The control of gene expression is an important tool for metabolic engineering, the design of synthetic gene networks, and protein manufacturing. The most successful approaches to date are based on modulating mRNA synthesis via an inducible coupling to transcriptional effectors. Here we present a biological programming structure that leverages a system of engineered transcription factors and complementary genetic architectures. We use a modular design strategy to create 27 non-natural and non-synonymous transcription factors using the lactose repressor topology as a guide. To direct systems of engineered transcription factors we employ parallel and series genetic (DNA) architectures and confer fundamental and combinatorial logical control over gene expression. Here we achieve AND, OR, NOT, and NOR logical controls in addition to two non-canonical half-AND operations. The basic logical operations and corresponding parallel and series genetic architectures represent the building blocks for subsequent combinatorial programs, which display both digital and analog performance. Successful approaches for controlling gene expression modulate mRNA synthesis by coupling it to inducible transcription effectors. Here the authors design 27 non-natural and non-synonymous transcription factors. Gene regulation in biological systems can be viewed as a molecular computer. Namely, gene expression can be modeled as on–off states of Boolean  logic, which can integrate multiple digital inputs into a desired output4. Currently, living cells can be programmed with genetic parts such as promoters, transcription factors, and metabolic genes9 to encode logical operations that integrate environmental and cellular signals12. Synthetic genetic logic gates have been engineered, including those capable of accomplishing AND, OR, and NOT functions15, which have been employed for pharmaceutical and biotechnological applications17. Moreover, combinations of such gates can be used to construct biological analogs of more advanced electronic circuits including switches21, logic23, memory25, pulse generators26, and oscillators30. Although logic in synthetic gene networks can be accomplished either at the transcriptional or translational levels, the former is more commonly employed in the development of synthetic gene networks via the use of transcription factors (TFs) to activate or repress genes of interest. Broadly speaking, TFs are DNA-binding proteins capable of blocking (or recruiting) RNA polymerase activity at the site of genetic promoters, and these functions can be combined in modular ways to engineer synthetic gene networks31. For the most part, early bacterial gene circuits were based on a core set of repressors, namely, TetR, LacI, and bacteriophage λ cI33, which have been extensively studied.Biological computation, at its core, is the ability to engineer and develop systems capable of converting information (inputs) into a programmable gene expression (output(s))+YQR . Under normal cellular conditions, LacI will bind to the O1 operator site and prevent transcription of downstream genes by physically blocking and compromising the activity of RNA polymerase34. In the presence of the chemical signal isopropyl-β-d-thiogalactoside or IPTG  LacI undergoes a conformational shift that results in decreased affinity for its cognate DNA operator. This event allows transcription of the downstream gene to proceed35. Although the structure of the 360 amino acid LacI protein can be divided into several (sub)domains, for simplicity we will group them into the DNA-binding domain (DBD) and the regulatory core domain (RCD).The first 60 residues constitute the N-terminal DBD containing the helix-turn-helix motif, which aids in the recognition of the O1 operator region. Residues 61–330 constitute the RCD, which encompasses the regions responsible for dimer assembly (C-subdomain), ligand binding, and for mediating and propagating the allosteric signal across the protein (N-subdomain). The functional unit of LacI is a dimer, however residues 331–360 make up the C-terminal tetramerization domain which facilitates the dimerization of two functional units39.The lactose repressor (LacI) is a workhorse in the field of synthetic biology. LacI or IYQR Fig.  is a canE. coli and other organisms40. Most members of this family are structurally similar and several respond to their respective inducers in much the same way LacI responds to IPTG. In recent years, a library of chimeric repressors has been engineered by replacing the RCD of LacI with regulatory core domains from homologous LacI/GalR family members. This has generated LacI-like transcription factors capable of regulating the lac promoter via the O1 operator, while still responding to the homolog’s corresponding natural inducer43. Moreover, the DBD of LacI has previously been modified to achieve alternate DNA recognition (ADR) via the introduction of the point mutations Y17T, Q18A, R22N  to create functionally orthogonal repressors which can be used to generate a variety of biological AND gates. For example, Shis et al. used several LacI/GalR chimeras to construct parallel AND gates, which used multiple TFs simultaneously with minimal cross-talk45.LacI is part of the larger LacI/GalR family of transcriptional repressors that regulate sugar metabolism in 49. These efforts are critically important because the development and discovery of non-natural transcription factors will facilitate an increase in the size and sophistication of synthetic gene networks, and expand the capabilities of achieving biological analogs of basic Boolean logical operations50. In this work we demonstrate a modular design approach capable of engineering non-natural transcription factors with orthogonal ligand response and non-natural alternate DNA recognition or X+ADR  control directly at the transcription factor level eliminates the need for ligand inducible promoters or the need to use sensors independent of the logical circuitry52. Finally, pairing these X+ADR repressors with our previously engineered collection of LacI suppressors (IA(X)YQR and IA(X)ADR54) allows for the development of combinatorial and non-canonical logical operations. Collectively, the engineered TFs and corresponding genetic architectures lay the foundation for the development of a nascent biological  programming language.Efforts in recent years have been made to expand the set of transcription factors that can be used toward the design of genetic circuitsADR Fig. . These t53. Here we extended this workflow to simultaneously vary the RCD and the ADR , while the YQR DNA-binding domain is complemented by the native O1 (or symmetric Osym) DNA operator. Mechanistically, when a given DNA operator is in proximity to a promoter element, transcription factor binding (to operator DNA) interferes with RNA polymerase function, inhibiting transcription  when paired with a cognate DNA operator element  regulatory core domain. Out of the ~200 functional alternate DNA-binding domains and cognate non-natural operators, we selected the 6 ADR-operator sets for this study based on the performance metrics observed in our previous study, Rondon and Wilson53. In the aforementioned study, we adapted a collection of engineered LacI suppressors  and suppressor (IA) phenotypes. The wild-type YQR/O1 set was selected for this study to enable the transcription factors created in this work to function in cooperation with the foundational antilacs (suppressors) engineered in Richards et al.54. Likewise, the 35 putative X+ADR variants tested in this study were selected to function as complementary systems to the suppressors adapted with ADR from our previous study53.In a previous study, we established a protein engineering strategy to confer alternate DNA-binding functions in the LacI scaffoldADR Fig. . In brieADR Fig. . CollectADR Fig. . In prinion Fig. . We postent Fig. . Alternaors Fig.  with 6 n+  in phenotype  has successfully been used throughout synthetic biology. Accordingly, we used I+YQR paired with O1 operator DNA (I+YQR|O1) as a reference system to score the relative performance of the engineered transcription factors observed in this study , relative to the reference system. Accordingly, the E+YQR|O1 system was designated as a more analog process. In contrast, the E+KSL|Oagg system can be classified as a more digital process, given the higher relative fold induction and stronger repression strength  developed in this study, and complementary antilac suppressors (IAADR) from our previously reported systems53  genetic architecture and two decoupled repressors  and the non-natural transcription factor CelR adapted with the alternate DNA-binding domain TAN (E+TAN). In the absence of both effector ligands (IPTG and cellobiose) GFP was not produced, as both I+YQR and E+TAN repressed gene expression . The addition of IPTG or cellobiose alone cannot induce the full production of GFP, as at least one transcription factor (I+YQR or E+TAN) remained in the repressed state. Only the addition of both effector ligands resulted in the full production of the reporter GFP, and led to the near digital performance of the engineered AND gate. In turn, we constructed a fully synthetic AND gate using two non-natural repressors I+TAN and E+YQR  vs. I+YQR|Osym  operations upon single ligand induction was not equivalent. The observed uneven off-states can be explained in part by the differences in performance of the engineered transcription factors  relative to the proximal position. Therefore, given equivalent transcription factor performance, the induction of a core genetic configuration resulted in lower gene production relative to the proximal configuration. Accordingly, a more digital AND gate was achieved via the incorporation of a more digital repressor-operator pair (R+YQR|Osym) at the proximal position  and hAND . In order to convert an infinite number of states into only two outcomes, voltage logic levels are created by defining which voltage bands or ranges represent a logic high (1) or logic low (0)udy Fig. . To a leudy Fig.  asymmetrion Fig. . Inspireal) Fig. .59). Rather than viewing the lack of isolated operations within our systems as an impediment, we can leverage this inherent connectivity to generate unique combinations of logical operations  with programming rules that are distinctive to biological logic. Moreover, these deviations in biological circuit functions  allow for the development of a vastly more dynamic programming language . To demonstrate the potential of our emerging non-natural transcriptional programming structure, we constructed two combinatorial logic gates via the SERI genetic architecture. The first combinational program used two-signal coupled repressors, G+TAN and I+YQR, and two cognate DNA operators Otta (core) and Osym  in series  and GalR(G+) share a conditional signal overlap45. Studies have shown that 10 mM IPTG competitively inhibited the GalR repressor, and resulted an insensitivity to the cognate effector ligand d-fucose. Accordingly, we hypothesized that the engineered G+TAN repressor would exhibit similar