E. coli single cell mRNA levels for two stress responses that depend on bacterial sigma factors with different mode of transcription initiation (σ70 and σ54). By fitting a stochastic model to the observed mRNA distributions, we show that the transition from low to high expression of the σ70-controlled stress response is regulated via the burst size, while that of the σ54-controlled stress response is regulated via the burst frequency. Therefore, transcription initiation involving σ54 differs from other bacterial systems, and yields bursting kinetics characteristic of eukaryotic systems.Transcription is fundamentally noisy, leading to significant heterogeneity across bacterial populations. Noise is often attributed to burstiness, but the underlying mechanisms and their dependence on the mode of promotor regulation remain unclear. Here, we measure  Transcription noise in bacteria is often attributed to burstiness, but the mechanisms are unclear. Here, the authors show that the transition from low to high expression can be regulated via burst size or burst frequency, depending on the mode of transcription initiation determined by different sigma factors. As a consequence, transcription is stochastic4 and may follow either a Poisson distribution —mRNA is synthesized in random, uncorrelated events, with a uniform probability over time—or is described as bursty—mRNA is synthesised in episodes of high transcriptional activity7. Cell-to-cell variability (noise) within a population3 is increased through bursty transcription7. Noise underpins bacterial bet hedging whereby genetically identical cells display population-wide divergent phenotypes11. Bet hedging may offer a competitive advantage ensuring survival and is important in responses of bacteria to antibiotics, acquisition of drug-tolerant persistence and the use of cells harbouring non-native gene control circuits in synthetic biology11. Gene-specific and genome-wide sources of noise have been described7. However, the contributions of many molecular events involved in transcription—in particular those occuring during transcription initiation—are currently largely unknown12.Transcription is a series of discrete interactions of transcription factors and RNA polymerase with the promoter alongside any biochemical steps that these factors undertake13. Specificity factors comprise two distinct classes: the σ70 family combines all sigma factors that bind to −10/−35 promoter elements and in E. coli includes σ70, σ19, σ24, σ28, σ32 and σ38; in contrast, σ54 binds to −12/−24 promoter elements and forms a class of its own. Transcription initiation from σ70-dependent promoters involves the spontaneous isomerisation of the closed RNA polymerase-promoter complex to the open complex14 , for the promoter DNA opening event19  and how it occurs is currently unknown.One major aspect of transcription initiation in bacteria is the need for a specificity factor termed sigma (σ) to direct RNA polymerase to the promoterSuf and Psp, each dependent upon a contrasting σ-factor class. One response manages stress arising from exposure to oxidants28 and iron starvation29, the other manages membrane stress27.In order to evaluate the impact of σ-factors in cell-to-cell variability, we analyse two paradigmatic stress responses, 30 and the Spätzcells software30 31 and burstiness 32. A Fano factor value of 1 corresponds to Poisson distribution (non-bursty), while Fano greater than 1 indicates bursty transcription. We also elaborated a model to provide a conceptual framework for interpreting our data. In the two-state Telegraph model of transcription35 the promoter transitions between an active and inactive state at rates λ and ν respectively. Transcription only occurs in the active state with a mean rate Κ, while mRNA decays at a rate δ. Transcription is bursty if ν ≫ λ and Κ, ν ≫ δ whereby bursts occur at an average frequency of λ/δ and an average size of Κ/ν. In line with previous propositions36 we considered that there may be multiple (nested) regulatory mechanisms at play, operating at different timescales to each other. This leads to a model in which there are multiple states of the transcription apparatus, only one of which is active in initiation  and a primed but inactive state (B). A single active state (A) then exists from which transcription can occur. If switching between states (C) and (B) is relatively slow, this leads to the behaviour displayed in Fig. 38 expressed mathematically as:We used RNA fluorescence in situ hybridizationω refers to the fraction of time in which the gene is in the deep inactive state (C), and is related to the parameters by ω = β/(α + β). This fraction is also the proportion of cells within a measured sample that are in this deeply inactive state. The parameters of the Negative binomial are related to the model parameters via r = λ/δ, and p = ν/(ν + Κ).Here, r, p, ω and consequently burst frequency λ/δ and burst size Κ/ν. Further details are given in the methods section.We then performed parameter estimation via model fitting. To infer parameters we used Bayesian inference via a custom Markov chain Monte Carlo (MCMC) algorithm, in order to obtain the probability distribution of these parameters, in light of the experimental data 39. We investigated the expression profile of the σ70 controlled Suf system to establish whether the behaviour of stress responses under the control of a more complex regulation deviates from that of simpler adaptive systems such as lactose utilisation. Suf is encoded in a single operon (sufABCDSE) and enables the assembly of Fe-S clusters under oxidative stress and iron limitation29. Expression from the PsufA promoter is induced in presence of hydrogen peroxide (H2O2) and/or the iron-chelator bipyridyl (BP)29. Regulation of PsufA in response to these stressors involves the interplay between two activators (OxyR and IscR) and one repressor (Fur)43  to the promoter43  or oxyR (ΔoxyR), under unstressed basal conditions and in presence of H2O2 and BP applied singly and in combination. Cells lacking iscR consistently showed nearly no expression at any condition (data not shown), suggesting that IscR is the major regulator of PsufA, in line with previous findings42. The lack of expression meant that no further insight could be gained from this mutant in understanding the source of noise and burstiness of this promoter.We measured the level of gene expression, transcriptional noise and bursting of native PsufABCD mRNA levels per cell are small         Fig.  suggesti50) Fig.  we inter29) Fig.  with a n20) Fig.  but not 49) Fig. . In cont04) Fig.  or oxyR 97) Fig.  is deletfur) the mean mRNA copy numbers per cell increase several fold  reduced the noise , since noise in wildtype and ΔoxyR under these conditions was largely the same. Generally, the levels of noise and burstiness correlate with the strength of the inducing signal. Application of the stronger inducer BP (singly and in combination with H2O2) resulted in stronger noise reduction and a stronger increase in burstiness than application of the weaker inducer H2O2. Interestingly, noise was slightly lower when BP was applied alone than in combination with H2O2. This was slightly more pronounced in Δfur     to cells that harboured the repressor  Fig.  than in 22) Fig. , while i64) Fig. . Our dation Fig. . Overallω, λ and K/ν underpinning bursty transcription  in which the σ70 controlled promoter is in the deep inactive state (C) is linked to the repression by Fur, since the value of ω is high when Fur is operating (i.e. in wildtype cells in absence of stress) yet ω is always low when cells are lacking Fur . Furthermore, the value of ω in presence of H2O2 showed a strong dependence on OxyR. Indeed, this was one of the strongest effects of OxyR in our entire data set. We thus conclude that a major role for OxyR in the regulation of PsufA transcription is to reduce the time and proportion of cells in which PsufA is in the deep inactive state and thus to prime PsufA for expression under oxidative stress.Fitting our extended Telegraph model to the experimental data of mRNA distributions  of PsufA was largely similar and did not scale with mean mRNA numbers per cell at higher levels of expression, i.e., when repression was removed e.g. through fur deletion or addition of BP (singly or with H2O2) in wildtype and ΔoxyR cells. We note however some modest burst frequency modulation at low expression in unstressed wildtype and ΔoxyR cells and also in ΔoxyR in presence of H2O2  from PsufA was small at low expression (e.g. in unstressed wildtype cells). Burst size scaled however with signal strength and mean mRNA numbers per cell to yield large bursts at high expression , the mean mRNA levels per cell produced from PpspA are low        Fig.  and burs19) Fig. . This ba74) Fig. ; strikin40) Fig.  compared54-dependent promoters strictly requires the mechanochemical action of an enhancer binding transcriptional activator (here PspF)16 .Recall that transcription initiation from σ)16 Fig. . Consequ45. This is due to autoregulation via a negative feedback exerted by PspF on its own expression45    Fig.  than at 92) Fig. . We prop54-dependent promoters, activation (and hence open complex formation) is a multi-step process that involves (i) ATP hydrolysis (inhibited by negative regulation) and (ii) sustained contact of the activator with the closed complex (RNA polymerase/σ54/promoter DNA)19  expressed at low levels which is unable to interact with the negative regulator PspA and thus escapes its inhibitory function49   . We presume that these differences arise from some residual negative regulation that occurs in the native system under stress. Strikingly however, removal of negative regulation of the activator has no marked effect on the burstiness of the σ54 controlled promoter  that has reduced affinity for the closed complex formed by RNA polymerase and σ54 at the promoter51  . Notably however, the promoter is markedly less bursty in presence of PspFT86S (FPspF_T86S = 7.57) , noise (CV2PspF_W56A+T86S = 0.58)     is due to the inefficient remodelling of the closed to the transcriptionally active open complex. Taken together, the quality of the contact between the activator and the closed complex is a determinant of the burstiness but not the level of noise during transcription from σ54 controlled promoters.We utilised a variant form of the activator PspF PspFW6A expres56) Fig.  than in ter Fig. . Followi96) Fig.  is simil57) Fig.  than und58) Fig.  returns 56) Fig. . The mRN89) Fig.  is large54 controlled promoter correlate with the level of expression as is seen with σ70  and increases exponentially with increasing expression level  , indicating non-Poisson mRNA production at all levels of expression  Fig. 4–7,39. Iion Fig. .54 controlled promoter is in the deep inactive state (C) correlates with the level of