seq library preparation were performed using the NEXTflex Rapid Directional mRNA-Seq Bundle . mRNA library sequencing was performed on a HiSeq2500 using the single-end method (50 bp reads), and the raw reads were aligned to the sacCer3 genome using the STAR aligner. The number of mapped reads was further analyzed using Cuffdiff−1 zymolyase . Lysed cells were spheroplasted at 30 °C for 20 min and chromatin was isolated. Pelleted nuclei were digested with 200 units of MNase (NEB) at 37 °C for 20 min , and then reverse cross-linked in 10 mM EDTA and 1% SDS buffer with proteinase K for 6 h at 37 °C. The DNA was extracted with phenol/chloroform, purified by ethanol precipitation, and resolved by 2% agarose gel electrophoresis in 1xTAE ) buffer. Mononucleosomal fragments were recovered with a gel extraction kit (Qiagen). MNase libraries were prepared using a TruSeq Nano DNA Library Prep Kit , and sequenced using a HiSeq2500 with the paired-end method. The obtained MNase-seq reads were aligned using Bowtie2, and nucleosome positions and occupancies were identified by DANPOS2. The average plot was graphed with the DANPOS2 profiler. The heatmap was clustered using Cluster 3.0 with 3 k-means and profiled using Treeview. The scatter plots and R2 values showing the sample variation of MNase-seq duplicates were included in Supplementary Fig. For preparation of MNase-seq library, formaldehyde-fixed cell pellets were resuspended in 10 ml of pre-incubation buffer , washed, and resuspended in 2 ml of zymolyase buffer  containing 2.5 mg ml−1 (mid-log phase), and spheroplasted in 200 μl of Sorbitol buffer  with 10 μl of 50 mg ml−1 of zymolyase . After washing twice with 100 μl of ice-cold Sorbitol buffer, the spheroplasts were incubated in fresh TD buffer  with transposase at 37 °C for 4 h. QIAquick PCR purification kit was used to purify DNA fragments, and transposition reactions were performed60. The transposition reaction was incubated in 37 °C water bath for 4 h in 23.75 μl of TD buffer containing 1.25 μl of Tn5 transposase. We used HiFi HotStart ReadyMix  for library amplification following provided manual except number of PCR cycle: we performed 14 cycle for PCR amplification. The amplified library was purified with a QIAquick PCR purification kit  and sequenced using a HiSeq2500 system with the paired-end method. The ATAC-seq data were analyzed as described in the ChIP-seq section. The scatter plots and R2 values showing the same variation of ATAC-seq duplicates were included in Supplementary Fig. Yeast cells (2.5 million cells) were harvested at O.D. 0.6~0.8 mlXenpous laevis core histones (200 ng) were incubated with Dot1, Dot1(101–140Δ) , or GST-Nap1p (2 μg) at 30 °C for 30 min, and then 200 ng of the 147 bp DNA was added to each reaction. After incubation for 90 min at 30 °C, 500 ng of pRS405 DNA plasmids were mixed into each reaction, using 5 × Reaction buffer , and incubation was continued for 15 min at 30 °C. The samples were then resolved by electrophoresis on a nondenaturing 5% (w/v) polyacrylamide TBE gel. Mononucleosomes were stained with EtBr, and western blotting analysis was performed with an antibody raised against histone H3 . The Uncropped scans of the western blot is shown in Supplementary Fig. For nucleosome assembly assay, 147 bp DNA fragments were amplified from pGEM-3z/601 with the primers listed in Supplementary Table ot1, Dot11–140Δ  and then mixed with chromatin remodelers and incubated for an additional 45 min at 30 °C. In all cases, a suitable concentration of chromatin remodeler was determined by prior titration. The reactions were stopped by the addition of 1 μg of HindIII-digested DNA and 1 μg of long-oligo-nucleosomes. The mixtures were resolved by 5% native polyacrylamide gel electrophoresis, and the gels were dried and exposed using a FLA-7000 (Fuji). The uncropped image of exposed native polyacrylamide gel is shown in Supplementary Fig. Short oligonucleosomes were reconstituted through an octamer-transfer method using native HeLa nucleosomes and 600 indicated that the yeast had reached stationary phase, equivalent amounts of cells (OD600 0.5) were serially diluted fivefold and spotted onto SC (synthetic complete) and SC-His+Gal plates. The plates were incubated at 30 °C for 2–3 days, and photographs were obtained when the yeast growth reached stationary phase.When the ODThe RNA-seq, MNase-seq, ATAC-seq, and ChIP‐seq datasets have been deposited to the GEO under accession number GSE106450.Supplementary InformationPeer Review File


A new type of RNAs was identified from genes traditionally thought to express messenger or linear ncRNA (noncoding RNA) only. They were subsequently named as circRNAs (circular RNAs) due to the covalently closed structure. Accumulating studies were performed to explore the expression profile of circRNAs in different cell types and diseases, the outcomes totally changed our view of ncRNAs, which was thought to be junk by-products in the process of gene transcription, and enriched our poor understanding of its underlying functions. The expression profile of circRNAs is tissue-specific and alters across various stages of cell differentiation. The biological function of circRNAs is multi-faceted, involving five main features  and varying differently from the locations, binding sites and acting modes of circRNAs. The regulating role of circRNAs is not isolated but through an enormous complicated network involving mRNAs, miRNAs and proteins. Although most of the potential functions still remain unclear, circRNAs have been proved to be ubiquitous and critical in regulating cellular processes and diseases, especially in cancers, from the laboratory to the clinic. Herein, we review circRNAs’ classification, biogenesis