Chromatin insulators shield promoters and chromatin domains from neighboring enhancers or chromatin regions with opposing activities. Insulator-binding proteins and their cofactors mediate the boundary function. In general, covalent modification of proteins by the small ubiquitin-like modifier (SUMO) is an important mechanism to control the interaction of proteins within complexes.Drosophila. SUMOylation augments the enhancer blocking function of four different insulator sequences and increases the genome-wide binding of the insulator cofactor CP190.Here we addressed the impact of dSUMO in respect of insulator function, chromatin binding of insulator factors and formation of insulator speckles in These results indicate that enhanced chromatin binding of SUMOylated CP190 causes fusion of insulator speckles, which may allow for more efficient insulation.The online version of this article (doi:10.1186/s13072-017-0140-6) contains supplementary material, which is available to authorized users. Drosophila [Drosophila, such as Su(Hw) [Insulators are DNA regulatory sequences that prevent functionally distinct chromatin domains from improper interference. They have basically two functions: the enhancer blocking and the barrier function , 2. The osophila –11. A tos Su(Hw) , BEAF-32s Su(Hw) –15, Zw5 s Su(Hw) , GAF [17s Su(Hw) , as wells Su(Hw) .Drosophila, dCTCF binds to six out of eight boundary elements of the bithorax complex (BX-C) and thereby contributes to the correct expression pattern of the homeotic genes in this gene cluster [Drosophila and to mediate insulator function [Drosophila S2-cells. Genome-wide RNAi depletion identified many factors required for Fab8-mediated insulation. Among these were the remodeling complexes NURF and dREAM, but also the histone variant H3.3, which have been functionally tested to contribute to insulation [Drosophila, Smt3 [Drosophila, SUMOylation is known to be required for the regulation of transcription by modifying transcriptional regulators, such as Mi-2, Groucho, Vestigal, CP190 and Mod(mdg4) [Drosophila has been controversially discussed. On the one hand, the IBP cofactors CP190 and Mod(mdg4) were found to be SUMOylated [gypsy insulator [gypsy-mediated insulation by reducing the amount of Mod(mdg4) required for the activity of Su(Hw)-dependent insulation [In  cluster , 20. One cluster , 21. dCTfunction . We usedsulation , 23. Onela, Smt3 , 25. SUMla, Smt3 –28. Manyod(mdg4) –33. The MOylated  and the nsulator . On the sulation .Drosophila, we expressed FLAG-Smt3 (FLAG-dSUMO) in Drosophila S2-cells. We find a striking co-localization of CP190 sites with SUMO and an increase in CP190 chromatin binding upon FLAG-dSUMO expression. SUMO depletion results in a loss of enhancer blocking activity and an increase in insulator speckle formation. Therefore, we can conclude that in the context of an enhancer blocking activity SUMOylation is required.To get more insight into the role of SUMOylation in the context of insulation and the function of IBPs in Drosophila are the dSUMO peptide Smt3, Activator of SUMO-1 (Aos1) and Ubiquitin activating enzyme 2 (Uba2), which form the hetero-dimer of the SUMO-activating enzyme [cis-regulatory elements in the genome. A second Fab8 or other insulator sequences were inserted in-between the enhancer and the promoter to block the enhancer activity. A corresponding control construct was lacking a functional insulator sequence at this position. Constructs were transfected into S2 cells, and stable clone pools were generated. These were challenged with double-stranded RNA directed against the known components of the SUMOylation pathway or, as a negative control, against GFP. Depletion of each of the six SUMO controlling factors resulted in an increase in luciferase activity of the Fab8 insulator construct  was performed. As a positive control for FLAG-IP, we used a S2 cell clone expressing a FLAG-HA-CP190 fusion. The Western blot showed that CP190 is SUMOylated . S2 cell clones for luciferase reporter assay were transfected as described in before [S2 wild-type cells, clones and clone pools were raised and cultured in Schneider’s Medium , 1% penicillin/streptomycin and glutamine). n before . A cell www.flyrnai.org ; α-dCTCF N-terminal, α-dCTCF C-terminal ; α-CP197 cells/20 ml flask. Inducible cells were induced with 10 ng CuSO4 24 h after seeding cells. After 48 h, the cells were harvested. Lysis was performed with SDS-Lysis-Buffer  (modified for Drosophila cells [2O) [S2 cells were seeded 2 × 10la cells ) for 15 lls [2O) . Sonicat7 cells/20 ml flask. Inducible cells were induced with 10 ng CuSO4 24 h after seeding cells. After 48 h, they were harvested and counted. The ChIP was performed as described previously [2O) was added to the cells and the DNA was fragmented with the Bio Ruptor sonicator (BioRad) for 2 × 10 cycles  within the ice-cooled sonicator. DNA was tested for fragment length of 200–500 bp on 1.5% agarose gel after phenol–chloroform extraction of DNA from chromatin. Chromatin samples were diluted 1:10 by dilution buffer , with addition of glycerol for stability of the chromatin. 10% input samples were separated from prepared chromatin and were stored at −20 °C. Pre-clearing of chromatin was performed with protein A/G agarose beads (Millipore) for 45 min at 4 °C. IP with specific antibodies against CP190, dCTCF, FLAG and Histone H3 was performed at 4 °C. Mouse IgG was used as IP control. After 4 h, beads were added and the solution was further incubated at 4 °C. The beads were than washed with increasing salt concentration: low salt buffer , high salt buffer  and LiCl buffer , as well as 2× TE buffer . Clean up of DNA was performed with 100 µl Chelex 100 agarose beads (BioRad). Proteinase K digestion was done with 10 µg/ml of the enzyme during Chelex DNA extraction. DNA from 10% input samples were also extracted by the Chelex method [S2 cells were seeded 2 × 10eviously  with min4) and non-induced (−CuSO4) was collected for the corresponding IP with CP190, dCTCF and FLAG antibody. Input DNA was collected as control. Sequencing libraries were prepared with the NEBNext ChIP-seq Library Prep Reagent (New England Biolabs) according to manufacturer’s instructions. Cluster generation was performed using the cBot (Illumina Inc.). Sequencing was done on the HiSeq 2500 (Illumina Inc.) using TruSeq SBS Kit v3-HS (Illumina) for 50 cycles. Image analysis and base calling were performed using the Illumina pipeline v 1.8 (Illumina Inc.). Individual ChiP-seq data were validated extensively by specific qPCR. Raw and processed data have been deposited in the NCBI gene expression omnibus (GEO) under accession number (GSE96581). For validation of the ChIP-seq experiments, ChIP-qPCR was performed. 1 µl of ChIP-DNA was used for qPCR. Dilutions of the input  were used to determine a standard curve. The PCR was performed using specific 0.2 µM primers (Invitrogen) for CP190/dCTCF, CP190-only and negative sites. Primers (Suppl. Table S1) were partially chosen from the ChIP-seq experiment, but were also taken from previous publications [For sequencing, replicates of IPs had to be pooled. The pooled samples were concentrated by speed vac. 10 ng DNA of the S2 FLAG-dSUMO expressing pool induced . Two peaks were determined to be overlapping in case they had a minimal overlapping interval of 1 bp.ChIP-seq reads were converted to fastq format and aligned to a precompiled dm3 reference index with BOWTIE . SequencIn order to identify differentially bound regions, we collapsed all binding regions determined for a given factor under different conditions and extracted the read numbers mapping to these collapsed intervals. DESeq  was usedmultiinter function of the BedTools suite [cluster option turned on. All intervals like binding peaks were annotated with respect to dm3 RefSeq annotations downloaded from UCSC genome browser repository.In case we were studying the overlap of multiple binding factors at once, we used the ls suite  with theStatistics for all meta-analyses were calculated using the Wilcoxon signed-rank test in R.After extension of reads, continuous coverage vectors were calculated and normalized per million reads to account for differential library sizes. These data were used to collect data in windows of different sizes spanning features of interest . The binding data were binned across binding sites in 50 bp windows, and the mean was calculated at each position in order to generate cumulative average binding profiles. For representation in genome browsers, profiles were additionally smoothed using kernel regression estimates. Data were visualized using the Gviz BioConductor package.fastq-dump tool of the SRA toolkit version 2.3.5. Reads were processed, and peaks were called as described above. Data sets used in this study were:GSM1015404, CP190; GSM1015406, Su(Hw); GSM1015408, dCTCF; GSM1015410, dCTCF; GSM1015410, Input .GSM1133262, Input; GSM1133264, CG8436 (IBF1) .GSM1278639, Beaf-32; GSM1278640, IgG .GSM1333827, Pc; GSM1333829, Input .GSM2042225, Pita; GSM2042228, Input .For comparison with other transcription factor binding data we downloaded processed binding data from ChIP-experiments for dSUMO, Pita, ZIPIC, Polycomb, IbF1, CP190 und dCTCF , 42 werep value <0.05) for one molecular function, were converted as a diagram in Microsoft Excel.GO analysis was used to identify factors which correlate with one or more biological function. Here factors identified in the RNAi screen with a z score >2 were used for GEO analysis. Gene names were supplied into the GO term analysis tool GeneCoDis3 . All facImmunostaining was performed as described . S2 cell® (IBM® SPSS® Statistics 22). Differences in corrected FLAG-dSUMO signal between mock knockdown and specific knockdown were analyzed by two-tailed Mann–Whitney U test. Significance level is defined as two-tailed asymptotic significance p < 0.05. Differences in number of CP190 intensity maxima between FLAG-dSUMO-positive and negative cells were also analyzed by two-tailed Mann–Whitney U test. Significance level is defined as two-tailed asymptotic significance p < 0.05.Analyses of CP190 intensity maxima as well as determination of FLAG-dSUMO signal intensity were performed with Fiji, the image processing package of ImageJ. FLAG-dSUMO signal intensity measured by integrated density was background-corrected. Statistical analyses were carried out using SPSSAdditional file 1: Figure S1. dCTCF and CP190 peaks co-localize with previously identified dCTCF and CP190 binding sites. Peaks for dCTCF and CP190 were identified using MACS2 based on publicly available (GSE41354) ChIP-seq profiles published in Ong et al. [g et al.   after RNAi against GFP, against dSUMO (Smt3) and against both components of the E1 SUMO-activating enzyme, Aos1 and Uba2 . ChIP-qPCR at CP190 example sites, in % of input. IgG was used as negative control . Sites without any binding CP190 (cg12054 neg. and lwr neg.) were used as control sites.Additional file 3: Table S1. All primer sequences are listed.Additional file 4: Table S2. Flag-SUMO and CP190 staining after luc or after Smt3 knockdown. Analyses of CP190 intensity maxima as well as determination of FLAG-dSUMO signal intensity were performed with Fiji, the image processing package of ImageJ. FLAG-dSUMO signal intensity measured by integrated density was background-corrected.Additional file 5: Table S3. Flag-SUMO and CP190 staining after luc or after Aos/Uba2 knock down. Analyses of CP190 intensity maxima as well as determination of FLAG-dSUMO signal intensity were performed with Fiji, the image processing package of ImageJ. FLAG-dSUMO signal intensity measured by integrated density was background-corrected.