negative regulation of the activator. It is lowest in the W56A mutant where negative regulation is absent, ATP hydrolysis constitutively active and remodelling of the closed to the transcriptionally active open complex efficient due to the proficient contact of this constitutive activator with the closed complex.Model fitting to the experimental data , but only modest at high level of expression in absence of negative regulation (compare PspFW56A+T86S with PspFW56A) . It increased in wildtype in presence of stress as well as at high activator levels (PspFhigh), in both cases some low level negative regulation still occurs. Yet, burst frequency was highest when negative regulation was completely abolished in the W56A and W56A + T86S mutant. The efficiency of the contact between activator and the closed complex however appears to have no major effect on the burst frequency. We discounted that our observations were simply due to differences in (i) gene dosage  Fig. . In contion Fig. . Burst f4 given that in contrast to other domains, in bacteria burstiness as well as burst size increased with higher expression in all promoters studied to date. This was further interpreted as being indicative of a global control of transcriptional bursting. Contradictory to this idea of gene-independent determinants of burst kinetics, it was shown that burstiness and burst size can be modulated by changing the efficiency of transcription regulation of an individual bacterial gene39.Although it is well established that noise and bursting is a fundamental property of inducible gene transcription, a precise picture of the underlying molecular events has not yet emerged. It is implied that domain specific differences in burst kinetics exist58. Yet, the contribution of sigma factors—whose role is to facilitate transcription initiation—has so far been overlooked. All bacterial promoters studied to date for noise and bursting are controlled by σ70. However, the evolutionarily distinct σ54 drives a fundamentally different mode of open complex formation. Whether or not the two sigma factor classes differ in the generation of noise and bursting is currently unknown. Yet, such knowledge is critical in order to gain a better appreciation of the scope of noisy and bursty transcription displayed by bacteria. It also enables a better understanding of the mechanisms underpinning cell-to-cell variability and thus phenotypic variation of σ54 controlled adaptive responses, e.g., biofilm formation, nitrogen fixation, virulence or antimicrobial persistence; such an understanding is essential when aiming to manipulate beneficial and pathogenic interactions of bacteria with their human, animal and plant hosts.A fuller understanding of the sources of transcriptional noise and bursting in bacteria requires an appreciation of the biochemical complexity of transcription regulation to avoid oversimplified views of the events that lead to open complex formation during transcription initiation. The impact of transcription initiation on noise and bursting has been acknowledged70-dependent promoters or from infrequent large bursts with high variability at σ54-dependent promoters  at σ70-dependent promoters or by increasing the burst frequency λ/δ at σ54-dependent promoters  and unregulated access of RNA polymerase to the promoter, both hallmarks of transcription initiation involving σ54. Strikingly, a similar behaviour is also observed in enhancer-dependent transcription in mamalian cells56. This implies that the type of regulation and not domain-specific constraints determine the transcriptional burst kinetics of a gene.The present study demonstrates that in bacteria the class of sigma factors, and as such the route to open complex formation during transcription initiation, determines the level and kinetics of transcriptional bursting. Low mean expression results from frequent small transcription bursts with low variability at σ59.While the two sigma factor classes yield opposing burst kinetics, noise (cell-to-cell variability of transcription within a population) simply correlates with the level of gene expression irrespective of the sigma factor. Our results imply that under basal and stress conditions bacteria utilise a universal adaptive behaviour. Prior to stress, transcription is noisier and hence cells are more heterogeneous; presumably reflecting opportunity to bet hedge. Upon stress perception, transcriptional noise is reduced through elevated gene expression yielding a more homogeneous cell population. Our observations therefore suggest a mixed strategy to cope with stress, using both environmental sensing and bet hedging. Environmental sensing is arguably more cost effective in that gene expression only occurs in presence of stress. Here we assume the low level expression of the sensory apparatus before stress is less costly than the full stress response. Examples where this may not be so can be envisaged, but is not the case for those studied here. The applicability of environmental sensing however is limited to stress conditions that are neither too sudden nor too severe given the phenotypic lag between sensing and responding. Bet hedging (gene expression in absence of stress) may be more costly but eliminates the phenotypic lag and thus provides an escape route for sudden and severe stress by priming the cells for less favourable conditions. This strategy may represent an adaptation to more stringent environmental pressure in line with the idea that gene expression noise may be subject to evolutionary selectionEscherichia coli K12 MG1655 strains. Genes were deleted by P1 transduction using strains from the KEIO collection of Escherichia coli mutants60 as donors or by lambda red recombineering61. Strains were typically grown in Luria-Bertani (LB) broth or on agar at 37 °C. For RNA fluorescence in situ hybridization, strains were grown in M9 minimal medium (Teknova) at 37 °C, from an inoculum of 150 μl from an LB overnight culture to a final OD600 of 0.4 in a culture volume of 30 ml in a 250 ml flask. The bacterial cultures were supplemented as required with antibiotics at the following concentrations: chloramphenicol 30 μg/ml; kanamycin 25 μg/ml. Exposure to isopropyl β-D-1-thiogalactopyranoside (IPTG) at 1 mM final concentration for 1 hour was used to express pIV from plasmid pGZ119EH. Arabinose 0.02–0.2% (w/v) and glucose 0.4–1% (w/v) were used to express PspF  from pBAD18-cm in cells lacking chromosomally encoded pspF.All experiments were performed with sufABCD and pspABC structural genes were designed using the Stellaris® Probe Designer version 4.2; the oligo length was set at 20 nt, the minimal spacing length at 2 nt and the masking level at 1–2. The probes were labelled by 6-carboxytetramethylrhodamine, succinimidyl ester (6-TAMRA).Fluorescent DNA probes (purchased from LGC Biosearch Technology) to detect the mRNA of 600 of 0.4 and collected by centrifugation. Cells were fixed in 1 ml of ice-cold 1× PBS in DEPC-treated water with 3.7% (v/v) formaldehyde (30 min incubation at room temperature), washed twice in 1 ml 1× PBS in DEPC-treated water, permeabilised in 1 ml 70% (v/v) ethanol in DEPC-treated water (1 h gently mixing at room temperature) and washed again in 1 ml 2× SSC in DEPC-treated water with 40% (w/v) formamide. Hybridisation was performed through incubation of the cells overnight at 30 °C in hybridisation buffer  formamide, 10% (w/v) dextran sulfate, 2 mM ribonucleoside-vanadyl complex, 0.2 mg/ml BSA and 1 mg/ml carrier E. coli tRNA) with 1 μM of the appropriate fluorescent probe(s). Subsequently, 10 μl of cells in hybridisation buffer were washed twice in 200 μl 2× SSC in DEPC-treated water with 40% (w/v) formamide and incubated for 30 min at 30 °C. Chromosomes were stained by incubating the cells for 30 min at 30 °C with 10 μg/ml DAPI  in 200 μl 2× SSC in DEPC-treated water with 40% (w/v) formamide. The cells were washed again in 200 μl 2× SSC in DEPC-treated water with 40% (w/v) formamide and resuspended in 10 μl 2× SCC in DEPC-treated water. For imaging, 2 μl of the cell suspension were immobilized using 1% (w/v) agarose pads on 35 mm, high μ-Dishes (ibidi).Bacterial cells were grown in M9 medium to an OD62. Cell segmentation masks from brightfield or DAPI images were generated via Schnitzcells63 in MATLAB (MathWorks). The mRNA copy number per cell was determined via Spätzcells30 in MATLAB (MathWorks) using images acquired through the Cy3 (6-TAMRA) channel in combination with the cell segmentation masks. Fluorescent spots within selected cells were detected automatically and differentiation of specific from nonspecific probe binding was achieved by selecting a false-positive threshold using non-expressing cells . The probability distribution of peak height and intensity of fluorescent spots was extracted   we assume therefore that 50 mRNAs per cell would be towards the top end of what might accumulate if the usual parameters of promoter activity and mRNA stability were being met. We note that some cells however appeared to accumulate several hundred target mRNAs. We propose that these high content mRNA cells are imaging or hybridisation artefacts e.g. through probe aggregation or non-specific genome-wide hybridisation. These cells were therefore omitted from the analyses presented in the main text. For completenesss however, we have included the data derived from the analyses including these cells . If the copy number of mRNA in the π(θ) are our prior distributions. Priors for ω and p were uniform  and therefore flat across the range of permissable values. The prior for r(=λ) was set as the half-Normal, truncated to positive values. This prior has minimal effect around the region of the inferred distribution, but ensures good convergence properties for the MCMC. A custom Metropolis-Hastings MCMC scheme was implemented to sample from the posterior distribution, the code for which is freely available. A multivariate Gaussian proposal distribution was used, in which the standard deviation was set at 5% of the current parameter values to ensure a reasonable acceptance ratio and good convergence. Chains were generally run to collect 500,000 samples with 100,000 dicarded as burn-in and thinning applied at a factor of 100. Each chain was examined for good convergence and restarted from the old chain when necessary. Outputs from the MCMC are displayed in Supplementary Figs. where 2) and Fano factor (F) as a function of the mean expression level and the other (non-varying) kinetic parameters. Expressions for the Fano factor are provided through Eqs.   2) and Fλ, and therefore the burst frequency, the following expression hold for each of the squared noise and Fano factor,If regulation is performed only via Conversely, if regulation is only via ν, and therefore the burst size, the following expressions hold,Suf data in which we infer variation in only the burst size, an average value of λ = 1.89 is obtained. For the Psp data, we take an average value of the ratio K/ν as 11.3. One must then still choose a value for K/δ, which here we set as 1000, although the results are not very sensitive to this choice.In plotting the predictions of these equations for Fig. Further information on research design is available in the Supplementary InformationReporting Summary


