CCM1, CCM2 or CCM3 gene within endothelial cells. They predispose to intracerebral bleedings and focal neurological deficits. Our understanding of the cellular and molecular mechanisms that trigger endothelial dysfunction in cavernous malformations is still incomplete. To model both, hereditary and sporadic CCM disease, blood outgrowth endothelial cells (BOECs) with a heterozygous CCM1 germline mutation and immortalized wild-type human umbilical vein endothelial cells were subjected to CRISPR/Cas9-mediated CCM1 gene disruption. CCM1−/− BOECs demonstrated alterations in cell morphology, actin cytoskeleton dynamics, tube formation, and expression of the transcription factors KLF2 and KLF4. Furthermore, high VWF immunoreactivity was observed in CCM1−/− BOECs, in immortalized umbilical vein endothelial cells upon CRISPR/Cas9-induced inactivation of either CCM1, CCM2 or CCM3 as well as in CCM tissue samples of familial cases. Observer-independent high-content imaging revealed a striking reduction of perinuclear Weibel-Palade bodies in unstimulated CCM1−/− BOECs which was observed in CCM1+/− BOECs only after stimulation with PMA or histamine. Our results demonstrate that CRISPR/Cas9 genome editing is a powerful tool to model different aspects of CCM disease in vitro and that CCM1 inactivation induces high-level expression of VWF and redistribution of Weibel-Palade bodies within endothelial cells.Cerebral cavernous malformations are slow-flow thrombi-containing vessels induced by two-step inactivation of the  CCM1 , CCM2  or CCM3 . In agreement with a Knudsonian two-hit mechanism, somatic inactivation of the corresponding wild-type allele in vascular endothelial cells (ECs) is widely accepted as the critical step in CCM initiation  are convolutes of dilated capillaries in the central nervous system. Familial occurrence of multiple CCMs  is associated with autosomal-dominantly inherited loss-of-function variants in one of the three genes itiation . In patiitiation .CCM1 variants in familial CCM cases   .in vitro modeling of hereditary and sporadic CCM disease, we here demonstrate that the inactivation of CCM1, CCM2 or CCM3 in ECs induces intracellular VWF accumulation and WPB redistribution. Analyses of human CCM tissue samples support the hypothesis that increased VWF levels in the endothelium of distended caverns contribute to the local hemostatic imbalance in these fragile vascular lesions.Using CRISPR/Cas9-based CCM1 germline variant (CCM1+/−) has been described before (https://imagej.net). To stimulate secretion of WPBs, cells were grown on 96- or 6-well plates, preincubated with Opti-MEM (Thermo Fisher Scientific) for 1 h and treated solely in Opti-MEM or with dimethyl sulfoxide , 150 nM Phorbol 12-myristate 13-acetate , 100 µM Histamine , 1 µM Desmopressin (Acetate)  or 1 µM DDAVP plus 100 µM 3-Isobutyl-1-methylxanthine  in Opti-MEM for 1 h according to Wang and colleagues  from a CCM proband with a pathogenic d before . Immortalleagues .CCM1−/− BOECs were generated with CRISPR/Cas9 genome editing from CCM1+/− BOECs as described before (CCM1+/− BOECs were transfected with crRNA:tracrRNA:Cas9 ribonucleoprotein (RNP) complexes that were specific to the CCM1 wild-type allele. Upon confluence on T25 flasks, the CRISPR/Cas9-treated cell mixture was seeded on 96-well plates with an average cell density of 0.5 cells/well. Emerging BOEC clones were genotyped by next generation sequencing in a custom two-step PCR enrichment approach. PCR products were pooled after purification with Agencourt AMPure XP beads  and sequenced with 2 × 150 cycles on a MiSeq instrument . The SeqNext software was used for data analysis . Only variants with quality scores ≥30 were called. BOEC clones with biallelic CCM1 loss-of-function variants (CCM1−/−) were selected for further expansion. Three to four individual CCM1+/− BOEC lines that had been clonally expanded from the blood of the CCM proband were used as controls. For complete knockout of CCM1, CCM2 and CCM3 in CI-huVECs, the following Alt-R CRISPR-Cas9 crRNAs (Integrated DNA Technologies (IDT), Coralville, Iowa, United States of America) were used: 5′-GGAGCTCCTAGACCAAAGTA-3′ (CCM1), 5′-GGTCAGTTAACGTCCATACC-3' (CCM2), and 5′-CAACTCACCTCATTAAACAC-3' (CCM3). A non-targeting crRNA  served as control. Reverse transfection, estimation of the genome editing efficiencies by T7 endonuclease I digestion or Sanger sequencing, and the expansion of knockout CI-huVEC clones were performed as delineated previously , rabbit anti-vascular endothelial (VE)-cadherin , CytoPainter Phalloidin-iFluor 488 , mouse anti-human KLF2 , monoclonal mouse anti-human vWF , Alexa Fluor 488-conjugated secondary goat anti-mouse IgG antibody , Alexa Fluor 555-conjugated goat anti-mouse IgG antibody  or Alexa Fluor 555-conjugated goat anti-rabbit IgG antibody . DAPI  was used to stain cell nuclei and image acquisition was performed with either an EVOS FL (Thermo Fisher Scientific) or a Zeiss LSM980 Airyscan 2 microscope  after addition of mounting medium . After the sensitivity and specificity of VWF staining had been verified, the focus was placed on the Quantification of WPBs was performed on cells grown on glass bottom imaging plates  and following secretagogues treatment using a high-content imaging microscope . In addition to the digital phase contrast channel , the images were acquired in three z-planes to detect VWF-positive granula (λex 475 nm/λem 500–550 nm) and DAPI-stained nuclei (λex 365 nm/λem 430–500 nm). The algorithm-driven image quantification was performed utilizing the Harmony 4.9 (Perkin Elmer) imaging and analysis software. The images were combined with their maximum projection intensities from all three z-planes and flatfield-adjusted. Per treatment and cell type, at least 2,500 cells in a total of 27 fields of view have been included in the analysis. The nuclei were segmented by their DAPI signal, followed by detection of the surrounding cytosolic area. A selection step was applied to exclude border region objects and dead cells with fragmented nuclei. In order to enhance the contrast, a sliding parabola function was applied, which allowed better discrimination of bright objects. Afterward, the number of WPBs and their intensity was determined inside the cytosolic cell region. To assess the amount of WPBs near the cell nucleus, a 5 µm ring-region was calculated and the granules were quantified inside this region.To quantify the secretion of VWF, cell culture supernatants were collected and concentrated . Proteins were analyzed in duplicates using Human VWF-A2 DuoSet ELISA  and Ancillary Reagent Kit  according to the manufacturer’s protocol. Absorbance at 450 and 540 nm was measured on a Paradigm platform . Values were corrected for 540 nm and normalized to untreated cells.Immunohistochemistry was performed on FFPE tissue slices as described before  using vWVmax = 6.00716, k = 8,210.36, n = 0.72, and R2 = 0.99319. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.Data analysis was performed using GraphPad prism software . Multiple t tests or two-way ANOVA were used for statistical analysis. Where appropriate, Šidák or Dunnet corrections for multiple comparisons were used. For quantification of ELISA data, VWF concentrations were calculated from the standard curve by linear regression performed with the Origin software . The standard curve was obtained by fitting the Hill equation [y=Vmax*xn/(kn+xn)] to our data, with in vitro (CCM1 loss-of-function germline variant (CCM1+/−) and wild-type CI-huVECs (CCM1+/+). Using CRISPR/Cas9 genome editing, we inactivated the CCM1 wild-type allele in CCM1+/− BOECs or induced biallelic frameshift variants in CCM1+/+ CI-huVECs and thereby generated pairs of CCM1+/− and CCM1−/− BOECs (hereditary CCM model) or CCM1+/+ and CCM1−/− CI-huVECs (sporadic CCM model), respectively.To model the hereditary and sporadic type of CCM disease in vitro , we usedCCM1 in human ECs, we first established thirty clonal CCM1−/− BOEC lines in a single cell cloning approach  were found in only 32% of CCM1+/− BOECs, over 90% of CCM1-deficient BOECs were SF-positive . Interestingly, a perinuclear accumulation of WPBs was found in unstimulated CCM1+/− BOECs. Stimulation with histamine, PMA or the vasopressin analog DDAVP which triggers cAMP-mediated exocytosis, reduced the number of WPBs in the perinuclear region of CCM1+/− BOECs  with our CI-huVEC model which mimics sporadic CCM disease (CCM1+/+ vs. CCM1−/−). Well-known features of CCM pathobiology such as actin stress fiber formation, disruption of the integrity of intercellular junctions or upregulation of the transcription factors KLF2 and KLF4 (Genome editing has become a powerful tool to model complex human diseases in vitro . Hence, and KLF4  were recCCM1, CCM2 or CCM3 gene disruption and, most importantly, the striking VWF immunopositivity within the lining endothelium of individual caverns of human CCMs support this hypothesis. Since stimulation with secretagogues induced proper VWF secretion in CCM1−/− BOECs, it is reasonable to assume that vascular stasis and transient procoagulant stimuli trigger local thrombus formation in cavernous lesions. The high affinity of thrombin for the anticoagulant endothelial receptor thrombomodulin which is strongly expressed on ECs of capillaries in the wall of cavernous blood vessels (Furthermore, we addressed a new aspect of the hemostatic imbalance in CCMs. CCM bleeding is one of the major concerns of CCM patients and their doctors. Since the risk of future bleeding events is even increased after a first hemorrhage , the exi vessels , may, on vessels . Loose iCCM1−/− BOECs with these secretagogues demonstrated that the second-hit inactivation of CCM1 does not attenuate the regulated exocytosis of WPBs. Although we cannot exclude that other stimuli such as forskolin, epinephrine, thrombin or VEGF (2+-dependent pathways, Rab proteins that are recruited to WPB, the dynein-dynactin complex, the SNARE machinery, the transcription factor KLF2, and various other signaling pathways influence the formation and secretion of WPBs (KLF2 is a key feature of CCM disease and was also observed in our in vitro model. Its misexpression interferes with endothelial quiescence and increases Rho as well as ADAMTS activity (VWF mRNA with a simultaneous decrease in ANGPT2 mRNA was described in HUVECs upon KLF2 overexpression (CCM1−/− BOEC model. Despite upregulation of KLF2 in CCM1−/− BOEC, we did not observe a shortening of WPBs which would have been indicative of reduced activity of primary hemostasis (VWF secretion and WPB distribution inside ECs are controlled by a highly regulated molecular network. The secretagogues histamine, DDAVP, and PMA used in this study cover the most important signaling cascades of WPB exocytosis from human ECs . Remarka or VEGF  might ha of WPBs . The obsactivity . Lentiviactivity . Furtherpression . These amostasis . In factmostasis . It is pmostasis . Interesmostasis ; (Gladinmostasis . SpecifiIn conclusion, our results point to the co-existence of pro- and anticoagulatory processes in CCM which provides an explanation for the favorable outcome seen in CCM patients who got antithrombotic therapies for other reasons . Future


During liver organogenesis, cellular transcriptional profiles are constantly reshaped by the action of hepatic transcriptional regulators, including FoxA1-3, GATA4/6, HNF1α/β, HNF4α, HNF6, OC-2, C/EBPα/β, Hex, and Prox1. These factors are crucial for the activation of hepatic genes that, in the context of compact chromatin, cannot access their targets. The initial opening of highly condensed chromatin is executed by a special class of transcription factors known as pioneer factors. They bind and destabilize highly condensed chromatin and facilitate access to other “non-pioneer” factors. The association of target genes with pioneer and non-pioneer transcription factors takes place long before gene activation. In this way, the underlying gene regulatory regions are marked for future activation. The process is called “bookmarking”, which confers transcriptional competence on target genes. Developmental bookmarking is accompanied by a dynamic maturation process, which prepares the genomic loci for stable and efficient transcription. Stable hepatic expression profiles are maintained during development and adulthood by the constant availability of the main regulators. This is achieved by a self-sustaining regulatory network that is established by complex cross-regulatory interactions between the major regulators. This network gradually grows during liver development and provides an epigenetic memory mechanism for safeguarding the optimal expression of the regulators. The liver participates in a variety of crucial biological processes such as hemopoiesis during embryonic life and metabolism, glycogen storage, detoxification, plasma protein secretion, acute phase reaction, and hormonal homeostasis in adulthood. The major cell type of the liver is the hepatocyte, which arises from endodermal precursors through a complex multistep differentiation process. During hepatocyte differentiation, the gene expression pattern of each