and metabolism, their well-studied and anticipated functions, the current understanding of the potential implications of circRNAs in tumorigenesis and cancer targeted therapy. CircRNA is an up-rising star in ceRNAs (competing endogenous RNAs) field, even in the whole RNA world, characterized by the distinct structure without terminated 5’ caps and 3’ tails . AlthougCircRNAs are a group of non-coding RNAs that are ancient yet newly recognized regulating molecules. After the earliest discover in plant-based virus in 1976 –12. But Spliceosome-Dependent Circulation Path.Most eukaryotic circRNAs are generated during alternative splicing, an essential step in the eukaryotic gene expression process that is catalyzed by either spliceosomal machinery or group I/II ribozymes –18. The Intron-Pairing-Driven Circularization Path and Lariat-Driven Circularization PathFirstly, in the ‘Intron-Pairing-Driven Circularization Path’ Fig. , a largeProtein Factors Associated Circulation PathAs in the ‘Intron-Driven Circulation Path’, some RBPs (RNA binding proteins) can bind to specific targets in introns and bring the donor-acceptor sequences in proximity to each other thus trigger several circularization processes instead of the intronic reverse complementary motifs. In this model of circularization, RBPs play a double role of both stabilizing the splicing motifs and inhibiting canonical linear splicing in the process of regulating circRNA generation Fig. . QKI, onForamtion rate is actully slow: Long time accumulation makes the abundancePrevious researches of circRNAs are all reported in genomics level, since the relatively stable characteristics of circRNA can achieve its accumulation within a certain time. The rate of circRNA formation is still a mystery until now. A newly published paper showed that the forming rate of circRNA is actually very low, and the generation process is related to the rapid extension of the polymerase II. The researcher took the neurons as example and drew a final conclusion that circRNA depends on the cis-elements (reverse complement sequence inside RNA) in the process of rapid transcription and the long-time cumulative effect in cells . We haveAlthough more and more human circRNAs were identified and uploaded online using molecular biology strategies combined with new bioinformatic approaches, the underlying mechanisms of circRNA generation are still not clear yet. There is an assumption that at least four distinct paths involved in the formation of circRNAs , 14, 15.Three subsets classification: component-orientedAccording to the most existing studies, comprehensive circRNAs can be classified into three types Fig. , exonic Five subsets classification: location-orientedWith the development of deep sequencing in varied species and tissues, some special circRNAs types was discovered, circRNAs should be classified into five types, “exonic”, “intronic”, “antisense”, “sense overlapping” and “intergenic” according to their location relationship with adjacent coding RNA Fig. . Except Due to the confusion of nomenclatural method in different databases and the poor understanding in biogenesis and characteristics of circRNAs, generating a systematic circRNAs classification is not an easy task. Up to now, there are two separate ways to divide circRNAs based on different principles.Online-databases of circRNA: different flowers bloom togetherhttp://starbase.sysu.edu.cn/ Accessed 10 Augst 2016) and circBase (http://www.circbase.org/ Accessed 10 August 2016) allow the user to search for functional classes or processes [http://gyanxet-beta.com/circdb Accessed 21 July 2016) and nc2Cancer (http://www.Bioinfo.Tsinghua.Edu.cn/nc2Cance Accessed 15 July 2016), provide plenty of information about cirRNA-associated diseases and the genomic positions of several circRNAs [http://circnet.mbc.nctu.edu.tw/ Accessed 12 September 2016) and deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/ Accessed 23 August 2016) contains the most CircRNA annotation (Annotation of circRNAs depends on sequencing reads spanning the backsplice junction and thus map as nonlinear reads in the genome), especially human CircRNAs [http://circinteractome.nia.nih.gov Accessed 8 September 2016) will be the only option if you want to match the circRNA with relevant RNA-binding proteins [There are many free online databases available for the circRNA research, each database has its own characteristics and covers the different part of the information about circRNAs. For instance, both starBase v2.0  only occupies about 2% of the genome, the rest massive number of transcripts were classified to non-protein coding RNAs (ncRNAs). CircRNA was found stable and abundant in cells across species, involving in complex regulatory networks of ncRNAs and playing a indispensable role in the development of non-neoplastic diseases and tumors , 44.AssuA super sponge for miRNACircRNAs has been identified as a new kind of ceRNAs that sponge and inhibit relevant miRNAs by complementary base paring Fig. .1. MiRNACircRNAs can affect the biological function of miRNA, regulating the activity of miRNA-target gene, thus playing an indirect role in the regulation on post-transcriptional level. CiRS-7 is a recently identified human circRNA which harbors more than 70 conventional miR-7 binding sites thus works as a super-sponge of miR-7 , 38. It Post-transcription regulationMost ciRNA (formed by the introns) position in the nucleus, almost has no miRNA binding function, yet what is intriguing is that knocking down ciRNA will hinder the transcription of the corresponding gene . Ci-ankrCircRNA can work together with RBPs to form a complex, inhibiting transcription, or directly regulating the expression of