While EC50 concentrations of emetine are achievable in the blood, studies show that concentrations of emetine can be almost 300 times higher in the lungs. Furthermore, based on the relative EC50s of emetine towards the coronaviruses compared with Entamoeba histolytica, emetine could be much more effective as an anti-coronavirus agent than it is against amoebiasis. This paper also discusses the known side effects of emetine and related compounds, how those side effects can be managed, and the optimal method of administration for the potential treatment of COVID-19. Given the serious and immediate threat that the COVID-19 coronavirus poses, our long history with emetine and the likely ability of emetine to reach therapeutic concentrations within the lungs, ipecac, emetine, and other analogues should be considered as potential treatment options, especially if in vitro studies confirm viral sensitivity.The COVID-19 coronavirus is currently spreading around the globe with limited treatment options available. This article presents the rationale for potentially using old drugs  that have been used to treat amoebiasis in the treatment of COVID-19. Emetine had amongst the lowest reported half-maximal effective concentration (EC Ipecac ectively . OutlineEmetine, as an isolated alkaloid, was used as an anti-infective agent from 1912 when Vedder  showed t50 for SARS and MERS coronaviruses, with all patients vomiting at the 30 mL dose [In a study looking at the amount of emetine absorbed after a 30 mL oral dose of ipecac (containing 13.9 mg of emetine) in 10 subjects, the mean plasma concentration between 1 and 3 h post-dosing was approximately 6.5 ng/mL (0.0135 μM), which equates to only 0.25 to ~1 times the EC mL dose . Similar mL dose .50  values of less than 0.042 μM against the Zika virus [It may therefore be possible that the lung concentrations of emetine after the oral administration of ipecac syrup are sufficient for anti-coronavirus activity, especially if other ipecac alkaloids in the syrup also have antiviral properties and it is taken with a 5-HT3 antagonist to avoid vomiting. As an example, cephaeline  has been shown to have low nanomolar ICka virus . Unfortunately, emetine absorption from oral ipecac displays considerable inter-patient variation . Some paIntramuscular administration of emetine can also result in nausea and vomiting  which, b50 of emetine against Entamoeba histolytica is 26.8 μM [50 for the SARS and MERS coronaviruses, implying that emetine is potentially far more potent as an anti-coronavirus agent than it is against amoebiasis. These results may cast doubt on the very low EC50 obtained with the SARS and MERS coronaviruses; however, studies by different authors have shown that emetine also has potent antiviral activity against the Zika virus (IC50 = 0.00874 μM) [50 = 0.04 μM) [50 = 10.2 μM) [50 values ranging from 0.12 to 1.43 μM, with the MERS coronavirus EC50 being 0.34 μM [50 for the MERS virus is higher than in previous studies [50 value of 0.16 μM [But how can we be sure that effective concentrations are achieved by the intramuscular route? Emetine, given intramuscularly, has been established over a long period as the most specific and highly potent agent against intestinal and extra-intestinal amoebiasis . The IC5 26.8 μM ; this is0874 μM)  and the 0.04 μM) . Interes10.2 μM) . At 0.0310.2 μM) . HIV rev10.2 μM) . In rela 0.34 μM . While t 0.16 μM .The main therapeutic issue with emetine use is perhaps its potential for cardiac toxicity. This was especially prevalent in India, where it was estimated that 10%–40% of the population had suffered from amoebiasis, resulting in the use of emetine being “widespread and lavish” . EmetineConsidering the substantially higher potency that emetine appears to have against the coronavirus, doses of one fifth to one tenth of the doses used for the treatment of amoebiasis  could potentially be used. These lower doses are likely to minimise or eliminate any significant cardiac toxicity and nausea while maintaining antiviral effectiveness. It should also be noted that the intravenous route was considered too toxic and offered no therapeutic advantages , and stiIf it was used in patients, it is beyond the scope of this article to suggest at which stage in the disease process an agent such as emetine should be used to treat coronavirus infection. Too early and prolific use could promote resistance. If therapy is left too late when acute respiratory distress  has deveA related compound to emetine that should be tested in vitro for coronavirus sensitivity is dehydroemetine, which was developed in response to the cardiovascular toxicity associated with emetine. Dehydroemetine is structurally similar to emetine but is recognised as having a lower cardiovascular risk profile then emetine  and has Under normal situations, it would be unexpected that an antibacterial compound would also be an antiviral compound. More unlikely, that the same mode of action would account for both antibacterial and antiviral activities. As emetine affects ribosomal protein synthesis in yeast and has been shown to inhibit viral-RNA synthesis , there m50 was used to initially rank a drug’s potential clinical significance as an antiviral agent against coronaviruses. Drugs that had higher ratios were deemed to be more clinically important, as it signified that therapeutic antiviral concentrations were more likely to be achievable at normal dosing. This system, by itself, proved inadequate, especially in the case of emetine. First, blood levels varied between studies. One study reported the range [50 ratio of approximately 8  that are known to effectively treat Given the serious and immediate threat that the COVID-19 virus poses, our long if not partially forgotten history with emetine and dehydroemetine, and the likely ability of emetine to reach therapeutic concentrations within the lungs, ipecac, emetine, and other analogues should be considered as potential treatment options, especially if in vitro studies confirm that the COVID-19 virus is sensitive to these agents.


In contrast, facultative heterochromatin regions exhibit reduced H3K9me2 and H3K9me3 levels in abo1∆. Genome-wide analysis showed that abo1∆ cells have silencing defects in both the centromeres and subtelomeres, but not in a subset of heterochromatin islands in our condition. Thus, our work uncovers a role of Abo1 in stabilising directly or indirectly Clr4 recruitment to allow the H3K9me2 to H3K9me3 transition in heterochromatin.Heterochromatin regulation is critical for genomic stability. Different H3K9 methylation states have been discovered, with distinct roles in heterochromatin formation and silencing. However, how the transition from H3K9me2 to H3K9me3 is controlled is still unclear. Here, we investigate the role of the conserved bromodomain AAA-ATPase, Abo1, involved in maintaining global nucleosome organisation in fission yeast. We identified several key factors involved in heterochromatin silencing that interact genetically with Abo1: histone deacetylase Clr3, H3K9 methyltransferase Clr4, and HP1 homolog Swi6. Cells lacking Abo1 cultivated at 30 °C exhibit an imbalance of H3K9me2 and H3K9me3 in heterochromatin. In  The chromatin regions that remain condensed throughout the cell cycle are defined as heterochromatin regions and are transcriptionally repressed3. In general, heterochromatin can be defined molecularly by the modification of histone H3, specifically the absence of methylation on lysine 4 (H3K4me) and presence of methylation on lysine 9 (H3K9me)6. A conserved group of heterochromatin proteins, the heterochromatin protein 1 (HP1) family, specifically recognise heterochromatin regions by binding to H3K9me. Both di- and tri-methylation (H3K9me2/3) enable the binding of HP1 proteins to silence heterochromatin8. Heterochromatin regions can be further classified into constitutive and facultative heterochromatin10. Fission yeast, Schizosaccharomyces pombe, is an excellent model organism for chromatin studies because its heterochromatin organisation is similar to human cells12. Examples of constitutive heterochromatin regions in S. pombe are the pericentromeric regions that contain repetitive DNA sequences13. These regions are regulated by RNAi-dependent silencing machinery that involves protein Dcr1 and Ago115. In contrast, the silencing of facultative heterochromatin, such as subtelomeric regions and heterochromatin islands, is mediated by RNAi-independent machinery that can be dynamically modulated to express genes required for the cell cycle or adaptation to the cell environment17. A subset of facultative heterochromatin islands, known as “determinant of selective removal” (DSR) islands, contain meiotic genes18. These islands are silenced through a specific RNA elimination process, together with Clr4, when the cells are not in meiosis19.In eukaryotic cells, the regions of the chromatin that contain active genes are termed euchromatin, and these regions condense in mitosis to allow for chromosome segregation and decondense in interphase to allow for gene transcriptionCaenorhabditis elegans, H3K9 is methylated sequentially and H3K9me2 and H3K9me3 are present in distinct localised chromatin domains21. Jih et al. recently reported that the transition from H3K9me2 to H3K9me3 can be blocked by introducing point mutations in the SET and chromodomain of S. pombe Clr4 22. These three Clr4 mutants exhibit increased H3K9me2 and reduced H3K9me3 levels in pericentromeric regions, which allows the transcription of dg-dh repeats22. However, the two Clr4 SET domain mutants (I418P and F449Y) block the transition to H3K9me3 in the pericentromeric region by inhibiting the methylation capacity of the enzyme, whereas the chromodomain mutant Clr4 (W31G) reduces the transition to H3K9me3 by blocking recognition and binding to methylated H3K923.Distinct roles have been observed for H3K9me2 and H3K9me3 in heterochromatin silencing. In the nematode S. pombe, H3K9me2 regions allow transcription, whereas H3K9me3 regions are transcriptionally silent22. The Clr4 enzyme is comprised of the chromodomain and SET domains and binds to methylated H3K924. The HP1 homologue Swi6 also binds H3K9me and acts together with the histone deacetylase Clr3 to prevent histone turnover in heterochromatin regions, repressing transcription25. Clr4 is responsible for both H3K9me2 and H3K9me3 in fission yeast, but the determinants and auxiliary proteins