IPTG interference, and that this behavior could be used as a conditional repressor when coupled with I+YQR in series. To test this hypothesis, we first demonstrated that 10 mM IPTG competitively inhibited the G+TAN repressor, and resulted in insensitivity to the cognate effector d-fucose in isolation . However, upon the addition of 10 mM IPTG and 10 mM d-fucose, the circuit reverts to the off-state . Practically, this combinatorial logic gate functions as an apparent bandpass filter that only allowed the production of the output interface (GFP) within a certain effector ligand concentration, and attenuated (rejected) GFP production at or above 10 mM IPTG  can be systematically combined to produce more sophisticated logical gates. In principle, the combinational logic strategies that are employed toward the development of advanced electronic circuits can be applied to the fundamental biological edifice we developed in this study. Bearing in mind that electronic digital circuits are physically isolated, and gene circuits do not share the same degree of physical sequestration  and Osym  configuration, but deployed two decoupled non-natural repressors (R+YQR and G+TAN) and one engineered antilac (IAYQR). In principle, the IAYQR suppressor is coupled to the R+YQR repressor via the DNA-binding function and is simultaneously coupled to the G+TAN repressor via the conditional ligand interaction (as described above). When assembled, the resulting combinational logical program consists of a two-signal AND operation controlled via d-ribose and d-fucose, in series with two NOT gates controlled via IPTG . Thus, this three-signal program represented a more granular bandpass filter.In our second iteration of a combinatorial logical gate via the SERI genetic architecture, we repurposed the OPTG Fig. . This thPTG Fig. . Howeveragg) located in the proximal position. Whereas, the bottom channel had a SERI architecture and included Osym (core) and one non-natural operator Otta . The genetic structure was complemented by three decoupled transcription factors I+YQR, E+KSL, and F+TAN, that were responsive to three disparate effector ligands . Both channels returned the same GFP output interface. Objectively, the system resulted in a non-canonical logical program (hAND [PARA] AND) controlled via three-signal inputs. This genetic program had a performance profile comparable to a digital-to-analog converter (DAC), where the system generated various output levels that corresponded to different digital input combinations  configuration composed of three non-synonymous DNA operators Fig. . The top+HQN repressor was controlled via a two-signal AND gate. When the signal IPTG or cellobiose was added the top channel produced intermediate amounts of the engineered repressor. Whereas, the maximum R+HQN repressor output was only produced upon the addition of both signal inputs at 10 mM. The production of the R+HQN transcription factor was coupled to a GFP output interface, located on the bottom channel. The R+HQN repressor was paired with the cognate operator Ottg, with a background of 10 mM d-ribose under every condition to alleviate repression. This DAC program presented an inverted series of outputs, relative to the previous DAC program  the relative position of the operator being acted on was moved from the core to the proximal position, (iii) the R+KSL repressor had a fold induction that was less than one third of that observed for R+HQN. Given the significant change in performance of R+KSL, relative to R+HQN  the NOR program was deactivated , we modified the single GFP output digital-to-analog converter developed in the last iteration Fig. . The objted Fig. . Thus, td-fucose + cellobiose + d-ribose) that was less than half that of the top channel (inputs = IPTG + cellobiose). Resulting output signals are non-discrete, and convoluted by operator coupling between Octt (top core) and Otta  facilitated via non-natural repressor E+HQN. In addition to operator coupling, this program was IPTG signal coupled via S+YQR and I+KSL, where 10 mM IPTG inhibits the GalS transcription factor (S+YQR). Program complexity notwithstanding, the fidelity of this system was remarkably high, implying that a priori (in silico) forecasting may be possible. Additional iterations of this master architecture paired with different non-natural transcription factors resulted in vastly different outcomes . In principle, the genetic master circuit can generate any of the logical programs presented in this study, provided the appropriate systems of engineered transcription factors and cognate operators are selected. Using the master architecture we constructed a full four-signal combinatorial program Fig. . The per+YQR) or engineered antilac suppressor (IAYQR), via a shared DNA operator O1 (or Osym). The six alternate DNA recognition (ADR) domains used in this study to confer repressive function in E+, G+, S+, F+ and R+ RCDs, are also shared among 46 engineered antilacs (IAADR) and 6 related I+ADR repressors. This will enable a broad range of functional coupling by way of shared DNA-binding functions. The coupling of engineered transcription factors can also be extended via shared ligand binding functions. Namely, seven I+ADR repressors, seven IAADR suppressors, seven G+ADR and six S+ADR repressors share a common binding function to IPTG – though disparate phenotypes upon effector ligand binding. While this system of engineered transcription factors is large , the putative combinatorial design space that can be used toward the development of additional non-natural transcription factors is even more astonishing. Swint-Kruse et al.43 estimates that there are more than 1000 regulatory core domains that share the same topology to those used in this study. In addition, Lewis et al.55 has identified ~200 non-natural  DNA-binding domains and cognate operator combinations that can potentially be adapted to any given RCD. Consequently, these parts represent a combinatorial design space of ~105 putative transcription factors. Even if only 1% of these engineered transcription factors are functional, this could lead to the generation of hundreds of non-natural regulatory proteins with a variety of performance characteristics.In this study we introduce a biological programming edifice based on an engineered system of non-natural transcription factors and complementary genetic architectures. The SERI and PARA structures were used to systematically arrange cognate DNA operators and facilitate the development of combinatorial logic gating. Given that the engineered transcription factors are adapted with alternate DNA recognition and operator DNA elements that are not represented in nature, the resulting logical operations can be constructed and operated alongside existing natural genetic programs. Moreover, we have adapted each representative regulatory core domain with the native LacI DNA-binding domain. This will facilitate program coupling with the wild-type LacI repressor  to establish traceability. The back of a given card displays the operator position, microbe chassis, relevant genetic elements and related information. Collectively, this represents a standardized set of 55 unit operations. Recent studies61 have demonstrated the impact that genetic architecture can have on the regulatory properties of a given transcription factor, justifying the need for our standardization approach (which will aid in both benchmarking and reproducibility). While we found the dynamic range of the unit operations useful in this study, the proposed metrology will enable other end-users to systematically tune the performance of a given transcription factor system to meet individual needs. The estimated scale-out of our current programming edifice is represented by approximately 86,184 putative biomolecular systems for the master architecture, when complemented by our current set of engineered transcription factors  defining the conditional units of measurement for a given transcription factor, (ii) reproducible realization of these units at steady-state, (iii) performance traceability via the comparison of the performance metrics of a given transcription factor to a reference system. We begin this metrology via the development of performance cards for each of the transcription factors developed or used in this study . Mutations to the DNA-binding domain were then introduced via routine site-directed mutagenesis using Phusion DNA polymerase with GC buffer (New England Biolabs). For the FruR variants, the traditional lacI promoter was replaced by the stronger lacIq promoter62, which leads to a tenfold increase in protein production. This single nucleotide change was introduced via NEB Q5 Site Directed Mutagenesis (New England Biolabs) using the NEBaseChanger tool to design primers. For each repressor variant, the entire gene reading frame along with the promoter driving expression was sequenced in the forward and reverse direction (Eurofins Genomics) to verify all mutations and correct assembly. A reporter plasmid system was constructed starting with the pZS*22-sfGFP reported in Richards et al.54. This plasmid features a low copy number pSC101* origin of replication, and a kanamycin resistance marker. The region of the plasmid excluding the promoter and operator was PCR amplified, visualized on an Agarose Gel, and Gel Extracted (Qiagen). A small fragment containing the constitutive trc promoter (hybrid of trp and lacUV5 promoters), a 5-bp spacer segment, and the operator sequence was constructed via oligos (Eurofins Genomics) and placed into the pZS*22-sfGFP vector through CPEC. This is the same reporter system utilized by Rondon and Wilson53. For each operator variant, the region upstream of the reporter gene, along with the GFP reading frame was sequenced in the forward and reverse direction (Eurofins Genomics) to verify correct assembly. Detailed sequence data , which features a low copy number p15A origin, a chloramphenicol resistance marker, and the gene for the repressor regulated with a constitutive LacI promoter. The different RCDs were obtained as follows: GalS (Addgene #60773), FruR (Addgene #60768), RbsR (Addgene #60773) from the Swint-Kruse and Bennett Labs12) — in the core56 position. The second operator was then introduced in the proximal13 position, 15 bp downstream of the end of the −10 hexamer . To build the parallel circuits, the GFP gene along with the rrnB T1 terminator was PCR amplified from the pZS*22-sfGFP plasmid, visualized on an agarose gel, and gel extracted (Qiagen). On this second copy of GFP, we used the strong pL promoter64 as the scaffold and replaced the 18 bp region between the −35 (TTGACA) and −10 (GATACT) hexamers with 18 bp of an operator sequence. The second operator was then introduced in the proximal position, 15 bp downstream of the end of the −10 hexamer. Like the original reporter, we employed and insulator part, specifically the self-cleaving ribozyme RiboJ to prevent transfer-function variability stemming from differences in operator sequence. In order to avoid evolutionary instability due to homologous