KIP1 (p27) and the E3 ubiquitin ligase component S-phase kinase-associated protein 2 (SKP2), which control each other by forming a triangular network. MYC is engaged in bidirectional crosstalk with each of these players; while MYC regulates their expression and/or activity, these factors in turn modulate MYC through protein interactions and post-translational modifications including phosphorylation and ubiquitylation, impacting on MYC’s transcriptional output on genes involved in cell cycle progression and senescence. Here we elaborate on these network interactions with MYC and their impact on transcription, cell cycle, replication and stress signaling, and on the role of other players interconnected to this network, such as CDK1, the retinoblastoma protein (pRB), protein phosphatase 2A (PP2A), the F-box proteins FBXW7 and FBXO28, the RAS oncoprotein and the ubiquitin/proteasome system. Finally, we describe how the MYC/CDK2/p27/SKP2 axis impacts on tumor development and discuss possible ways to interfere therapeutically with this system to improve cancer treatment. MYC is a pleiotropic transcription factor that controls a number of fundamental cellular processes required for the proliferation and survival of normal and malignant cells, including the cell cycle. MYC interacts with several central cell cycle regulators that control the balance between cell cycle progression and temporary or permanent cell cycle arrest (cellular senescence). Among these are the cyclin E/A/cyclin-dependent kinase 2 (CDK2) complexes, the CDK inhibitor p27 MYC, MYCN, and MYCL, here collectively referred to as “MYC”, encodes transcription factors that contain a basic region/helix-loop-helix/leucine zipper (bHLHZip) type of DNA-binding and protein interaction domain. All MYC proteins heterodimerize with the obligatory bHLHZip protein MAX, which enables the MYC:MAX heterodimer to bind so-called E-box DNA sequences (CACGTG and similar sequences) situated in regulatory regions of target genes [The MYC oncogene family, consisting of et genes ,2,3,4,5.et genes  . In lization ,46,47,48KIP1 (referred to from now on as p27) and the S-phase kinase-associated protein 2 (SKP2), together constituting the CDK2/p27/SKP2 network, and the impact this pathway has on MYC-regulated transcription and tumorigenesis. We further elaborate on possible therapeutic interventions within this pathway as a potential new strategy to target MYC in cancer.This review focuses on regulation of MYC through protein–protein interactions and post-translational modifications at MYC-boxes 1 and 4 involving cyclin-dependent kinase 2 (CDK2), the cyclin-dependent kinase inhibitor p27The N-terminal MYC-box 1 (MB1) in the transactivation domain is one of the important regulatory regions of MYC, playing a role in transcriptional activity and transformation. It also encompasses a conserved phospho-degron, i.e., an amino acid sequence containing a phosphorylation site(s) involved in protein degradation ,49. Two cis to trans by PIN1 in Thr-58/Ser-62 double phosphorylated MYC enables dephosphorylation of Ser-62 by PP2A, and subsequent FBXW7-mediated MYC degradation  . AccordiIt is, however, still unclear why de-phosphorylation of Ser-62 would be required for FBXW7-mediated degradation, since FBXW7 binds a double Ser-62/Thr-58 phosphorylated MYC peptide with as high or higher affinity , and sinAlthough still somewhat of a conundrum, evidence is accumulating that phosphorylation at Ser-62 not only primes for Thr-58 phosphorylation and MYC destruction, but also plays an independent role in MYC regulation  did not stabilize the protein during mitosis in neuronal cells, but instead resulted in rapid degradation of MYCN via GSK3β-mediated phosphorylation of Thr-50 (corresponding to MYC Thr-58). Further, inhibition of PP2A also increased degradation of MYC in this system, correlating with increased Ser-62 phosphorylation . Although there seems to be a general agreement that Thr-58 phosphorylation destabilizes MYC, there are thus conflicting data on whether Ser-62 phosphorylation affects MYC stability. If Ser-62 phosphorylation stabilizes MYC, one would expect a S62A mutant to decrease stability. However, this mutant has been reported to exhibit a similar half-life to the WT MYC in REF52 cells , or incrWhat could be the explanation for these discrepancies? The literature seems to agree that as a priming event, Ser-62 phosphorylation does promote FBXW7-induced MYC degradation. However, the execution of this signal can be enhanced/speeded up or dampened/delayed by a number of other players, including PP2A, PIN1 and GSK3β in response to RAS, PI3K/AKT or WNT signaling, as mentioned above. In addition, FBXW7-induced degradation of MYC is fine-tuned by the de-ubiquitylation enzyme USP28 , which sTaken together, although stabilization of MYC by Ser-62 phosphorylation may contribute to the increased potency of MYC in the biological systems described above, this is unlikely to be the sole explanation. thrombospondin (TSP-1) gene, a regulator of angiogenes, as well as activation of the well-known MYC target gene ornithine decarboxylase 1 (ODC1). Expression of the MYC S62A mutant reversed these changes in TSP-1 and ODC1 expression, and apparently behaved as a dominant negative mutant in this setting.Experiments performed already in the early 90s using the MYC TAD fused to the DNA binding domain of GAL4 in promoter/reporter assays suggested that the activity of the MYC TAD was enhanced by serum stimulation or by ERK2, and was regulated during the cell cycle in a Ser-62 dependent manner ,89. Simiglutamyl-cysteine synthetase (γ-GCS) gene—a key enzyme in glutathione synthesis. Although expressed at the same level as WT MYC, MYC S62A was not recruited to the promoter, and expression of this mutant increased oxidative stress-induced cell death. Our laboratory showed that MYC suppressed RAS-induced cellular senescence in REFs in a Ser-62-dependent but Thr-58-independent manner. Pharmacological inhibition of CDK2-mediated Ser-62 phosphorylation resulted in depletion of MYC from genes involved in senescence regulation despite continuous MYC expression, resulting in abrogation of MYC-mediated senescence suppression. These studies suggest that Ser-62 phosphorylation is involved in recruitment and maintenance of MYC at subsets of MYC-regulated genes involved in managing oncogene-induced and other types of stresses that can cause apoptosis or senescence [In what way could Ser-62 phosphorylation affect MYC-regulated transcription mechanistically? Benassi et al.  found thnescence ,58. The question remains how phosphorylation of Ser-62 affects the association of MYC with certain genomic loci and MYC-dependent gene regulation? Sears’ laboratory recently reported that the prolyl isomerase PIN1 increases the rate of recruitment of MYC to chromatin . This reEvidence is emerging that both the phosphorylated and unphosphorylated Ser-62 residue may act as binding sites for different factors. The tumor suppressor protein BIN1 was shown to interact with unphosphorylated Ser-62, but displaced upon Ser-62 phosphorylation . AnotherTaken together these reports suggest that phospho-Ser-62-interacting proteins influence activity, target gene selection and intracellular localization of MYC  and RNA-sequencing and proteomics studies. Further, mice expressing phosphorylation mutants of MYC, some of which were exemplified above, could be depleted of Ser-62 kinase candidates by any of the currently available technologies  techniques, etc.) to fully decipher the role of Ser-62 phosphorylation in regulating MYC transactivation activity and stability. In the next sections we put particular focus on the role of CDK2, not only as a Ser-62 kinase but also with respect to its other substrates and interaction partners including p27 and SKP2, in MYC regulation. CDK2 is a core cell cycle component that is mainly active from late G1-phase and throughout the S-phase due to its activity being dictated by E-type and A-type cyclins fluctuating in their expression during the cell cycle . Growth Progression and completion of S-phase is regulated by A-type cyclins, which in complex with CDK2 promotes DNA replication and have functions in DNA damage repair ,126 Fig. Cyclin Our research laboratory and the laboratory of Bruno Amati demonstrated a direct connection between MYC and CDK2 in modulating cellular senescence in normal cells and cancer cells ,134. MouhTERT, BMI-1, INK4A and p21CIP1p16 [Further, our laboratory demonstrated that MYC is directly targeted by CDK2-mediated phosphorylation at Ser-62, an event counteracted by pharmacological inhibitors of CDK2 and by overexpression of p27. As mentioned above, MYC is phosphorylated at Ser-62 in response to oxidative stress, which was shown to be required for MYC to balance this stress , thus fu p21CIP1 . Further p21CIP1 ,134. Pho p21CIP1 , and dep p21CIP1 . It remaSKP2 ubiquitin ligase complex [One of the gatekeepers for G1-S transition of the cell cycle is p27, which by binding to cyclin A/E-CDK2 in the nucleus blocks the catalytic activity of these complexes ,156,157  complex ,161,162 SKP2 [SKP2, SKP2 binds to and targets numerous substrates, typically tumor suppressor proteins and oncoproteins linked to gene transcription, metabolism, cell survival, and cell cycle progression including, in addition to p27, p21CIP1, cyclin A, MYC, E2F1, p130, FOXO1, SMAD4, BRCA2, MLL, LKB1 and CDK9 [SKP2 may differ depending on target. As examples, SKP2 targets tumor suppressors such as p27 [SKP2 was initially discovered as an interacting partner of the cyclin A/CDK2 complex , but wasSKP2 ,165,166.and CDK9 . The modh as p27 ,162 and h