leading to the transition from H3K9me2 to H3K9me3 remain unknown.In 26. The human bromodomain-containing proteins, ATAD2 and ATAD2B, belong to the AAA (ATPases Associated with diverse cellular Activities) ATPase family. Proteins belonging to this family hydrolyse ATP to mediate conformational changes in substrate proteins, resulting in diverse cellular processes28. ATAD2 plays a key role in cell proliferation and is upregulated as a potential oncogene in cervical, colorectal, breast, and lung cancers32. In the budding yeast Saccharomyces cerevisiae, the ATAD2 homologue Yta7 binds to histones and participates in the heterochromatin boundary function in the HMR region, where it counteracts the spread of silencing into the neighbouring euchromatin34. This ScYta7 boundary function is possibly related to the regulation of nucleosome density through eviction of H3-H4 tetramers from the chromatin35.The bromodomain is an extensive evolutionarily conserved protein domain that recognises acetylated lysine residues and plays multiple roles in transcriptional activationS. pombe, Abo1 and Abo236. A recent study showed in vitro that Abo1 structure works as a hexamer using Cryo-EM analysis and is able to load H3-H4 onto DNA in a ATP-hydrolysis dependent manner37. In vivo, cells lacking Abo1 exhibit a global reduction in nucleosome levels, nucleosome redistribution, silencing defects at centromeric heterochromatin regions, and a chromosome segregation phenotype. Abo1 plays a role in centromere formation, as Abo1 is localised at pericentromeric heterochromatin and abo1∆ mutant cells present with silencing and defects in chromosome segregation 38Intriguingly, pericentromeric heterochromatin in abo1∆ has a slight increase in H3K9me2 enrichment, with clear defects in silencing38. However, the mechanism underlying Abo1 function is still unknown.Two AAA-ATPase bromodomain family members have been identified in S. pombe revealed several heterochromatin factors, including the histone deacetylase Clr3, H3K9 methyltransferase Clr4, and HP1 homologue Swi6. We found that cells lacking abo1 exhibit a strong imbalance in H3K9me2 and H3K9me3 levels within different heterochromatin regions, resulting in transcriptional deregulation. We show that Abo1 is involved in the heterochromatin assembly process by stabilising Clr4 binding and mediating the transition between H3K9me2 and H3K9me3.Here, our synthetic genetic screening of Abo1-interacting genes in 39 in which S. pombe strains harbouring deletions of genes involved in chromatin regulation were crossed with a strain carrying an abo1 deletion. Among 711 investigated genes, the strongest negative genetic interactions were recorded with genes encoding proteins involved in heterochromatin assembly: histone deacetylase Clr3, H3K9 methyltransferase Clr4, and chromodomain HP1 homologue Swi6  assay40. Therefore, we also analysed the cell viability of wild-type and abo1 single/double-mutant cells during temperature stress. At the standard growth temperature (30 °C), the population of dead cells was increased in the abo1∆ culture compared to wild-type. A synergistic increase in cell death was observed for the double mutants of abo1∆ combined with clr4∆ and swi6∆ . No additive effects were detected with abo1∆ and clr3∆ . Silencing of tel2L::ura4 was examined by the growth of colonies in media without uracil and media containing FOA. In abo1-deleted cells, silencing of tel2L::ura4 was clearly reduced, confirming that Abo1 is required for gene silencing in subtelomeric regions , other mutations affecting heterochromatin-associated factors, such as the deacetylate Sir2, putative histone H3 demethylase Epe1, siRNA-producing enzyme Dcr1, telomere binding protein Taz1, and HP1 family members Chp1 and Chp2, were included. The effects of these mutations on H3K9me2 levels in the different heterochromatin regions were compared to the H3K9me2 levels in cells lacking Abo1  and the tlh1 locus, abo1∆ cells had increased H3K9me2 enrichment and reduced H3K9me3 compared to wild-type  and centromeric (dhK) repeat regions (see Discussion).To confirm our observation on the role of Abo1 in histone methylation, we validated H3K9me2/me3 enrichment by ChIP-qPCR and expression by RT-qPCR of several genes located at subtelomeres, in the pericentromeric ats Fig. . The foulls Fig. . All fouype Fig. . These cype Fig. , possiblabo1∆ ChIP-seq results to publicly available data42. The average H3K9me2 levels in abo1∆ and clr4W31G cells exhibited a decreased level of H3K9me2 in 9 of 10 DSR islands, whereas the levels of H3K9me2 were increased in 3 of 4 non-DSR islands . In S. pombe, Abo1 was found in association with Swi6 that specifically bind domains containing H3K9me25 with a similar enrichment level  . Interestingly, when Swi6 nChIP-MS was performed in the clr4 catalytically dead (clr4∆CD) and H3K9R substitution mutants, Abo1 was not found  and telomeric repeats (tlh1), Clr4 occupancy was also significantly reduced in abo1∆ cells  followed by transcriptional gene silencing (RNAi-TGS)22. In the RNAi-CTGS step, the dg-dh repeats can still be transcribed, and siRNAs are generated by Dicer and Ago1, consequently activating the RITS complex  are present, allowing for a low level of transcription. In the RNAi-TGS step, H3K9 is tri-methylated, Swi6 binds, and the Clr3 deacetylase enzyme removes acetylation of H3 to stop transcription  and PMG media lacking uracil. The plates were incubated at 30 °C.Culture, genetic manipulation, and fission yeast strains are described in Supplementary Table S. pombe cells were grown to log phase in triplicate for fluorescence-activated cell sorting (FACS). Cells were then washed with 1x PBS and subsequently resuspended in 50 mM sodium citrate (pH 7) with 50 μg/ml propidium iodide (PI) for 30 min in the dark at room temperature (Sigma Aldrich). Cells were analysed immediately using a flow cytometer . Cells were sorted by forward (FSC) and side (SSC) light scattering. After gating for 20,000 single cells using FSC and SSC, dead cells stained with PI were counted through the FL2- channel.abo1∆ strains (Hu2318) were cultured to mid-log exponential phase and continuously induced at 25 °C, 30 °C, or 37 °C for 4 hours. The cells were then harvested by centrifugation at 3000 rpm and lysed by incubation with TES buffer . RNA was extracted using the hot acid-phenol method. Biological duplicates of RNA samples were sent for hybridisation to Gene Chip S. pombe Tilling 1.0FR Arrays  at the Affymetrix core facility (BEA) of Karolinska Institutet. For RNA microarray analysis, the signal raw data files were normalised using Affymetrix Tiling Analysis Software (TAS). The normalised microarray data were mapped to the S. pombe genome (Sanger 2007). Microarray results were visualised using SeqMonk software (https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). The microarray data can be accessed at GEO accession GSE 125910.Wild-type (Hu2185) and For each cell culture, 1.0 μg of the extracted RNA was treated with DNase I  and isolated for reverse transcription by SuperScript™ III First-Strand Synthesis Master Mix . RT-qPCR was then applied by using the SYBR™ Green PCR Master Mix . The reactions were performed using an Applied Biosystems® 7500 Real-Time PCR System. The primer sequences are listed in Supplementary Table abo1∆ cells (Hu2318), Flag-clr4 cells (SPJ390), and abo1∆ Flag-clr4 cells (Hu3040) were isolated in duplicate and subjected to ChIP according to Durand-Dubief et al.65. A total of 50 μg (for ChIP-seq) or 4 μg (for ChIP-qPCR) of H3K9me2 antibody , 4 μg (for ChIP-seq) or 1 μg (for ChIP-qPCR) of H3K9me3 biotinylated antibody , 4 μg of Flag M2 antibody , and 4 μg of H3 antibody  were added to 30 μl of sheared chromatin (for ChIP-qPCR) or 100 μl of sheared chromatin (for H3K9me2 ChIP-seq) or 200 μl of sheared chromatin (for H3K9me3 ChIP-seq) for each experiment. Protein A Dynabeads  were used for H3K9me2 ChIP, and m280 streptavidin Dynabeads  for H3K9me3 ChIP with an additional antibody pre-incubation step. ProteinA/G Magnetic beads  were used for Flag ChIP and H3 ChIP. ChIP DNA was sequenced and the sequencing data processed by the core facility (BEA) of Karolinska Institutet. Processed data were quantified using a read count per million reads normalisation, and then relative enrichment compared to the DNA input using SeqMonk software (https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). ChIP-DNA was also quantified for quantitative PCR using Applied Biosystems® 7500 Real-Time PCR Systems. SYBR™ Green PCR Master Mix . Primers are listed in Expanded View Table EV2. ChIP-seq data can be accessed at GEO accession GSE125911.Chromatin samples from wild-type (Hu2185), S. pombe strains harbouring deletions of genes involved in chromatin processes were selected from the Bioneer deletion library V.566 using Gene Ontology terms “chromatin binding”, “DNA binding”,”chromosome binding”, “chromosome”, and “transcription”. These strains have gene∆::KanR deletions and carry the auxotrophic markers ade6-M216 (or ade6-M210) ura4-D18 and leu1-32. The 760 strains were crossed with the smt0 abo1∆::hygR strain (Hu3021), spores transferred in YES media, and successively selected three times on YES with G418 (200 μg/ml) and YES with hygromycin (200 μg/ml). The cross resulted in 716 double mutants carrying a deletion for a gene involved in the chromatin process combined with the abo1 gene deletion using the RoToR robot (Singer Instruments). For analysis, single and double mutants were grown for 2 days using semi-solid YES media at 30 °C with the 384-plate format in triplicate. Plates were scanned and the colony sizes measured for four replicates and normalised using SGAtools67. Next, the colony fitness score was obtained using the normalised colony sizes of the single and double mutants. Negative scores indicate that the double mutant induces an increased fitness defect than expected; conversely, positive scores correspond to greater fitness compared to the control. The scoring distribution of these strains was analysed and a cut-off score of <−0.45 applied.A total of 760 haploid http://cshprotocols.cshlp.org/content/2006/1/pdb.prot4153.full)69. Eluted samples were quantified and sent for mass spectrometry at the chemical core facility in Karolinska Institutet70.Six litres of Abo1-TAP (Hu2368) and wild-type cells were cultured to log-phase. Pelleted cells were then ground using the grinding machine (6875 Freezer/Mill® High Capacity Cryogenic Grinder) with liquid nitrogen. The frozen yeast powders were resuspended in lysis buffer . Cell lysates were ultra-centrifuged and the supernatant removed. The supernatant was flowed through pre-washed IgG Sepharose 6 Fast Flow beads . Using TEV protease , TAP-tagged Abo1 protein was eluted (TAP affinity purification protocol according to (Supplementary Dataset.Supplementary information file.


Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes6. However, only a minority of the more than 1,600 transcription factors encoded in the human genome has been assayed. Here we present, as part of the ENCODE (Encyclopedia of DNA Elements) project, data and analyses from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) experiments using the human HepG2 cell line for 208 chromatin-associated proteins (CAPs). These comprise 171 transcription factors and 37 transcriptional cofactors and chromatin regulator proteins, and represent nearly one-quarter of CAPs expressed in HepG2 cells. The binding profiles of these CAPs form major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalogue of DNA sequence motifs for transcription factors, and describe motifs that correspond to other transcription factors that are co-enriched with the primary ChIP target. For example, FOX family motifs are enriched in ChIP–seq peaks of 37 other CAPs. We show that motif content and occupancy patterns can distinguish between promoters and enhancers. This catalogue reveals high-occupancy target regions at which many CAPs associate, although each contains motifs for only a minority of the numerous associated transcription factors. These analyses provide a more complete overview of the gene regulatory networks that define this cell type, and demonstrate the usefulness of the large-scale production efforts of the ENCODE Consortium.Transcription factors are DNA-binding proteins that have key roles in gene regulation ChIP–seq and CETCh–seq data are used to analyse binding maps for 208 transcription factors and other chromatin-associated proteins in a single human cell type, providing a comprehensive catalogue of the transcription factor landscape and gene regulatory networks in these cells. A typical TF binds to a short DNA sequence motif, and, in vivo, some TFs exhibit additional chromosomal occupancy mediated by their interactions with other CAPs10. CAPs are vital for orchestrating cell type- and cell state-specific gene regulation, including the temporal coordination of gene expression in developmental processes, environmental responses, and disease states13.There are an estimated 1,639 transcription factors (TFs) in the human genome16. In addition to highlighting potentially active regulatory DNA elements by direct measurement, ChIP–seq data can define DNA sequence motifs that can be used, often in conjunction with expression data and chromatin accessibility maps, to infer likely binding events in other cellular contexts without performing direct assays. Although motifs identified by ChIP–seq are often representative of direct binding, this is not always the case, as co-occurrence of other TFs could lead to the enrichment of their motifs. Furthermore, the ChIP–seq method identifies both protein–DNA and, indirectly, protein–protein interactions, such that indirect and even long-distance interactions  can be captured as ChIP–seq enrichments.Identifying genomic regions with which a TF is physically associated, referred to as TF binding sites (TFBSs), is an important step towards understanding its biological roles. The most common genome-wide assay for identifying TFBSs is ChIP–seqA long-term goal is comprehensive mapping of all CAPs in all cell types, but a more immediate aspiration is to create a catalogue of all CAPs expressed in a single cell type. The resulting consolidation of hundreds of genome-wide maps for a single cellular context promises insights into CAP networks that are otherwise not possible. Such comprehensive data will also provide the backdrop for understanding large-scale functional element assays, and should improve the ability to infer TFBSs in other cell types that are less amenable to direct measurements.18. Of all human CAPs, approximately 960 are expressed in HepG2 cells above a threshold RNA value of 1 FPKM (fragments per kilobase of transcript per million mapped reads), the lowest level at which we can routinely generate successful ChIP–seq and CETCh–seq results. This resource contains ChIP–seq and CETCh–seq maps for about 22% of these 960 CAPs, of which 171 are sequence-specific TFs and 37 are histone-binding or histone-modifying proteins, or other chromatin regulators or transcription cofactors  regions are driven by strong motifs for one or a few TFs and weaker, more degenerate motifs for many other CAPs.Here we present an analysis of 208 CAP occupancy maps in the hepatocellular carcinoma cell line HepG2 performed as part of the ENCODE project, composed of 92 traditional ChIP–seq experiments with factor-specific antibodies and 116 CRISPR epitope tagging ChIP–seq (CETCh–seq) experimentsAs an initial analysis, we investigated how the binding of each of the 208 CAPs is distributed in the genome relative to known transcriptional promoters. We calculated the fraction of each of the called peaks of each CAP that was within 3 kilobases (±3 kb) of transcription start sites (TSSs), analysing only the TSSs of genes expressed (≥1 TPM (transcripts per kilobase million)) in HepG2 cells Fig.  and, sepTo further summarize the occupancy landscape, we merged all the called peaks from every experiment into non-overlapping 2-kb windows, limited to those windows in which two or more CAPs had a called peak, and performed principal component analysis (PCA) on these DNA segments, using the presence or absence of each CAP at each genomic segment. This analysis captured global patterns of ChIP–seq peaks, with principal component 1 (PC1) explaining about 28% of the variance and correlating strongly with the number of unique CAPs associated with a given genomic region Fig. . PC2 sep19. This IDEAS HepG2 epigenomic segmentation inferred 36 genomic states based on eight histone modifications, RNA polymerase ChIP–seq, CTCF ChIP–seq, and DNA accessibility data sets ). Notably, IDEAS states for HepG2 cells were classified using mainly histone marks, augmented by only two chromatin-associated ChIP–seq maps included in our data set (CTCF and RNA polymerase). These segregate the anticipated major classes of correlations between epigenomic states in the IDEAS segmentation and CAP associations, such as enrichment of H3K4me3 at annotated promoters and H3K27ac at candidate active enhancers, as well as open chromatin status as assayed by DNA accessibility experiments, typical of TF-bound DNA. As expected, the resulting IDEAS states classified only a minority of the HepG2 genome as potential cis-regulatory elements , a machine-learning method for biochemical mark-based genomic segmentation20. Specifically, we found a subset of CAPs that are preferentially associated with promoters, another subset associated with candidate active enhancers, and a third group distributed across both proximal promoter regions and candidate active enhancers  and even as alternative promoters-TFs. Each approach required a subset of roughly any 30 CAPs to achieve approximately 80% accuracy.Because CAPs localize to specific genomic states, we were able to reproducibly train random forest models to predict the IDEAS state of a genomic region using binding information for only a small number of CAPs Fig. . The precis-regulatory elements classified as promoters, ‘strong’ enhancers, or ‘weak’ enhancers, with merging of similar features within 100 base pairs (bp), resulting in a broad size distribution from 200 bp to 12–16 kb. We then calculated how many CAPs were associated in each region  database8 to determine whether our de novo derived motifs matched previous findings from various in vivo and/or in vitro assays27. Overall, more than 80% of the 293 motifs had a similar motif in these databases ; the direct matching motifs of the assayed TFs were closer to the centres of called peaks and the discordant motifs for other TFs were more offset, providing evidence for co-occurrence at these locations . The resulting correlation heat map also showed clusters of related CAPs as well as both known and potentially novel interactions , and cohesin subunits its Fig. .36. GATAD2A has a very degenerate DNA-binding domain and is not predicted to bind DNA independently, and indeed the called GATAD2A motif matched FOXA3  as those sites with more than 70 CAPs  within a 2-kb region. Intersecting HOT regions with IDEAS segmentations revealed that more than 92% of HOT regions map to candidate promoter or strong enhancer-like states . We determined using GREAT (genomic regions enrichment of annotations tool) analysis that promoter-localized HOT regions are associated with housekeeping genes and that distal HOT regions are near genes associated with liver-specific pathways .We define HOT regions in these data . We tested the predictive ability of the SVM as the number of TFs increased and found that predictions remained constant, rather than declining, further strengthening the notion that these sites are not artefacts  when compared to an equal number of enhancer regions (as defined by IDEAS segmentation) with only 2–10 associated CAPs, or to a null set of random enhancer elements with any number (0–208) of associated CAPs. Sites with 2–10 CAPs had substantially smaller numbers of both high-affinity and low-affinity TF motifs, and the random enhancers were essentially devoid of strong motifs , and both were significantly higher than that of the null set of random enhancer elements , indicating that the information imparted by the DNA sequence of HOT sites exceeds that of other cis-regulatory elements . In brief, HepG2 cells were obtained from ATCC (HB-8065), confirmed by morphological observation, and tested for mycoplasma (ThermoFisher C7028). Pools of cells were grown separately to represent replicate experiments. Crosslinking of cells was performed with 1% formaldehyde for 10 min at room temperature and the chromatin was sheared using a Bioruptor Twin instrument (Diagenode). Antibody characterization standards are published on the ENCODE web portal and consist of a primary validation (western blot or immunoprecipitation–western blot) and a secondary validation (immunoprecipitation followed by mass spectrometry) for traditional antibody ChIP–seq. With CETCh–seq experiments, a molecular validation (PCR or Sanger sequencing confirmation of edited genes) in addition to one of the immunological validations  is required for release. Raw fastq data were downloaded from the publicly available ENCODE Data Coordination Center, and aligned to the human reference genome (hg19) using the BWA-0.7.12 (Burrows Wheeler Aligner) alignment algorithm54. Post-alignment filtering steps were carried out using samtools-1.355 with MAPQ threshold of 30, and duplicate removal was performed using picard-tools-1.88 (http://broadinstitute.github.io/picard/). After filtering, each CAP’s genome-wide binding sites (peak enrichment) were computed using phantompeakqualtools, implementing the SPP algorithm57, with replicate consistency and peak ranking determined by irreproducible discovery rate (IDR) using the IDR-2.0.2 tool56 to generate narrow peaks passing IDR cutoff 0.02 (soft-idr-threshold). ENCODE blacklisted regions  were filtered out. In addition, we note that plasmids used to generate edited cells with epitope-tagged CAPs have been deposited to Addgene, the non-profit plasmid repository, and are available for researchers to tag particular CAPs in other cell lines of interest. We