recombination, an engineered variant, RiboJ1050 was used upstream of GFP to introduce sequence diversity, while maintaining function. This region  was constructed via oligos and the three fragments were then combined via splicing by overlap extension (SOE)65 and introduced into the linearized form of the reporter plasmid described previously via CPEC. For each construct, the region upstream of the reporter gene , along with the GFP reading frame was sequenced in the forward and reverse direction (Eurofins Genomics) to verify correct assembly.The trc promoter was used as a scaffold, but the 18 bp region between the −35 (TTGACA) and −10 (TATAAT) hexamers was replaced with 18 bp of an operator sequence . Finally, the PBR322 origin of replication was PCR amplified from the pet28b vector (a gift from the Kane lab), visualized on an agarose gel, and gel extracted (Qiagen) and combined with the X+ and AmpR coding regions via CPEC. This plasmid also uses the strong lacIq promoter to drive expression of the transcription factor and was then co-transformed along with the pLacI plasmid and the reporter plasmid for assaying. In the case that more than two TFs were needed for a Transcription Factor system, plasmids containing more than one open reading frame (ORF) needed to be constructed. First, a linearized form of the pLacI plasmid was created by amplifying a 3764-bp region of the plasmid, visualized on an agarose gel, and gel extracted (Qiagen) the fragment. Next, the ORF for another X+ variant was PCR amplified from pSO and dpnI digested in order to remove any background template. The two were then combined using the NEBuilder HiFi kit (New England Biolabs). Similarly, a 3648-bp region of pSO was amplified, visualized on an agarose gel, and gel extracted (Qiagen) and combined with the ORF of a given X + from pLacI. The latter was dpnI digested and the two fragments were combined once again using the NEBuilder HiFi kit. This allowed for the introduction of up to four TFs to be simultaneously transformed along with the reporter plasmid into E. coli. For these plasmids, each Transcription Factor was sequenced independently along with their respective promoters to ensure proper assembly. Vector maps are given in Supplementary Fig. To introduce additional transcription factors (TF), a third plasmid bearing another LacI/GalR family chimera was created. A plasmid with an alternate selection marker and compatible origin of replication was needed and to this end, the AmpR coding region of pLS1 was PCR amplified, visualized on an agarose gel, and gel extracted (Omega). This was then combined with the X66 (Genotype lacZ13(Oc), lacI22, LAM–, el4–, relA1, spoT1, and thiE1, Yale CGSC #5237), which is an E. coli K12 strain that has LacI and the lac operon deleted. The plasmids were co-transformed and plated on LB agar with the appropriate antibiotics . Microplate assays were performed as outlined by Richards et al.54. Briefly, colonies were inoculated in 1 mL of LB and grown overnight at 37 °C shaking at 300 rpm. After this initial growth period, cultures were diluted into 200uL wells in M9 minimal media supplemented with 0.2% casamino acids, 1 mM Thiamine HCl and the appropriate antibiotics containing the appropriate effector ligand . Ligands used are as follows: IPTG , d-Fucose , Cellobiose , d-Ribose (CAS 50–69–1 Arcos Organics), d-Fructose (CAS 57–48–7 Alfa Aesar). Each sample was aliquoted in six samples in a clear, sterile, conical-bottom 96-well plate (Fischer Scientific) and grown in a 37 °C shaker at 300 rpm covered with a Breathe-Easier sealing membrane (Midwest Scientific) to prevent evaporation. After 20 h, all wells were transferred to a black 96-well plate (COSTAR) for assaying and GFP fluorescence  and optical density (OD600) were measured using a Synergy HT plate reader (BioTek). Corrections for pathlength were made using OD900 and OD975 and the fluorescence values were normalized to the optical density and averaged across all biological replicates. For each operator variant, the maximum GFP expression was determined using the LacStop control plasmid. LacStop is a plasmid that contains the LacI coding sequence on the pLacI plasmid but includes a stop codon at positions 2 and 3  in the presence and absence of inducer utilizing a student’s t-test with unequal variances and allowing for unequal sample sizes. The significance level was set to 0.001 and a one-tailed test was used. The rationale for a one-tailed rather than two tailed test is that the ligands utilized in this study have been previously shown to be inducers of their respective RCD and therefore, we were strictly concerned with cases in which the mean fluorescence was higher in the presence of inducer than in the absence. For a full table of descriptive statistics, including effect size  see Supplementary Data − or Xs depending on the magnitude of the fluorescence output. Variants with a GFP expression of less than 50% of LacStop under the same conditions (same operator and same ligand) were classified as Xs or super-repressor phenotypes while a variant with GFP expression greater than 50% of LacStop was classified as an X− phenotype.Phenotypes were determined by first comparing the mean GFP (fluorescent) output for Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Software 1Supplementary Data 1Supplementary Data 2Reporting SummarySource Data


RUNX1 gene, a master regulator of the hematopoietic process, participates in pathological conditions as a partner for several genes in chromosomal translocations. One of the most frequent chromosomal translocations found in acute myeloid leukemia patients is the t, in which RUNX1 and ETO genes recombine. In RUNX1 gene, the DNA double-strand breaks that originate the t are generated in the intron 5, specifically within three regions designated as BCR1, BCR2, and BCR3. To date, what determines that these regions are more susceptible to DNA double-strand breaks is not completely clear. In this report, we characterized RUNX1 intron 5, by analyzing DNase-seq and ChIP-seq data, available in the ENCODE Project server, to evaluate DNaseI hypersensitivity and the presence of the epigenetic mark H3K4me3 in 124 and 51 cell types, respectively.Our results show that intron 5 exhibits an epigenetic mark distribution similar to known promoter regions. Moreover, using the online tool YAPP and available CAGE data from the ENCODE Project server, we identified several putative transcription start sites within intron 5 in regions BCR2 and BCR3. Finally, available EST data was analyzed, identifying a novel uncharacterized long non-coding RNA, which is expressed in hematopoietic cell lines as shown by RT-PCR. Our data suggests that the core promoter of the novel long non-coding RNA locates within the region BCR3.RUNX1 intron 5, transcribed from a promoter located in the region BCR3, one of the chromosomal breakpoints of RUNX1 gene.We identified a novel long non-coding RNA within The online version of this article (10.1186/s40246-019-0219-1) contains supplementary material, which is available to authorized users. RUNX1  and ETO  recombine, generating a fusion protein that disrupts the hematopoietic process  for 28 cycles. The three primer pairs used were , , and . Finally, PCR products were resolved using electrophoresis in 1% agarose gels, dyed with SYBR® Safe DNA Gel Stain (Invitrogen), a fluorophore that allows visualization under UV light.RT-PCR was performed to evaluate RNA expression in the different cell lines, using primer sets to detect Additional file 1:Table S1. Genomic coordinates of the analyzed regions. (XLSX 11 kb)Additional file 2:Table S2. Presence of DNaseI hypersensitivity along RUNX1 intron 5 and control regions, in 124 cell types. (XLSX 23 kb)Additional file 3:Table S3. Presence of the epigenetic mark H3K4me3 along RUNX1 intron 5 and control regions, in 51 cell types. (XLSX 15 kb)Additional file 4:Table S4. EST sequences found in RUNX1 intron 5. (XLSX 12 kb)


The balance of the protease/inhibitor gene expression ratio is vital in preventing the oncogenic potential of matriptase. The intestinal cell lineage is regulated by a transcriptional regulatory network where the tumor suppressor, Caudal homeobox 2 (CDX2) is considered to be an intestinal master transcription factor. In this study, we show that CDX2 has a dual function in regulating both ST14 and SPINT1, gene expression in intestinal cells. We find that CDX2 is not required for the basal ST14 and SPINT1 gene expression; however changes in CDX2 expression affects the ST14/SPINT1 mRNA ratio. Exploring CDX2 ChIP-seq data from intestinal cell lines, we identified genomic CDX2-enriched enhancer elements for both ST14 and SPINT1, which regulate their corresponding gene promoter activity. We show that CDX2 displays both repressive and enhancing regulatory abilities in a cell specific manner. Together, these data reveal new insight into transcriptional mechanisms controlling the intestinal matriptase/inhibitor balance.The type II membrane-anchored serine protease, matriptase, encoded by  Dysfunction of barrier integrity can contribute to local and systemic diseases including inflammatory bowel disease, allergic disorders and malignancies2.The intestinal epithelium represents one of the most significant permeability barriers for exposure to environmental toxins and microorganisms6. Matriptase  is a type II transmembrane serine protease encoded by the gene suppression of tumorgenicity-14 (ST14), and is expressed in most epithelia10. The ST14 gene has been established as a critical tumor-suppressor gene in the gastrointestinal tract, as well as a suppressor of colitis and colitis-associated colon carcinogenesis in mice11. However, it is currently unknown how the ST14 gene is involved in maintaining epithelial barrier integrity, and in suppressing intestinal carcinogenesis.Evidence from the last decade has demonstrated the crucial importance of the serine protease matriptase in the generation and maintenance of epithelial integrityserine peptidase inhibitor Kunitz type -1 and -2 (SPINT1 and SPINT2)14. Evidence suggests that matriptase becomes highly oncogenic, when not kept under strict post-translational regulation by either HAI-1 and/or HAI-2, as deregulated matriptase has been shown to cause squamous cell carcinoma formation in transgenic mice overexpressing only wild-type matriptase in the epidermis, while a simultaneous increase in either the HAI-1 and/or HAI-2 expression completely reverses the oncogenic potential caused by matriptase overexpression17. Furthermore, an increase in the matriptase/HAI-1 protein ratio has been identified in late-stage ovarian tumors18, as well as for the mRNA ratio in colorectal cancer adenomas and carcinomas19, proposing that matriptase and/or HAI-1 may be deregulated during tumor progression.Matriptase is known to be kept under strict post-translational control by its two inhibitors, HAI-1 and HAI-2, encoded by the genes ST14 and SPINT1 might be co-regulated at the transcriptional level by intestine-specific transcription factors.While the regulation of matriptase at the protein