intermediate cell type is generated by the action of transcription factors, which bind to the regulatory regions of their target genes and activate transcription at specific times during development. Developmental cell fate decisions are determined by cell-to-cell communication and the action of complex signaling pathways. Signaling molecules exert their function through the modulation of transcription factor activity, either directly or indirectly.In this review, we summarize current knowledge about the function of major transcription factors involved in different stages of liver development. Furthermore, we present our current understanding of the regulatory mechanisms of developmental gene activation.Albumin, Transthyretin, HNF4α, and α-fetoprotein are first activated at this stage [In early embryos, liver organogenesis is initiated from the definitive endoderm. In mice, during gastrulation at embryonic day 6.5 (E6.5), the primitive node is formed on the posterior side of the epiblast. Later on, this knot of cells forms a structure called a primitive streak. Cells migrating from the primitive streak give rise to the mesendoderm, which is the precursor of mesoderm and endoderm. Signaling factors such as Nodal lead these bipotential mesendoderm cells to segregate and generate the definitive endoderm. Through morphogenetic movements, the endoderm forms the primitive gut, which is surrounded by mesoderm. Subsequently, the gut tube is divided along the anterior–posterior axis into the foregut, midgut, and hindgut. At E8.5, cells in the ventral foregut endoderm receive BMP (bone morphogenic protein) signals from the septum transversum mesenchyme, parallel to FGF (fibroblast growth factor) signals from the adjacent developing heart, which facilitates their differentiation into hepatoblasts . Hepatocis stage ,2.After hepatoblast specification, the hepatic epithelium thickens and transforms into a pseudostratified epithelium, resulting in the formation of the liver diverticulum. By E9.5, the layer that surrounds the hepatoblasts is destroyed, enabling them to migrate into the septum transversum mesenchyme and produce the nascent liver bud. The septum transversum mesenchyme also contributes to the formation of hepatic stellate cells. From E9.5 until E15, hepatoblasts proliferate and the liver bud grows. Around E13, hepatoblasts begin to differentiate into hepatocytes or cholangiocytes. Hepatoblasts that are in contact with the portal vein will differentiate into cholangiocytes, whereas the rest of the hepatoblasts gradually differentiate into mature hepatocytes .Single-cell RNA-seq analyses revealed that cellular transcriptomes are very dynamic at the specification stage (E9.5–E15.5). On the other hand, gene expression profiles remain similar between E11.5 and E15.5, suggesting that liver specification mainly occurs prior to this time period . SpecifiThe functional importance of this set of transcription factors in hepatocyte specification and differentiation is best exemplified by the fact that the forced expression of certain combinations of these factors can convert other somatic cells such as fibroblasts into functional hepatocytes. Successful reprogramming of embryonic or adult fibroblasts has been reported by the co-overexpression of FoxA3, GATA4, and HNF1α  or HNF1βFoxA1 gene are characterized by low glucagon mRNA levels, hypoglycemia, weakness, and dehydration, leading to lethality between postnatal days 2 to 12 (P2 to P12). This study showed that FoxA1 has a significant role in the transcriptional control of genes regulating glucose homeostasis [FoxA proteins belong to the subfamily of the Forkhead box (FOX) transcription factors and are thought to be essential for the transcriptional regulation of virtually all genes expressed in the liver, lung, and pancreas ,10,11,12eostasis . Mouse meostasis ,24. FoxAeostasis . Initialeostasis ,28,29,30(A/T)GATA(A/G) in the cis-regulatory elements of target genes [GATA transcription factors participate in the regulation of embryonic morphogenesis and cellular differentiation . The GATet genes ,34. Initet genes ,35.Data from knockout and rescue experiments suggested that the function of GATA4 is important for ventral morphogenesis, especially for the expansion of the liver bud and the formation of the ventral pancreatic bud ,38,39,40N-terminus, and a highly acidic region in the C-terminus. The last two motifs are considered to be essential for the transcriptional activation [The haematopoietically expressed homeobox (Hex) transcription factor is a homeobox-containing protein essential for the development of the liver, and more specifically, for the expansion of the liver bud. It contains a DNA-binding domain, a proline-rich region in the tivation . Hex firtivation . On E10,tivation . In micetivation ,45. The tivation . Liver btivation ,48. Finativation . All theProspero-related homeobox 1 (Prox1) transcription factor is essential for the formation of several organs and tissues such as liver, pancreas, eye, lymphatic vessel, nerve, and cardiac muscle ,50,51. IHnf4α gene in embryonic hepatocytes showed that this transcription factor affects gluconeogenesis, glycogen synthesis, the architecture, and the functionality of hepatocytes [Hnf4α gene in the liver results in an aberrant accumulation of lipid, a reduction of serum cholesterol and triglyceride levels, and an increase in the serum bile acid concentration [HNF4α belongs to the orphan nuclear receptor family and constitutes the main transcriptional activator for many genes expressed in the liver . It contatocytes . HNF4α iatocytes . Finallyntration . All theHNF6 transcription factors belong to the ONECUT class, which contains two significant domains in the C-terminus that constitute the DNA-binding domain: a single-cut domain and a divergent homeodomain ,67,68,69N-terminal part of the proteins contains a transactivation and attenuation domain [N-terminus is relatively low, resulting in differences in their transactivation and attenuation properties [C/EBPα gene results in an abnormal liver phenotype due to the increased capacity of hepatocytes to proliferate [Two members of the CCAAT/enhancer-binding protein (C/EBP) family, C/EBPα and C/EBPβ, are important regulators of liver development . C/EBP pn domain ,90,91,92operties . During operties . In aduloperties . Mice laoperties ,96. The liferate ,96. The liferate ,98, and liferate .C-terminus [Hepatocyte nuclear factor 1 (HNF1) is a member of the POU homeobox gene family that mediates the transcriptional activation of cell type-specific genes in