also note that the GC content of DNA has been reported as a source of bias in ChIP–seq data, leading to over-representation of TFBSs and false positive peak calls, which could confound subsequent analyses59. To address this concern, we performed ChIP–seq experiments in unedited cell lines using the FLAG antibody (Sigma F1804) that we use in CETCh–seq, and used these libraries as background for peak calling. In these experiments, the only variable is the edited cell line used as foreground, and most biases should be accounted for.All protocols for ChIP–seq and CETCh–seq have been previously published and are available at the ENCODE web portal 64, and used to identify 57 genes that were highly and specifically expressed in liver as compared to all other cell and tissue types, and also found in HepG2 cells with at least 10 TPM. On average, these 57 liver-specific genes were 151.21 times more highly expressed than in any other cell type.RNA-seq quantification data for 56 cell lines and 37 tissues were retrieved from the Human Protein Atlas . All promoter-like and enhancer-like regions identified in at least one of five other cell lines were merged using pybedtools66 and these regions were filtered from the HepG2 segmentation. Significant enrichment of CAPs in the cis-regulatory regions was evaluated using Fisher’s exact test  against random or null sequences with matched length, GC content and repeat fraction using null sequence python script from Kmer-SVM67. Heat maps were generated using the heatmap.2 function from R gplots package (https://cran.r-project.org/web/packages/gplots/).IDEAS segmentation for six cell-types  were collected from the Penn State Genome Browser  by associating the genomic regions to genes from various ontologies such as GO molecular function, MSigDB and BioCyc pathway. The parameters used for GREAT analysis were Basal+extension  for all enhancer-associated sites, and Basal+extension  for all promoter-associated regions with whole-genome background. MSigDB pathway70 was noted for genomic region enrichment analysis.cis-regulatory sites, categorized into promoter or enhancer-associated, correlate with increased evolutionary constraints. A highly constrained elements bed file containing high-confidence regions (significant P) generated from per base GERP scores was retrieved from the Sidow laboratory at Stanford (http://mendel.stanford.edu/SidowLab/downloads/gerp/). The fraction of overlapping bases for each bin of the ‘CAP bound category’ (low to high) with highly constrained elements was computed using bedtools-2.26.066 and pandas-0.20.3, python2.7, further normalized by the fraction of ‘highly constrained elements’ overlapping per 100-bp region of CAP bound categories. In addition, the Kolmogorov–Smirnov test was performed to evaluate statistically significant differences in distribution between the highly bound (20+ CAP bound) and not highly bound regions (1–19 CAP bound sites) for both promoter- and enhancer-associated sites.Genomic evolutionary rate profiling (GERP) was performed to assess whether highly bound 66. All other computations, and the pairwise peak overlap percentage for each CAP to build the pairwise matrix, were performed using pandas-0.20.3, python2.7 (Python Software Foundation) to construct network plots, using R igraph, implementing the Fruchterman Reingold algorithm. The interconnection between CAP shared binding sites for 208 CAPs was built with a minimum threshold of 75% or more overlap between any two CAPs. The sizes of vertices and nodes in the graph are representative of the number of connections each CAP has with its connected partner, while edges represent the degree of overlap between CAPs.Pairwise overlap of binding sites between each of the 208 CAPs was performed with 50 bp up- and downstream from the summit of peaks using python-based pybedtools71. A minimum of 1 bp overlap was required and resultant peaks greater than 2 kb (~1%) were filtered from downstream analysis. Hierarchical clustering, using the Euclidean distance metric and Ward clustering method, of CAPs based on degree of co-binding was performed in R with the ‘heatmap.2’ function of the gplots package.Co-binding was characterized by merging IDR-passing narrow peak files from 208 CAPs with the ‘merge’ function from the bedtools software package72 were trained on a random sample of up to 5,000 narrow peaks  as a positive set against 10× random/null sequence with matched size, GC-content and repeat fraction as a negative set. At motif level, LS-GKM support vector machines (SVMs)72 were trained on a sample of 5,000 random motif sites found by FIMO (MEME-suite), extending ±15 bp, for all TFs (n = 171), as a positive set against the 10× random-null sequence with GC content and repeat fraction matched sequence as a negative set.At peak level, LS-GKM support vector machines (SVMs)x) was set to ten and the random seed (−r) was set to 1. SVMs were trained using the ‘gkmtrain’ function with a k-mer length (−l) of 11, kernel function (−t) of 4, regularization parameter (−c) of 1, number of informative columns (−k) of 7, and maximum number of mismatches (−d) of 3. Precision-recall areas under the curve (PR-AUC) were calculated by obtaining the tenfold cross-validation results from ‘gkmtrain’ (after setting the –x flag to 10), and inputting the results into the ‘pr.curve’ function of the PRROC R package, resulting in mean PR-AUC of 0.66 at the peak level, and 0.74 at the motif level. Classifier values for all bound sequences were obtained using the ‘gkmpredict’ function, and HOT sites  were scored with each CAP to assess their putative binding affinity at HOT regions, and percentile ranked to obtain the top 5% and bottom 75% k-mer compared to enhancers with 2–10 associated TFs  and to random enhancers with any number of associated factors (0+) .Null genomic sequences matched to observed binding events were obtained using the ‘nullseq_generate.py’ function available with the LS-GKM package. The fold number of sequences  using the R package ranger76, a faster implementation of random forest in R, and also tested using Sklearn 0.19.0. The median OOB (out-of-bag) error estimate was computed for 100 instances of randomly sampled  loci iterations, to compute the element classification and misclassification accuracy using confusion matrix.PCA was performed on a CAP binding matrix composed of the presence or absence of motif in merged peaks as a binary matrix of loci, and implementing the python-based ML library scikit-learn Sklearn (0.19.0)P < 0.05. SEQUEST results were reported with ProteinProphet protXML Viewer (TPP v4.4 JETSTREAM) and filtered for a minimum probability of 0.9. For ENCODE antibody characterization standards, all protein hits that met these criteria were reported, including common contaminants. Fold enrichment for each protein reported was determined using a custom script based on the FC-B score calculation77. Following ENCODE antibody characterization guidelines, the CAP must be in the top 20 enriched proteins identified by immunoprecipitation–MS, and the top CAP overall for release. For GATAD2A co-associated TFs, the peptides with minimum 0.9 probability were present in smaller quantities than those of GATAD2A.Whole-cell lysates of FLAG-tagged or unedited HepG2 cells (~20 million) were immunoprecipitated using a primary antibody raised against FLAG or the CAP, respectively. The immunoprecipitation fraction was loaded on a 12% TGX gel and separated with the Mini-PROTEAN Tetra Cell System (Bio-Rad). The whole lane was excised and sent to the University of Alabama at Birmingham Cancer Center Mass Spectrometry/Proteomics Shared Facility. The sample was analysed on a LTQ XL Linear Ion Trap Mass Spectrometer by liquid chromatography electrospray ionization with tandem mass spectrometry (LC–ESI–MS/MS). Peptides were identified using SEQUEST tandem mass spectral analysis with probability based matching at 52. ENCODE HepG2 DNase-seq raw FASTQs (paired-end 36 bp) of roughly equivalent size  were downloaded from the ENCODE portal and processed using ENCODE DNase-seq standard pipeline (available at https://github.com/kundajelab/atac_dnase_pipelines) with flags: -species hg19 -nth 32 -memory 250G -dnase_seq -auto_detect_adapter -nreads 15000000 -ENCODE3. Processed BAM files were merged and used as input for PIQ TF footprinting using each TF’s top motif position weight matrix (PWM). Next, identified TF footprints from every TF that met a specified PIQ purity  were intersected with all identified ChIP–seq binding sites using BEDtools to correlate the number of unique TF footprints with the number of ChIP–seq factors identified at a given ChIP–seq binding site.To identify TF footprints for comparison to ChIP–seq binding sites, we used PIQ33 using the previous chromatin analysis partitioning strategy32 with modifications as described below. We calculated the RPKM of each data set’s first replicate over each of the 951,022 genomic segments to build a training matrix. We used each data set’s second replicate to build a separate scoring matrix. The training matrix was used to train five trial self-organizing maps with a toroid topology with size 40 × 60 units using 10 million time steps (~10 epochs) and selected the best, based on fitting error using the scoring matrix, for further analysis, and segments were assigned to their closest units based on the scoring matrix.The SOM was trained with the SOMatic packagek-means clustering of the unit profiles to determine centroids for groups of units. Metaclusters were built around these centroids so that all of the units in a cluster remained connected. SOMatic’s metaclustering function attempts all metacluster numbers within a range given and scores them on the basis of Akaike information criterion (AIC)78. The penalty term for this score is calculated using a parameter called the dimensionality, which is the number of independent dimensions in the data, which in this case are the individual cell subtypes. To estimate this number, we used a 60% cut on a hierarchical clustering done on the SOM unit vectors. For this work, the dimensionality was calculated to be 6. For metaclustering, all k between 50 and 250, with 64 trials, were tested and metacluster number 196 had the lowest AIC score and was chosen for further analysis.To properly fit the data, SOM units with similar profiles across experiments were grouped into metaclusters using SOMatic. In brief, metaclustering was performed using n, n segments of other clusters were chosen randomly, and this set of positive and negative examples was split, using 80% of the examples for training and 20% for scoring. The training data were fed through an R script using the rpart and rattle packages to create, score, prune, and re-score a tree for each metacluster. This entire process was repeated for 100 trials with only the tree with the highest accuracy drawn.To generate decision trees for these metaclusters, each of the segments in the training matrix was labelled with its final metacluster. For each metacluster, if the metacluster is of size Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-020-2023-4.Supplementary InformationThis file contains Supplementary Notes , Supplementary Fig. 1, and additional references.Reporting SummarySupplementary TablesThis file contains Supplementary Tables 1-6.