level has been well studied, less is known about the transcriptional regulation. The critical importance of the protease/inhibitor mRNA ratio found in the intestine suggests that the expression ratio of 22, and the caudal-related homeobox (CDX2) transcription factor, CDX2, has been revealed as an intestinal master transcription factor. Studies in mice have shown that Cdx2 plays a vital role during intestinal development, differentiation, and during adult tissue homeostasis24. Loss of CDX2 affects the tissue expression pattern and morphology in both the adult small intestine and the colon25. Cultured in vitro 3D-grown mouse-derived small intestinal organoids have shown that intestinal stem cells lacking CDX2 fail to commit to the intestinal differentiation program and instead turn into a more gastric-like lineage26. Several clinical studies have observed dysregulated CDX2 levels in colorectal cancer27, and reduced levels of the transcription factor have been reported to be a prognostic biomarker for stage II and III colon cancer28 and metastatic colon cancer29. Moreover, studies have demonstrated that overexpression of CDX2 has an inhibitory effect on colon cancer growth in in vitro experiments and in tumor-transplantation studies performed in mice31.The intestinal epithelium-specific regulatory network of transcription factors has been well studiedST14 and SPINT1 basal gene expression is not dependent on CDX2 in intestinal cells; however, induced expression of CDX2 affects the ST14/SPINT1 balance. We have identified and characterized functional intestine-specific transcriptional DNA regulatory enhancers for both the ST14 and the SPINT1 genes, and shown that CDX2 binding is enriched within these enhancers, using chromatin immunoprecipitation, as well as described the specific binding sites, using gel shift assays. Collectively, these results provide evidence that the ST14 and the SPINT1 enhancers functionally activate their corresponding promoter activity, in an intestinal- and CDX2-regulated manner. Thus, we suggest that the intestine-specific co-expression of matriptase and its inhibitor HAI-1 involves transcriptional regulation by the transcription factor CDX2.In this study, we report that the 32. The LS174T cells, harboring trans-activator elements (TET3G) and the PrIITE system, were engineered to disrupt the endogenous CDX2 locus, and upon stimulation by doxycycline, to induce ectopic expression of a codon-optimized CDX2 construct. The analyses showed that loss of CDX2 (- Dox) did not have an impact on either ST14 or SPINT1 mRNA expression, as compared to wild-type LS174T cells (wt), suggesting that their basal gene expression is not maintained by CDX2, thus showing CDX2 independence  and between +3331 to +3863 relative to the SPINT1 TSS  relative to the TSS. The cloned plasmids were transiently transfected into Caco-2 cells, and the reporter activity was normalized to the co-transfected LacZ plasmid expression. The ST14 promoter reporter assay revealed that the ST14 promoter activity exceeded the activity baseline of the empty pGL4.10 vector, with a significant 27.1-fold stimulation  and SPINT1 (+3331 to +3863) were next analyzed for possible CDX2 binding sites. Using the transcription factor binding prediction tool, Transfac database, we found five predicted CDX2 binding sites  within the ST14 enhancer region  and supershift assays were conducted. Double-stranded probes covering the putative CDX2-binding sites were labeled with  ATP (Perkin Elmer), using T4 polynucleotide kinase (Fermentas). Nuclear extracts were prepared as previously described70. The EMSA protein/DNA binding reaction contained 2 μl of 10 day differentiated Caco-2 nuclear extracts, 3 μl dialysis buffer , 10 μl Gelshift buffer , 0.5 μl dI-dC (homopolymer of deoxyinosine and deoxycytidine). For the competition assay, a 100-molar excess of unlabelled unspecific, wild-type or CDX2-binding site-mutated oligonucleotide was added to the reaction mixture. For supershift assays, 1 µl of CDX2-antibody  or 1 µl of non-immune serum  was added. The reaction mix was incubated on ice for 20 minutes and then 1 μl of radiolabeled probe was added followed by a further incubation for 20 minutes on ice. Finally, 2 μl of Gelshift loading buffer ) was added to the binding reaction mixture. The protein/DNA complexes were resolved on a 5% non-denaturing polyacrylamide gel which afterward was dried on a slab gel dryer SGD4050 (Savant) before exposure using a phosphor-image screen. The screen was scanned on a Storm 840 scanner (Molecular Dynamics) and the image was analysed using the Image-Quant Software version 5.2.Electrophoretic Mobility Shift Assay (EMSA) was conducted to verify DNA-protein binding. Oligonucleotides used in this experiment, listed in Table 21. Sonication was verified for yielding DNA fragments of approximately 100–500 bp. The immunoprecipitations were performed in four replicates with either CDX2 antibody  or HA (hemagglutinin) antibody , using Dynabeads Protein A/G beads. From each replicate, 1% was removed before immunoprecipitation and used as input control. Analysis of immunoprecipitated DNA enrichments was performed with qPCR using the primers listed in Table 71.Caco-2 cells were grown for 5–6 days after confluence before harvest for the ChIP experiment. Cross-linking of protein/DNA, sonication, immunoprecipitation and DNA purification was done as previously described72, Caco-2 CDX2 ChIP tracks21 (available upon request), Caco-2 NNF4a- and H3K4me2 CHIP (NCBI GEO data: accession No GSE23436), LS174T CDX2 ChIP (NCBI GEO data: accession No GSE97273). The sequencing data were mapped to hg19 and the CisGenome version273 software was used to analyze the data. Tracks were uploaded into UCSC Genomebrowser74 for visualization images precented in this study.The genomic sequence tracks presented in this study derive from previously published and available data; Caco-2 CAGE-tracks (NCBI GEO data: accession No. GSE54074)Both Students T-test  and ANOVA test  were used on data presented in this study. Graphs and calculated statistical significance were created using the GraphPad Prism 5 software.Supplmentary Information


In many organisms, early embryonic development is driven by maternally provided factors until the controlled onset of transcription during zygotic genome activation. The regulation of chromatin accessibility and its relationship to gene activity during this transition remain poorly understood. Here, we generated chromatin accessibility maps with ATAC-seq from genome activation until the onset of lineage specification. During this period, chromatin accessibility increases at regulatory elements. This increase is independent of RNA polymerase II-mediated transcription, with the exception of the hypertranscribed miR-430 locus. Instead, accessibility often precedes the transcription of associated genes. Loss of the maternal transcription factors Pou5f3, Sox19b, and Nanog, which are known to be required for zebrafish genome activation, results in decreased accessibility at regulatory elements. Importantly, the accessibility of regulatory regions, especially when established by Pou5f3, Sox19b and Nanog, is predictive for future transcription. Our results show that the maternally provided transcription factors Pou5f3, Sox19b, and Nanog open up chromatin and prime genes for activity during zygotic genome activation in zebrafish. In eukaryotes, DNA is packed inside the cell nucleus in the form of chromatin, which consists of DNA, proteins such as histones, and RNA. Chromatin accessibility influences when and where DNA-binding proteins such as transcription factors and RNA polymerase II find their targets in order to activate transcription. It is unclear, however, whether the accessibility of regulatory regions precedes and predicts future transcription. To address these questions, we took advantage of ATAC-seq, which is a powerful technique to probe chromatin accessibility states. We analyzed zebrafish embryos as they gear up to start transcription for the first time and related the accessibility of regulatory regions to the activity of associated genes. This revealed that chromatin at regulatory regions is often accessible prior to gene activity and that this has predictive value for future transcription. Analyzing transcription factor mutants, we further determined that Pou5f3, Sox19b and Nanog play an important role in opening up chromatin. Thus, our study shows that transcription factors open regulatory elements to prime gene activity during development. Chromatin structure is an important determinant of eukaryotic transcription regulation. At gene regulatory regions such as promoters and enhancers, chromatin needs to be in an accessible state to allow for binding of the transcriptional machinery. Consequently, the vast majority of transcription factor (TF) binding co-localizes with accessible chromatin regions ,2. ChromDrosophila, mouse, and human embryos harbor accessible chromatin at regulatory elements prior to gene activation We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,A. Aziz AboobakerAssociate EditorPLOS GeneticsWendy BickmoreSection Editor: EpigeneticsPLOS GeneticsTwo of the expert reviewers have some substantial questions regarding the current manuscript. On balance I agree, that given what we already know, you could consider further analyses with the current data set to support your conclusions. I do not think it is necessary within the context of this study to provide further experimental validation of putative enhancers unless you already have these data available.