as p27  through h as p27 ,170. SKPh as p27 . SKP2 knockout systems suggests that SKP2 primarily functions as an oncogene [SKP2 deletion was shown to abrogate tumor formation in the pituitary and prostate in mice devoid of the major tumor suppressors p53 and pRB [SKP2-induced p27 degradation [SKP2 complex, SKP2 can also target p27 for ubiquitylation and degradation by another SCF complex containing CUL4A and the damaged DNA-binding protein DDB1, which is associated with the COP9 signalosome, a complex with isopeptidase activity [Genetic analysis using multiple oncogene . For ins and pRB . Numerou and pRB . p27 has and pRB ,174. Nak and pRB ,175. The and pRB . The abi and pRB . Express and pRB ,178,179, and pRB ,180, a cradation ,182. In activity .Expression and activity of SKP2 are regulated at multiple levels including mRNA expression, protein degradation and intracellular localization. SKP2 mRNA expression is activated by both E2F1 and MYC, and is under negative regulation by pRB through repression of E2F  109,184184,185. SKP2 gene expression around the G1-S transition [SKP2 and the 26S proteasome associated together with MYC at target gene promoters, indicating that ubiquitylation and degradation of MYC occurred directly at the site of MYC activity. In fact, ubiquitylation of MYC through the SCFSKP2 complex appeared to have dual outcomes; in addition to proteasome degradation, SKP2 also promoted and was even required for MYC-driven transcription of cell cycle-associated genes and for MYC-induced S-phase entry. This suggested that SKP2 acts as a transcriptional cofactor for MYC and feeds back to MYC in a positive forward loop where MYC and SKP2 activate each other . On ansition ,191. Sinch other  [190,191SKP2 complex also induced p53-independent cellular senescence [As discussed earlier, both MYC depletion, CDK2 inhibition/depletion and p27 activation cause senescence induction even in p53-independent MYC-driven systems through downregulation of MYC target genes involved in senescence suppression ,134,196.nescence . A potenARF, which is induced by MYC as a result of oncogenic stress, inhibits the interaction between MYC and SKP2 resulting in decreased MYC ubiquitylation, which in turn switches the repertoire of genes regulated by MYC towards induction of apoptosis [The interaction between MYC and SKP2 is also subjected to competition from other proteins, affecting MYC’s transcriptional output. Hann’s laboratory reported that the tumor suppressor protein p19poptosis . SNIP1 ipoptosis ,199.SKP2 conjugates K63-linked ubiquitin chains to certain substrates [The apparent connection between the transcriptional activity of MYC and its destruction by SKP2 is a good example of what has been called the “activation by destruction”, “Kamikaze” or “Black Widow” models of transcription ,203,204.bstrates ,170,171,bstrates ,191. Twobstrates ,9, at lebstrates . The latWhat could be the mechanism(s) by which SKP2 and other E3 ligases promote MYC-driven transcription? There are several findings, both for MYC and other transcription factors, that gives some insight to how this might work: (1) Recruitment of cofactors. Ubiquitin conjugated to transcription factors in the form of mono-, oligo- or poly-ubiquitylation with various types of linkage may serve as binding sites for cofactors with a role in transcription ((2) Recruitment of the proteasome. Conjugated ubiquitin chains are bound by the 26S proteasome through interaction with ubiquitin-binding subunits of the 19S regulatory subcomplex . The 19SSo, why would recruitment of the proteasome to an ubiquitylated transcription factor stimulate transcription? There are different models for how this might work: (a) Non-proteolytic role of the 19S regulatory subcomplex of the proteasome. There are a number of reports suggesting that the ATPase activity of the 19S AAA ATPase subunits could also participate in remodeling of transcription complexes in a non-proteolytic fashion in order to facilitate transcription ((b) Proteolytic role of the 26S proteasome. It is still not entirely clear if the 19S particle ever exist separated from the 20S proteasome particle in cells, and if it can be uncoupled from the catalytic activity of the proteasome. Studies of the distribution of 19S and 20S over the genome in yeast by ChIP suggest a very similar distribution . There aCDK8 [What discrete steps of the MYC-driven transcription cycle involve SKP2 and other parts of the ubiquitin/proteasome system? Although this has not been investigated specifically for SKP2, mutation of all potential ubiquitylation sites in MYC did not alter MYC’s chromatin association or RNA Pol II recruitment, but did affect recruitment of TRRAP/HATs and factors involved in transcription elongation such as BRD4 and P-TEFb . These cCDK8 . MacroH2CDK8 , raisingCDK8 ,196,197.SKP2 E3 ligase complex and simultaneously stimulates the activity of cyclin E/CDK2, thereby targeting p27 for proteasome-mediated degradation  By radation  [10,158,How does the interaction with p27 affect MYC? IFN-γ-induced association of p27 with MYC at chromatin correlated with loss of total and Ser-62-phosphorylated MYC from target promoters, reduced expression of MYC target genes, growth arrest, differentiation and induction of senescence in myeloid tumor cells ,125. EctFurther, since p27-induced degradation of MYC seemed to be independent of the SKP2 and FBXW7 E3 ligase complexes, these results points to the existence of a-yet-to-be-identified proteolytic E3 ligase whose activity towards MYC is mediated through p27 . As discussed above, binding of p27 to MYC may possibly compete with the binding of FBXO28, HUWE1/HECTH9 and SIRT1, which all stimulate MYC-induced transactivation, in the former cases by enhancing recruitment of p300. In this way, the outcome of p27 binding to MYC would be two-fold; reduced transcription of MYC target genes and degradation of MYC . One shoConsidering that p27 is a target for CDK2 and SKP2, and that all three proteins can associate with MYC at chromatin, an intriguing possibility is that p27-mediated repression of MYC at target gene promoters is reversed during G1-S phase transition or in tumors through elevated cyclin E and/or SKP2 expression. In such a scenario, cyclin E/CDK2-induced phosphorylation of p27 would be followed by SKP2-mediated degradation and loss of p27 from promoters. Through phosphorylation of Ser-62 and ubiquitylation of MYC and possibly other targets at promoters, cyclin E/CDK2 and SKP2 would subsequently contribute to further activation of transcription (It should be emphasized that this model is hypothetical; it remains to be demonstrated whether p27, CDK2 and SKP2 coexist at the same promoters simultaneously. Future studies need to dissect how these factors are distributed across the genome, if they are co-dependent and what influence they have on the transcriptome in different cellular contexts, including cell cycle and senescence regulation. Does the crosstalk between MYC and p27 described above have any relevance for human cancer? Since p27 expression is predominantly regulated at the level of mRNA translation and protein turnover  and p27 While MYC is a bona fide oncogene transcribed from the most frequently amplified locus across human cancer types, p27 does not exhibit the typical tumor suppressor properties, mostly obvious from its chromosomal locus not displaying focal and frequent deletions in cancers . This isIn addition to the relationship between MYC and p27, there is a well-established inverse correlation between SKP2 and p27 the expression in many human tumors as mentioned above, including thyroid, oral, breast, prostate, ovarian and lung cancer as well as lymphoma ,178,179.MYCN-amplified neuroblastoma and myeloid leukemia cell lines [What possibilities are there to target the MYC/CDK2/SKP2/p27 axis in cancer, i.e., reducing the activity of MYC, CDK2 or SKP2, or boosting nuclear p27 expression? There are numerous potential ways that can be considered. One is to utilize pharmacological inhibitors of CDK1/2. Treatment with CDK1/2 inhibitors or depletion of CDK2 efficiently inhibit growth and/or induce apoptosis in triple negative/basal breast cancer, ll lines ,249,250 ll lines ,249. Clill lines . While tll lines ,252. Anoll lines . Other ill lines ,255,256.ll lines ,231,232,ll lines ,257,258.ll lines ,75,259. ll lines ,261.Finally, the induction of p27 by cytokines like IFN-γ and TGF-β or by differentiation-, cell density- and adhesion-signals ,158,159 One of the most prominent oncogenic features of MYC is its ability to promote cell proliferation despite unfavorable conditions, which causes severe cellular stress, usually leading to cell death. This clearly limits MYC’s oncogenic potential. MYC therefore needs to cooperate with other factors in order to overcome these limitations, such as oncogenic RAS, the anti-apoptotic members of the BCL-2 family and mutated p53. This review has focused on the role of the CDK2/p27/SKP2 network, which is regulated by MYC but also feeds back to MYC through protein–protein interactions and post-translational modifications in positive or negative loops, thereby modulating MYC’s ability to regulate genes involved in cell cycle progression and senescence suppression.There are a number of questions that remains to be answered concerning the interplay between MYC and the CDK2/p27/SKP2 network. It is still unclear what subsets of MYC target genes are under control of this network. Considering the well-documented role of CDK2, p27 and SKP2 in cell cycle regulation, genes involved in this process are good candidates, and examples of such genes have also been documented ,125,191.


