Recently, a new technology combining CRISPR with TEs was reported, which provides a novel targeted insertion system to both academia and industry. We suggest that interrogating biological processes that generally depend on the actions of TEs with TEs-derived genetic tools is a very efficient strategy. For example, excessive activation of TEs is an essential factor in the occurrence of cancer in humans. As potent mutagens, TEs have also been used to unravel the key regulatory elements and mechanisms of carcinogenesis. Through this review, we aim to effectively combine the traditional views of TEs with recent research progress, systematically link the mechanistic discoveries of TEs with the technological developments of TE-based tools, and provide a comprehensive approach and understanding for researchers in different fields.It has been 70 years since Barbara McClintock discovered transposable elements (TE), and the mechanistic studies and functional applications of transposable elements have been at the forefront of life science research. As an essential part of the genome, TEs have been discovered in most species of prokaryotes and eukaryotes, and the relative proportion of the total genetic sequence they comprise gradually increases with the expansion of the genome. In humans, TEs account for about 40% of the genome and are deeply involved in gene regulation, chromosome structure maintenance, inflammatory response, and the etiology of genetic and non-genetic diseases. In-depth functional studies of TEs in mammalian cells and the human body have led to a greater understanding of these fundamental biological processes. At the same time, as a potent mutagen and efficient genome editing tool, TEs have been transformed into biological tools critical for developing new techniques. By controlling the random insertion of TEs into the genome to change the phenotype in cells and model organisms, critical proteins of many diseases have been systematically identified. Exploiting the TE’s highly efficient   Transposable elements (TEs) are mobile DNA sequences that mainly range in length from 100 bp to 10,000 bp, and the dynamic features of their interaction with host genomes drive evolutionary novelty and shape genome compositions and functions in multiple ways. TEs were found to exist in all organisms throughout prokaryotic and eukaryotic kingdoms. TEs-derived sequences constitute a large portion of most eukaryotic genomes, which comprise approximately 10% of some fish genomes, 37% of the mouse genome, 45% of the human genome, and more than 90% of some plant genomes. An interesting discovery is that the genome sizes of different species are linearly related to the portion of its contents. Similar to viruses, TEs are multi-faceted selfish genetic elements that encode and regulate proteins with multiplexed functions and noncoding regulatory elements by replication or remobilization themselves. To differentiate them from other invasive genetic elements like viruses, a TE is defined as a genetic element with the potential for chromosomal and replicative mobilization, and they exhibit an increasing frequency of vertical transmission through the germline.In the decades following Barbara McClintock’s groundbreaking discovery of mobile DNA sequences, the profound impact of TEs on both evolution and human health has become evident. To understand how TEs have impacted biological functions, we have to understand the diversity, classification, and transposition mechanism of TEs. TEs can be categorized into two major classes based on their transposition intermediates: class I, retrotransposons, and class II, DNA transposons  Wicker . Class IIn contrast, class II TEs are usually mobilized in the genome based on a cut-and-paste mechanism, in which a DNA transposon is excised and moved to a new location of the genome by itself with the catalysis of a DNA transposase. At present, there are four main classifications based on the remobilization mechanism for DNA transposons: a) “cut and paste” elements mobilized by DDE-transposase ; b) “cut and paste” elements mobilized using tyrosine recombinase (YR) (known as Cryptons); c) “rolling-circle” elements (known as Helitrons); and d) “self-synthesizing” elements  . Comparein vivo or in vitro, providing an influential strategy for high-throughput sequencing library preparation or high-resolution imaging. In this review, we offer a comprehensive guide to TEs in molecular genetics, human health, and biotechnology. For an in-depth review of classification, evolution, and applications in lower-level organisms like yeast, fruit flies, and plants, we recommend these other reviews  Can the hyperactivation of transposable elements serve as a diagnostic marker for human cancer? b) When do somatic LINE-1 insertions become influential in tumorigenesis and cancer progression? c) Are there emerging mechanisms, like DNA transposons, that play a role in tumorigenesis as well?Nearly half of human cancers exhibit abnormal levels of somatic LINE-1 retrotransposition . A recenEndogenous TE can offer many regulatory sequences contributing to upstream and downstream gene expression by functioning as enhancers, promoters, silencers, and boundary elements . UnbiaseThis raises a question: Is LINE-1 a viable diagnostic biomarker for early cancer detection? There are two essential proteins associated with LINE-1: ORF1p and ORF2p. ORF1p encodes an RNA-binding protein that forms trimers to bind single-strain RNA, and ORF2p consists of an endonuclease domain and a reverse transcriptase domain, which play a critical role in LINE-1 remobilization. The expression level of ORF1p is much higher than ORF2p, and it is considered a hallmark of human cancers, especially for breast, ovarian, and pancreatic cancers . A studyLINE-1 ORF2p immunostaining shows that it could be detected in human colon, prostate, lung, and breast tumors at an early stage . Using MThe primary regulators that control TE transcription are epigenetic alterations, including both DNA methylation and histone modifications. LINE-1 promoter hypomethylation is a notable hallmark of cancer. In 1993, Thayer and colleagues first revealed that the methylation status of the LINE-1 promotor region is related to LINE-1 activation in cancer cells . This fiAt what point do somatic LINE-1 insertions start to contribute to tumorigenesis and cancer progression? Human tumors exhibit significant levels of cellular and genetic heterogeneity . The conStress and other harmful environmental stimulations can induce hypomethylation of the human genome and lead to the reactivation of TEs such as LINE-1 . Analysiin vitro and in vivo J antigen receptor recombination system . In 2013 in vivo   was identified. In this mutation, two Alu elements, respectively, in intron one and intron 3 of paired chromosomes, exchanged from one to the other and formed a non-functional HPRT transcript with duplicated exon two and exon 3 . Over exDrosophila, two repressive markers, H3K9me3 and HP1, have been observed to decrease as individuals age, particularly in heterochromatic regions  response serves as the primary line of defense against invading viruses, activated upon detecting the presence of these viral invaders. Hence, mammalian cells have established a series of sensors that could recognize typical characteristics of viral nucleic acids in the cytoplasm and trigger the activation of the type I IFN response. However, due to the similarities between TE-derived and viral nucleic acids, cells can misidentify TEs-triggered hyperactivation as viral infection and induce the same type I IFN response. Recently, a study analyzing the Gene Expression Omnibus database has described an interesting phenomenon that genome-wide transposon upregulation is associated with virus infections in humans and mice . It indiHere, we will first describe the mechanisms of free DNA and RNA sensing existing in mammalian cells . SensorsTEs DNA sequences and RNA intermediates could accumulate in the cytoplasm under conditions such as Aicardi–Goutières syndrome (AGS) . AGS is During cellular senescence in human fibroblasts, LINE-1 transcription becomes activated, leading to the accumulation of LINE-1 intermediates. This activation has been attributed to the involvement of three factors: TREX1, RB1, and FOXA1 . In SIRTTEs have been utilized as powerful genetic tools to reliably and effectively introduce an array of foreign DNA sequences into genomes. This includes selectable markers, epitope/fluorescent reporters, barcode cassettes, mutagenic gene or enhancer trap cassettes, shRNA expression cassettes, and therapeutic constructs. Three DNA transposon systems, namely, Tol2, Sleeping Beauty (SB), and piggyBac (PB), stand as the most extensively employed techniques for genome manipulations in vertebrates. These systems have been used for genome manipulations in vertebrates, such as integrating foreign genes into tissue culture cells, generating transgenic animals, conducting forward genetic screens for functional gene annotation, and serving as therapeutic carriers to introduce beneficial genes into individuals with genetic disorders. Other novel transposons discovered in fish also exhibit similar behaviors as a genetic editing tool . SB has With the development of high-throughput sequencing technology, the Tn5-based transposon system has been innovatively applied to improve the efficiency of sample processing for high-throughput sequencing. For example, an assay for transposase-accessible chromatin using sequencing (ATAC-seq) has been developed to utilize the Tn5 transposon system to detect open chromatin sites in mammalian cells . Linear The Clustered Regularly Interspaced Palindromic Repeats (CRISPR-Cas) system, an adaptive immunity system