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Zygotic genome activation is important for early embryonic development and the mechanisms underpinning this process should be of interest to the readership of PloS Genetics. Here Palfy and colleagues investigate ZGA in zebrafish, specifically focussing on the interplay between chromatin accessibility and transcription. They performed ATACseq on a series of early stages pre and post ZGA and found that from genome activation to onset of lineage specification chromatin accessibility increased in regulatory regions and preceded the transcription of associated genes. Using CRISPR mutants they show that this was dependent on the maternally expressed TFs, Pou5f3, Sox19B and Nanog, whose association with accessible elements was indicative of future transcription. They conclude that these TFs are involved in priming genes for transcription.A lot is known about the timing of zygotic genome activation and Pou5f3, Sox19B and Nanog have previously been identified as key TFs involved. It was shown that they bind putative regulatory elements and a recent paper  used MNase-seq and reports that these factors promote the open chromatin state. This study also provided chromatin accessibility data from similar stages (pre ZGA to dome stage). Thus, the present manuscript complements these previous reports and extends previous findings.The experiments and associated bioinformatics analyses are well done, however, the overall advance is somewhat incremental.They first attempt to address whether transcription is required for chromatin accessibility and compare a-amanitin treated and untreated embryos at the oblong stage, this treatment leads to arrest at sphere stage. Accessibility changes significantly only at the highly transcribed miR430 locus and not at any other highly transcribed genes. Thus, they conclude that RNA polymerase II activity is not required for chromatin accessibility, with the exception of the mir-430 cluster. It is not clear why they chose the id1 and fbxw4 loci to show ATACseq tracks and maybe the authors can comment on this selection also in the text (they do comment in the figure legend).Looking at the timeline of developmental stages and publicly available expression data the authors observe that chromatin accessibility precedes transcription. They look at promoters and putative enhancers of genes that are activated at sphere and shield stages. The enhancers are defined by distance from TSS and presence of particular TF binding sites, but none of these putative enhancers is confirmed experimentally and their function and dependency on particular TFs is not confirmed.They investigate the roles of Pou5f3, Sox19B and Nanog by creating CRISPR mutants followed by ATACseq at the oblong stage. The effects on accessibility at promoters and putative enhancers are quite small based on the volcano plots shown in Fig 4b, particularly in the MZsox19b and MZnanog mutants. Although for the selected genes shown in Fig 4c the effects are very clear and these genes and associated enhancers would be good candidates for experimental validation.Overall, their analysis confirms a previous publication which suggested that these TFs regulate chromatin accessibility. In addition, the authors suggest that sites bound by a single TF or two of the TFs have reduced accessibility in the mutants, but this is not very clear from the plots shown in Fig 4d.Finally, the authors find a correlation between increased accessibility of chromatin prior to gene expression at a subsequent stage and they suggest that Pou5f3, Sox19B and Nanog are priming gene expression.The analysis reveals interesting associations, however the manuscript would benefit from experimental validations of some of the putative enhancers identified, including the confirmation of TF binding sites as being functionally important.Reviewer #2: This manuscript investigates the process of zygotic genome activation (ZGA) in zebrafish embryos to explore the relationship between transcription, chromatin accessibility and how this is regulated, including the regulatory proteins involved. These are interesting and important questions because we still do not fully understand how chromatin accessibility is regulated and how changes in accessibility are related to gene expression during development i.e. whether open chromatin is a reliable predictor the genes being expressed in cells or the future genes that will be expressed.The authors carry out ATAC-Seq in replicates at seven different stages of zebrafish embryogenesis covering from the 256 cell stage - approximately when zygotic gene expression begins – to gastrulation. I was curious however as to why earlier stages were not assayed since the authors themselves state that some zygotic transcription is observed earlier at the 64-cell stage. Comparison of the ATAC-Seq profiles across these stages allowed the authors to visualise and compare changes in open chromatin and how this is associated with changes in gene expression. They found five clusters of genes with different chromatin accessibility dynamics representing increasing numbers of genes with promoters and enhancers that become more accessible as development proceeds. They then provide convincing evidence that this increasing accessibility is generally not dependent on transcription except for the very highly expressed miR-430. They then show that accessibility of promoters and enhancers preceeds the expression of the associated genes. This is an important result that, consistent with data from other organisms, strongly suggests while generally open chromatin is not a great predictor of which genes are expressed at a given stage or tissue, it can be predictive of future gene expression during development.The authors then investigate the effect of loss of three candidate transcription factors on chromatin accessibility during ZGA: Pou5f3, Sox19b and Nanog. They found that loss of any of these factors results in a decrease in chromatin accessibility and that this effect is caused by a loss of binding of these factors individually and in combination at different loci. Analysis of gene expression evidences that these three transcription factors likely prime genes for expression by increasing chromatin accessibility.Overall I think this is a very interesting and well written paper that reports convincing data to advance our understanding of the regulation of gene expression during development.Reviewer #3: N/A**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , and click on the Fetch/Validate link next to the ORCID field.  This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.In the meantime, please log into Editorial Manager at If you have a press-related query, or would like to know about one way to make your underlying data available , please see the end of this email. If your institution or institutions have a press office, please notify them about your upcoming article at this point, to enable them to help maximise its impact. Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,A. Aziz AboobakerAssociate EditorPLOS GeneticsWendy BickmoreSection Editor: EpigeneticsPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have responded in detail to the comments made by reviewers. This is a well written manuscript which makes an important contribution.Reviewer #3: The authors have addressed the vast majority of my concerns and provided explanations for the others. I am therefore happy with the manuscript in its present form.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , one way to make that data available is to deposit it in the The following link will take you to the Dryad record for your article, so you won't have to re‐enter its bibliographic information, and can upload your files directly: http://datadryad.org/submit?journalID=pgenetics&manu=PGENETICS-D-19-00995R1http://www.datadryad.org/depositing. If you experience any difficulties in submitting your data, please contact help@datadryad.org for support.More information about depositing data in Dryad is available at data availability policy requires that all numerical data underlying display items are included with the submission, and you will need to provide this before we can formally accept your manuscript, if not already present.Additionally, please be aware that our ----------------------------------------------------Press Queriesplosgenetics@plos.org.If you or your institution will be preparing press materials for this manuscript, or if you need to know your paper's publication date for media purposes, please inform the journal staff as soon as possible so that your submission can be scheduled accordingly. Your manuscript will remain under a strict press embargo until the publication date and time. This means an early version of your manuscript will not be published ahead of your final version. PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  2 Jan 2020PGENETICS-D-19-00995R1 Chromatin accessibility established by Pou5f3, Sox19b and Nanog primes genes for activity during zebrafish genome activation. Dear Dr Vastenhouw, We are pleased to inform you that your manuscript entitled "Chromatin accessibility established by Pou5f3, Sox19b and Nanog primes genes for activity during zebrafish genome activation." has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Soon after your final files are uploaded, unless you have opted out or your manuscript is a front-matter piece, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.Thank you again for supporting PLOS Genetics and open-access publishing. We are looking forward to publishing your work! With kind regards,Kaitlin ButlerPLOS GeneticsOn behalf of:The PLOS Genetics TeamCarlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdomplosgenetics@plos.org | +44 (0) 1223-442823plosgenetics.org | Twitter: @PLOSGenetics


Escherichia coli and the plant pathogen Dickeya. We argue that, while in E. coli the gradient of DNA thermodynamic stability and gene order along the origin-to-terminus axis represent major organizational features orchestrating temporal gene expression, the genomic sequence organization of Dickeya is more complex, demonstrating extended chromosomal domains of thermodynamically distinct DNA sequences eliciting specific transcriptional responses to various kinds of stress encountered during pathogenic growth. This feature of the Dickeya genome is likely an adaptation to the pathogenic lifestyle utilizing differences in genomic sequence organization for the selective expression of virulence traits. We propose that the coupling of DNA thermodynamic stability and genetic function provides a common organizational principle for the coordinated expression of genes during both normal and pathogenic bacterial growth.Recent studies strongly suggest that in bacteria, both the genomic pattern of DNA thermodynamic stability and the order of genes along the chromosomal origin-to-terminus axis are highly conserved and that this spatial organization plays a crucial role in coordinating genomic transcription. In this article, we explore the relationship between genomic sequence organization and transcription in the commensal bacterium  Escherichia coli [Coordination of the bacterial chromosome structure and function is mediated by changes in genomic DNA supercoiling associated with DNA replication and its cessation and corresponding alterations of transcription. The mechanism coordinating genomic transcription is not fully understood, but recent findings made it increasingly clear that the genetic regulation of bacterial growth cannot be satisfactorily explained on the basis of the classical operon model. First of all, genomic transcription turned out to be both pervasive and fuzzy, so that the operon model does not hold the promise to explain all the alterations of genetic activity during bacterial growth and adaptation, as thought before ,3,4,5,6.hia coli ,8,9, conhia coli ,13,14,15hia coli ,17,18, whia coli ,22,23,24hia coli ,25,26. Ahia coli ,28, reguhia coli , bindinghia coli ,31,32, DWhatever the nature of the factors determining the subdivision of the chromosome into separate expression domains, recent studies made it obvious that the coordination of genomic function involves the temporal emergence of coherent domains of transcription  comprisE. coli, the role of chromosomal sequence organization in the regulation of the genetic function is quite conspicuous. Initiation of replication at the chromosomal origin of replication (OriC) requires high negative superhelicity both in vitro and in vivo and is assisted by several DNA architectural proteins