For many years, gene expression in the three cellular domains has been studied in an attempt to discover sequences associated with the regulation of the transcription process. Some specific transcriptional features were described in viruses, although few studies have been devoted to understanding the evolutionary aspects related to the spread of promoter motifs through related viral families. The discovery of giant viruses and the proposition of the new viral order Megavirales that comprise a monophyletic group, named nucleo-cytoplasmic large DNA viruses (NCLDV), raised new questions in the field. Some putative promoter sequences have already been described for some NCLDV members, bringing new insights into the evolutionary history of these complex microorganisms. In this review, we summarize the main aspects of the transcription regulation process in the three domains of life, followed by a systematic description of what is currently known about promoter regions in several NCLDVs. We also discuss how the analysis of the promoter sequences could bring new ideas about the giant viruses’ evolution. Finally, considering a possible common ancestor for the NCLDV group, we discussed possible promoters’ evolutionary scenarios and propose the term “MEGA-box” to designate an ancestor promoter motif (‘TATATAAAATTGA’) that could be evolved gradually by nucleotides’ gain and loss and point mutations. Poxviridae, Asfarviridae, Iridoviridae and Phycodnaviridae  of Bohle iridovirus (another Ranavirus member)—looking for conserved regions to be considered as regulatory elements , Mimiviridae family; the same early promoter motif was identified in the upstream region of 35% of genes [Mimiviridae family, which suggests that such a robust transcriptional apparatus was already present in the last common ancestor.In a distant relative, the of genes . Howeverof genes . MoreoveMarseilleviridae [After the discovery of mimiviruses, other giant viruses infecting amoebae were described, such as marseilleviruses, which is currently classified in the family eviridae . Other veviridae , pandoraeviridae ,126, phieviridae ,128 and eviridae . Althougeviridae ,9. In alThe proposed Megavirales order comprises viral families that exhibit some unique features that allow their clustering into a monophyletic group . In addiThe origin of the members of the Megavirales order is still under debate, but the evolutionary history of some of its members is already being told, at least concerning genome evolution. The first members to be analyzed were the poxviruses. It has been demonstrated by phylogenetic analysis based on the presence/absence of genes that genomes from this family have been subject to frequent events of gene duplication, deletion, and HGT from their hosts. Many of these genes can interfere with host immune signaling, such as homologues of cytokines receptors which could confer some advantages in the interaction with the hosts ,131,132.Iridoviridae family. It is particularly interesting the fact that iridoviruses infecting the same host-range exhibited a similar pattern of gene gain and loss, but this was slightly different when the viruses infected different hosts (fish vs. insect-infecting viruses), suggesting that such a pattern was driven by host–virus co-evolution [Phycodnaviridae and Mimiviridae has recently been described. The genomic comparisons of closely related viruses belonging to the Mimiviridae and Phycodnaviridae families show that genomes accumulating genomic mutations occur on successive cycles of genome expansion and reduction. In addition, there is no general tendency of genome expansion or contraction. Each family exhibits a specific pattern for gene acquisition, which might be a reflex of interaction with distinct hosts [The “accordion-like” pattern of evolution was also identified in different members of the volution . Finallyct hosts . Since tConsidering a common origin for the NCLDVs, a possible scenario is that the Megavirales’ common ancestor presented a ‘TATATAAAATTGA’ promoter motif, which we named here as the “MEGA-box” . Over time, with the Megavirales’ order radiation, the MEGA-box has been gradually evolved by nucleotides’ gain and loss, analogously to that reported for the entire genome, which evolved through gene gain and loss. The MEGA-box was slightly modified in the poxviruses lineage, at least concerning the early promoter motif. Considering the intermediate and the late promoter motifs of poxviruses, if they truly came from the MEGA-box, this could have happened through a series of nucleotide loss. However, it is also possible that the emergence of other promoters, rather than the early one, have emerged after the establishment of the poxvirus’ lineage, thus not originating from the ancestral promoter sequence. The same might be true for mimiviruses, phycodnaviruses and iridoviruses. Considering asfavirus and ascoviruses, their promoter sequences might have originated from the MEGA-box through successive gain and loss of nucleotides. However, another scenario is also possible, wherein their promoter motifs emerged from the poxviruses and iridoviruses lineages respectively (closest evolutionary groups). This scenario is in agreement with the proposition that the Megavirales’ ancestor was already a giant virus with a large genome . In thisWhether the NCLDVs came from a simple entity ,136, or Most of the giant viruses have a powerful genetic arsenal, encoding several proteins necessary for the transcription system which provides a relative independence of their hosts for this process. In addition, the transcription of this high gene content is temporally regulated by promoter regions that exhibit some similarities, indicating a common origin of these regulatory elements. Although many studies have already been done in relation to almost all viral families of the Megavirales order, most of them remain without biological confirmation; i.e., the promoter motifs in many giant viruses were predicted, but not experimentally validated. Therefore, the performance of biological studies to confirm the existence and the effect of all promoter motifs described so far in giant viruses is imperative. This analysis will truly establish the common temporal regulation pattern predicted in these viruses, and will also corroborate (or even refute) the hypothesis of an A/T-rich promoter in the Megavirales common ancestor. Moreover, the deep analysis of the genome of the recently described giant viruses , and also the discovery of new complex viruses, will strongly contribute to complete the puzzle of the origin and evolution of Megavirales.On the other hand, the biotechnology field will also be boosted by the advance in the studies of promoters and gene expression in giant viruses. Among the NCLDVs, the poxviruses are by far the best characterized group regarding the genome expression, especially the VACV. These viruses have been used as expression vectors for the synthesis of proteins and as vaccine candidates to prevent infectious diseases and treat cancer, mainly due to their high gene expression levels ,138. Thi


Developmental morphogens play an important role in coordinating the ductular reaction and portal fibrosis occurring in the setting of cholangiopathies. However, little is known about how membrane signaling events in ductular reactive cells (DRCs) are transduced into nuclear transcriptional changes to drive cholangiocyte maturation and matrix deposition. Therefore, the aim of this study was to investigate potential mechanistic links between cell signaling events and epigenetic regulators in DRCs.in vivo models, we examine the mechanisms whereby sonic hedgehog (Shh) overcomes an epigenetic barrier in biliary precursors and promotes both cholangiocyte maturation and deposition of fibronectin (FN).Using directed differentiation of induced pluripotent stem cells (iPSC), isolated DRCs, and in vivo results show that EZH2 KO mice fed the choline-deficient, ethanolamine supplemented (CDE) diet have an exaggerated cholangiocyte expansion associated with more robust ductular reaction and increased peri-portal fibrosis.We demonstrate, for the first time, that Gli1 influences the differentiation state and fibrogenic capacity of iPSC-derived hepatic progenitors and isolated DRCs. We outline a novel pathway wherein Shh-mediated Gli1 binding in key cholangiocyte gene promoters overcomes an epigenetic barrier conferred by the polycomb protein, enhancer of zeste homolog 2 (EZH2) and initiates the transcriptional program of cholangiocyte maturation. We also define previously unknown functional Gli1 binding sites in the promoters of cytokeratin (CK)7, CK19, and FN. Our in vitro by overcoming an EZH2-dependent epigenetic barrier and this mechanism also promotes biliary expansion in vivo.We conclude that Shh/Gli1 signaling plays an integral role in cholangiocyte maturation  Chronic biliary diseases and their complications continue to be the cause of significant morbidity and mortality and these disorders have very limited therapeutic options. Cholangin vitro model of the stem cell-to-cholangiocyte transition in general and provides basic insights into biliary maturation and the ductular reaction that occurs in biliary disease.iPSC are derived from somatic cells by forced expression of various pluripotency factors, rendering them capable of self-renewal and diverse differentiation . iPSC-deShh is a developmental morphogen with classical roles in embryologic patterning and morphogenesis in a variety of species and tissues. More recently, the Shh pathway has been implicated in regeneration and differentiation of stem cells  includinEpigenetic modifications are increasingly implicated in liver cell differentiation in various liver diseases , 27. TheIn this study, we tested the overarching hypothesis that Shh interacts with immature biliary precursors, promoting cholangiocyte maturation and FN deposition, through EZH2-mediated mechanisms. We employed specific pharmacologic and genetic interventions in two complementary models of differentiation, iPSC and isolated DRC, along with the CDE diet in genetically-modified mice. This study reports the following novel findings: 1) Shh simultaneously promotes both cholangiocytic maturation in biliary precursors and release of FN; 2) Previously unknown regulatory sites within the promoters of CK7, CK19, and FN bind Gli1 upon Shh stimulation; 3) This process overcomes an epigenetic barrier and promotes cholangiocyte gene expression by reducing EZH2-dependent transcriptional repression; and 4) Animals genetically lacking EZH2 have an exaggerated response to the CDE diet with enhanced ductular reaction, including biliary proliferation, FN production, and peri-portal fibrosis. Overall, these findings identify new molecular targets involved in biliary regeneration and the associated fibrosis. Therefore, these studies not only provide new mechanistic insights, but also have important biomedical relevance.NHC cells were a gift from N.F. LaRusso. NHC and iPSC-derived cholangiocytes (iDC) were cultured in H69 media   * 100.ChIP assays were performed at DE, HS, and HP phases of our iPSC-to-cholangiocyte differentiation protocol. At the last day of each phase, the cells were fixed with 1% formaldehyde. ChIP assays were performed using EZ-Magna-ChIPflox/flox mice have global knockout of EZH2 upon tamoxifen exposure. These mice were exposed to tamoxifen for 5 days by intraperitoneal injection and subsequently fed the CDE diet (choline-deficient chow with ethionine-supplemented drinking water) for 3 weeks. Control animals received standard mouse chow and non-supplemented water. Additional control animals included Cag Cre- EZH2flox/flox mice, and mice receiving vehicle in place of tamoxifen . Under Ketamine/Xylazine anesthesia, mice were sacrificed at week 3 for liver tissue collection or isolation of DRCs.All animal procedures were approved by the Mayo Clinic Institutional Animal Care and Use Committee . The EZH2 mice were generated in collaboration with Alexander Tarakhovsky. The EZH2 floxed mouse model was originally generated at the Rockefeller University, following standard homologous recombination techniques to inactivate the endogenous WT locus in embryonic stem cells, generating chimeras, and isolating colony founders carrying the knock-out of this gene. The loxP sites were introduced to flank exons 16–19 which encode the SET domain of EZH2. The genotype was preserved through the archiving of frozen sperm. Frozen sperm was used to recover heterozygous mice in the FVB/NJ strain and subsequently crossed back into a pure C57BL/6 background for more than 20 generations to produce the inbred strain used in this study. Genotyping was confirmed by Southern blot and routinely checked by PCR. The resulting CAG Cre+ EZH2Data are presented as the mean ± standard error. Data represents typical experiments reproduced at least three times. Analysis was performed using Graph Stat Prizm software . Statistical analyses used one-way analysis of variance (ANOVA) with a Bonferoni post-test.Using an iPSC-based endodermal cell differentiation model that recapitulates liver maturation , we findCCCACCCACAA), one putative site in the CK7 promoter (GGCCACCCCTCT), and another putative site in the CK19 promoter (GGCCACCCCTCT). To determine whether and which consensus sites recruit Gli1 in response to Shh, we performed ChIP with and without Shh stimulation. These experiments identified specific functional promoter elements within FN, CK7, and CK19 that bind Gli1. These experiments not only validated the consensus Gli1 sites as functional but also showed significant increases in the binding of this transcription factor in response to Shh treatment  via RT-PCR  as well reatment . Subsequreatment . In the reatment . NotablySince transcription factors such as Gli1 do not directly modify the genome, but rather work with key enzymatic complexes to epigenetically regulate gene expression through chromatin remodeling, we next sought to gain insight into complexes that may mediate the function of Shh on cholangiocyte-like differentiation. Previous studies have revealed that in order to activate gene expression, Gli1 works within epigenetic complexes that decorate histone tails with various activating or repressive marks. To expain vitro system. In contrast, pharmacologic inhibition of EZH2 with GSK126 enhanced the Shh-induced luciferase activity Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 FigH&E staining, trichrome staining, and immunohistochemistry in Cre negative control animals.(DOCX)Click here for additional data file.S2 FigPVportal vein, *bile duct.40X images of Masson’s Trichrome staining. (DOCX)Click here for additional data file.