found in most archaea and many bacteria, is currently the most suitable strategy for genome editing in mammalian cells . Recent In 2005, two pioneering studies demonstrated that somatic mutagenesis induced by engineered SB transposition platforms in mice could be applied to discover novel cancer genes . SB-drivIn 2010, Rad and colleagues reported a new design of mutagenic transposon vectors that combines both PB and SB transposition systems to maximize the utility of their different integration preferences . In tranRosa26 locus, with a loxP-flanked stop cassette (LSL) positioned in between. This study successfully unveiled a set of driver genes specific to pancreatic cancer, proving challenging to identify using alternative approaches  that identified distinct environmental signal pathways involved in HCC tumorigenesis , KRAS (KrasG12D), SMAD4 (Smad4KO), and TP53 (p53R172H)) that mimic different stages of colorectal cancer (CRC) developments. This approach allowed us to understand better the evolutionary forces driving different stages of CRC progression . Other Srf model , a condi1c model , a condiE model , a condih1 model  in both h1 model . A serieigenesis .Ptch+/−/Math1-SB11/T2Onc or T2Onc2) with micro-neurosurgical tumor resection and image-guided radiotherapy identified a very poor overlap between primary tumors and their recurrences   . Given tYeast, Caenorhabditis elegans, and Drosophila melanogaster for many years, which help to discover critical genes and pathways in various biological circumstances  conjugated DNA probes and PALM super-resolution imaging to visualize the marked open chromatin.Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is a technique to identify regions of open chromatin in the genome, which is based on Tn5 transposition ability in vitro   fusion protein could be used to achieve RNA-guided transposition in human cells . In thisin vivo . The resin vivo in mouse liver. This competent design has achieved the on-target delivery efficiency of 25% after directed evolution  combining Cas9 with the PB transposase . Unlike volution .Compared to the classical CRISPR-Cas9 system, RNA-guided transposition systems provide an extensive external DNA delivery system to the mammalian cell genome. Compared to classical transposon techniques, RNA-guided transposition systems can provide an accurate delivery that will protect most essential genes or oncogenes in the genome. Another advantage is that RNA-guided transposition systems could restrict the total copy number of insertions, and classical transposon systems could not easily control it. As an example, transcriptome analysis of two cases of T cell lymphoma following PB-mediated CAR T cell therapy revealed significant copy-number alterations. These included widespread gains in oncogenes and losses in tumor-suppressor genes, all attributed to uncontrolled random insertions . By emplattTn7, and then it will recruit TnsABC and donor DNA complex to integrate the transposon into the attTn7 site. TnsE is a protein that prefers to bind the plasmid DNA, and then it recruits the TnsABC and donor DNA complex, integrating the donor at the target site. TniQ is a homolog of TnsD, which is incompletely characterized at the molecular level  type I-F CRISPR-Cas System ; and 2) Vibrio cholera  the core transposition element, TnsA-TnsB-TnsC; 2) the CRISPR machinery, TniQ-Cas8-Cas7-Cas6, along with a native CRISPR array encoding mature CRISPR RNAs (crRNAs); 3) the donor DNA. Transfection experiments confirmed that all these three plasmids are essential for RNA-directed transposition. Next, the authors attempted to knock out or to make mutants on each of the genes and found that all the encoding genes and crRNA are essential for this RNA-directed transposition.To test the hypothesis that a transposon-encoded CRISPR-Cas system could direct transposons to genomic sites through guided RNA, Klompe and colleagues studied the transposition mechanism of the type I-F CRISPR-Cas system from  cholera . The elecyanobacteria Scytonema hofmanni CAST into E. coli system to analyze the transposition mechanism, including a helper plasmid containing TnsB, TnsC, TniQ, and Cas12k, along with the endogenous tracrRNA region and a crRNA; a donor plasmid containing the kanamycin resistance gene flanked by the transposon; and a target plasmid containing a synthetic protospacer sequence flanked by a short random motif upstream of the protospacer. Using this system, ShCAST was confirmed to catalyze RNA-guided DNA transposition by inserting DNA segments to 60–66 base pairs downstream of the protospacer unidirectionally with high efficiency in microbiome genomes.Strecker and colleagues used a similar plasmid-based approach to demonstrate site-directed transposition with a single Cas effector using the type V-K system, which they named CAST (CRISPR-associated transposase) . The autUnfortunately, until now, transposon-encoded CRSIPR-Cas systems do not exhibit enough efficiency in eukaryotic cells . In addiThe studies above highlight the breadth of current research interests involving transposable elements. The mechanics of transposition and the dynamic interplay between transposable elements and their host organisms have been the central subjects of intensive investigation for an extended period. During evolution, transposable elements have played a critical role in forming the structures and regulatory elements of genomes in different species. A recent study showed that transposable elements could promote exon shuffling by inserting transposase domains in new genomic contexts. This mechanism can generate host-transposase fusion genes through alternative splicing and provides a believable path for the evolution of several ancient transcription factors with crucial developmental functions . This fiConversely, when examining the activity of particular transposable elements within distinct tissues of the organism, a different picture emerges. For instance, some transposable elements are not silenced in the brain, especially in the hippocampus. Some transposable elements mediate cell-to-cell interactions and DNA, RNA, or protein exchanges. These new findings indicate that the functions of transposable elements are extremely abundant. Transposable elements should be considered highly regulated rather than deeply repressed in the human body.Secondly, transposable elements have served as indispensable tools for extensive phenotypic screening, leading to the discovery of numerous pivotal signaling pathways through this technology. Transposable elements have been instrumental in generating specific libraries for high-throughput sequencing, encompassing techniques like ATAC-seq for exploring open reading frames and LIANTI technology for the linear amplification of single-cell genomes. Retrotransposons, critical players in mechanism studies, have yet to be fully applied to the development of biological tools. In the near future, there is a growing inclination towards advancing retrotransposon-based tools. Recently, a new CRISPR-Transposon has been discovered with a novel mechanism of RNA-directed targeting of transposition, and a series of novel technologies have been developed based on a similar principle. This technique has a high potential to facilitate targeted DNA integration without the off-target mutagenesis potential of methods utilizing homologous recombination.Additionally, another potential direction is that TEs harbor an abundance of native cis-regulatory sequences that allow special and temporal regulation of both upstream and downstream genes, guide copies, deletions, and recombination. These resources warrant further exploration and investigation .In addition, we should also note that mechanistic studies of TEs often go hand in hand with TE-based technology development. As a mutagen, TE plays a vital role in the occurrence of cancer. At the same time, the modified transposition system has also been employed to decode the essential genes involved in cancer development systematically. At the same time, the efficient mutagenicity of TEs can accelerate efforts to understand the mechanisms of cancer treatment and recurrence. In the context of brain cancer, recurrent tumors following surgical resection and radiotherapy exhibit substantial distinctions from the primary tumor. As a mutagen, TE is also critical in the occurrence of various genetic diseases. At the same time, the use of modified TE systems allows investigators to systematically analyze the key regulatory elements in individual growth and development. For example, Chang and colleagues developed a highly efficient forward genetic screening system in mice and discovered a critical gene that causes milk-feeding disorders in mice . Now, ma