mRNA through Watson-Crick base pairing rules  Fig. 4..4.2. StaThere are also many other circRNAs containing translation-initiating site possess the similar function and act as mRNA trap to regulate expression of their host gene. It hence appears that involving in the regulation of gene transcription process is another important functional model of circRNA except for miRNA Sponge.Rolling Circle TranslationCircular mRNA have been proved to be translated in eukaryotic cell if it contains a IRES  sequence combining directly with ribosome –62. The Generating the circRNA-derived pseudogenesGiven the unique structure, highly conservative sequence, absolute stability and rich content of circRNAs, is there any chance that stable circRNA molecules could possibly be reversely transcribed and integrated into the genome, forming the RNA-derived pseudogenes? Researchers analyzed the corresponding circularization sites (exon 6- exon 2) of circRFWD2 in mouse genome, found 33 “highly credible circRFWD2-derived pseudogene”, 9 “lowly credible circRFWD2-derived pseudogenes “, and six pseudogene containing exon sequences located outside of circRFWD2. LINE-1 mediated RNA reverse transcription usually require the existence of Poly (A), yet 39 of 42 circRFWD2 related pseudogenes contain no Poly (A) sequence, This illustrates that some circRFWD2 may be reversely transcribed to cDNA and integrated into the genome based on an unknown way  because of its transcription in the antisense orientation with respect to the CDR1 gene, which has been implicated to be crucial in autoimmune neurologic disorders like Alzheimer’s disease –71. CircSome circRNAs are even able to possess the function of regulating cell cycle progression. Circ-Foxo3 is a highly expressed circular RNA in non-cancer cells, silencing endogenous circ-Foxo3 accelerates cell proliferation while ectopic expression of circFoxo3 hampers cell cycle progression . Circ-FoSome researchers provide evidences that circ_001569 acts mechanically as a miRNA sponge to inhibit miR-145 activity, and subsequently up-regulates miR-145 targets E2F5, BAG4 and FMNL2 to promote cell proliferation and invasion in CRC  . Cir-ITCChromosomal translocations give rise to oncogenic fusion gene thus encode fusion proteins which have been shown to contribute to tumorigenesis , 80. PMLThere is research shows that circular and linear expression exhibits an inverse trend in many cancer related pathways. Signaling pathways like NF-kB, ILK and TGF-β, which are essential for epithelial to mesenchymal transition and typically over expressed for mRNA in metastatic tumors, are suggested down regulated for circRNA . SimilarMajority of current studies all lend a strong support for the notion that circRNAs play fundamental roles in various disease, especially tumors. However, the function of large amounts of circRNAs which have been detected inside animal cells still remains largely unknown and awaits further detailed study. Thankfully, the results of existing researches have several interesting implications concerning circRNAs biology and toward disclosing the authentic involvement of circRNAs in various disease and cellular process.As already stated in our previous section, the expression levels of circRNA was observed an increase after ADAR knockdown compared with the host mRNA. This finding is actually very interesting. You can imagine that if the level of ADAR decreases transiently in physiological state, it is perhaps the perfect time when circRNAs start to play a role. Prior studies have shown that stem cells can differentiate into neurons if ADAR gene expression in embryonic stem cells is reduced. Then, whether circRNA is involved in stem cell differentiation? What role dose circRNA plays in abnormal differentiation of hematopoietic stem cell associated hematological malignancy? Whether the high stability of circRNA has a relationship with the memory retention of human brain and the memory-mediated killing effect of T-lymphocytes? Maybe circRNA can help us fully understand and effectively treat the brain degenerative diseases and haematological malignancies.We all know that circRNAs highly exist in permanent cells (neuron and cardiomyocyte) and accumulate as time goes on, then how to explain this phenomenon? Is the process of aging gives enough time for circRNAs to gather or the accumulation effect of circRNA promotes the occurrence of aging? What about the target miRNAs? Do they still bind with circRNAs sponge and increase synchronously during the aging process? What will happen to miRNAs after their release in a inactive state from the cleaved circRNAs? What contributes do the interaction between circRNA, miRNA and their parental gene serves in the process of aging and functional organ degeneration? These questions are all worthy of further study. Maybe 1 day human can unveil the mystery of aging by researching these promising RNAs.As a footnote to distinguish varied circRNAs, the naming of circRNAs today is still based on the RNA sequencing tools, which differs significantly from each other. This chaos existing in naming method causes the difficulty of circRNA classification. A normative and widely accepted naming system is necessary. With the gradual understanding of circRNA’s biogenesis and functions, there is a hope that majority of the circRNAs can be named according to the well-studied miRNA they bind or the star gene they regulate. The most famous example is ciRS-7 and CDR1as. Considering the many-to-one relationship between miRNA, cirRNA and their mother-gene, an internal numbering system is required. With this complete naming system, we can assume a circRNA’s source and potential function after the first sight of its name, isn’t great?