Chromatin remodeling complexes (CRCs) use ATP hydrolysis to maintain correct expression profiles, chromatin stability, and inherited epigenetic states. More than 20 CRCs have been described to date, which encompass four large families defined by their ATPase subunits. These complexes and their subunits are conserved from yeast to humans through evolution. Their activities depend on their catalytic subunits which through ATP hydrolysis provide the energy necessary to fulfill cellular functions such as gene transcription, DNA repair, and transposon silencing. These activities take place at the first levels of chromatin compaction, and CRCs have been recognized as essential elements of chromatin dynamics. Recent studies have demonstrated an important role for these complexes in the maintenance of higher order chromatin structure. In this review, we present an overview of the organization of the genome within the cell nucleus, the different levels of chromatin compaction, and importance of the architectural proteins, and discuss the role of CRCs and how their functions contribute to the dynamics of the 3D genome organization. Eukaryotic DNA is compartmentalized into hierarchically organized levels within the nuclear space. To achieve this, the genetic material interacts with diverse proteins in a non-random 3D array that helps to form a complex called chromatin. This DNA–protein complex functions to maintain the architecture of the genome, stabilize it, and regulate the accessibility of the transcriptional machinery to certain regions, while maintaining other regions silenced .To enable the accessibility of proteins to their target sequences, the chromatin must be remodeled into a less compacted structure, whereas a more compacted structure is associated with transcriptional repression. Furthermore, the chromatin structure is highly dynamic, and its remodeling contributes to many functions in the cell .To understand how the domains derived from the hierarchical organization of chromatin are formed, and how this organization is highly dynamic, it is necessary to visualize how DNA interacts with diverse proteins. At the first level of compaction, in an interphase chromosome, there exists a 6.5 nm diameter cylinder-like structure called nucleosome, which is formed by histone octamers with 146 base pairs (bp) of DNA wrapped around this core in 1.6 turns .This tetramer is formed by two heterodimers of the histones H3 and H4, which are flanked by two heterodimers of H2A and H2B histones in a structure known as the “histone core.” From this core, eight N-terminal and two C-terminal ends project out at defined locations. These are susceptible to a large number of post-translational modifications, some of which are recognized by protein complexes involved in the remodeling and maintenance of chromatin .Super-resolution nanoscopy  revealed that the nucleosomes can organize into discrete groups called “nucleosome clutches” that lack an organized structure. The number of nucleosomes per clutch is variable; they are interspersed with nucleosome-depleted regions, and the nucleosome density is cell-type specific .The next level of compaction consists of the so called “chromatin loops.” These structures have an average size in the kilobase (kb) scale . They arIn vertebrates, these chromatin loops are formed and stabilized through interactions with the architectural protein called CCCTC-binding factor (CTCF) and the cohesin complex. Analysis of Hi-C data has revealed that CTCF-binding motifs occur in a convergent orientation (forward-reverse), which serve as docking sites for CTCF to bind to DNA in a way that facilitates its positioning in a restricted 3D space .Interestingly, CTCF positioning along the genome is independent of the presence of cohesin, but cohesin localization is dependent of CTCF. This shows that CTCF recruits and leads cohesin to the target loci . This obin silico analyses  have shown a higher level of compaction known as “chromatin compartments.” These mega-structures are classified as compartment A for open chromatin state or compartment B for closed chromatin state , dependiType A compartments are characterized by a high content of transcriptionally active genes and correlate with active histone marks including H3K9ac and H3K27ac, high GC content, as well as hypersensitivity to DNAse I. Thus, A compartments have permissive transcriptional environment, although it should be noted that genes that are silenced may also exist to a lesser extent within these regions .On the other hand, type B compartments are characterized by the opposite features, including a high content of silenced genes and correlate with repressive histone marks such as H3K9me2, H3K9me3, and H3K27me3, poor or null DNAse I hypersensitivity, and late replication timing. Further, as in the case of type A compartments, the type B compartments may also contain exceptions in terms of genes that are transcriptionally active .The localization of the chromatin compartments is non-random in the nucleus, and this preferential distribution is highly correlated with its intrinsic characteristics. Hi-C data have shown that A compartments are located preferentially in the central region of the nucleus as well as in adjacent regions close to the nuclear pore complexes (NPCs) .B compartments are preferentially located at the periphery of the nucleus, interacting with elements of the nuclear lamina, which constitutes a predominantly repressive environment. These results are supported by electron microscopic studies that have shown heterochromatin to be preferentially located and clustered near the nuclear lamina in most cell types .It is important to mention that this array of compartments generally occurs in almost all cell types, but there are some exceptions where B compartments may be found located inside the nucleus and A compartments located adjacent to or interacting with the nuclear lamina . It is, The regions where B compartments interact with the components of the nuclear lamina are known as “lamina associated domains” (LADs). It has been reported that in mammals approximately 10% of the total genes are located in these domains, whereas up to one third of the whole genome are represented in these domains .in situ hybridization (FISH) techniques where each chromosome can be labeled with a different fluorescent probe for individual detection of each chromosome  . The firromosome .Further refined methods such as 3D FISH in combination with light optical serial sectioning of nuclei by laser confocal microscopy and 3D image reconstruction, allowed for the determination of the spatial arrangement of CTs and their substructures . BecauseChromosomal territories refer to the position of each chromosome in the nucleus. Experimental data have revealed that, globally, the sequences contained in each chromosome tend to interact with sequences located in the same chromosome, and at the same time tend to be excluded from sequences in other chromosomes. Thus, in this way, chromosomes are restricted to specific loci instead of being scattered across the nucleus .Currently, it is well known that chromosomes possess variable gene content among them, and previous studies have shown that chromosomes with higher gene density tend to be located at the interior of the nuclei, whereas chromosomes with a poor gene content are preferably located in the nuclear periphery .Interestingly, CTs have been shown to be susceptible to relocalization across the nucleus depending on the differentiation state of some cell types. During cellular differentiation of murine cerebellar Purkinje neurons, CTs change their positions at the end of the fifth day post-partum . WhereasInterchromosomal contacts between CTs are approximately three orders of magnitude weaker than intrachromosomal contacts . IntrachAs detailed earlier, the chromatin architecture is intrinsically linked with cellular and developmental patterns. This begs the question whether the nuclear architecture is different between the eukaryotes given the variations in genome sizes , and difIn principle, at a very basic level of the chromatin (at the histone level), there seem to be no major differences in their composition, and most of the canonical and variant histones are highly conserved (discussed below). However, the first clear difference in the genome architecture between eukaryotes is found at the level of the chromatin loops. As discussed earlier, in vertebrates, these structures are formed by interaction and stabilization between CTCF and the cohesin complex .Caenorhabditis elegans)  . Over 93elegans) . The 11  protein . The N-  protein .Drosophila, dCTCF is not essential for the establishment of chromatin loops , which is located at the boundaries of these domains. Besides, it was observed that these domains diminished or lost strength in DCC mutants, providing insights into how DCCs reshape the topology of the X chromosome and their implications in gene expression in C. elegans . These data arise the question if in organisms like Up to this point, far from being a static entity, the chromatin structure is highly dynamic, varying between euchromatin or heterochromatin states to allow for transcription of specific regions of the genome. However, it is important to mention that all the known ways in which chromatin can be remodeled occur at the nucleosome level, and only three mechanisms have been described.First, histone post-translational modifications (HPTMs) generally occur at the N-terminal ends of histones. They are the result of the activity of specialized groups of enzymes, such as histone acetyltransferases, which are involved in the acetylation of certain lysine residues , and hisAmong various HPTMs, acetylation on K9 and K14 of histone H3, as well as H4K5S is correlated with a transcriptionally active state, whereas deacetylation of these residues is involved in silencing of transcription . SimilarMultiple modifications can also occur on the same residue. Di and trimethylation have an important role in some physiological processes; H3K4me2 marks genes that are both transcriptionally active and silenced, whereas H3K4me3 is only found in genes that are transcriptionally active .Another type of HPTM includes ubiquitylation. H2AK119ub is reported to have a role in transcriptional repression because of its role in the repression of a subset of chemokine genes , PolycomCrotonylation is another HPTM that has been reported across species from yeast to humans . H3K9cr Further, diverse types of HPTMs have also been described in the histone globular domains including methylation, acetylation, or ubiquitylation of K residues; methylation of R residues; or phosphorylation of S residues that contribute to remodeling of chromatin and have a role in the regulation of gene expression . The funAll the specific modifications on histones have relevant biological implications at different organizational levels. They can direct different activities in different regions and regulatory elements. Through regulatory elements such as enhancers, they can influence groups of genes at a domain level. They may also have an effect at the chromosomal level, as in the case of the silencing of the X chromosome .Second, remodeling may be mediated by histone variants. These are histones that differ from the canonical histones in several aspects. For example, canonical histones are deposited during the S phase of the cycle, whereas deposition of the variants can occur at different stages. Additionally, the variants often have different amino acid residues, extra domains, or lack some domains compared to the canonical histones. The nucleosomes that contain these variants also have different properties. They tend to be either more labile or more stable. Further, the presence of some variants in the nucleosome indicate regions where DNA damage has occurred. Thus, the presence of these variants in the nucleosome can trigger a specialized function .For example, the histone variant H3.3 is deposited at telomeric regions by a complex composed by the chaperone DAXX and the ATPase subunit ATRX which is a CRC member of the SWI/SNF family. H3.3 histone deposition specifically at telomeres by DAXX/ATRX complex of pluripotent and non-pluripotent cells has been proposed as a mechanism to facilitate the access to chromatin . FurtherFinally, the ATPase activity of CRCs can evict, slide, remove or deposit nucleosomes or histones, and are involved in the regulation of transcriptional activation by modifying the chromatin architecture at different regions. CRCs allow for regulation of transcription through the activation or repression of genes that control alternating euchromatin and heterochromatin states, which in turn, allows for regulation in gene expression . It is iChromatin remodeling complexes (CRCs) can be described as specialized multiprotein machineries that allow access to DNA by temporarily