various organs, including the liver ,105,106.terminus ,104,109.terminus ,111,112.terminus . Apart fterminus .As mentioned above, the transcriptional activation of genes during development is mediated by several key hepatic regulators, which act in concert with specific signaling pathways to establish expression profiles that define differentiation-specific cellular states. Accumulating evidence suggests that regulatory regions (enhancers and promoters) of tissue-specific genes often reside in compacted genomic regions that cannot be accessed by transcription factors, thus acting as a barrier to transcription. Initial gene activation requires a defined sequence of transcription factor–DNA interactions and chromatin transitions, which can cope with the structural obstacle of chromatin condensation. This has become the prevailing view, following the discovery of a special class of transcription factors, now known as pioneer factors. These pioneer factors possess the ability to bind their recognition sequence when embedded into a highly condensed chromatin state.Alb gene was silent, and in the nascent liver bud, where the Alb gene was expressed [Pioneer factors were discovered in an attempt to uncover the first transcription factor that binds to the enhancer of the liver-specific albumin gene during embryogenesis. In vivo footprinting studies in an enhancer of the serum albumin gene showed that FoxA and GATA factors occupied their target sites both in pluripotent endoderm, where the xpressed ,116. Whexpressed . In thisxpressed ,119.C-terminal α-helical region of FoxA1, which is able to bind to core histones, is necessary for the opening of the chromatin [FoxA proteins bind to nucleosomal target sites via their H1-like DNA-binding domain ,120,121.hromatin ,123. Thehromatin ,125.Gene activation during development includes several steps. Initially, pioneer factors scan the genome and bind to particular regions as they encounter their binding sites . There aAs explained above, pioneer factors act as priming factors to establish the transcriptional competence of their target genes during development, but their binding is not accompanied by immediate transcription activation  ,140,141.A recent study has shown that the recruitment of two prominent hepatic regulators, HNF4α and C/EBPα, similarly to FoxA1, is not linked to concomitant gene activation during development . The timInsights into the abovementioned questions were provided by studying the dynamics of transcription factor recruitment and chromatin structure changes during developmental gene activation. It was observed that dynamic binding events, i.e., the transient binding of transcription factors, without gene activation is the most common phenomenon during development. The stable and transient association of transcription factors with different cis-regulatory elements in promoter and enhancer regions facilitates the recruitment of chromatin remodelers and the generation of active chromatin configurations. The length of time during which such dynamic interactions take place in a continuous fashion allows for the cumulative increase in histone modifications characteristic of active enhancers and the progressive expansion of stably open chromatin domains. In this way, bookmarking is part of a highly dynamic developmental maturation process during which regulatory regions are prepared for the acquisition of an optimal configuration that supports an efficient and stable transcription .The model above was supported by the analyses of mice that were deficient in the bookmarking factors HNF4α or C/EBPα. In both cases, a significant deregulation of transcription of most early-bound hepatic genes was observed in parallel to the blocking of acquisition in active chromatin states and the reciprocal accumulation of repressive histone modification marks .The link between the gene expression signature of embryonic and cancerous cells and tissues has been demonstrated by many previous studies ,145,146.Other studies have demonstrated the role of various pioneer factors  in establishing a gene expression profile that is permissive to cancer initiation and progression ,153,154.Liver regeneration relies on the intrinsic ability of the differentiated quiescent hepatocytes to enter the cell cycle, or on the cellular plasticity of other liver cells to transdifferentiate into hepatocytes in order to repopulate the damaged liver . It is kDuring mitosis, considerable alterations occur in the nuclear and chromosomal architecture. These include increased chromosome condensation, nuclear envelope breakdown, loss of long-range interactions between promoters and enhancers, and the displacement of many transcription regulators ,159. ThiThe first mechanistic insights into the process of transcription memory across many cell generations have been provided by the observations that histone modifications, characteristic of active chromatin states, are retained in parental nucleosomes following DNA replication . The perThe abovementioned features illustrated the model that the propagation of the proper reconstitution of transcription patterns during mitotic exit is assisted by epigenetic memory marks partially retained from the parental cells, which would guide transcription factors in their re-association with the previously established locations. These assisted global binding events may result in simultaneous transcriptional induction ,164. A rAs an analogy, one may assume that the mechanistic basis for the observed stable association of bookmarking factors with their targets during developmental gene activation involves cell cycle-mediated association/dissociation events in the same locations. This scenario has been challenged by several studies, demonstrating that a number of cell type-specific transcription factors remains bound to mitotic chromosomes ,168,169,The ever-growing list of transcription factors (TFs) that were found to be associated with mitotic chromatin includes C/EBPα, GBP, HSF1, HSF2, and HNF4α ,158,175,Taken together, the earliest pioneer factor in liver development, FoxA1, binds potently to mitotic chromatin; the subordinate pioneer factor GATA4 and non-pioneer factors are bound moderately; whereas other factors that act later in development bind very loosely or are excluded altogether from the chromosomes in mitosis. Thus, the analogy between the developmental and mitotic bookmarking mechanisms points to the common principles employed by living organisms for the execution of different processes, which utilize distinct functional properties of transcription