Pax6 locus in three mouse cell lines with different transcription states. While transcriptional activity minimally affects movement of 40-kbp regions, we observed that motion of smaller 1-kbp regions depends strongly on local disruption to chromatin fiber structure marked by H3K27 acetylation. This also substantially influenced locus configuration dynamics by modulating protein-mediated promoter-enhancer loops. Importantly, these simulations indicate that chromatin dynamics are sufficiently fast to sample all possible locus conformations within minutes, generating wide dynamic variability within single cells. This combination of simulation and experimental validation provides insight into how transcriptional activity influences chromatin structure and gene dynamics.In living cells, the 3D structure of gene loci is dynamic, but this is not revealed by 3C and FISH experiments in fixed samples, leaving a notable gap in our understanding. To overcome these limitations, we applied the highly predictive heteromorphic polymer (HiP-HoP) model to determine chromatin fiber mobility at the  To study the dynamic 3D structure of specific loci, the authors combine a computer modeling scheme based on polymer physics with experimental validation. Their results indicate that chromatin dynamics are sufficiently fast to sample all possible locus conformations within minutes, generating wide dynamic variability within single cells. At larger length scales (>5 kbp), this could involve the way chromatin forms loops bringing together promoters and their enhancers. Advances in microscopy4, and in next-generation sequencing methods such as chromosome-conformation-capture (3C) and its variants5, have revealed much about gene locus structure. More recently, the development of structural probes operating at the single-cell level has revealed striking variability within a tissue or population of cells that are phenotypically homogeneous6. The compatibility of such cell-to-cell variability with a robust transcriptional program and emerging phenotype is remarkable and still awaits full explanation. The variability of gene loci was exemplified by our recent modeling work on the locus of Pax6, a highly regulated and highly conserved developmental gene. Our simulations showed that chromatin interaction patterns revealed by CaptureC experiments could be generated by locus conformations that vary widely from cell to cell, and that the level of variation ) also markedly depends on cell type and expression level7.The spatial organization of the chromosome around a gene is thought to be intimately linked to its expression, providing an important mechanism for gene regulation. At short length scales (<5 kilobase pairs (kbp)), this mechanism might involve nucleosome repositioning and disruption of the chromatin fiber structure8, have been developed to monitor chromatin diffusivity in vivo. However, it remains difficult to reach high temporal resolution while labeling multiple points of interest simultaneously. Here, we use biophysical modeling and computer simulations9 to study and predict dynamics at the Pax6 locus. This approach provides mechanistic insight as well as new hypotheses and testable predictions, which we hope will stimulate further experiments.Although experimental methods probing chromosome organization continue to improve, the majority of studies to date have focused on fixed cells, providing information on only a ‘snapshot’ in time. An understanding of how locus conformations evolve dynamically remains largely elusive. For example, it is unclear whether the structural variation observed across a population is representative of the configurations adopted dynamically within a single cell. Or, does the chromatin in a single cell only visit, or sample, a small part of this ‘configuration space,’ with the observed variability arising only when gathering data from many cells? In other words, if one were to track locus configuration in a live cell, would one observe wide variations or a relatively static picture? The answer to this may give insight into how such variability can still give tight control of expression and phenotype. Although live-cell imaging has advanced markedly in recent years, challenges remain. Super-resolution microscopy allows ever-increasing spatial resolution to be achieved, and high-throughput techniques, such as Hi-DPax6 in mouse, SOX2 (ref. 7) and CCND1 (ref. 10) in human, and (using an earlier version) mouse alpha- and beta-globin11. Here, we evolved HiP-HoP such that it can be used to analyze the dynamics of the Pax6 locus in three mouse tissue-derived cell lines that express the gene at different levels13. We first examined the dynamic properties of simulated chromatin regions at different length scales and found that these vary substantially across the locus. The mobility of a given segment depends both on its biophysical properties  and on its surroundings, with local macromolecular crowding playing a pivotal role (we use the term ‘mobility’ to refer to a measure of how far a given segment will move or diffuse within a given time). We then studied the dynamics of the overall locus configuration, analyzing the timescale over which promoter-enhancer interactions change. The results suggest that the locus can sample all of its different configurations within minutes, strongly indicating that wide dynamic variation of structure would be observed within a single cell. Finally, we performed interventional experiments, in which we gave cells treatments either to inhibit transcription or to release topological strain; we made measurements in fixed cells and compared these with dynamic simulations. Interestingly, we found that removing proteins representing polymerase complexes from the simulation did not reproduce the changes observed in the transcription inhibition experiment; this led us to an alternative model scenario.Previously, we developed the HiP-HoP simulation framework to predict structural information on a gene locus at both the single-cell and population level. This was applied to several gene loci, including those of 10 to study the structure of the Pax6 locus in three mouse cell lines, denoted Pax6 OFF, ON, and HIGH (indicating the expression state of the gene). The model represents a chromosome region as a chain of beads and combines three key mechanisms that organize the locus  fiber21 . In HiP-HoP, proteins continually switch between a chromatin-binding and a non-binding state26, modeling post-translational modifications and resulting in clusters having realistic protein dynamics in terms of the exchange between the cluster and the soluble pool.We have previously used HiP-HoPcus Fig. . First, er21 see . Here, w27. Finally, ChIP data on CTCF and cohesin (Rad21) are used to identify positions of loop-stabilizing anchors ) are used to identify protein-binding sites (we use a simplifying assumption that ATAC peaks coincide with binding sites for ‘active’ proteins). Second, chromatin immunoprecipitation (ChIP) data on acetylation of lysine 27 on histone H3 (H3K27ac) are used to identify regions that are in the more open chromatin state: this decision is based on previous studies suggesting that chromatin possessing this mark has a disrupted structurehors see .Pax6, we used HiP-HoP to generate a population of simulated locus structures, which provided good predictions of experimental measures at both the population  and single-cell  level. CaptureC is a many-to-all 3C method which probes genome-wide interactions at a set of selected target locations29.In our previous study of Pax6  was selected and concatemerized 10 times along a 40-Mbp fiber  region around Pax6 promoters and two previously identified enhancers, denoted the upstream and downstream regulatory regions ) is 17.6 nm, determined by comparing simulated and experimental FISH measurements. The simulation time unit (τ) is approximately 2.07 ms, determined by comparison with previous motion-tracking experiments30. This gives, for each simulation, an approximate total duration of 27 min  than the other two cell lines, suggesting that protein-mediated chromatin looping may lead to reduced mobility.MSD curves for different probes, and for a given probe in different cell lines look highly similar, indicating that the mobility of these regions is under similar constraints. For a freely diffusing object, the MSD would grow linearly with time, but as expected for motion of a polymer segment, the MSDs here grew more slowlyM) as the MSD after a fixed lag time of 104 × τ ≈ 20.7 s. For each cell line, M was calculated for each 1-kbp bead to obtain a ‘mobility profile’ over the locus .Measurements of local macromolecular density around each polymer bead Fig.  revealedφe) for each bead was defined as the fraction of time in which it was in the vicinity of an extruder. In HiP-HoP, φe depends only on the positions of and occupancy at CTCF-binding sites, and it shows peaks at high-occupancy CTCF sites  exhibited a broad range of mobility values .We hypothesized that the chromatin properties giving rise to the variation in mobility might lead to differences in chromatin interactions. A common measure extracted from 3C methods is the ratio between the amount of local and long-range chromatin interactions at a given site−2 , consistent with protein-binding sites having low localness values, being in higher density regions, and having lower mobility.Plotting localness for each 1-kbp bead across the simulated region Fig.  revealedN) in the chain. A more tractable approach  is to track a smaller number of points. Here, we considered the same three FISH probe regions as above  and took the three pairwise separations of these probes as a description of the locus configuration. These separations are represented by a point in a three-dimensional space , where the components xUP, xUD, and xPD are the separations of the URR and Pax6, the URR and DRR, and the Pax6 and DRR probes, respectively. In Figure Pax6 ON cells. The volume of the cloud of points represents the range of structures that the locus can adopt . Examination of these trajectories suggests that the whole volume can be explored within a single simulation of duration 7.96 × 105τ, equivalent to roughly 27 min—considerably shorter than a typical cell cycle ) was defined, which tracks the mean change in locus configuration over a lag time (t). Mathematically, this is the MSD of X as it moves through configuration space  reaches a plateau at large times, because the locus explores a finite volume within configuration space , gives information about the dynamics of the locus structure: all three cell lines show α < 1, characteristic of sub-diffusive behavior , indicating that the configuration changes more