These observations suggest that the craniofacial phenotypes of ciliary mutants like ta2 are caused either by excessive activity of the GLIA or reduced activity of GLIR. To decipher between these two scenarios, we examined GLI3 occupation at the regulatory regions of target genes and subsequent target gene expression. Using in silico strategies we identified consensus GLI binding regions (GBRs) in the avian genome and confirmed GLI3 binding to the regulatory regions of its targets by chromatin immunoprecipitation (ChIP). In ta2 mutants, there was a strikingly low number of GLI3 target genes that had significantly increased expression in facial prominences compared to the control embryo and GLI3 occupancy at GBRs associated with target genes was largely reduced. In vitro DNA binding assays, further supported ChIP results, indicated that the excessive GLI3FL generated in ta2 mutants did not bind to GBRs. In light of these results, we explored the possibility of GLI co-regulator proteins playing a role in regulatory mechanism of GLI-mediated transcription. Taken together our studies suggest that craniofacial ciliopathic phenotypes are produced via reduced GLIT production, allowing for target gene transcription to be mediated by the combinatorial code of GLI co-regulators.The primary cilium is a ubiquitous, microtubule-based organelle that cells utilize to transduce molecular signals. Ciliopathies are a group of diseases that are caused by a disruption in the structure or function of the primary cilium. Over 30% of all ciliopathies are primarily defined by their craniofacial phenotypes, which typically include midfacial defects, cleft lip/palate, micrognathia, aglossia, and craniosynostosis. The frequency and severity of craniofacial phenotypes in ciliopathies emphasizes the importance of the cilium during development of the craniofacial complex. Molecularly, many ciliopathic mutants, including the avian  Primary cilia are ubiquitous organelles that serve as cellular hubs for transduction of numerous signaling pathways. Most notably, cilia have been identified as transducers of the Hedgehog (Hh) pathway. Identification of the primary cilium as a signaling hub for the Hh pathway came from seminal experiments reporting that anterograde and retrograde intraflagellar transport (IFT) proteins in the cilium are required for Sonic Hedgehog (SHH) signal propagation  or truncated into the repressor isoform (GLIR) , the activation of SHH target genes is dictated by the alleviation of repression via loss of GLIR.Three basic models have been proposed to depict the potential mechanism of how SHH target genes are activated by a gradient of GLI isoforms: (1) the ratio sensing model, (2) the threshold repression model and (3) the threshold activation model  is a naturally occurring avian mutant that is best characterized by severe polydactyly and its oral-facial phenotype   associated with those targets in the developing frontonasal, maxillary and mandibular prominences  in order to uncover the mechanism by which GLI mediated transcription is being impacted in talpid2 (ta2) heterozygous carriers were mated, eggs were collected and shipped from the UC Davis Avian Sciences Department. Embryos were incubated at 37°C for approximately 5–7 days when embryos reached Hamburger Hamilton stage 25–31 (HH25-31).ALX4 (105 bp) F: GTTACGGTAAGGAGAGCAGTTT, wordR: CTTTCACTCCAGCCTCCTTC, BMPR1A (100 bp) F: GTGCTGTCGGACTGATTTCT, wordR: TGCCATCCAACGAATGCT, WIF1 (100 bp) F: CAACCTGTTTCAATGGAGGAAC, R: GGCTGATGGCATTTACTGATTT, OSR2 (140 pb) F: CCACTTCACCAAGTCCTACAA, R: TCTCTTTGGAATGGAT GTACCG. The statistical significance of the data was evaluated through two-tailed Student's t-test. p-values less than or equal to 0.05 (95% confidence level) were considered as statistically significant differences.FNPs, MXPs, and MNPs were harvested from day 5 chick embryos. mRNA was prepared with TRIzol reagent (Thermo Fisher Scientific), and then converted to complementary DNA through reverse transcription reaction . Different amounts of cDNA  was used for quantitative PCR to test PCR efficiency and a linear range of duplication . Expression of genes known to play a role in craniofacial development were examined with the following primer sets: 4, 1X complete protease inhibitor cocktail, EDTA-free), and slightly sonicated with a microprobe to recover chromosome bound GLI2 and GLI3 proteins. BCA assay (Pierce) was used to measure protein concentration of cell lysates. Proteins were boiled with 1X Laemmli sample buffer and run on 6% SDS-PAGE for GLI2 and GLI3, or 12% SDS-PAGE for GAPDH, which later were wet-transferred to Polyvinylidene difluoride (PVDF) membrane. Anti-GLI2, anti-GLI3  and anti-GAPDH  were prepared in 1X TBST (0.1% Tween-20)/6% nonfat milk, as well as secondary antibodies . Proteins were detected by Electrochemiluminescence assay .FNPs, MXPs, or MNPs were pooled and lysed in RIPA buffer containing protease inhibitors and phosphatase inhibitors  at −2549 from the TSS site (GGAAGAAGTGTCAGTGTAAGAGTCTCCACGTGGGTGGTCAAGGCCATGGCTGCCTCACGG). 100 pmole of biotin-conjugated positive oligonucleotides and complementary oligonucleotides (Integrated DNA Technologies) were annealed in 1X TE/50 mM NaCl buffer in a PCR cycler and incubated with Dynabeads Streptavidin . FNPs, MXPs, or MNPs from day 5 control or ta2 embryos were pooled and processed as described for Western blotting. Cell lysates were incubated with oligonucleotides-bound Dynabeads at 4°C for 2 h. Beads were washed with 1 ml RIPA buffer three times and processed for Western Blotting analysis.ta2 embryos were harvested, pooled and crosslinked in 1% formaldehyde. Tissue was homogenized in RIPA buffer and sonicated  at 5 cycles of 30 s on/45 s off. Sheared DNA was distributed around 0.3 kb to 1 kb on 1% agarose gel. Cell lysates were pre-cleaned with Dynabeads protein G (ThermoFisher Scientific) and quantified by BCA assay. Dynabeads for immunoprecipitation were blocked with 20 μg/ml Glycogen, 20 μg/ml BSA, 20 μg/ml yeast RNA in RIPA buffer at 4°C for an hour. GLI3 antibody  and pre-blocked Dynabeads Protein G were incubated with 90% of cell lysates at 4°C overnight. 10% of cell lysates were kept as Input. Beads were washed with IP wash buffer I , IP wash buffer II , IP Wash Buffer III  and TE buffer . ChIP samples were reverse-crosslinked by boiling with 10% Chelex-100 (BioRad), and treated with 0.2 mg/ml Proteinase K at 55°C for 30 min. Immunoprecipitated DNA samples were analyzed with quantitative real-time PCR (BioRad) with primers to GBR of target genes. Error bars in all figures represent standard error of the mean (S.E.M.) from five to seven independent experiments. The statistical significance of the data was evaluated through two-tailed Student's t-test. p-values less than or equal to 0.05 (95% confidence level) were considered as statistically significant differences.FNPs, MXPs, or MNPs from day 5 control or Sequences for GBRs from previous publications  mutant, a naturally occurring avian ciliopathic mutant that has been established as a model for the human craniofacial ciliopathy, Oral-facial-digital syndrome 14  .Our previous work determined that aberrant ciliogenesis in s Figure . The losFL isoform typically goes on to act as an activator, these results suggest that craniofacial phenotypes in the ta2 mutant could be caused by increased GLI activator function leading to increased expression of GLI target genes. To test this hypothesis, we utilized an in silico approach to look for potential GLI target genes within the avian genome. Using previously published sequences of GBRs  with mRNA from facial prominences of day 5 control and ta2 embryos  promoter. We synthesized a 60 base pair biotin-labeled oligonucleotide of the PTCH1 promoter containing an endogenous GLI binding motif found at position −2549 proximally upstream of the transcription start site (TSS). Through high affinity of Biotin-Streptavidin interaction, we were able to evaluate the DNA binding ability of GLI3 isoforms by Western blot. Pre-incubation of non-labeled PTCH1 oligonucleotides depleted GLI3 protein signals, which confirmed the specificity of the GLI3-GBR interaction. Under the same exposure, the affinity based pull-down assay showed that GLI3T predominantly bound to the biotin-labeled oligonucleotides in both control and ta2 mutants. Interestingly, despite the high level of GLI3FL production in ta2 embryos, GLI3FL failed to bind to the biotin-labeled oligonucleotides , a conserved protein known to regulate the activity of GLI proteins via modulating GLI processing, stabilization and subcellular localization  to begin to address this process.in silico GBR analysis, ES harvested and photographed embryos, SB conceived the project, analyzed data and wrote the manuscript with input from all authors.YC designed and tested all primers, and performed and interpreted all ChIP and qPCR experiments, PC performed This work was funded by the National Institutes of Health, National Institute of Dental and Craniofacial Research (NIDCR) [R00-DE01985 and [R01-DE023804] to SB] and by C.T.O. and Research in Progress (RIP) funds from the Cincinnati Children's Research Foundation to SB.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.