modifying the structure or composition of nucleosomes . These cBecause of the large number of genetic interactions and the difficulty in characterizing them biochemically, CRCs have been classified based on the degree of conservation of the helicase/ATPase subunit and by the unique flanking domains that confer different functions . To dateS. cerevisiae. It is made up of 15 principal subunits, which are as follows; INO80 (ATPase domain), Rvb1, Rvb2, Arp4 (actin related protein-4), Arp5, Arp8, actin, Nhp10 (non-histone protein 10), Anc1/Taf14, Ies1 (Ino eighty subunit-1), Ies2, Ies3, Ies4, Ies5, Ies6 (The INO80 (Inositol requiring 80) complex is a member of the CRC family and was initially purified and characterized in s5, Ies6 , 2003.Drosophila, where it has 19 subunits  (INO80 CRCs members participate in nucleosome editing  which is (SRCAP) .INO80 translocates along the DNA and promotes the exchange of H2A.Z-H2B dimer more efficiently than H2A-H2B containing dimers . Cryo-EMDrosophila embryo extracts through in vitro assays (ISWI (imitation switch) members were initially identified for their nucleosome remodeling activities in o assays . The memo assays . Togetheo assays .Drosophila which were named dNURF , bromodomains or additional DNA binding motifs . ISWI ised dNURF , dCHRAC ed dNURF  and dACFed dNURF . In mammed dNURF , each coed dNURF .Saccharomyces cerevisiae also triggers DNA distortion and translocation after ISWI activation, showing an unperturbed histone core structure with the exception of the H4 tails, this mechanism is identical to the human SNF2H mechanism, suggesting a common DNA translocation mechanism  family, was originally identified in o humans . CDH memo humans  alternato humans  at theiro humans   and a post-HSA near the catalytic domain and a bromodomain (which can bind acetylated residues of histones) at the C-terminal end  proteins, which were originally described in inal end   BAosophila , and  of the eight catalytic subunits were compared to other epigenetic marks such as DNA methylation, histone modifications, nucleosome positioning, and chromatin contacts revealed by Hi-C experiments in the prostate cancer cell line LNCaP. A classification was proposed in which the chromatin remodelers are clustered into two functional groups .Group 1 contains chromatin remodelers that are mainly (but not exclusively) associated with actively marked chromatin. This group contains SMARCA4, SNF2H, CHD3, and CHD4. Group 2 containing BRM, INO80, SNF2L, and CHD1 is mainly associated to repressed chromatin. Interestingly, both group 1 and 2 chromatin remodelers occupy sites within the TAD boundaries, intra-TADs, and around CTCF-binding sites. However, only group 1 remodelers are significantly enriched at active enhancer, promoter loop anchors, and even at long range chromatin loops. On the contrary, regions associated with LADs do not seem to require these chromatin remodeling activities as neither of the groups presented an enrichment at these sites. However, the latter does not exclude the possibility that other ATPases that were excluded from this study may be associated with or enriched at the LADs .Consistent with this classification, through remodeler-nucleosome interaction assay  at the transcription start sites (TSS) . InteresSeveral factors are involved in regulating the access to DNA: DNA base composition, HPTMs, presence of histone variants in the nucleosomes, histone chaperones, chromatin remodelers, and transcription factors. Transcription factors are implicated in recruiting chromatin remodelers and HPTMs to modulate their activities. RSC (remodeler of structure of chromatin) is a member of the SWI/SNF family. RSC participates in promoter clearance of a nucleosome-depleted region (NDR) by shifting +1 nucleosomes in the direction of the open reading frame (ORF), making the promoter more accessible  Lorch a.Other chromatin remodelers are also important for this function including ISWI and INO80, and some CHD’s such as CHD1 . In humaRecently, the structure of RSC bound to a nucleosome has been resolved using CRYO-EM, in these studies it was found that RSC contacts not only the “partially wrapped” nucleosomes at the NDR, but also establishes contacts with the DNA promoter elements. RSC is organized into five main lobes, each with different functions. Through the main lobes it contacts the acetylated core histones, while the lobe that includes the ATPase contacts the promoter sequence, where the translocase activity of the complex takes place . The abiDrosophila is involved in acetylating canonical H2A-H2B dimers, it promotes the exchange of these dimers at the body of certain stress response genes and, aids in RNA Pol II promoter release and elongation  was used as the reporter construct. CTCF is important in directing CHD8 to the insulator site, and knockdown of CHD8 affected the insulator activity, but not CTCF binding. Moreover, CpG methylation of the adjacent loci was affected in the CHD8 knockdowns, and various sites were hypermethylated. Further, a reduction in the acetylation state of the nucleosomes around the insulator, but not at the CTCF-binding site was observed. The data revealed that CHD8-CTCF complex functions in altering the methylation state and histone modification in the vicinity of the insulator site .Recent studies have also highlighted the importance of chromatin organization in the vicinity of the CTCF-binding sites. Specifically, TAD boundaries often contain several CTCF motifs, which in turn arrange the TAD boundary structure in a specific 3D nucleosome organization . SNF2H aThese experiments suggested that SNF2H affects cohesin loading at a subset of CTCF sites by affecting nucleosome spacing. It was also established that SNF2L functions to maintain nucleosome organization of nucleosomes surrounding the CTCF sites, and it does so as part of the nucleosome remodeling factor (NURF) complex. Another interesting observation was that depletion of SNF2H affected the distance between the nucleosomes by causing an average increase of 25 bp. The opposite effect was observed for SNF2L depletion, in which case the distance between nucleosomes was reduced by 10 bp around transcription factor binding sites  Wiechen. DepletiAnother ATPase found to affect nucleosome positioning around the CTCF-binding site is SMARCA4 . It is one of the ATPases of the SWI/SNF complex. SMARCA4 has been shown to regulate interchromosomal interactions between tissue-specific promoters during myogenesis  and bindHi-C experiments following the perturbation of the level of SMARCA4 using shRNAs showed an increase in both intra- and interchromosomal associations in the subtelomeric regions, placing SMARCA4 as a regulator of higher order structures at these regions of the genome. Additionally, SMARCA4 perturbation led to changes in nucleosome positioning around the CTCF-binding sites, leading to an increase in nuclease accessibility around the CTCF-binding sites, thus affecting the TAD border strength, and allowing further intra-TADs interactions. These results placed SMARCA4 as a regulator of higher order chromatin structure  Barutcu.Later, another group identified SMARCA4 as a partner of CTCF . SMARCA4Given the association of SMARCA4 with p68, and the roles in the maintenance of a subset of TAD boundaries, the role of SMARCA4 at sites shared by DDX5 and CTCF was examined. These sites were shown to include a subset of genome wide CTCF sites located around the TSS and associated with marks of transcriptionally active chromatin . The datBiochemical studies have identified members of the cohesin complex that associate with CRC. Isolation of human ISWI (SNF2H)-containing CRC revealed that RAD21 interacts directly with this ATPase, as well as with members of the NuRD complex. Furthermore, they were found to bind together to specific Alu-rich regions in the genome and the absence of SNF2h impaired the binding of cohesion to these sites. As Alu sequences are rich in CpG dinucleotides, DNA methylation state was also found to modulate the association of cohesin to these sites . This stDrosophila, remodelers are also associated with architectural proteins. RNAi screening was used to identify regulators of the enhancer blocking activity of the Fab-8 insulator, whose activity depends on both CP190 and dCTCF. This screening led to the identification of approximately 80 genes. Among them, there were several ATPases, particularly ISWI and CAF-1 (chromatin assembly factor-1) members of the NURF and dREAM  remodeling complexes, and other subunits of these complexes. It was established that lack of ISWI leads to a change in nucleosome phasing at dCTCF-binding site, making these sites less accessible to MNase digestion. Furthermore, it was demonstrated that CP190 directs the binding of these ATPases to specific insulator sites promoting an open chromatin structure . DREF is a transcription factor that binds to the DNA motif 5′-TATCGATA-3′ in the core promoter element  are still able to form ES colonies and show normal morphology. Two thousand differentially expressed genes were identified following Snf2h loss, which led to reduced proliferation and differentiation potential. Analysis of nucleosomal phasing using ATAC-seq (Active Transposase Accessibility Assay) and MNase-seq  revealed that the regulatory regions, but not TSS, were affected.Importantly, nucleosome repeat length  revealed an increment of 9 bp in the absence of Snf2h, which was specific to the depletion of this gene since SMARCA4 depletion had no effect on nucleosome repeat length . In addiIn summary, these results show that SNF2H impacts chromatin at the nucleosome level by changing nucleosome phasing, promotes changes in DNA methylation, and affects transcription factor binding. Additionally, this loss also affected the loop formation and TAD insulation. However, unlike in the case of ARID, mentioned above, it does not affect chromatin compartmentalization.Other activities that are important are those of the topoisomerases. These enzymes are recruited by ATPases to these sites and contribute to the maintenance of the strength of the borders . For insDNA methylation is recognized as a heritable epigenetic modification. Proteins from the methyl-binding domain (MBD) group, recognize these modifications and recruit several enzymatic activities to the specific DNA region, such as histone modifications and chromatin remodeling activities . All theIgf2/H19 imprinted genes, CTCF binds to the DMR  upstream of the H19 gene on the maternal allele. Several enhancers are located downstream of the H19 gene, while the Igf2 gene is located further upstream of the DMR. CTCF binding at DMR acts as an insulator with enhancer blocking activity, preventing Igf2 activation from the maternal downstream enhancers, which in turn, can only activate the H19 gene. On the paternal allele, the DMR is methylated and CTCF does not bind to this region. Therefore, the enhancers can activate the Igf2 gene  and an increase of histones H3 and H4 acetylation levels in the DMR. ATRX deficiency promotes a decrease of cohesin and CTCF occupancy at the DMR region, indicating that ATRX is necessary for cohesin and CTCF occupancy at these sites (Igf2 expression. These chromatin loops are lost upon ATRX depletion and lead to an aberrant transcription of the H19 gene (In mouse forebrain cells MeCP2 directs ATRX (which is a member of SWI/SNF CRC) to the DMR that acts as an imprinting control region (ICR). MeCP2 binds specifically to the maternal allele along with cohesin and ATRX, and this complex favors CTCF occupancy at the DMR avoiding se sites  , the global outcome, such as loss of architectural protein binding  and insulation is the same. These data indicate that different species may use different strategies to achieve a correct control of chromatin organization.It is becoming clearer that CRCs’ subunits affect chromatin structures at different levels, whether it is affecting nucleosome phasing, impeding the union of transcriptional and architectural factors to DNA, modulating chromatin loops and some even modulating TAD insulation and higher order chromatin compartmentalization. Moreover, evolution has conserved many of these activities, even though loss of the orthologs display different phenotypes (such as the case of ISWI in CRC’s carry different enzymatic activities and although the current reviewed research has focused mainly on the ATPase activity, we can expect that other activities may also be involved in the control of higher order chromatin structures. Also, other physical properties which may also be promoted by CRCs such as liquid-liquid phase separation, will be worth studying. The relation of CRCs and higher order chromatin dynamics will shed light on different biological processes and enrich our understanding of these important complexes in development and disease.Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.