factors.A common feature of all developmentally regulated hepatic gene regulatory regions is the combinatorial binding of many transcription factors. The stable association of multiple factors with different cis-regulatory elements is a prerequisite for high-level transcription. This depends on the excess availability of transcription factors. How is the required hepatocyte-specific and high concentration of the main regulators achieved?During liver development, the expression of the main hepatic regulators, described in a previous section, follows a sequential pattern. For instance, FoxA factors are highly expressed in all stages and their function is crucial, not only for the initial activation of developmental genes but also for the maintenance of hepatic gene expression . GATA faAt subsequent stages of hepatocyte maturation, a promoter occupancy analysis of the main hepatic regulators demonstrated multiple cross-regulatory interactions between a core set of six hepatic transcription factors, including HNF1α, HNF1β, HNF4α, HNF6, FoxA2, and LRH-1 . This reThe significance of the transcription factor network in setting up liver-specific transcriptional profiles during development and in preserving the hepatic gene expression program throughout the embryonic and adult life is demonstrated by the complexity that offers sustainability. The transient or permanent loss of one component of the circuit may have small effects on hepatic gene expression patterns once the complexity of the cross-regulatory network reaches a critical level. In this way, the hepatic network functions as a fundamental epigenetic memory mechanism, which secures the maintenance of the expression pattern in differentiated hepatocytes . In thisThe gene expression pattern of fully differentiated hepatocytes is generated by multiple regulatory signals involving the sequential action of hepatic transcription factors during embryonic and postnatal development. The process is initiated by pioneer factors that bind to and destabilize the chromatin at gene regulatory loci, which allows for the recruitment of additional transcription factors necessary for the activation of the target genes. The recruitment of transcription factors is not accompanied by immediate gene activation, but it initiates a lengthy maturation process involving the progressive expansion of active chromatin marks and the generation of a configuration that is competent for transcription initiation. A group of genes that are highly active in embryonic hepatocytes are fully shut down after birth, and many of them are reactivated in hepatocellular carcinoma. These genes are also marked by hepatic transcription factors throughout adult life. The association of transcription factors with their targets, without triggering immediate transcription activation either in embryonic or postnatal life, is called “bookmarking”. The bookmarking function of hepatic transcription factors is important for the developmental activation of the genes and the precise re-establishment of hepatic gene expression patterns following the mitotic phase of each cell duplication event. While we now have a good understanding of the chromatin maturation process, which accompanies bookmarking factor association, the critical step that determines the actual timing of the activation of hepatic genes is less understood.During the past years, it has been increasingly recognized that nuclear topology may be critical in determining the active and inactive states of genes. Given the high level of plasticity of the nuclear architecture in different cellular conditions, it is intriguing to assume that developmentally regulated loci may partition in different nuclear compartments at the priming, maturation, and activation stages. Such compartmentalization is likely to be virtual, generated by distinct long-range interactions with other genomic loci. We envisage that the contribution of gene topology and that of the different neighboring genomic regions may provide a novel regulatory layer that could influence the transcription factor binding and chromatin remodeling processes. The multiplicity of regulatory processes is expected to provide an additional level of plasticity to developmental decisions and orchestrate developmental gene expression patterns.


The transcription factor CTCF appears indispensable in defining topologically associated domain boundaries and maintaining chromatin loop structures within these domains, supported by numerous functional studies. However, acute depletion of CTCF globally reduces chromatin interactions but does not significantly alter transcription.Here, we systematically integrate multi-omics data including ATAC-seq, RNA-seq, WGBS, Hi-C, Cut&Run, and CRISPR-Cas9 survival dropout screens, and time-solved deep proteomic and phosphoproteomic analyses in cells carrying auxin-induced degron at endogenous CTCF locus. Acute CTCF protein degradation markedly rewires genome-wide chromatin accessibility. Increased accessible chromatin regions are frequently located adjacent to CTCF-binding sites at promoter regions and insulator sites associated with enhanced transcription of nearby genes. In addition, we use CTCF-associated multi-omics data to establish a combinatorial data analysis pipeline to discover CTCF co-regulatory partners. We successfully identify 40 candidates, including multiple established partners. Interestingly, many CTCF co-regulators that have alterations of their respective downstream gene expression do not show changes of their own expression levels across the multi-omics measurements upon acute CTCF loss, highlighting the strength of our system to discover hidden co-regulatory partners associated with CTCF-mediated transcription.This study highlights that CTCF loss rewires genome-wide chromatin accessibility, which plays a critical role in transcriptional regulation.The online version contains supplementary material available at 10.1186/s13059-021-02466-0. H19-IGF2 and β-hemoglobin loci [CCCTC-binding factor (CTCF) is a highly conserved zinc finger–containing transcription factor known as “the master weaver of the genome” . It is tbin loci , 4. CTCFbin loci , 6. In gbin loci –9, and tbin loci –14. At tbin loci , 16. Morbin loci –21.MLL-rearranged human B cell lymphoblastic leukemia (B-ALL) cell line SEM, allowing acute depletion of CTCF protein through the auxin-inducible degradation (AID) [BLCAP induced transcription, consistent with the data collected from acute depletion of CTCF. Last, we discovered 40 CTCF co-regulatory partners in controlling