Sox3/SOX3 is one of the earliest neural markers in vertebrates. Together with the Sox1/SOX1 and Sox2/SOX2 genes it is implicated in the regulation of stem cell identity. In the present study, we performed the first analysis of epigenetic mechanisms (DNA methylation and histone marks) involved in the regulation of the human SOX3 gene expression during RA-induced neural differentiation of NT2/D1 cells. We show that the promoter of the human SOX3 gene is extremely hypomethylated both in undifferentiated NT2/D1 cells and during the early phases of RA-induced neural differentiation. By employing chromatin immunoprecipitation, we analyze several histone modifications across different regions of the SOX3 gene and their dynamics following initiation of differentiation. In the same timeframe we investigate profiles of selected histone marks on the promoters of human SOX1 and SOX2 genes. We demonstrate differences in histone signatures of SOX1, SOX2 and SOX3 genes. Considering the importance of SOXB1 genes in the process of neural differentiation, the present study contributes to a better understanding of epigenetic mechanisms implicated in the regulation of pluripotency maintenance and commitment towards the neural lineage. SOX2 gene, the closest relative of SOX3, is one of the core pluripotency factors involved in the regulation of stemness and differentiation . After short centrifugation, pellets were lysed in nuclei dropping buffer . High molecular weight DNA was extracted using phenol-chlorophorm-isoamylalcohol extraction, and precipitated with sodium acetate and isopropanol.™ Kit . For each conversion 2 μg of DNA was used and protocol provided by the manufacturer strictly followed. Upon conversion the reaction recovery rate was considered as 100% and hence concentration of DNA was not measured.Sodium-bisulfite conversion of isolated genomic DNA was performed using EZ DNA Methylation-LightningSOX3 promoter. PCR cycling conditions were 95°C for 10 minutes; 95°C for 20 seconds, 50–55°C for 20 seconds, 60°C for 2 minutes, for 40 cycles. PCR reactions were performed using non-modified forward primer and 5’-biotin-labeled reverse primer listed below. BSP primers were designed using MethPrimer tool . Cells were dounced on ice and nuclei separated by centrifugation. Nuclei were lysed in sonication buffer ]. Chromatin was sonicated to fragments of 500–1500 bp. Precleared chromatin in immunoprecipitation buffer  was incubated overnight with following antibodies: anti-Flag , anti-H3 , anti-H3K4me3 , anti-H3K79me2 , anti-H2B , anti-H2BK16ac , anti-H2BK120ac , anti-H2BK5ac . Following day, chromatin and antibodies were incubated for 4 hours with Protein G agarose  saturated with salmon sperm DNA and BSA overnight. Samples were centrifugated and Flag supernantants saved for Inputs. Resins were washed 5 times with RIPA buffer , followed by wash with LiCl buffer  and wash in TE buffer, pH 8.0. Resins were resuspended in TE buffer, and cross-links reversed by overnight incubation with RNase A  at 65°C. Following day all samples were adjusted to 0.5% SDS and treated with 20 μg of Proteinase K  for immunoprecipitated samples and 40 μg for Input samples for 3 hours at 50°C. DNA was extracted with phenol/chlorophorm/isoamylalcohol and precipitated overnight at -20°C with 3M sodium-acetate, tRNA  and ethanol. Following day DNA samples were centrifuged and DNA resuspended in HSOX3 (RefSeq NM_005634) upstream (-673/-578), SOX3 core promoter (-224/-63), SOX3 5’ downstream coding region (+503/+606), SOX2 (RefSeq NM_003106) promoter (-107/+56) and SOX1 (RefSeq NM_005986) promoter (-147/+25) were analyzed using qPCR with following primers:DNA sequences of SOX3 upstream5’–GCAGTCCTGAAGCCTGTCTC-3’ (forward)   5’–GCGTCTCCAAGAAGCTCTCC-3’ (reverse)   SOX3 core promoter5’- AGGGCTCCCCGAACTTTT-3’ (forward)   5’- GCTGGGCCCCTTATATACCT-3’ (reverse)   SOX3 downstream5’- TGGAGAACCCCAAGATGCAC-3’ (forward)   5’- CTTGGCCTCGTCGATGAATG-3’ (reverse)   SOX2 promoter5’- GCCCCCTTTCATGCAAAACC-3’ (forward)   5’- CTCTGCCTTGACAACTCCTG-3’ (reverse)   SOX1 promoter5’- ACCCCTCCCCATTCTTCTCT-3’ (forward)   5’- CAGGTCGGTCTCCATCATCA-3’ (reverse)   The enrichment was calculated relative to Flag and normalized against H3 or H2B. In comparative experiments, the enrichment in undifferentiated cells was assigned the value 1 and other samples were normalized to this value . ResultsData were presented as mean ± S.D. and were analyzed using Student’s T test. *P<0.05 was considered statistically significant.in vitro model to study the process of human neural differentiation [SOX genes expression and regulation [SOX3 gene [SOX3 is considered as one of the key regulators of neural development in vertebrates . We analntiation ,28,29. Antiation . Followintiation ,31. Moregulation ,23,32–37OX3 gene . In the Nanog mRNA levels 4 days following RA treatment, confirming the exit of NT2/D1 cells from the pluripotency  to assess methylation status of the human st CpG island to be difficult for the analysis due to GC-rich sequence which could give rise to homopolymers and secondary structures following bisulfite conversion of DNA. These structures prevent optimal amplification and subsequent pyrosequencing of the region encompassing 1st CpG island within the SOX3 promoter and therefore we proceeded with the analysis of the second island. Genomic DNA isolated from undifferentiated NT2/D1 cells and cells treated with RA for 2, 4 and 7 days were subjected to the sodium-bisulfite conversion. Converted DNA was used as a template to amplify 2nd CpG island within SOX3 promoter using non-modified forward primer and biotin-labeled reverse primer and PCR products were analyzed by pyrosequencing [SOX3 promoter in undifferentiated NT2/D1 cells  using ENCODE datasets (http://genome.ucsc.edu). ChIP-Seq signals for H3K4me2 and H3K4me3 in the promoter and the SOX3 coding region are high (SOX3 promoter (H3K9ac and H3K27ac) and coding region (H3K36me3 and H4K20me1), and H3K79m2 is low within the coding region , the SOX3 core promoter and a region within SOX3 gene, ~550bp downstream from TSS (SOX3 downstream)  . We detenstream) . Upon RA 4 and 7 . This prOX3 gene , providintiation .SOX3 gene throughout the course of RA induction of NT2/D1 cells  cells, characterized by high expression of Sox3, lack of DNA methylation and positive histone PTMs [Sox3 has also been discussed as part of study by Azuara et al. [Sox3, in mESCs. Finally, hypermethylation of SOX3 was detected in chronic lymphocyte leukemia [Despite the growing amount of data regarding the regulation of the human one PTMs . Epigenea et al.  showing leukemia .SOX3 gene in pluripotent cells as well as in cells responding to neural differentiation-inducing stimuli. Molecular events during RA induced differentiation of NT2/D1 cells reflect the steps in the development of human brain [SOX3 [Sox3 gene [Embryonal carcinoma cell line NT2/D1 provided us with a valuable model system to study the epigenetic state of an brain . We demoin [SOX3 ,32,35. Tox3 gene –66.SOX3 promoter is hypomethylated. This hypomethylation persists through neural differentiation induced by RA. Similar results were obtained for the SOX1 promoter, which is also hypomethylated during the first 7 days of neural differentiation of NT2/D1 cells. While methylation of CpG–rich promoters is considered a hallmark of silent genes, nonmethylated promoters are not necessarily associated with active transcription [SOX2 which are unmethylated in undifferentiated NT2/D1 cells become methylated in neurons, with no detectable SOX2 expression [Despite low SOX3 expression levels in undifferentiated cells, the cription ,68. Rathcription . Some ofcription . Moreovepression . At the pression .SOX3 gene. This finding is supported by the study by Lauberth et al. showing that H3K4me3 facilitates TFIID recruitment and enhances preinitiation compex (PIC) assembly, thus promoting transcription [SOX3 and SOX1 are among the genes with bivalent promoters. In the study by Mikkelsen et al., Sox3 was one of the genes with bivalent promoter in mES cells, resolved into H3K4me3+ in lineage committed NP cells, while in mouse embryonic fibroblasts (MEFs) it is devoid of any histone marks [Sox1 was found to be bivalent in both mES and NP cells, and H3K4me3-/H3K27me3+ in MEFs [SOX1 was found to be marked by both H3K4 and H3K27 trimethylation during RA- and BMP4- induced differentiation of ES cells [Sox3 and Sox1 is typical for high CpG content promoters of genes with key roles in the development [We demonstrate that H3K4me3 on the core promoter and upstream of TSS is associated with transcriptional induction of cription . Many prne marks . In the  in MEFs . FurtherES cells . This stelopment . FurtherSOX3 at day 2 was not accompanied by an increase in H2B acetylation. There is limited literature data regarding the contribution of H2B acetylation in regulation of transcriptional regulation: a study by Kurdistani et al. demonstrated negative correlation of H2B acetyl marks with transcription in Saccharomyces cerevisiae [SOX3 gene upon RA treatment is associated to other histone marks. Nevertheless, substantial depletion of all three H2B acetyl marks on the promoter of SOX3 at day 7 coincides with the reduced expression of SOX3 gene. These findings suggest that H2BK5ac, H2BK16ac and H2BK120ac at the core promoter of SOX3 gene contribute to the regulation of SOX3 gene expression, primarily at the end of the first week of neural differentiation of NT2/D1 cells, when they interact coordinately with other deposited histone marks and transcription apparatus or transcription factors. It would be interesting to evaluate chromatin marks on other potential regulatory regions of SOX3 gene. For example, recent analysis has revealed that distal enhancers of numerous genes are targeted by NF-Y, a transcriptional regulator of SOX3 gene and transcription factor with nucleosome-like properties