quickly. Sub-diffusion is expected, owing to the polymeric nature of the chromosome; loops being transiently stabilized by protein bridges or loop extruders likely act as dynamical traps, further inhibiting motion. The Pax6 OFF S(t) curve shows a smaller α, perhaps reflecting the very different pattern of protein-binding sites in those cells.To examine this quantitatively, a shape-change parameter  measurements7. However, it is possible that there are distinct sets of parameters that give similar static predictions, but different dynamic behavior. Although experimental determination of the parameters remains challenging, we can use the simulations to examine the effect of varying them.How well do simulations reproduce the dynamics of real locus structures in vivo? It is likely that the dynamics depend heavily on the model parameters, for example the number of proteins, the rate at which proteins switch between binding and non-binding states, the number of extruders, or extrusion speedksw) were performed for Pax6 ON cells  to ksw = 2 × 10−4τ−1 ≈ 0.097 s−1 (black points). Less frequent switching led to a smaller volume in the configuration space being explored , Fig. 26: protein-stabilized loops are more likely to form for extended times, slowing the dynamics and favoring more compact configurations (including multi-loop ‘rosette’ structures).First, simulations with different rates at which proteins switch between binding and non-binding states  when loop extrusion parameters were varied . Other recent work using HiP-HoP suggests that loop extrusion plays an important role in cell-to-cell variability of expression22; it would be interesting in the future to study configurational dynamics in other loci where, for example, enhancers and promoters sit at opposite ends of a CTCF loop domain.If the switching rate is kept constant, but instead the number of proteins is decreased, there is a smaller decrease in variability Fig. , and conrio Fig. . These rPax6, the choice to consider three probes is natural because there are two distal enhancers. One could consider more probes and examine how the system moves through a higher-dimensional configuration space. That might be more relevant for larger, more complex loci with many regulatory elements, but the general conclusions are unlikely to be affected. A complementary approach for characterizing locus dynamics is to measure time intervals between enhancer-promoter collisions and the duration of their interaction; we include such an analysis in Finally in this section, we note that for Pax6 HIGH cells after treatment with drugs to perturb either transcription or topology. Alpha amanitin was used to inhibit transcription through selective degradation of elongating polymerases37. Surprisingly, but as observed previously38, CaptureC profiles looked very similar to those from untreated cells, with similar, if slightly higher, interaction peaks  are enhanced.Given the apparent links between locus configuration, gene activity, and dynamics, we performed new CaptureC and FISH experiments on aks Fig. . Separatent Fig. . TogetheTo interpret these results, we considered how transcription inhibition could be modeled within the simulations. A loss of transcription could be implemented as a loss of protein binding, because our model proteins represent complexes of polymerase and transcription factors Fig. . However26 to model chemical reactions  and provide a realistic turnover of proteins in foci. Another possible effect of alpha amanitin is that it might interrupt such reactions, for example, in the polymerase transcription cycle, and polymerases could become stuck in an initiation state. A modified simulation was used to test this: all protein-chromatin bonds were ‘fixed,’ so that from the point of simulated alpha amanitin treatment, any protein bound to a chromatin bead remained bound  results in increased mobility and local decompaction, but some other enhancer-promoter bridging  may be stabilized, resulting in more pronounced CaptureC peaks. A proper representation of the action of alpha amanitin in simulations would require a more explicit inclusion of transcription than is currently possible within HiP-HoP.Pax6 HIGH cells were treated with bleomycin to perturb the local chromatin topology . After perturbation, CaptureC profiles looked very similar to those from untreated cells, whereas FISH revealed no significant change  at different points within a locus, or in different cell lines, showed highly similar dynamics; by contrast, different 1-kbp regions could have very different mobilities. This was dependent on two factors: local chromatin fiber disruption (marked by H3K27ac), and bridging interactions between regulatory elements. Typically, disrupted chromatin40. Conversely, experiments using an ANCHOR/ParB DNA-labeling system to track a promoter showed motion becoming constrained upon activation41. Several studies tracking multiple points across the genome  showed that dynamics tend to increase as activity is reduced  degradation39). These observations can be reconciled if, as our model suggests, gene activation leads to chromatin decompaction (increasing mobility) in some regions, but additional looping (decreasing mobility) in others.The prediction that the position and size of a probe determine the observed dynamics will be critical when designing future live-cell imaging experiments, and could explain some previous contradictory results. In live-cell imaging in which single enhancers and promoters were labeled using CRISPR–dCas9 with guide RNAs spanning ∼2 kbp, these became more mobile when active26 had subtle effects. Intriguingly, we found that different changes to the parameters that have the same effect on static properties do not necessarily have the same effect on dynamics. We note that configurational dynamics will also depend strongly on locus size: we would expect, for example, a 1-Mbp TAD to take longer to re-organize than the ∼200-kbp region studied here. This is consistent with previous modeling based on fitting to 5C data42 that suggested that multiple TAD configuration states should be dynamically accessible during a cell cycle.The concept of promoter-enhancer interactions often leads to a notion of somewhat static gene locus configurations; by contrast, our study introduces the concept of sampling between regulatory elements, where interactions continually change over time. Our results indicate that, for a typical locus, many possible interaction events can be sampled within tens of minutes Fig. , suggestPax6 locus structure, leading only to small increases in CaptureC interaction peak heights. Our experiments stand in contrast with recent Hi-C, HiChIP, and OCEAN-C data showing that acute degradation of Pol II led to a small decrease in looping interactions43; another study using MicroC in mice found that transcription inhibition using triptolide or flavopiridol did not greatly affect promoter-enhancer interactions, but other features in the data were affected44. Other work showed that alpha amanitin did not disrupt enhancer interaction hubs38, and that elongation inhibition via DRD treatment did not disrupt Pol II foci45. Together, these results suggest that there is a complicated relationship between transcription and chromatin contacts and point to subtleties in the action of different inhibitors, which are not understood.We found that inhibiting transcription using alpha amanitin did not greatly alter Pax6 locus give insight into the factors affecting chromatin dynamics and suggest that for a locus of this size, in a given time, a single cell would show the same level of variability as that observed from cell to cell across a population. We expect that a similar order of magnitude for the timescale for locus rearrangement would be found in other polymer models for chromatin ; however, our results suggest that differences across a locus or between loci depend on details of the model . In the future, it would be interesting to simulate larger regions or to include additional model ingredients that might shed light on other observations made in live cells . HiP-HoP could be extended to include, for example, repressive proteins that compact DNA, nuclear lamina interactions, or proteins such as SAF-A that are thought to form an RNA-dependent gel constraining chromatin motion47.These simulations of the 7. In brief, a chain of beads connected by springs represented a chromosome region, with 1 kbp of DNA per bead. Diffusing spheres represent complexes of proteins which bind at active chromatin sites, and these switch back and forward between a binding and a non-binding state with rate ksw = 10−3τ−1 (unless otherwise stated), where τ is the simulation time unit. DNA accessibility data (ATAC-seq) were used to identify binding sites. Loop extrusion was modeled by introducing additional springs between adjacent beads in the chain, which were then moved at regular time intervals to extrude a loop. Extruders were initiated at random positions, moved at a rate kex = 2 bp τ−1, and were removed with rate koff = 2.5 × 10−5 τ−1. ChIP followed by microarray hybridization (ChIP–chip) data for CTCF and Rad21 binding were used to identify loop anchor sites, and extrusion was halted at loop anchor sites in a direction-dependent manner. In some regions of the bead chain, additional springs were added to ‘crumple’ it into a more compact structure. Histone modification data (H3K27ac ChIP–chip) were used to identify regions that do not have the crumpling springs. The dynamics were evolved through a Langevin dynamics scheme (implicit solvent) using the LAMMPS molecular dynamics software50. In our previous work7, the simulations matched the average diffusion properties of the model chromatin