a different subset of downstream genes, of which many CTCF co-regulators exhibited alterations to their downstream genes’ expression but did not show changes at their expression levels, highlighting the advantage of our system to discover hidden co-regulatory partners associated with CTCF-mediated transcription. In summary, we propose a model that acute CTCF loss rewires genome-wide chromatin accessibility, which plays an essential role in transcription regulation.Despite a global reduction of chromatin interactions upon CTCF loss, its observed effect on mRNA expression by RNA-seq is not dramatic , 22. Thuon (AID)  system. CTCF locus and generated three clones of CTCFAID cells [Acute CTCF depletion alters chromatin accessibility. Although many crude loss-of-function models targeting CTCF have been extensively studied , 23–28, ID cells . In the ID cells .Fig. 1AAID cells treated with or without IAA. Wild-type SEM cells and SEM cells with CRISPR double knockdown of two unrelated targets, USF1 and USF2, were included as additional controls   are associated with these DARs, we performed de novo motif analysis (Homer v4.9.1) . Our datp value < 2.2 × 10–16, Wilcoxon test), with a median distance very close to 0 bp. In contrast, the increased DARs were significantly distant (~ 100 bp) from the CTCF motifs than the decreased DARs  but were significantly closer than the control regions (~ 10 kb)   or decreased DARs . These 2xCTSes are proposed to regulate chromatin loops [2 fold change CTCF loss vs. control) were significantly more than the loops overlapping the control NFRs  and increased DARs . The lost contact of the loops overlapping control NFRs were also significantly stronger  than the loops overlapping with the increased DARs, although the difference appeared marginal  and more increased DARs are often associated with more expression . For the genes shown consistent changes, we further reviewed the ATAC-seq signals assigned to their promoters and confirmed that the pattern was as expected  and confirmed the reproducible pattern . We next matched them to the upregulated genes in our RNA-seq results  if the TSSs were located 2 to 50 kb away from the DARs. In summary, 67 genes passed these criteria  detected about 61% overall indel frequency in the targeted pool population   within the CTCF binding peak in the distal non-coding region upstream of the ion Fig. A, which NT) Fig. B. Alternein Fig. . As expeout Fig. D. In sumAID treatments at 12, 24, and 48 h compared to control cells were carried out via an advanced TMT-LC/LC-MS/MS platform  between global proteome and transcriptome . Moreover, MYC’s binding sites were significantly enriched in CTCF-mediated DARs upon acute CTCF loss highlighting it can work synergistically with CTCF; (2) hidden master partners that do not show an evident difference of their expression following acute CTCF depletion but exhibit substantial proteomic changes to their respective downstream gene targets . As expected, most of these 40 CTCF co-regulatory partners also co-localize within CTCF-mediated DARs, confirming their potential direct co-regulatory role with CTCF. Furthermore, we investigated the co-regulation pattern of CTCF and selected candidate TFs ZBTB7A and YY1. We hypothesized that if acute depletion of CTCF impaired chromatin accessibility and adjacent TF binding, we should be able to detect the proximity of both binding motifs within a subset of loci. To this end, the overall distance between the CTCF binding motif and ZBTB7A or YY1 motifs was calculated among increased, decreased, and control DARs identified previously. The data demonstrated that the physical distance between pairwise CTCF/ZBTB7A and CTCF/YY1 motifs are notably closer  in decreased DARs compared with the others. As a negative control, this pattern was not observed between CTCF and DUX motifs (p = 0.47). Notably, the list of CTCF co-regulatory TFs was significantly enriched in survival essential genes  when compared to the complete list of TFs in the CRISPR-Cas9 library (1639 TFs) dropout screening (Fisher exact test p = 0.0237) [Consistent with the immunoblotting and Q-PCR results, mass-spectrum (MS)-based proteomics and RNA-seq analyses confirmed the substantial loss of CTCF expression at the protein level and increased expression at the mRNA level following acute loss of CTCF. While the loss of CTCF is lethal in SEM cells , the mol 0.0237) , underliCTCF haploinsufficiency destabilizes DNA methylation [CTCF knockdown in prostate cancer cells leads to hypermethylation at CTCF-binding sites [DNMT1 and DNMT3B double knockout in HCT116 cells, the DMRs exhibited increased chromatin accessibility and were enriched for CTCF motifs [Despite recent advancements in understanding CTCF biology, several aspects remain unresolved because of a lack of adequate research tools. hylation , and CTChylation . CTCF knng sites . In contng sites , 38, 53,ng sites . Whetherng sites , 56. UpoF motifs . This isF motifs , furtherF motifs . Indeed,Using a state-of-the-art acute CTCF degradation system and rich available datasets, we provide direct evidence that CTCF regulates chromatin accessibility but not DNA methylation. These data successfully fill a knowledge gap by uncoupling the direct effects of CTCF from its downstream effects. Our results shed light on the mechanism by which CTCF exerts numerous molecular functions. To our knowledge, this is the first report that shows sites with decreased DARs upon CTCF loss exhibit a tandem CTCF-binding pattern that is associated with CTCF motif orientation. These data also support the hypothesis that transcriptional regulation most likely occurs where CTCF and CTCFL/BORIS reside together . BecauseWe also found that the distance between tandem CTCF sites previously reported for 2xCTSes (~ 33 bp)  was shorWhile acute deletion of CTCF can profoundly disturb global chromatin interactions and accessibilities, transcription is often not significantly altered , 22. We We and others demonstrated that long-term loss of CTCF induced massive cell death , highligAID knockin clones. For instance, although CTCF protein seems to completely disappear when monitored by immunoblotting, flow cytometry and CUT&RUN still can detect some positive signals. There are many possible explanations to address this observation. Dr. Gerd Blobel’s group recently reported that a minimal amount of chromatin-bound CTCF is retained upon auxin treatment [AID lines may also contribute to incomplete protein degradation. To mitigate this challenge, a more powerful acute depletion system is required, which