that mediates establishment of permissive chromatin modifications [SOX3 gene.Interestingly, activation of revisiae . Analyserevisiae ,73. The ications ,74,75. FSOXB1 genes are differentially regulated in the initial phases of neural differentiation. Based on the presented data, early phase of neural differentiation of NT2/D1 cells could be divided into two stages. First stage falls within the first 2 days of RA induction and is characterized by the increase in SOX3 and reduction in SOX2 expression levels, whereas SOX1 remains unchanged. Second stage (days 4 and 7) is accompanied by the reduction in both SOX2 and SOX3 levels, while SOX1 is upregulated. In the first stage, H3K4me3 profile of the SOX2 promoter is not strongly correlated with the decline in the expression of SOX2, as opposed to SOX3 whose upregulation at day 2 is likely operated by H3K4me3. This is in concordance with the findings by Barrand et al. indicating that H3K4me3 is associated with SOX2 promoter in different cell types, regardless of SOX2 expression [SOX2 than for SOX3 promoter, providing an additional proof of different states of SOX2 and SOX3 promoters in cells undergoing differentiation. As for SOX1 promoter, we detected raise in H3K4me3 at day 2, preceeding the actual increase in SOX1 expression levels at day 4 and 7 of RA induction.One of the interesting findings emerging from this study is that pression . MoreoveSOX1, SOX2 and SOX3 genes. These implies that same epigenetic mechanisms could contribute to the observed reduction in SOX2 and SOX3 expression levels at later stages of neural differentiation. Conversely, detected decrease in H3K4me3 and H2B acetyl marks at SOX1 promoter suggests that these activating histone modifications do not have considerable role in transcriptional activation of SOX1. Therefore, other mechanisms such as a depletion of repressive mark H3K27me3 or changes in TFs activity and signaling pathways could be attributable to changes in SOX1 expression during the neural differentiation. It would be significant to analyze epigenetic mechanisms acting on SOXB1 genes in later stages of neural differentiation, as well as in cells undergoing the process of dedifferentiation, as this could have impact on our understanding of the exact roles of SOXB1 genes in pluripotentcy maintenance and on improvement of guided differentiation protocols.In the second stage, at day 7 we detected significant decrease in all analyzed histone PTMs at promoters of SOXB1 genes, we have previously described the modulation of SOX2 and SOX3 gene expression during the early phases of neural induction of NT2/D1 cells by RA [SOX3 promoter involved in RA-responsiveness [SOX2 and SOX3 genes during first 48h of RA induction [SOX3 gene, while no significant effect of this TF on the SOX2 expression has been observed. Also, in contrast to significant impact of NF-Y on RA-induced activation of SOX3, this TF had only mild effect on SOX2 expression in RA-induced NT2/D1 cells [SOX2 promoter activity is down-regulated upon RA induction of mouse F9 EC cells [Sox2 promoter upon differentiation of EC cells [SOX2 transcriptional regulation acting as an enhancer [SOX1, there are several studies demonstrating the roles of signaling pathways in SOX1 gene regulation [Regarding the other mechanisms implicated in the regulation of ls by RA . Also, wsiveness ,14,15,17nduction . Among mD1 cells . This obEC cells . Indeed,EC cells . Apart fEC cells ,78–80 anEC cells –87. It iEC cells ,89. Alsoenhancer . As for gulation ,86,91,92SOX3 in pluripotent cells and during the early phases of neural differentiation. We found SOX3 gene to be non methylated from undifferentiated NT2/D1 to cells committed towards neural lineage. Furthermore, we presented data regarding epigenetic mechanisms acting on SOX2 and SOX1 genes during the initial phases of neural differentiation, pointing out to potential similarities and differences in the epigenetic control of SOXB1 genes. These findings could contribute to the elucidation of complex events during neural differentiation and ultimately provide better means for the development of therapies based on the use of epigenetic-modifying drugs.In summary, we provide a first map of the epigenetic landscape of S1 Fighttp://www.roadmapepigenomics.org/ and converted in bigwig files through Galaxy tool followed by the visualization in UCSC genome browser.Raw data were retrieved from (PPTX)Click here for additional data file.


Over the past few years, tools that make use of the Cas9 nuclease have led to many breakthroughs, including in the control of gene expression. The catalytically dead variant of Cas9 known as dCas9 can be guided by small RNAs to block transcription of target genes, in a strategy also known as CRISPRi. Here, we reveal that the level of complementarity between the guide RNA and the target controls the rate at which RNA polymerase “kicks out” dCas9 from the target and completes transcription. We use this mechanism to precisely and robustly reduce gene expression by defined relative amounts. Alternatively, tuning repression by changing dCas9 concentration is noisy and promoter‐strength dependent. We demonstrate broad applicability of this method to the study of genetic regulation and cellular physiology. First, we characterize feedback strength of a model auto‐repressor. Second, we study the impact of amount variations of cell‐wall synthesizing enzymes on cell morphology. Finally, we multiplex the system to obtain any combination of fractional repression of two genes. If the target is chosen downstream of the promoter, dCas9 serves as a roadblock that blocks transcription elongation. Here, we characterize this system at the single‐cell level, with interesting implications for the native CRISPR immune system. We then develop a strategy to use this system for precise and noise‐preserving relative gene repression that is independent of promoter strength.A powerful way to investigate genes and their regulation in bacteria is to vary their expression levels and investigate the response of the cell. To that end, genes are typically placed under inducible promoters. While easy to implement, this approach has multiple disadvantages: First, native expression can lie outside the dynamic range of the inducible promoter. Second, inducible promoters typically increase expression noise in comparison with native promoters  and mCherry coding for a red fluorescent protein (RFP) at two different chromosomal loci of E. coli strain MG1655. To repress either of these genes using CRISPR knock‐down, we integrated the dcas9 gene from S. pyogenes under a Ptet promoter, inducible by the addition of anhydrotetracycline (aTc)  ≤ 1 that depends on guide RNA‐target complementarity  = 1, the system efficiently blocks RNAP every time RNAP and dCas9 collide. At the opposite extreme, if P(stop) = 0, dCas9 never blocks RNAP  is the probability that dCas9 is occupying the target.Here, γP(stop) only depends on guide RNA‐target complementarity. Therefore, repression is independent of dCas9 concentration, if the occupancy is very close to 1, that is, if the target is saturated. In these conditions, cell‐to‐cell fluctuations of dCas9 concentration also no longer affect the repression of the target, thus explaining the low and constant noise obtained for different degrees of repression  that dCas9 blocks RNAP does not depend on promoter strength. Repression strength should thus not be measurably affected for promoters of different strengths, if dCas9 is saturating the target, that is if P(bound) is very close to 1. To verify this prediction, we put sfgfp under the control of two promoters of different strengths (P127 and PPhlf) and blocked expression using four different guide RNAs with an increasing number of mismatches. While the strain with PPhlF expressed about three times more GFP than the strain with P127  if dCas9 does not saturate the target, for example, if repression is controlled by dCas9 concentration. According to our kick‐out model of dCas9 ejection by RNAP, the occupancy is given byAs transcription can be successful if dCas9 is saturating the target, we wondered whether dCas9 would be ejected from the target by RNAP during successful transcription events. While physical displacements would not affect repression in saturating conditions, they could measurably reduce the occupancy of the target kon[dCas9] is the rate of binding and kout is the combined rate of RNAP‐induced ejections, spontaneous unbinding and possibly replication‐fork‐based displacements , which, in turn, would reduce the repression fold. Indeed, we observed a weaker repression of the stronger PPhlf promoter compared to P127 at low dCas9 concentrations. This observation quantitatively agrees with our kick‐out model for full and intermediate (14 bp) levels of complementarity, respectively .Precision, robustness, and large dynamic range make complementarity‐based CRISPR knockdown a versatile repression strategy. To repress genes of interest in their native context, we propose to insert In the following, we demonstrate that this system has broad applicability for the study of genetic regulation and cellular physiology: first, we study the regulation of a model transcriptional feedback circuit, and second, we quantify the effect of fractional protein repression on cell morphology during steady‐state growth.Pseudomonas fluorescens  that blocks binding of PhlF to the promoter. Accordingly, higher DAPG concentrations lead to higher steady‐state concentrations of PhlF and GFP , while a Hill coefficient of n = 1 was required to describe our observations at 50 μM. PhlF proteins dimerize in vitro and are thought to bind the operator as a dimer  and RodA (encoded by mrdB) are inner membrane proteins with, respectively, transglycosylase  is the probability of dCas9 blocking RNAP if dCas9 is occupying the target, and P(bound) is the probability