to experimental measurements, but in that case only a short region of the genome was simulated, so spatial variations in chromatin density were not accurately represented. In this work, a much larger chromatin fragment (40 Mbp) was simulated to better match the overall density found in vivo. For efficiency, 10 instances of a 3-Mbp region around Pax6  were placed along this 40-Mbp  fragment. Periodic boundary conditions were used, and the system size was chosen to give a roughly realistic chromatin density. We used data from 3 mouse cell lines, and in a single simulation, a mixture of loci from the 3 were included among the 10 instances. In repeat simulations, the positions of the loci in different cell lines were randomized . As noted above, to ensure that this new set up still gave good predictions of locus conformation, results were compared with both CaptureC data and FISH . We obtained nine such distributions , and used the mapping that minimized the average of the nine Kolmogorov–Smirnov statistics. This gave an estimate for the length unit of σ ≈ 17.6 nm. To map the simulation time unit, we calculated an MSD for every chromatin bead in every simulation, and obtained an average. This was compared with data from motion-tracking experiments in ref. 30, where MSDs were obtained for several chromosome regions in Saccharomyces cerevisiae. We used a linear fit to find the mapping that minimized the difference between the simulated and experimental MSD curves. This led to an estimate for the simulation time unit of τ ≈ 2.07 × 10−3 s.Simulation length and time units were mapped to physical units by comparison with experimental data. To estimate the length unit, for a given pair of FISH probes, a distribution of separations was obtained from simulations and experimentsPax6 HIGH cells  were isolated from a mouse insulinoma51 and were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Thermo Fisher) supplemented with 10% fetal calf serum and 1% penicillin–streptomycin at 37 °C in 5% CO2. No cell authentication was performed, and the sex of the cell line is not known.Pax6 HIGH cells were plated in a 6-cm dish and, once at ∼80–90% confluence, treated with 100 µM alpha amanitin (Sigma A2263-1MG) for 7 h or with mock treatment . Inhibition was assessed by real-time quantitative reverse transcription PCR (qRT–PCR); samples were washed with PBS, and RNA was extracted using RNeasy mini-Kit (Qiagen). All RNA samples were treated with on-column DNase1 treatment (Qiagen); the RNA concentration was corrected across the sample and reverse transcribed to complementary DNA (cDNA) using SuperScript II (Thermo Fisher) standard first-strand synthesis protocol with oligo(dT) primers (Promega), with two biological replicates per condition. Real-time qPCR on cDNA was performed using a LightCycler 480 II (Roche) and SYBR Select Master Mix (Thermo Fisher), using the standard manufacturer’s protocol. Gene-specific primers were designed for intron-exon junctions to assay nascent RNA, and the fold change against mock treatment control was calculated using the 2CT–∆∆ method, normalizing against 18S as a housekeeping gene.To block transcription, Pax6 HIGH cells at 80–90% confluency were trypsinized to a single cell suspension; 1 × 106 cells were treated with 250 µM of bleomycin  for 15 min in PBS, centrifuged to remove the bleomycin, and washed with PBS. Samples were processed using a standard protocol for genomic DNA preparation from a Pure link genomic DNA extraction kit (Invitrogen). To assay DNA nicking, a comparison between 250 µM bleomycin and PBS treatment alone was performed on 1 µg extracted genomic DNA and run on an alkaline agarose gel under denaturing conditions, before staining with ethidium bromide and imaging, using a standard alkaline gel protocol52.For bleomycin treatment, Pax6 HIGH cells that had been treated with alpha amanitin or bleomycin, NG CaptureC was performed as previously described29, but with the following alterations. Two replicates of 5 × 106 cells were processed for each case; cells were fixed with 2% formaldehyde and lysed with standard 3C lysis buffer for 15 min before being snap frozen. Cells were further lysed by re-suspension in water and then in 0.5% SDS for 10 min at 62 °C. Each replicate was split between three tubes, re-suspended in 800 µL 1× DpnII buffer (NEB) with 1.6% Triton X-100, and digested with 3 sequential additions of 750 units DpnII enzyme at 37 °C with 1,200 r.p.m. shaking over 24 h. Samples were heat inactivated at 65 °C for 20 min, and 3 samples from each replicate were combined into 7 mL with 1× T4 DNA Ligase Buffer (NEB), with 1% Triton X-100 and 12,000 units of T4 DNA ligase at 16 °C overnight. Samples were treated with Proteinase K overnight at 65 °C and RNase A/T1 (Thermo Fisher) for 1 hour at 37 °C, before a standard phenol–chloroform extraction and ethanol precipitation was performed. Complete digestion and ligation were assessed by gel electrophoresis.For Pax6 locus elements, as in ref. 7 . Capture oligonucleotides from each of the two pools were mixed at equimolar amounts and pooled to a final concentration of 13 pmol in a volume of 4.5 µL per sequence capture. Libraries were sized and quality controlled on a D1000 Tapestation tape (Agilent).Purified 3C DNA from each sample was sonicated to 200–400 bp with a Soniprep 150 probe sonicator at 4 °C and purified with a standard Ampure XP Bead protocol (Beckman Coulter) using a 1/1.5 DNA to bead ratio. Two Illumina sequencing libraries were prepared per capture pool replicate, with 6 µg of starting DNA in each, and generated using NEBNext DNA Library Prep Kit (NEB). Samples were indexed with unique barcodes using NEBNext Multiplex Oligos for Illumina (NEB). Two separate capture pools were designed to the following 29, using each of the two capture pools, with 1.5–2 µg 3C library DNA per hybridization reaction. Each hybridization reaction was performed on a thermocycler at 47 °C and incubated for between 66 and 72 h. Each hybridization reaction was then bound to streptavidin beads from SeqCap EZ Pure Capture Bead Kit and washed with SeqCap EZ Hybridization and Wash Kit (Nimblegen), following the manufacturer’s protocol. Hybridization reactions were split into two and libraries were re-amplified using Post LM-PCR oligonucleotides (Nimblegen) and Q5 High-Fidelity DNA polymerase (NEB) directly from the beads, and then the DNA was purified using Ampure XP Beads, with a 1/1.8 DNA to bead ratio. A second hybridization reaction was performed as above on the re-amplified 3C libraries with 2 reactions pooled together (∼1 µg in each) and incubated for 22–24 h. Washed and re-amplified double-captured libraries were sized and quality-controlled on a D1000 Tapestation tape (Agilent), and paired-end sequenced on an Illumina Hi-seq 2500 or Hi-seq 4000.NG CaptureC sequence capture was performed using SeqCap EZ HE-Oligo Kit A or B (dependent on the multiplex barcode) and SeqCap EZ Accessory Kit (Nimblegen)53  at 37 °C for 60 min. Slides were then rinsed briefly with 2× SSC, dehydrated through an ethanol series, and air dried. Slides were warmed by incubation in a 70 °C oven for 5 min before denaturation for 1 min in 70% formamide in 2× SSC, pH 7.5, at 70 °C. Slides were then transferred to 70% ethanol on ice, dehydrated through an ethanol series, and air dried before overnight hybridization at 37 °C with pairs of fosmid probes  Zeiss Axioplan II fluorescence microscope with Plan-Neofluar objectives, a 100-W Hg source (Carl Zeiss), and a Chroma 83000 triple band-pass filter with single excitation filters installed in motorized filter wheels (Prior Scientific Instruments). Image capture and analysis were done using in-house scripts written for Iola Spectrum . For FISH, images were collected from at least 50 randomly selected nuclei for each experiment and then analyzed using custom Iola scripts that calculate the distance between two probe signals.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-023-01059-8.Supplementary InformationContains Supplementary Notes and Supplementary Figure 1Reporting SummaryPeer Review FileSupplementary Table 1DpnII restriction enzyme fragments (mm9 coordinates) targeted in CaptureC experiments, together with the oligo sequences used and where these map to. In experiments oligos were grouped into two pools, with each pool being used in separate samples. Supplementary Table 2: Table giving details of FISH probes. Coordinates are for the mm9 genome. Extended Data Fig. 2 shows data from Ref. 12 which used probe set 1. Probe set 2 was used in new experiments where Pax6 HIGH cells were treated with alpha amanitin  or bleomycin Supplementary Table 1: Table showing the