including highly abundant NAPs, such as integration host factor (IHF), factor for inversion stimulation (FIS), and histone-like protein first isolated from E. coli strain U13 (HU) [E. coli in particular, the sequences around OriC demonstrate a higher thermodynamic stability compared to those around the chromosomal terminus (Ter) of replication (In U13 (HU) ,40,41,42lication . E. coli, the G/C-rich OriC end is activated early on commitment of cells to exponential growth [rrn) and protein genes, as well as of the RpoZ (ω) subunit of RNA polymerase (RNAP), which stabilizes the vegetative (σ70) holoenzyme assembly [rrn transcription FIS [rrn operons on both chromosomal arms, is activated aberrantly on the expense of genes requiring high negative superhelicity for transcription, suggesting that HU is involved in buffering the accumulation of excessive negative superhelicity in the OriC end, which otherwise, could impair the coordinated transcription of the genome [In l growth . This eal growth  and incrassembly ,45,46 antion FIS ,53,54,55tion FIS ,21 and otion FIS ,57,58. Ntion FIS ,60. Althtion FIS , thus fae genome . The NAPe genome ,64.E. coli growth cycle appears to be governed by coupling the static gradient of DNA thermodynamic stability with the dynamic gradient of negative superhelicity along the chromosomal OriC–Ter axis, which acts as a coordinate system [On cessation of replication, when the processes increasing the supercoil dynamics in the G/C-richer chromosomal Ori end subside, the less G/C-rich Ter end gains a competitive advantage in terms of energy requirements for duplex melting associated with the initiation of transcription. This switch in the relative activity of chromosomal ends is coordinated with a change in RNAP holoenzyme composition and promoter recognition specificity ,66,67, ae system ,61. E. coli, the spatial order of genes along the chromosomal OriC–Ter axis and the temporal expression of genetic function is coupled with a strategic organization of sequences with distinct thermodynamic stability around the opposite chromosomal poles. This superimposition of the gradients of DNA thermodynamic stability and supercoiling on a conserved chromosomal gene order with anabolic and catabolic functions, allocated respectively to the OriC and the Ter ends, provides a bona fide device for the spatiotemporal coordination of genomic expression during the E. coli growth cycle [E. coli growth cycle and featuring distinct sequence organization are consistent with this notion [E. coli is likely streamlined for fast growth. The question is whether a similar organization can be revealed in related bacteria with more sophisticated lifestyles, such as, e.g., bacterial pathogens. One excellent model to explore this question is the plant pathogenic bacterium Dickeya dadantii, which during plant infection, has to cope with various types of severe environmental stress [Importantly, the chromosomal OriC and Ter ends also differ in terms of genetic function. The former is enriched in anabolic genes and the latter in catabolic genes . More coth cycle . The dyns notion . Howeverl stress ,75. D. dadantii genome markedly differs from that of E. coli. In particular, it features four  thermodynamically distinct DNA regions. These regions appear to be highly conserved, although they have suffered extensive rearrangements during the evolution of Dickeya species  and the distribution of digital parameters (unique genes and genetic functions) . Ultimatd stress ,26. In td stress , in keep[E. coli . HoweverE. coli, the measurements of the distribution of DNA analog  and digital  parameters of the genomic sequence revealed a strict anti-correlation between thermodynamic stability and codon variability in the OriC and Ter ends of the genome. In the G/C-rich OriC end, the randomness of codon distribution is low, whereas in the relatively A/T-rich Ter region, where thermodynamic stability is low, codon randomness is high [D. dadantii revealed the same regularity: the G/C-rich SD1 and SD2 regions demonstrated low sequence randomness, whereas the A/T-rich LD1 and LD2 regions demonstrated high sequence randomness . Since the emergence of a CODO is correlated with a directionally coherent alteration (either activation or repression) of gene expression, a faithful reporter construct should comply with the behavior of the CODO it is targeted to. For example, peculiar influence of the encompassing chromosomal regions on the inserted reporter cassettes has been reported in laxation . D. dadantii, the correlation between domain behavior and reporter activity was examined using the supercoiling-sensitive fisP-YFP construct, which was previously shown to faithfully reproduce the regional differences in supercoil dynamics induced by deletion of HU in E. coli [D. dadantii CODOs d5 and d7 emerge synchronously and harbor adaptation/virulence determinants, the expression of which significantly correlates with that of the CODOs [fisP-YFP reporter inserted in d5 and d7 demonstrates a behavior reflecting that of the targeted CODOs, i.e., activation under oxidative stress in both CODOs and activation in d5 vs repression in d7 under osmotic stress (fisP-YFP reporter declined after the relaxation of DNA induced by novobiocin treatment, as expected. However, when this construct was inserted into the CODO d8, which is “asynchronous” in a sense that it does not appear in any of the examined growth conditions in combination with either d5 or d7, the reporter responded poorly to both oxidative and osmotic stresses, whereas DNA relaxation by novobiocin increased instead of decreasing the reporter activity. At the same time, the response of the fisP-YFP reporter to acidic stress was uniform and did not depend on the behavior of the targeted CODO. This versatile response of the fisP-YFP reporter markedly differed from that of the dpsP-YFP construct, in which the reporter is under the control of the promoter of the dps gene encoding Dps, the major stationary-phase NAP. In E. coli, this latter construct demonstrated relative independence from the chromosomal context [D. dadantii (In  E. coli . D. dadahe CODOs . These tc stress A. Notabl context  and was dadantii B. fisP-YFP construct inserted in CODOs d5 and d7, as well as these CODOs themselves, respond to osmotic stress in opposite ways? Osmotic shock stimulates K+ uptake [fisP-YFP reporter inserted into these CODOs—a conjecture that can be tested, for example, by examining the reporter behavior in the hns mutant strain. However, why does the + uptake , compens+ uptake . This ch+ uptake ,84. Osmo+ uptake , could a+ uptake ,88,89,90+ uptake  is modul+ uptake ,95,96,97+ uptake ,99. This+ uptake , as wellShewanella oneidensis, E. coli, the ethanol-producing bacterium Zymomonas mobilis, and the sulfate-reducing bacterium Desulfovibrio alaskensis it was reported that there is little correlation between the importance of genes for optimal growth or fitness and their upregulation [Pseudomonas syringae reported the absence of correlation between mutant fitness in planta and either the magnitude of expression or the degree of induction for most genes in planta compared to in vitro conditions [It is noteworthy that, while it is generally accepted that alterations of transcript patterns are adaptive, there are reports about the absence of correlation between the transcript pattern and the expressed function. For example, in a study comparing the expression and mutant fitness of biosynthetic genes in several bacteria including the metal-reducing bacterium gulation . Also, anditions . Howevernditions ,24,80 arE. coli and in the plant pathogen Dickeya, this coordination of structure and function is achieved by the strategic organization of large chromosomal domains of gene expression containing sequences with distinct thermodynamic stability and supercoiling response, termed CODOs. The Dickeya CODOs and similar regions in other pathogens harbor various adaptation/virulence determinants that are co-regulated with and expressed within the CODOs, emerging in responses to environmentally induced changes of DNA supercoiling. Since global DNA supercoiling changes as a function of metabolic state, while the latter depends on growth conditions, it is likely that specific constellations of CODOs emerging in response to environmental change aid the facile adaptation of pathogenic bacteria to stress. We believe that further studies of the coupling between the physicochemical properties of DNA, genomic expression dynamics, and genetic function will pave the way towards understanding how the coordinated transcriptional response to environmental change is encoded in the primary sequence organization of bacterial genomes.The sequence organization of the bacterial genomes reflects the necessity of facile accessibility of genomic regions encoding particular functions to regulatory factors during bacterial growth and adaptation to changing environmental conditions. We have argued that both in the commensal


The Polycomb repressive complexes PRC1 and PRC2 act non-redundantly at target genes to maintain transcriptional programs and ensure cellular identity. PRC2 methylates lysine 27 on histone H3 (H3K27me), while PRC1 mono-ubiquitinates histone H2A at lysine 119 (H2Aub1). Here we present engineered mouse embryonic stem cells (ESCs) targeting the PRC2 subunits EZH1 and EZH2 to discriminate between contributions of distinct H3K27 methylation states and the presence of PRC2/1 at chromatin. We generate catalytically inactive EZH2 mutant ESCs, demonstrating that H3K27 methylation, but not recruitment to the chromatin, is essential for proper ESC differentiation. We further show that EZH1 activity is sufficient to maintain repression of Polycomb targets by depositing H3K27me2/3 and preserving PRC1 recruitment. This occurs in the presence of altered H3K27me1 deposition at actively transcribed genes and by a diffused hyperacetylation of chromatin that compromises ESC developmental potential. Overall, this work provides insights for the contribution of diffuse chromatin invasion by acetyltransferases in PRC2-dependent loss of developmental control. Polycomb repressive complexes PRC1 and PRC2 act non-redundantly at target genes to regulate transcription. Here the authors present engineered mouse ESCs targeting the PRC2 subunits EZH1 and EZH2 to discriminate between contributions of distinct H3K27 methylation states and the presence of PRC2/1 at chromatin, and provide evidence for the role of H3K27 acetylation in PRC2-mediated functions. The importance of PRCs is highlighted by the early embryonic lethality of knockout mice5, as well as the failure to establish proper in vitro differentiation of embryonic stem cells (ESCs) lacking core PRC1 and PRC2 subunits9. PRCs maintain a critical role also in adult life, with both PRC1 and PRC2 activities playing specific roles in controlling cell identity and tissue homeostasis1. Importantly, these same activities are also frequently deregulated in different type of human tumors by genetic lesions that preferentially target PRC2 activity1. Such mutations can