that dCas9 is occupying the target .Here, γP(stop) only depends on guide RNA‐target complementarity, the occupancy P(bound) generally depends on complementarity, dCas9 concentration, and possibly on transcription‐initiation rate (see the following paragraph). Therefore, repression is independent of dCas9 concentration only if the occupancy is very close to one (1 – P(bound) ≪ 1), that is, if the target is saturated.While the probability According to straight‐forward reaction kinetics, the occupancy is given bykout is the rate of dCas9 leaving the target. dCas9 can in principle leave the target by two different mechanisms, by transcription‐independent unbinding (with rate koff if bound to the target), or by being kicked out from the target during all or part of the successful RNAP passage events, that is, during collisions where transcription continues. Alternatively, dCas9 could stay bound during all successful passage events. According to the two different models, kout is given by0 should thus lead to lower target occupancy. If the spontaneous unbinding model was correct (δ = 0), occupancy should be independent of γ0.msfgfp placed under two promoters with different promoter strengths at an intermediate level of dCas9 concentration, where the target is not saturated by dCas9 and where changes of occupancy due to promoter strength should be clearly visible. We found that the relative GFP expression  of a promoter with 2.6‐fold higher promoter strength is increased by 1.7‐fold as compared to the weaker promoter for full complementarity between guide RNA and target  is the ratio of the rate of induced dCas9 displacements over the rate of dCas9 rebinding, α = koff/(kon[dCas9]) is the ratio of transcription‐independent unbinding rate over rebinding rate, and r = 1 − P(stop) is the probability of successful transcription in the presence of target‐bound dCas9. The latter probability is known to be r = 0.026 ± 0.003 for full complementarity between guide RNA and target from independent experiments in saturating conditions .To test this hypothesis, we compare the predicted expression level equation  for zeroP127 Fig C. In App127‐GFP as a reference to infer the normalized kick‐out rate λ(P127)r corresponding to the experimental GFP expression levels of γ*ex(P127) = 0.32 ± 0.04 ‐times stronger than P127  = (2.6 ± 0.2)λ(P127). The predicted expression level of the PPhlF promoter is thusP127 Fig A and thePhlf) = 0.54 ± 0.05. This value is in great quantitative agreement with the measured expression level of γ*ex(PPhlF) = 0.54 ± 0.02. On the contrary, values of α > 0.14 lead to predicted expression levels significantly lower than the experimental value (koff ≪ kon[dCas9]).According to our hypothesis of α = 0, we obtain γ*. As in the case of full complementarity, we found an excellent agreement between the experimental and predicted GFP expression levels (γ*ex(PPhlF) = 0.79 ± 0.04 vs. γ*(PPhlF) = 0.77 ± 0.03) for a transcription‐independent unbinding rate of α = 0 while predicted expression values for α > 0.35 are significantly lower than the measured expression level. We also carried out experiments with a guide RNA of 10 mismatches. However, the experimental data showed uncertainties too large to make conclusions about the level of α. We thus conclude that the dCas9‐target complex is stable even for reduced degrees of complementarity of down to 14 bp and maybe less.We then wondered whether the transcription‐independent unbinding rate α would increase for reduced degrees of complementarity. To that end, we performed the same analysis as above on experimental data for GFP expression from the two promoters at non‐saturating levels of dCas9 but now for guide RNA that carries six mismatches  ≈ 0.43 and r ≈ 0.026. P127 is based on the consensus promoter sequence and thus not expected to be stronger than well‐studied model promoters in E. coli, which display transcription rates of not more than about 20 min−1 =2tC(1−|Φ|)+tD≈2, where Φ = 0.66 is the relative distance of the gene locus from the origin of replication, if distance is normalized with respect to the distance between origin and terminus. Thus, the lifetime of a single dCas9‐DNA complex (n/koff) is greater than about 12 min (full complementarity) or 5 min (6 mismatches). Notably, these timescales should be regarded as lower bounds, while the actual lifetimes might be significantly larger. This estimate is thus in agreement with recent single‐molecule tracking and restriction‐protection assays . Di‐acetyl‐phloroglucinol (DAPG), anhydrotetracycline (aTc), and isopropyl β‐D‐1‐thiogalactopyranoside (IPTG) were used, respectively, for induction of PPhlF, PTet, and PLac promoters. All oligonucleotides were obtained from Eurofins Genomics.For all flow cytometry measurements, the cells were grown in Luria‐Bertani (LB) broth. As a minimal medium for the Unless stated otherwise, all cultures were grown at 30°C. Strains were first re‐streaked from a freezer stock. Independent single colonies were picked for each replicate. Cells were then grown overnight in 96‐deep‐well plates using a tabletop shaker in 1 ml of medium with 100 ng/ml of aTc and 50 μg/ml of kanamycin (Eppendorf). The day of the measurement, cultures were back‐diluted 250 times in fresh medium with aTc and kanamycin, and grown for 1 h 45 min into exponential phase. We then fixed the cells with 4% formaldehyde (30 min on ice) and washed with phosphate‐buffered saline (PBS).E. coli with various induction levels of dCas9, we prepared the cells into steady‐state exponential growth then diluted the cultures 1/250 in a flat‐bottomed 96‐microwell plate (Greiner) and recorded optical density along growth using a microplate reader (Tecan). We fitted an exponential function to the data points corresponding to the exponential phase in order to calculate the doubling time.To determine the doubling times of t‐test on the natural logarithms of the average fluorescence  carrying a non‐targeting pCRRNA plasmid was used to measure the auto‐fluorescence background. We calculated the mean fluorescence signal of each population and subtracted the mean auto‐fluorescence signal. To test whether differences in expression were significant, we performed Student's ® (EMD Millipore) software suite. For each condition, at least 10,000 events were recorded per replicate. Cells that were out of focus or tilted were identified by calculating the average gradient of a pixel normalized for variations in intensity levels (Gradient RMS feature in IDEAS®). Additionally, we used the Feature Finder script of IDEAS® to remove contaminating particles, images with multiple cells and beads. After filtering, at least 2,000 images remained per sample. The fluorescence channels were not used for filtering. A color compensation matrix was calculated to account for spectral overlap of GFP and RFP emission spectra, so cultures of AV02 (GFP only) and AV04 (RFP only) would each have a null signal on the converse channel. As a proxy for the reporter's intracellular concentration, we used the average image intensity inside the area corresponding to each cell. The cell area was determined by using a threshold on the bright field images. Single points located more than three standard deviations away from the population average were discarded as outliers, as they can disrupt the noise computations. The average fluorescence μ of each sample was calculated by taking the mean of the single‐cell fluorescence. The noise was defined as σ/μ, with σ=σsample−σblank where σsample is the standard deviation of the intracellular average intensity of the sample, and σblank is the standard deviation of a sample with no fluorescent reporter (noise from auto‐fluorescence).An Amnis ImageStreamX (EMD Millipore) imaging cytometer was used to image the cells in high‐throughput in brightfield, GFP, and RFP channels. Images were analyzed using the IDEASFixed cells were transferred to PBS microscopy pads with 1.5% UltraPure Agarose (Invitrogen) and imaged using an inverted microscope  equipped with a 100× phase‐contrast objective , a solid‐state light source , a multiband dichroic . mCherry fluorescence was measured using excitation (560/32) and emission (632/60) filters. Images were acquired using a sCMOS camera  with an effective pixel size of 65 nm.et al, MATLAB code adapted from the Morphometrics package  was used to separate RNAs. The gels were blotted onto Nylon membranes (Invitrogen), which were subsequently cross‐linked with 1‐ethyl‐3‐(3‐dimethylaminopropyl) carbodiimide  buffer  and T4 PNK (NEB) reagents. A labeled probe specific to the guide RNA R20 (5′ GCATAGCTCTAAAACTCCGTATGAAGGCACCCAGA 3′) was column purified (Macherey‐Nagel PCR cleanup kit) and used for overnight hybridization. The intensity of the shortest band, corresponding to the fully processed guide RNA, was quantified using the Fiji software package.Total RNA was extracted from cultures in early stationary phase using TRIzol. Electrophoresis on NovexDB, SvT, and AV designed the study. AV constructed the strains and plasmids and performed all measurements and data analysis. SvT developed the mathematical models. LC took part in strain construction, and EO performed the compensation of uneven illumination for the measurement of mCherry‐PBP2 by microscopy. AV, DB, and SvT wrote the manuscript.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Source Data for AppendixClick here for additional data file.Review Process FileClick here for additional data file.Source Data for Figure 1Click here for additional data file.Source Data for Figure 2Click here for additional data file.Source Data for Figure 3Click here for additional data file.Source Data for Figure 4Click here for additional data file.Source Data for Figure 5Click here for additional data file.Source Data for Figure 6Click here for additional data file.Source Data for Figure 7Click here for additional data file.