The microrchidia (MORC) proteins are a family of evolutionarily conserved GHKL-type ATPases involved in chromatin compaction and gene silencing. Arabidopsis MORC proteins act in the RNA-directed DNA methylation (RdDM) pathway, where they act as molecular tethers to ensure the efficient establishment of RdDM and de novo gene silencing. However, MORC proteins also have RdDM-independent functions although their underlying mechanisms are unknown.In this study, we examine MORC binding regions where RdDM does not occur in order to shed light on the RdDM-independent functions of MORC proteins. We find that MORC proteins compact chromatin and reduce DNA accessibility to transcription factors, thereby repressing gene expression. We also find that MORC-mediated repression of gene expression is particularly important under conditions of stress. MORC-regulated transcription factors can in some cases regulate their own transcription, resulting in feedback loops.Our findings provide insights into the molecular mechanisms of MORC-mediated chromatin compaction and transcription regulation.The online version contains supplementary material available at 10.1186/s13059-023-02939-4. Caenorhabditis elegans, MORC-1 can compact DNA through topological entrapment [The MORC proteins are a family of highly conserved GHKL-type ATPases involved in gene silencing and chromatin compaction . In Caentrapment , while itrapment . In micetrapment , and MORtrapment .FWA transgenes [The Arabidopsis genome encodes six MORC proteins: MORC1, 2, 4, 5, 6, and 7 (MORC3 being a pseudogene) [ansgenes , 9. HoweTOPLESS (TPL) and LEUNIG (LUG) are both Grocho (Gro)/TLE-type transcriptional co-repressors in plants. They are characterized by a conserved glutamine-rich C-terminal domain and an N-terminal WD-repeat domain . The gluHere, we use MORC-unique sites to study the RdDM-independent functions of MORC proteins. We show that MORC proteins compact chromatin and reduce DNA accessibility to TFs, thereby repressing the transcription of stress-responsive genes.We previously reported that approximately 80% of MORC7 binding regions overlap with sites of RdDM [Mouse MORC3 recognizes and localizes to regions of H3K4me3-marked chromatin through its CW domain ; howeverWithin these MORC7-unique regions, we identified two subgroups: MORC7A and MORC7B. The ChIP-seq data for MORC7, Pol V, ATAC-seq, transposable element (TE), and small RNA density indicated that MORC7A was within a region of high chromatin accessibility and low TE density Fig. : Fig. S2MORC7B contained a high density of TE with no apparent active histone marks, reflective of its heterochromatic localization Fig. a, b. We p-value = 3.12E − 36); these included PHYTOCHROME INTERACTING FACTOR (PIF), ethylene and auxin-responsive transcriptional factors, and Myb transcriptional factors  and Pol V-unique .The genomic distribution enrichment data showed enrichment of MORC7A peaks over promoters Fig. a, but noGene Ontology (GO) term analysis of genes proximal to MORC7A showed an enrichment of negative regulation of auxin metabolic process (~ 80-fold), shade avoidance (~ 30 fold), and the primary shoot apical meristem specification pathway (~ 30-fold) Fig. b, c, d. p = 7.89E − 12)  mutants [morc4 morc7, morc6, and morchex — with morchex showing the most pronounced phenotype  in ype Fig. : Fig. S5ype Fig. d. This smorc4 morc7, morc6, and morchex mutants [morc4 morc7, morc6, and morchex mutants — with morchex showing the strongest binding changes, and the random control regions showing no differences  or non-gain of DNA methylation (n = 8580), and observed a substantial decrease of the TF footprints over both groups of sites in ZF-MORC6 plants  and morchex mutant without treatment. This showed that the genes proximal to MORC7A were slightly (p = 0.002) up-regulated in the morchex mutant without treatment  in the lug mutant, suggesting that LUG may facilitate expression of these genes  in morchex, particularly after heat treatment  and LEUNIG (LUG), for ChIP-seq analysis, because they were present in the MORC7 IP-MS data [hex Fig. e, f — cohex Fig. g. To teslow Fig. g. Overalals Fig. g. We fouFig. p = .7E − 13 Fig. p = .7E − 13 SDC gene and an SDC transgene, as well as other DNA-methylated targets in the genome [morc mutants display various disease phenotypes; for example, Kang et al. [morc1 is susceptible to Turnip Crinkle Virus (TRV), while Harris et al. [morchex is susceptible to the Hyaloperonospora arabidopsidis (Hpa) strain, Emwa1. However, the molecular mechanisms underlying the additional functions of the MORC proteins remain unknown.We previously reported that MORC proteins are localized to sites of RdDM throughout the genome, and function as molecular tethers to facilitate the efficient establishment of RdDM . We showe genome . In addig et al.  reporteds et al.  reportedC. elegans MORC1 homodimers can topologically entrap and condense DNA through further oligomerization of MORC1 proteins [morc mutants display pericentromeric heterochromatin decondensation [C. elegans MORC1 — compacting chromatin by topological entrapment, thereby reducing its accessibility to TFs.Here, we investigated the function of MORC7 in regions where RdDM does not occur, particularly those near genes where no DNA methylation is present. We found that MORC proteins reduce chromatin accessibility within these regions. Previous in vitro studies showed that proteins . In addiensation , which tensation . We showmorc4 morc7; although, the underlying mechanism of this was unknown [Plant MORCs have been implicated in plant pathogen responses. MORCs promote resistance in some plant species and inhibit defense responses in others . Upregul unknown . Here, w unknown . Howevermorchex. Like with its role in plant pathogen defense response, it seems likely that the role of MORCs in stress responses relates to its effects on chromatin compaction of promoter regions and TF networks.Finally, we showed that MORC proteins are important for the regulation of gene expression, particularly under stress conditions. We also found altered expression of heat-responsive genes in MORC proteins have a broad binding spectrum in the genome and appear to participate in at least three separate processes. They co-localize to sites of RdDM, facilitating efficient DNA methylation establishment , they armorchex consisting of morc1-2 (SAIL_893_B06), morc2-1 (SALK_072774C), morc4-1 (SALK_051729), morc5-1 (SALK_049050C), morc6-3 (GABI_599B06), and morc7-1 (SALK_051729). For heat treatments, plants were grown under 37 °C for 0.5 h and put back to normal temperature for 48 h for recovery.All plants in this study were grown in standard greenhouse conditions . The following plant materials were used in this study: Full-length genomic DNA fragments, including native promoter sequences, were cloned into pENTR/D vectors (Invitrogen), and then into modified destination vectors carrying 3xFLAG with LR Clonase (Invitrogen). All primers used in this study are available in Additional file 2, 5 mM KCl, 0.2% Triton X-100, 5 mM β-mercaptoethanol, and 35% glycerol); the samples were then ground with Omni International General Laboratory Homogenizer at 4 °C, and filtered through a two-layer Miracloth using a 40-µm nylon mesh Cell Strainer (Fisher). Samples were spin filtered for 10 min at 3000 g, the supernatant was discarded, and the pellet was resuspended with 25 ml of grinding buffer using a Dounce homogenizer. The wash step was performed twice in total. Nuclei were then resuspended in 0.5 ml of freezing buffer . Nuclei were then subjected to a transposition reaction with Tn5 (Illumina). For the transposition reaction, 25 µl of 2 × DMF  was mixed with 2.5 µl Tn5 and 22.5 µl nuclei suspension at 37 °C for 30 min. The transposed DNA fragments were then purified with ChIP DNA Clean & Concentrator Kit (Zymo). Libraries were prepared with Phusion High-Fidelity DNA Polymerase (NEB), in a system containing: 12.5 µl 2 × Phusion, 1.25 µl 10 mM Ad1 primer, 1.25 µl 10 mM Ad2 primer, 4 µl ddH2O, and 6 µl purified transposed DNA fragments. The ATAC-seq libraries were sequenced on a NovaSeq 6000 sequencer (Illumina).The nuclei collection process from inflorescence and meristem tissues was performed in accordance with previously described methods , 27. FreTotal RNAs were extracted from ~ 100 mg of flower buds using TRIzol and the Direct-zol RNA Miniprep kit . Sequencing libraries were prepared using the TruSeq Stranded mRNA Library Prep kit (Illumina), according to the manufacturer's instructions, and sequenced on a NovaSeq 6000 sequencer (Illumina).10 g of inflorescence and meristem tissues were used for ChIP-seq. ChIP assays were performed as has been described previously . BrieflySmall RNA-seq reads were downloaded from a previous paper . Adaptorhttp://jaspar.genereg.net/) [ATAC-seq read adaptors were removed using trim_galore. The reads were then mapped to the Arabidopsis thaliana reference genome, TAIR10, using Bowtie2 (-X 2000 -m 1) . Reads oeg.net/) .p-value < 0.05 and fold change > 2 were used to decide whether there were any significant differences in expression between samples.Cleaned short reads were aligned to the reference genome, TAIR10, by Bowtie2 (v2.1.0) . ExpressChIP-seq data was aligned to the TAIR10 reference genome with Bowtie2 (v2.1.0) , only inmorc mutants and wild type was reanalyzed [http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to trim adapters. BS-seq reads were aligned to the TAIR10 reference genome by BSMAP (v2.90), allowing two mismatches and one best hit (-v 2 -w 1) [Previously published whole-genome bisulfite sequencing data for analyzed . Briefly 2 -w 1) . Reads wAdditional file 1: Figure S1. Chromatin states of MORC7. Figure S2. Small RNA data over MORC7 peaks. Figure S3. Examples showing MORC7 enrichment over the promoter regions of the TOPLESS genes. Figure S4. MORC7 associates with some TFs. Figure S5. Metaplot and heatmap showing chromatin accessibility changes. Figure S6. Volcano plot showing TF changes for ZF off-target sites. Figure S7. Expression levels of genes in the primary shoot apical meristem specification pathway, with and without heat treatment, in Col-0 and morchex mutants. Figure S8. Motif enrichment of TPL and LUG peaks identified in Col-0 or morchex mutant. Figure S9. Metaplot and heatmap showing ChIP-seq signals over peaks detected in morchex mutant.Additional file 2: Table S1. Genes proximal to MORC7A-unique, MORC7B-unique, MORC7-Pol V Common, and Pol V-unique peaks.Additional file 3: Table S2. List of MORC7 interacting proteins.Additional file 4: Table S3. Expression level of LUG directly regulated genes in Ler and lug mutant.Additional file 5: Table S4. Primers used in this study.Additional file 6: Table S5. Published ChIP-seq data used for ChromHMM states analysis.Additional file 7. Review history.