is currently not available to our knowledge.We also acknowledge that residual CTCF protein can be detected upon auxin treatment in CTCFreatment . Also, rIn brief, here we unveiled many CTCF co-regulatory partners through systematic integration of ATAC-seq, RNA-seq, Hi-C, Cut&Run, CRISPR-Cas9 TF library dropout screening, and MS-based deep proteomic and phosphoproteomic data, largely broadening our understanding of the transcriptional network mediated by CTCF. The mechanism and functional consequence behind the interactions between CTCF and these partners is worth further investigation into other systems. Finally, the rich next-generation sequencing data collected from the acute CTCF degradation model will be valuable resources for the research community.44. The BCLAP-CBS targeted pool population was generated by delivering guide RNA (5′- AGGTCTGTGGTGACACCTAA-3′) into the Cas9-expressing SEM cell line. All cells were negative for mycoplasma infection, and their identity was confirmed by short tandem repeat analysis.The human B-ALL cell line SEM (DSMZ) carrying bi-allelic miniAID-mClover3 knockin tags was established in a previous study . Three sCell lysates were prepared in RIPA buffer, subjected to SDS-PAGE (Thermo Fisher Scientific), and transferred to PVDF membranes (Bio-Rad) at 100 V for 1 h. After blocking, the membranes were incubated with 5% nonfat milk in TBS-T  containing antibodies against GAPDH , USF1 , USF2 , and CTCF  at 4 °C for 12 h with gentle shaking. The membranes were washed three times for 30 min and incubated with a 1:2000 dilution of horseradish peroxidase-conjugated anti-mouse or anti-rabbit antibodies for 2 h at room temperature. The blots were washed with TBS-T three times for 30 min and developed with the ECL system (Amersham Biosciences).Three single-cell derived knockin clones  were treated with complete medium supplemented with 500 μM IAA (Sigma) for 24 h or 48 h to induce CTCF degradation. IAA washout was performed by centrifuging and resuspending the cells in PBS three times and then maintaining in culture for 48 h in a regular medium without IAA.CTCF, BLCAP, MEM173, MXRA7, STAT3, STAT5A, and GAPDH. Relative gene expression was determined by using the ΔΔCT method [Reverse transcription was performed by using the High-Capacity cDNA Reverse Transcriptase kit . The real-time Q-PCR was performed using FAST SYBR Green Master Mix , with specific primers to amplify T method .2, and 0.1% IGEPAL), followed by centrifugation. The pellets were resuspended in 25 μL Tagment DNA Buffer  and then used directly in the transposition reaction. Nextera Tn5  was added to the resuspended nuclei, and the transposition reaction was incubated at 37 °C for 30 min. After transposition, the DNA was purified using a Qiagen MinElute PCR purification kit . Indexing PCR was carried out for 12 cycles with NEBNext HiFi 2X PCR Master Mix  and indexing primers, as previously described [M-value normalization method and applied empirical Bayes statistical tests after linear fitting from the voom package  [p value < 0.05 (Benjamini–Hochberg procedure) and fold change > 2. Cutoff p value > 0.5 and fold change < 1.05 were used for control NFRs (no change). To detect TF-enriched DARs, we scanned the TRANSFAC motif database [Briefly, 75,000 cells were collected per sample in duplicate and resuspended in cold PBS with protease inhibitors. After centrifugation at 500 rpm for 5 min at 4 °C (Eppendorf 5417R refrigerated centrifuge), cell pellets were resuspended in cold lysis buffer with protease inhibitors  . DARs wedatabase  by usingdatabase . For eacdatabase . Qualityp value ≤ 0.01 as a cutoff. The minimum length for DMRs was 50 bp, and the minimum number of CpG sites for DMR was 3. DMR information was provided in Additional file Genomic DNA collected from Clone 27 with or without IAA treatment for 24 h was extracted with the PureLink Genomic DNA Mini kit (Thermo Fisher Scientific). Approximately 2 μg of genomic DNA was used for bisulfite DNA conversion and subsequent cleanup of the converted DNA with the EpiTect Fast Bisulfite Conversion kit (Qiagen), followed by library construction and next-generation sequencing. The FASTQ sequencing files were first trimmed by removing adapter sequences with trimgalore and mapped to the human genome (hg19) by BSMAP  . CpGs weA dropout CRISPR screen was done in SEM cells to target 1639 human transcription factors with seven sgRNAs designed against each gene . The CasAID SEM cells from 4 treatment groups: no IAA, + IAA 12 h, + IAA 24 h, and + IAA 48 h, each with triplicates, were applied for deep proteomic and phosphoproteomic analyses using a well-established protocol [2-based phosphoproteomic enrichment was then performed on the pooled sample, and flow-through was further desalted and applied for off-line basic-PH fractionation. Peptides were separated into 80 fractions via a 2 h gradient, and every other fraction was dried and reconstituted in 5% formic acid for MS analysis. Peptides were analyzed on an Orbitrap HF mass spectrometer (Thermo Fisher Scientific) after separated on a 20 cm × 75 μm id column packed with 1.9 μm C18 resin  and heated at 55 °C. Peptide separation was achieved through a 2 h ~ 15–40% buffer B  gradient. The mass spectrometer was set in DDA mode with 60,000 resolution, 1 × 106 AGC target, and 50 ms maximal ion time for MS1, Top 10, 1 × 105 AGC target, 105 ms maximal ion time, 1 m/z isolation window and 0.2 m/z offset, 38 NCE, and 15 s dynamic exclusion for MS2. Proteomic data were processed by the hybrid JUMP software suites for improving sensitivity and specificity [Two million CTCFprotocol , 66. In cificity , 64. Bricificity . Detailet-test from two or three independent biological replicates with GraphPad Prism 6.0. Fisher exact tests and Wilcoxon tests were performed with R. The figures were plotted with R , deeptools, or Excel.Statistical analyses for Q-PCR data were calculated with a two-tailed Additional file 1: Supplementary Figures-Figures S1-S13.Additional file 2: Supplementary Tables-Tables S1. ATAC-seq peak data.Additional file 3: Supplementary Tables-Tables S2. ATAC-seq statistics and QC.Additional file 4: Supplementary Tables-Tables S3. DMR profiles.Additional file 5: Supplementary Tables-Tables S4. Insulator targets.Additional file 6: Supplementary Table-Tables S5. MAGeCK analysis of CRISPR screen.Additional file 7: Supplementary Tables -Tables S6. Differential expression analysis of whole proteome and phosphoproteome data.Additional file 8: Supplementary Tables -Tables S7. Summary of RNA-seq data.Additional file 9.