result in either gain- or loss-of-function of PRC2, depending on the tissue and the environmental context15.The proper establishment and regulation of transcriptional programs is of fundamental importance during development. Polycomb group (PcG) proteins act as epigenetic regulators that ensure the maintenance of cell-specific transcriptional programs by exerting a crucial role during establishment of cellular identity and cell fate transitions. This is guaranteed by the activity of two major PcG repressive complexes (PRCs), PRC1 and PRC2, that act non-redundantly at the same target genes to ensure proper gene repression via post-translational modifications of histone proteins16. PRC2 activity relies on the methyltransferases EZH1 and EZH2, which deposit all three methylation states of lysine 27 onto histone H3 (H3K27me)19. EZH1 and EZH2 are mutually exclusive within PRC2 and retain distinct enzymatic proprieties in vitro, with EZH2 showing higher methyltransferase efficiency under the same reaction conditions19. While loss of EZH1 is dispensable for embryogenesis and mice viability20, EZH1 fails to compensate EZH2 loss of function that results in early embryonic lethality during gastrulation2. Both EZH1 and EZH2 are expressed in mouse ESCs and are found associated within PRC218. PRC2-EZH1 and PRC2-EZH2 complexes are associated to a set of ancillary proteins that are not required for intrinsic PRC2 enzymatic activity but play distinct roles in regulating chromatin recruitment and activity26.PRC1 is responsible for the deposition of histone H2A lysine 119 mono-ubiquitination (H2Aub1), catalyzed by activity of its redundant E3-ligase subunits RING1A or RING1BDrosophila, recruitment occurs via Polycomb response elements (PRE), which are distal cis-regulatory elements about a few hundred base pairs long that are devoid of nucleosomes27. The recruitment mechanisms in mammals appear to be divergent and are still not clear. CpG islands attract PRC1 and PRC2 complexes, and several subunits, such JARID2 and Polycomb-like proteins , have direct binding affinities to these DNA motifs29; how specificity is acquired, however, remains an open issue. PRC2 has been proposed to be actively excluded from CpG islands (CpGi) by RNA polymerase II activity, which would imply that PRC2 has the potential ability to bind CpGs by default30. In this scenario, neither the role that H2Aub1 exerts in vivo on PRC2 recruitment, nor the role that PRC2 exerts on PRC1 recruitment, is clearly defined.The mechanisms by which PRC1 and PRC2 complexes are recruited to, and stabilized at, chromatin still remain points of discussion. In 31. It was also shown that PRC2 retains the ability to bind H2Aub1, suggesting that deposition of H2Aub1 can control PRC2 recruitment and/or stabilization at target loci34. Studies in Drosophila melanogaster have elegantly shown that preventing H2Aub1 deposition by mutating K117, K118, K121, and K122 of histone H2A, or by expressing a dRING catalytically inactive mutant, did not result in homeotic transformations35. Similarly, inactivating point mutations of RING1B in mice postpones embryonic lethality from embryonic day E10.5 to E15.536. Although RING1A was still expressed in these mice, these results strongly suggest that lack of H2Aub1 deposition cannot phenocopy loss of PRC2 activity in vivo. Importantly, substituting lysine for arginine in H3 (H3K27R) resulted in homeotic transformations identical to E(z) loss-of-function in the developing fly embryos, pointing to H3K27me3 as the central hub for PcG functions in fly development37. Whether this also applies to mammalian development remains to be addressed.Multiple PRC1 subcomplexes with distinct biochemical proprieties have been demonstrated to exist. These complexes are commonly referred to as canonical or non-canonical, based on their dependence on H3K27me3 for recruitment to target loci17. We have previously reported that while mono-methylated H3 (H3K27me1) is preferentially deposited at highly transcribed gene bodies, tri-methylated H3 (H3K27me3) is deposited at promoter regions concomitantly with PRC2 and PRC1 association17. Like the CBX proteins of canonical PRC1, EED can bind H3K27me3 with its WD40 domain to stabilize PRC2 at its target sites and to allosterically stimulate PRC2 enzymatic activity38. This also generates an intrinsic biochemical competition for this modification between PRC1 and PRC2; binding of PRC2 reinforces its activity at target sites but also serves as a docking site for canonical PRC1.PRC2 activity controls all forms of H3K27 methylation17. Essentially, H3K27me2 “fills the gaps” between H3K27me1 and H3K27me3 chromatin domains in intergenic and non-transcribed intragenic genomic space. Importantly, we and others have previously reported that global loss of H3K27 methylations results in an aberrant accumulation of H3K27 acetylation mediated by CBP and p300 activity40. This occurs at all sites in which H3K27 methylation is lost, including hyperacetylation of PcG bound promoters and non-lineage-specific enhancer elements17. This observation raises the question of whether early developmental failure in the absence of PRC2 activity is primarily a consequence of loss of control of promoter repression due to lack of H3K27me3 deposition, or a general failure caused by diffuse chromatin hyperacetylation due to a lack of H3K27me2 deposition.In contrast, di-methylated H3 (H3K27me2) is a broadly diffused modification that, in ESCs, covers 70% of total H3To address these questions, we generate a set of mutants to entirely delete EZH1 and EZH2 proteins or completely inactivate EZH2 catalytic activity without altering PRC2 assembly. We now demonstrate that PRC2 is recruited to target sites independently of H3K27me3 and H2Aub1 deposition. We further show that the activity of EZH1 is sufficient to deposit H3K27me3 at all PRC2 target sites but is unable to spread this modification to neighboring chromatin. Although this is sufficient to fully recruit PRC1 activity at these sites and to prevent hyperacetylation of all PcG target promoters, EZH1 activity is unable to counteract diffused chromatin H3K27ac hyperacetylation caused by H3K27me2 loss or to support proper ESC differentiation. Together, these data strongly suggest that early developmental failure induced by loss of PRC2 activity is a consequence of chromatin H3K27 hyperacetylation rather than specific loss of repressive control at target genes.41. To bypass this problem, we generated macrodeletions that eliminate the production of any polypeptide from the Ezh2 gene (Ezh2 knockout [KO]). Following a similar strategy, we have also generated a macro-deletion that targets independently the Ezh2 paralog Ezh1 (Ezh1 KO). To further create double-KO (dKO) ESCs, we sequentially targeted Ezh1 and Ezh2 with the same gRNAs  homozygous mutations in Ezh1 KO ESCs using CRISPR/Cas9  completely impaired EZH2 catalytic activity. We thus generated physiological dKO Fig. . It is iSCs Fig. , and thaSCs Fig. . MoreoveSCs Fig. . OverallEzh1/2 dKO and Ezh2 Y726D ESCs displayed a complete genome-wide loss of H3K27me3 deposition at PcG bound sites defined by association of PRC2 (SUZ12) or PRC1 (RING1B) in wild-type ESC  of human EZH2 for 24 h 42. Indeed, after 6 h of MG132 treatment in Ezh2 Y726D ESCs, H2Aub1 deposition was nearly abolished, but the global levels of core PRC2 components were not affected . We have further introduced in these cells a tetracycline inducible form of EZH2 to activate first, the loss of PRC1 activity by OHT treatment and second, the activation of EZH2 expression with doxycycline  in WT ESCs, we observed that H3K27me3 and H3K27ac were deposited in a mutually exclusive manner, with PcG target promoters showing undetectable H3K27ac levels, while active promoters showing accumulation , the levels of H3K27ac increased globally, invading promoters that were previously decorated by H3K27me3  in cell culture. Consistent with the complete compensation for all H3K27 methylation states exerted by EZH2, lls Fig. . In conties Fig. . This hiers Fig. . ImportaESC Fig. . While aESC Fig. , Ezh2 KOges Fig. . In contogy Fig. . These d46. A series of biochemical studies revealed the importance of allosteric stimulation of PRC2 for the nucleation and spreading of H3K27me3 domains at a genome-wide level, demonstrating its importance in sustaining H3K27me2/me3 deposition47. Notably, minor differences in the amino acid sequence of the stimulatory responsive domain between EZH1 and EZH2 render EZH1 less responsive to allosteric stimulation than EZH248. Importantly, PRC2-EZH1-mediated H3K27me2/3 deposition at target genes was sufficient to preserve PRC1 binding and gene repression  identified in WT ESCs, and compensated by H3K27me2 Fig. . Thus, aon Figs. .50, long non-coding RNAs53, and ancillary proteins associated to PRC231. Recent work has further identified specific CG-rich consensuses that seem to serve as nucleation sites for PRC2 recruitment and spreading47. Different forms of PRC2 also have distinct affinities in binding chromatin templates. For instance, EZH1 was shown in vitro to bind nucleosome templates with a greater affinity than EZH248. Moreover, different analyses studying the function of ancillary proteins, such as MTF2, JARID2, and AEBP2, have shown that all of these activities contribute to stabilizing PRC2 at chromatin and to stimulating its in vitro activity54. In particular, JARID2 can be methylated by EZH2 on K116, resulting in allosteric stimulation with the same mechanism as for H3K27me355. While JARID2 plays a crucial role in the context of ESCs, its function in more-committed cell types is marginal, suggesting that different mechanisms for stimulatory actions and recruitment exist. Recent work from the Helin laboratory has further shown that a form of SUZ12 unable to associate with PRC2 is still recruited to PcG target sites41. Our results in Ezh1/2 dKO ESC also show that SUZ12 binding is retained at PcG sites, however with lower intensity respect to wild-type ESC. The catalytically inactive form of PRC2 supports this notion, showing that PRC2 can associate to all its target sites at a genome-wide level in the absence of any form of H3K27 methylation , H3K27me3 deposition and PRC2 target association were fully restored within 24 h after EZH2 re-expression  that were linked to PRC2 de novo nucleation47. The lack of transcription from CpG-rich promoters was further proposed as a determinant for PRC2 recruitment, which would imply that all CpGi are potential targets of PRC230. Importantly, our catalytically inactive EZH2 mutant was recruited to all PRC2 target sites, despite its inability to deposit any form of H3K27 methylation. Although in agreement with the H3K27me3 nucleation and spreading model47, this result further suggests that de novo binding of PRC2 to all target sites is uncoupled from H3K27me3 nucleation. Overall, our data link the models of PRC2 being tethered by transcriptional silencing with the mechanisms of spreading H3K27me3 domains.How PRC2 acquires specificity for target gene recognition in specific cell lines is also an