Our understanding of the organization of the chromatin fiber within the cell nucleus has made great progress in the last few years. High-resolution techniques based on next-generation sequencing as well as optical imaging that can investigate chromatin conformations down to the single cell level have revealed that chromatin structure is highly heterogeneous at the level of the individual allele. While TAD boundaries and enhancer–promoter pairs emerge as hotspots of 3D proximity, the spatiotemporal dynamics of these different types of chromatin contacts remain largely unexplored. Investigation of chromatin contacts in live single cells is necessary to close this knowledge gap and further enhance the current models of 3D genome organization and enhancer–promoter communication. In this review, we first discuss the potential of single locus labeling to study architectural and enhancer–promoter contacts and provide an overview of the available single locus labeling techniques such as FROS, TALE, CRISPR–dCas9 and ANCHOR, and discuss the latest developments and applications of these systems. Human diploid cells need to handle a genome consisting of little more than 6 Gigabases in a way that protects it against damage and can be propagated unchanged over generations. Genes need to be activated and silenced depending on external signals, cell-fate decisions and to maintain cell identity. This depends on a tightly controlled access of gene-regulatory proteins to the genome. At the same time, the whole genome undergoes major reorganization in proliferating cells. The entire functional genome is duplicated during replication and then highly compacted upon entry into mitosis to allow segregation of sister chromatids and equal distribution of the genome onto the daughter cells.The packing of the DNA with its bound proteins (chromatin fiber) within the cell nucleus is fundamental for these processes. In interphase, the chromatin of individual chromosomes locates in distinct chromosome territories with little intermingling. Genome-wide conformation capture studies such as Hi-C revealed that chromosomes can be divided into more transcriptionally active regions (A compartment) and inactive regions (B compartment). These are marked by specific histone modifications and interact with the same type of regions while avoiding the other compartment. The driving forces between this compartmentalization are complex and involve the folding of the chromatin fiber, general transcriptional activity and clustering of proteins and DNA, invoking mechanisms of phase separation. Liquid–liquid phase separation (LLPS) is a little understood multifactorial process underlying the self-organization of membraneless chromatin compartments, driven by intrinsic properties of the chromatin fiber, associated RNA and chromatin-binding proteins via disordered domains  and/or by accumulation of transcription activators  (for a review see ).Chromatin conformation capture approaches revealed regions with strong directionality of chromatin contacts within these compartments, visible as characteristic triangles in contact maps and referred to as topologically associating domains (TADs) Fig. . ContactKey proteins for the formation of structural loops are the cohesin complex and the chromatin insulator protein CTCF. Depletion of critical cohesin complex subunits such as RAD21 or CTCF leads to the apparent loss of chromatin loops –5. ModelThe loops provide a dynamic structural scaffold to chromatin and impact gene expression, by directly recruiting enhancers to promoters or indirectly through confining the search space of enhancers.A general depletion of cohesin or CTCF in cells only leads to mild misexpression of a few hundred genes , indicatChromatin conformation capture methods  cannot show the dynamics of chromatin loops since they analyze millions of cells simultaneously; while available single cell Hi-C data  as well The observation and eventual manipulation of chromatin contacts in live cells by imaging techniques provides a new perspective on chromatin dynamics. Fundamental questions can be approached from a different angle, for example the dynamics of promoter–enhancer interactions, the stability and duration of looping interactions, the role of proteins (in addition to CTCF and cohesin) and protein aggregates/droplets  for stabilizing or destabilizing specific loops and chromatin-reorganizing processes (replication and cell division). These observations require the labeling of DNA loci with fluorophores and live-cell imaging at high resolution. Major challenges include the selection of suitable labeling approaches to obtain a high signal-to-background ratio that permits visualization and tracking of the signals, to allow imaging over long periods of time  . In thisThe first approach used to visualize specific sequences in mammalian cell nuclei was the lac operator–repressor system. In a pioneer study, Robinett et al.  insertedThe use of the Lac repressor–operator system was followed by the successful adaption of several other repressor–operator combinations  , 22 whicEscherichia coli [HJH elements and the Eμ enhancer of the immunoglobin locus failed to result in germline transmission. The integration of the lac operator array in mouse embryonic stem cells resulted in an abnormal chromosome count of 70–80 chromosomes, indicating that the insertion of the lac operator array can lead to chromosome instability [In these initial FROS experiments, repressor proteins were used because their high affinity to the operator binding sites would create a spot with a detectable signal-to-background ratio. However, it was shown later that the tight binding of LacR and TetR molecules to their operator sequences can have undesired biological consequences such as gene silencing. In the human osteosarcoma U2OS cell line, insertions of lac repeats were shown to induce an arrest in early S-phase, possibly by stalling DNA polymerase . The samhia coli . In moustability .As shown by these examples, FROS labeling might induce genomic instability, and the effect of genomic insertion of FROS arrays needs to be carefully controlled.Saccharomyces cerevisiae [Before the advent of CRISPR-based genome editing, the use of operator–repressor arrays to image specific loci of interest was largely limited to species with high rates of homologous recombination such as revisiae . In mammrevisiae . Tasan erevisiae .Xanthomonas, are proteins that bind to specific DNA sequences [Transcription activator-like effectors (TALEs), discovered in the plant pathogenic bacteria equences . TALEs bequences , 31.Since TALEs can be modified to bind specific DNA sequences, it has also been an attractive method to fluorescently label such sequences Table . SeveralWhile studies on repetitive sequences have provided valuable insights, it is still limited to a set of specific repetitive regions. To research TAD boundaries or enhancer–promoter interactions, it would be necessary to label non-repetitive single loci. Ma et al. demonstrated that TALEs can be used to visualize single DNA loci by mapping HIV-1 proviral DNA sequences in the human genome using TALEs labeled with quantum dots (QDs), which have unique optical properties including excellent brightness and photostability compared with traditional organic dyes . The QDsWhile the other methods mentioned in this review are currently more prevalent, TALEs can be a valuable tool for DNA visualization. It might be interesting to develop a repetitive array, similar to FROS arrays, that can recruit a set of the same TALEs without the need to develop many different TALEs each targeting a specific DNA sequence. This would yield another system that can be used in combination with other DNA labeling systems such as FROS, dCas9 or ANCHOR. In addition, in cases were the binding of repressors to operator arrays induces replication roadblock effects, TetO targeting TALEs could be an alternative. An advantage of TALEs is that the residence time of TALEs can be adjusted by varying the length of the DNA binding domain, whereas repressors can currently only be altered by previously described mutations .The CRISPR/Cas9 (Cas9 for short) genome editing tool that revolutionized biomedical research can be repurposed for labeling of genomic regions. Originally an antiviral defense system of prokaryotes, the Cas9 protein together with a guide RNA (sgRNA) sequence can induce a double stranded break with near base pair precision . This maMuc4 gene with combinations of 26 or more sgRNAs. A similar approach was used to label enhancers and promoters with 36 sgRNAs each [Caenorhabditis elegans genome (up to 6 repeats with at least 24 sgRNA sites) in the genomic region of interest [Chen et al. were the first to describe imaging of telomeres with dCas9-eGFP in combination with one sgRNA . They alNAs each . An alteNAs each . They ininterest  to recruMUC4 gene [While it is very attractive to use dCas9 to label a specific locus, a major problem of dCas9-FP-based methods remains the low signal due to the limited number of fluorophores that can be recruited to the locus. Some groups succeeded in amplifying the signal by fusing the supernova tagging system (SunTag) to dCas9 –44. The UC4 gene , 45–47.IER5L promoter and its super-enhancer, a known ~ 500-kb chromatin interaction dependent on RAD21 [Alternative approaches that allow the recruitment of a large number of fluorophores per dCas9 molecule involve extending the sgRNA with RNA aptamer repeats such as repeats of the MS2 RNA binding sequence, bound by fluorophore-fusions of the MS2 bacteriophage coat protein, or other analogues –51. EachThe advances on the dCas9 labeling techniques have significantly improved its signal-to-background ratio, resistance to photobleaching and ability to visualize loci over long time periods. Successful dCas9 labeling in these systems requires the expression of multiple genetic components ) either via transient transfection or the generation of stable cell lines. While dCas9 imaging systems can be relatively easily established on telomeres or other repetitive genomic loci, the step to label a single, non-repetitive locus is often large. This can be explained by the delicate balance of getting both a sufficient fluorescent protein level to label the locus and avoiding a high background signal that could obstruct the single locus signal. Expressing the fluorescent moiety at a low but sufficient level is therefore almost always an advantage in obtaining a high signal-to-background ratio, because expressing the fluorescent molecule at high levels increases the number of molecules in the background while the number of locus-binding molecules