Several members of the Hes/Her family, conserved targets of the Notch signalling pathway, encode transcriptional repressors that dimerise, bind DNA and self-repress. Such autoinhibition of transcription can yield homeostasis and, in the presence of delays that account for processes such as transcription, splicing and transport, oscillations. Whilst previous models of autoinhibition of transcription have tended to treat processes such as translation as being unregulated (and hence linear), here we develop and explore a mathematical model that considers autoinhibition of transcription together with nonlinear regulation of translation. It is demonstrated that such a model can yield, in the absence of delays, nonlinear dynamical behaviours such as excitability, homeostasis, oscillations and intermittency. These results indicate that regulation of translation as well as transcription allows for a much richer range of behaviours than is possible with autoregulation of transcription alone. A number of experiments are suggested that would that allow for the signature of autoregulation of translation as well as transcription to be experimentally detected in a Notch signalling system. The Notch signalling pathway plays a crucial role in many different developmental contexts , is involved in the function and maintenance of adult tissue  and is Notch signalling occurs when a Notch ligand  interacts with a Notch receptor. Upon activation of the Notch receptor, the Notch intracellular domain is cleaved and transported to the nucleus where it activates target gene expression . InteracN-component cascade that is typically formulated such that the Negative feedback loops play an essential role in biological oscillations . The Goo cascade ,9. It ha cascade ,11. A coPositive feedback loops have also been identified in many biological oscillators. For example, in models of the cell cycle positive feedback loops give rise to hysteresis that results in cell cycle phase transitions . In neurLevels of gene products are regulated by processes such as transcriptional activation, post transcriptional regulation (from splicing to RNA stability) and protein degradation. mRNA sequences contain untranslated regions (UTRs) that flank the open reading frame at the 5′ and 3′ ends. UTRs often contain conserved sequence elements that facilitate control of translational efficiency and mRNA stability through the binding of specific regulatory factors. Regulators at the post transcriptional level include RNA binding proteins (RNA-BPs), microRNAs (miRNAs) and long-noncoding RNAs (lncRNAs). The 5′UTR is mostly involved in regulating translation efficiency with elements such as upstream open reading frames and secondary structures inhibiting translation. Other sequences such as 5′ terminal oligopyrimidine sequences and internal ribosome entry sites may enable translation under conditions where most other mRNAs are translationally inactive. The 3′UTRs often contain several binding sites for miRNAs and/or RNA-BPs that mostly inhibit translation and/or target the mRNA for degradation.In mouse neural progenitor cells it has been identified that: (i) the miRNA mir-9 is required for Hes1 oscillations, (ii) Hes1, a Notch target gene, represses miR-9 transcription; and (iii) miR-9 decreases Hes1 mRNA stability, resulting in a double negative feedback loop . The disThe aim of this study is to investigate a potential role for translational regulation in a model of a transcriptional repressor. The layout is as follows: in t. It is assumed that transcription is repressed by protein dimers such that the transcription rate is given byX, i.e. translation occurs at rateX at which X-dependent translation is half-maximal. Finally, X is assumed to be under the same transcriptional regulation as M.Let by Lewis . It is aX and X.The total rates of change with respect to time are assumed to be given by the ordinary differential equationsX. This assumption could be realised, for example, in a situation where the production and degradation rates of X are relatively large. HenceX into Equations  transcription of mRNA is inhibited by protein dimers ; (ii) X P. In contrast, the transcription rate is a decreasing function of P  mRNA is more stable than protein (The inhibition of translation by X introduces a double negative feedback loop (P represses production of X which represses translation of P), i.e., a positive feedback loop. Hence the translation rate is an increasing sigmoidal function of of P see b. For laM and P evolve to the steady state . Note that the P nullcline has, for the chosen parameters, a local maximum and minimum, but that the M nullcline is monotonically decreasing. The solution trajectory ultimately tends to the intersection of the nullclines. In the limit of low transcription rate, the system behaves like a standard model of auto-inhibition of transcription .In the limit of a low baseline transcription rate  cells exhibit intermittent oscillations whereby successive cycles are followed by periods of non-oscillatory behaviour . Upon inReturning to the deterministic model, further increase in ycle see . The staycle see . At A, lThe numerical results presented in r . AlthouUpon transcriptional block  of a pathway repressing further transcription, thus resulting in a stable steady state. In oscillatory systems negative feedback loops in the presence of delays can give rise to oscillatory gene expression patterns.Post-transcriptional modifications encompass a wide range of processes that involve the processing, translation and stability of mRNAs. Whilst they have been shown to play a crucial role in the regulation of the circadian oscillator, far less is known about the role of post transcriptional modifications in the Notch signalling pathway. Indeed, most mathematical models of mRNA degradation and translation in the Notch signalling pathway assume that such processes are linear and unregulated. In this study we consider a feedback loop that ultimately results in the translation rate of a protein increasingly sigmoidally with protein levels . Our results show that, in principle, such post-transcriptional regulation can give rise to a system that is reminiscent of the FitzHugh-Nagumo ,23 modelFor intermediate background transcription rates, the model is excitable. In this regime small perturbations to the steady state result in the system quickly returning to steady state. However, a sufficiently large perturbation results in a transient pulse of protein levels before the system eventually returns to the stable steady state. Notably, it has previously been suggested that the segmentation clock oscillator is excitable . Whilst For larger transcription rates the model is bistable: there is a stable steady state and a stable limit cycle. Depending on the initial conditions that are chosen, model solutions tend to one of the identified attractors. Moreover, upon the inclusion of additive white noise, the model solutions can switch between the two stable states, behaviour that is reminiscent of observations of intermittent oscillations in isolated zebrafish PSM cells . NotablyPhenomenology in the model is consistent with some experimental observations of the zebrafish segmentation clock. When Notch signalling is inhibited, via DAPT treatment , the osck1 see . HoweverA further prediction of the model is that a reduction in the transcription rate could lead to higher levels of mRNA. In a system of uncoupled cellular oscillators, where one samples the average level of mRNA using a technique such as qPCR, the steady state level of mRNA measured close to the point where oscillations are lost will be higher than the average over the oscillatory cycle. Hence one could observe that, at the population average level, transcriptional inhibition counterintuitively results in an increase in mRNA levels.In this study a reference set of parameter values has been chosen based upon a previous model of delayed negative feedback oscillations. So as to allow focus of the interaction between nonlinearities in translation and transcription, we have not explicitly accounted for delays that represent, for example, splicing and transport. Moreover, we have focussed here only on the sensitivity of model solutions to the parameter X allows for the model analysis to be simplified and therefore for parallels with excitable medium theory to be explored. Whilst the quasi-steady state approximation could be realised, for example, if X were relatively unstable and produced at a relatively large rate, further experimental work is required to firstly identify molecular regulators of post-transcriptional regulation and then to quantify their kinetics. In mouse neural progenitors [The quasi-steady state approximation for the variable genitors  it has bIt is a well-established result that combining positive and negative loops can give rise to nonlinear behaviours such as excitability. Here the major novelty is the application of this idea to the Notch signalling pathway. However, in the derivation of the proposed model we hypothesised that mRNA translation is negatively regulated by an unidentified molecule X. Whilst there are prospective molecular candidates that could satisfy the assumptions made in the model development, further work is required to experimentally determine the molecular players.