important open question. Here, we showed that in ESCs that had been cultured for an indefinite number of passages in the absence of any form of PRC2 activity . This accounts for approximately 90% of total RING1B associated at PcG sites in agreement with previous reports using Eed KO ESCs58. The massive RING1B displacement . Importantly, while H3K27me2/3 deposition and PRC1 association at target sites were sufficient to maintain target genes repression, they were not sufficient to prevent developmental failure when Ezh2 KO ESCs were challenged to differentiate into EBs. This suggests that additional features to the direct control of target genes repression could contribute to proper development. Our data using the Ezh2 R685C mutant further suggest that diffused chromatin H3K27ac could be a major determinant in PRC2-mediated developmental defects.One critical activity of PRC2 is its ability to affect PRC1 recruitment to chromatin. PRC1 can exist in different forms that can be roughly divided in canonical and non-canonical forms. Canonical PRC1 complexes are characterized by the presence of CBX proteins, which retain binding affinities for H3K27me3 substratesent Fig.  further 17. In addition, we and others have shown that loss of PRC2 enzymatic activity always results in an aberrant increase in the levels of H3K27 acetylation40. We have previously mapped the sites of H3K27ac accumulation in Eed KO ESCs and have shown that such deposition occurs at both H3K27me3-decorated promoters and in the intra- and intergenic space at which H3K27me2 is broadly diffused17. As this can result in an altered activation of enhancer elements, we speculate that loss of H3K27me2 deposition could have a major role in PRC2-dependent deregulated control of cell identity. Although Ezh2 KO ESCs showed a similar gain in H3K27ac as Ezh1/2 dKO and Ezh2 Y726D ESCs, our ChIP-seq analyses for H3K27ac in Ezh2 KOs demonstrated that all PcG target promoters were more protected from H3K27 hyperacetylation  in Ezh1 KO cells. Introduction of the desired point mutation was confirmed by restriction analysis followed by Sanger sequencing.ESCs KO clones were generated in E14TG2a by CRISPR/Cas9 targeting mESCs were grown on 0.1% gelatin-coated dishes in 2i/LIF-containing GMEM medium (Euroclone) supplemented with 20% fetal calf serum (Euroclone), 2 mM glutamine (Gibco), 100 U/ml penicillin, 0.1 mg/ml streptomycin (Gibco), 0.1 mM non-essential amino acids (Gibco), 1 mM sodium pyruvate (Gibco), 50 µM ß-mercaptoethanol phosphate-buffered saline , 1000 U/ml leukemia inhibitory factor , and GSK3β and MEK 1/2 inhibitors (ABCR GmbH) to a final concentration of 3 μM and 1 μM, respectively.Ring1A KO Ring1B fl/fl conditional mESC and Ring1b KO mESC lines were generated elsewhere9. Ring1A-/-; Ring1B fl/fl;Rosa26::CreERT2 conditional mESC were subjected to CRISPR/Cas9 editing in order to obtain double Ezh1/Ezh2 KO. Cells were transduced with 5 μg/ml polybrene and lentiviral particles delivering pLIX_402 vector (Addgene #41394) expressing human wild-type EZH2 for 36 h and then subjected to 72 h -long puromycin-selection (1 μg/ml). For inducible EZH2 re-expression, cells were treated for 24 h with 0.5 μM 4-hydroxytamoxifen  in order to obtain Ring1b KO; then were treated with a combination of 4-OHT (or EtOH) and doxycycline (1 μg/ml) for 48 h prior harvesting.Rosa26:Cre-ERT2 mESCs were induced to differentiate to EBs by LIF removal in hanging drops (1000 cells/20 μl drop) on the lid of 15 cm petri dishes for 48 h. EBs were then collected and stimulated with 0.5 µM all-trans retinoic acid (ATRA) for 3 days. EBs were left in culture in non-coated petri dishes in ES medium without LIF until day 7. Medium was replaced every second day.Where indicated, undifferentiated mESCs were treated with 10 μM MG132  dissolved in 100% DMSO for 6 h.For rescue experiments, pCAG vectors encoding Flag-Avi-tagged human wild-type EZH2 or EZH2 Y731D mutant or pCAG vector encoding Flag-HA-tagged human wild-type EZH1 were transfected into mESCs using Lipofectamine 2000 (ThermoFisher Scientific), according to the manufacturer’s instructions. Cells were subjected to puromycin selection (2 µg/ml) for 24 h and then collected for subsequent analyses.For western blot analysis, mESCs were lysed and sonicated in ice-cold S300 buffer  and supplemented with protease inhibitors (Roche). For co-immunoprecipitation experiments, cells were lysed in the same lysis buffer. Immunoprecipitation was performed on 1 mg protein extracts using homemade anti-EZH2 (AC22) cross-linked sepharose beads  for 3 to 4 h at 4 °C. Immunocomplexes were washed 5× with S300 buffer and eluted in Laemmli sample buffer. Protein lysates were separated on sodium dodecyl sulfate polyacrylamide gel electrophoresis gels and transferred to nitrocellulose membranes. After probing with the suitable primary and secondary antibodies, chemoluminescence signals were captured with the ChemiDoc Imaging System (Bio-Rad).3), anti-Suz12 , anti-Eed , anti-p53 (1:5 dilution: homemade), anti-flag , anti-Mtf2 , anti-Jarid2 , anti-Phf19 , anti-EPOP (1:1000 dilution)66, anti-P300 , anti-CBP , anti-HA , anti-H3K27me1 , anti-H3K27me2 , anti-H3K27me3 , anti-H3K27ac , anti-H3K9K14ac , anti-H3K14ac , anti-H3K18ac , anti-H3K23ac , anti-H4K5ac , anti-H4K8ac , anti-H4K12ac , anti-H4 , anti-H2AK119ub , anti-H3 , and anti-H2A .Western blot analyses were performed with: anti-Vinculin , anti-Oct3/4 , anti-Ezh2 , anti-HA , anti-Jarid2 , anti-Mtf2 , anti-Phf1967, anti-EPOP66, anti-H3K27me1 , anti-H3K27me2 , anti-H3K27ac , anti-H3K36me3 , anti-H2AK119ub  and purified rabbit IgG .ChIP assays were performed using: anti-Suz12 , anti-Ring1b  and retro-transcribed with ImProm-II™ Reverse Transcription System (Promega) according to the manufacturer’s instructions. Quantitative real-time PCR (qPCR) was carried out using GoTaq qPCR master mix (Promega) on CFX96 Real-Time PCR Detection System (Bio-Rad). R by function “pheatmap”. Primer sequences are available upon request.Graphical representations of qPCR analysis were generated in 68 with minor modifications. Briefly, poly-A containing mRNA molecules obtained from 1 μg of total RNA were copied into first-strand cDNA by reverse transcription and template-switching using oligo(dT) primers and an LNA-containing template-switching oligo (TSO). Resulting cDNA was pre-amplified with KAPA HotStart Taq enzyme (Kapa Biosystems) and then purified with Ampure beads (Agencourt AMPure XP- Beckman Coulter). Two nanograms of pre-amplified cDNA were tagmented with in-house produced Tn5 transposase and further amplified with KAPA HotStart Taq enzyme. After purification with Ampure beads, the quality of the obtained library was assessed by Bioanalyzer , prior sequencing.RNA-seq was performed following SMART-seq2 protocol69. Duplicates were removed using PICARD (http://broadinstitute.github.io/picard/). HTseq-count v0.8.070 was used to calculate counts with parameters --stranded = no --mode = intersection-nonempty using RefSeq mm9 annotation downloaded from UCSC. DESeq2 v1.20 R package was used to perform differential expression analyses using default parameters71. Genes with an absolute log2 fold change of 1 were considered as differentially expressed.Reads were aligned to the mouse reference genome mm9 using TopHat v2.1.17. Briefly, 1% formaldehyde cross-linked chromatin was sheared to 500–1000 bp fragments by sonication and incubated overnight in IP buffer  at 4 °C with the indicated antibodies. For ChIP for SUZ12 and RING1B, 400–500 μg of chromatin and 5 μg of antibodies were used. For ChIP for histone modifications, 200–250 μg of chromatin supplemented with 5% spike-in of S2 Drosophila chromatin (prepared in the same manner) and 3 μg of antibodies were used. The next day, chromatin lysates were incubated for 2 to 4 h with protein-A sepharose beads . Beads were washed 3× with low-salt buffer  and 1× with high-salt buffer , and then re-suspended in de-crosslinking solution . DNA was purified with QIAquick PCR purification kit (Qiagen) according to the manufacturer’s instructions. DNA libraries were prepared with 2–10 ng of DNA using an in-house protocol72 by the IEO genomic facility and sequenced on an Illumina HiSeq 2000.ChIP experiments were performed according to standard protocols73, favoring only unique alignments and filtering out duplicates. For ChIP-RX experiments, sequencing reads were also aligned to the Drosophila reference genome (dm6) and used to compute a normalization factor for individual datasets. Peak calling was performed from aligned reads using MACS274. Significant bound regions were defined using a cut-off of p = 1 × 10–1. BigWig files were built from Bam files with 200 bp reads, extension, input subtraction, and library size scaling using the function bamCompare from deepTools 2.075. For ChIP-Rx samples the scaling factor was calculated as described in ref. 76. Heatmaps were generated using the computeMatrix and plotHeatmap utilities from deepTools75. The matrix was normalized by a factor calculated as 1/(max – min) when samples were subjected to ChIP analysis with the same antibody. The reference point for plotting was selected with respect to the transcription start site (TSS), and the 5000 bp distances upstream and downstream of TSS (TSS ± 5 kb) were graphically represented. Tracks were subsequently visualized using the IGB genome browser77.Sequencing reads were aligned to the mouse reference genome (mm9) using BowtieR. H3K27me3 density distribution was computed within SUZ12 peaks and within a 1.2 kb region of 5′ and 3′ of SUZ12 peak boundaries. Target regions (N = 3968) were selected considering SUZ12 peaks colocalizing with RING1B with P ≥ 1 × 10–1 in wild-type mESCs. Correlation analysis was performed between wild-type and Ezh2 KO samples. The IN/OUT density ratio was plotted, and linear regression analysis was calculated in R. T-test statistics were applied.Boxplots were generated with the package ‘ggplot’’ of 78 with minor modifications. Briefly, 50,000 mESCs were directly lysed , re-suspended in 50 μl of TAPS-buffer 1 ×  added with 8% PEG and in-house produced Tn5 transposase and incubated at 37 °C for 1 h at slow agiting (500 rpm). After a double purification with Ampure beads (Agencourt AMPure XP- Beckman Coulter) recovered tagmented DNA was amplified and barcoded by PCR performed with KAPA HotStart Taq enzyme (Kapa Biosystems).ATAC-seq was performed following previous methodsAfter size selection step with Ampure beads, libraries were eluted in 22 μl of pure water, quantified with Qubit fluorometer (Invitrogen) and checked with Bioanalyzer instrument , prior sequencing.73, favoring only unique alignments and filtering out duplicates with PICARD. Heatmaps were generated using the computeMatrix and plotHeatmap utilities from deepTools75. Boxplot of normalized intensity was performed with ggplot. Enhancers annotation was obtained from previous work in ESC79.Paired sequencing reads were aligned to the mouse reference genome (mm9) using Bowtie v1.2.2Further information on experimental design is available in the Supplementary InformationReporting SummarySource Data