remains the same  number, certainly when single non-repetitive loci are targeted. At best the binding equilibrium at the target is somewhat improved, but this will be negligible relative to the increase in background fluorescent molecules, particularly when the fluorescent molecule has a long residence time at the target sequence. The flexibility of the dCas9 system has a great appeal for single locus tracking techniques, and ultimately a combination of solutions as described above might improve robust labeling of single loci.In 2014, Saad et al. introduced a novel protein–DNA imaging approach called the ANCHOR DNA labeling system . This laSaccharomyces cerevisiae. Later, the ANCHOR1–ANCHOR3 pair was used in mouse embryonic stem cells to investigate the heterogeneity of chromatin dynamics [Drosophila showed that ANCHOR2 labeled two regions of interest (ROIs) equally efficiently as LacI-LacO labeling, whereas ANCHOR1 showed less efficient labeling at both ROIs. In addition, both ANCHOR systems showed sensitivity to the design of the ParB-FP construct and cellular context [Since different bacteria strains and replicons  within the same bacterium contain different ParB-INT combinations, multiplexing non-crossreactive ANCHORs to label multiple loci became possible. For example, the ANCHOR1 and ANCHOR2 system were used in combination to visualize the dynamics of double strand break repair in dynamics . Recentl context . This suThe ANCHOR DNA labeling system shows great promise as the latest addition to the available single locus tracking techniques. The ease of CRISPR-based genomic insertion of the short ANCH sequences ~ 1 kb) in combination with the ability to non-invasively label genomic regions of interest make the ANCHOR DNA labeling system a promising tool for studying locus dynamics , Alexander et al. have excluded the presence of a long-lived state in which the enhancer and promoter physically interact [Active transcription has been widely associated with 3D enhancer–promoter proximity. Using a dual locus labeling of the interact . Similarinteract . This isinteract . We antiinteract , 60.Sox2 locus, not only the promoter and the Sox2 control region (SCR) displayed confined motion, also the intermittent and downstream genomic region showed a higher level of confinement [HoxA locus) measured in the same cell type [Several studies show an interplay between transcription activity and locus mobility. For example, Gu et al. have shown with a dCas9-based labeling approach that enhancers and promoters diffuse at a faster rate when a locus is transcriptionally active in comparison to an inactive locus . Using afinement  than a tell type .Sox2 enhancer diffuses more freely at transcriptionally poised alleles compared to transcribing alleles, whereas the level of confinement of the Sox2 promoter remains similar [The abovementioned results indicate that transcriptional activation of a locus can have effects on the mobility of regulatory and non-regulatory elements. However, nascent transcription has been shown to occur in transcriptional bursts . Studies similar .In the future, experiments in which the movement of single loci are tracked across a longer time window spanning multiple transcriptional events might increase our understanding of the relationship between active transcription and chromatin dynamics.As the insertion of a fluorogenic array or the fluorescent labeling of an endogenous genomic region has the potential to disrupt the genomic feature of interest, the choice of the place for insertion and labeling a region is a careful balancing act between minimizing the linkage error without causing perturbation. In a recent article, Brandão et al. simulated the effect of label placement and localization error on the ability to detect a 500 kb region in a looped and nonlooped state. When the linkage and localization error become too large, looped and nonlooped states can no longer be detected by locus tracking experiments .In endogenous contexts, loop anchors such as enhancers, promoters and CTCF sites have been successfully tracked by FROS, dCas9-based and ANCHOR approaches with linkage distances ranging from several kilobases to 10 kb , 59, 65.Drosophila cells, the presence of LacI molecules at a 20mer LacO array placed at the Ubx intronic enhancer decreased Ubx protein levels, whereas no perturbation was observed when using the ANCHOR1 and ANCHOR2 labeling technique [Nanog gene as part of the STREAMING tag system [Another important consideration for selecting the type and the position of the label is the impact on the chromatin context for gene regulatory elements and gene expression. For example, Delker et al. found that in echnique . In lineg system . TherefoApproaches based on dCas9 can avoid interference with ongoing transcription by targeting the template strand, since guide RNAs targeting the non-template strand have a higher gene silencing effect compared to those targeting the template strand , 66.Important limitations of all approaches are the signal-to-background ratio as discussed above and the stability of the selected fluorophores against fluorescence bleaching. Depending on the imaging setup, these factors limit the experiment and complicate image analysis. One system that efficiently increases the signal-to-background ratio are fluorogenic tags, green fluorescent protein–nanobody arrays, and monomeric wild-type green fluorescent protein binders that are initially dim but brighten ~ 26-fold on binding with the array , 47  and equDrosophila, transcriptional activation by a distal enhancer only occurred when the enhancer and promoter were in a proximal state (< 340 nm) [Sox2 enhancer control region (SCR), promoter and nascent RNA revealed no correlation between enhancer–promoter proximity and nascent transcription. This study of Alexander et al. revealed no evidence of sustained physical contact between enhancers and promoters. The authors propose that the absence of a correlation between enhancer–promoter proximity and Sox2 transcription might be the result of long-lived activation of the promoter, long-range gene activation, for example by formation of large transcription factor condensates, or by limitations in the spatiotemporal resolution of the experiment [SOX9 locus, interactions between structural elements positioned between the enhancer cluster and the promoter facilitate enhancer–promoter communication and transcription [Looking ahead, one important problem is the correlation of enhancer–promoter proximity with transcription, which was suggested in many biological systems –73 but i 340 nm) . In contperiment . We antiperiment , revieweperiment ) will lecription . At the cription . Similarcription , whereascription .Revisiting these observations by investigating chromatin contacts in live single cells and eventually the combination of DNA, RNA and protein labeling to visualize all components contributing to the nanoscale organization of the cell nucleus, will lead to a deeper understanding of gene regulatory mechanisms.Many proteins have been shown to accumulate in transient (seconds) and stable foci (minutes to hours) in which the individual molecules exchange within seconds –82. DualIn addition, recent advances in super-resolution techniques such as STED have enabled the detection of sub-diffraction sized clusters of regulatory factors proximal to transcription sites , 73, 85.In the future, similar perturbation experiments can be performed to evaluate foci formation and stabilization at specific genomic locations. Alterations, deletions, inversions and rearrangements of DNA sequences powered by novel synthetic regulatory genomic approaches  combinedUltimately, these experiments might not only reveal to what extent foci influence the biophysical behavior of individual molecules, but also how foci and the number of individual molecules within foci are related to the output of local molecular processes such as DNA damage repair, DNA replication and transcription.


Here, we report the identification of ARE sequences in the promoter regions of genes encoding several epigenetic regulatory factors, such as histone deacetylases (HDACs), DNA methyltransferases (DNMTs), and proteins involved in microRNA biogenesis. In this research, we study this possibility by integrating bioinformatic, genetic, pharmacological, and molecular approaches. We found ARE sequences in the promoter regions of genes encoding several HDACs, DNMTs, and proteins involved in miRNA biogenesis. We confirmed that NRF2 regulates the production of these genes by studying NRF2-deficient cells and cells treated with dimethyl fumarate (DMF), an inducer of the NRF2 signaling pathway. In addition, we found that NRF2 could be involved in the target RNA-dependent microRNA degradation (TDMD) of miR-155-5p through its interaction with Nfe2l2 mRNA. Our data indicate that NRF2 has an epigenetic regulatory function, complementing its traditional function and expanding the regulatory dimensions that should be considered when developing NRF2-centered therapeutic strategies.The epigenetic regulation of gene expression is a complex and tightly regulated process that defines cellular identity and is associated with health and disease processes. Oxidative stress is capable of inducing epigenetic modifications. The transcription factor NRF2 (nuclear factor erythroid-derived 2-like 2) is a master regulator of cellular homeostasis, regulating genes bearing  The cells of multicellular organisms have the same genetic content, yet their functions are diverse because they express different genes. These differences are due in large part to epigenetic mechanisms that lead to heritable and stable changes in gene expression programs that occur through alterations in chromatin structure. Epigenetic gene regulation provides an adaptive layer of control of gene expression to enable the organism to adjust to a varying environment by eliciting histone modifications, DNA methylation, and gene silencing via microRNAs (miRNAs) ,2. EpigeOxidative stress arises from an imbalance between reactive oxygen species and the cell’s antioxidant capacity, leading to an accumulation of ROS and a disruption of the epigenetic state of the cell. In turn, oxidative damage triggers epigenetic changes in the chromatin structure, histone modifications, DNA methylation, and DNA-binding proteins . One of However, until now, whether NRF2 influences epigenetic processes by modulating the expression of DNA methyltransferases (DNMTs), histones deacetylases (HDACs), and miRNAs has not been investigated. Here, we identify for the first time several HDACs, DNMTs, and genes encoding proteins involved in the biogenesis of miRNAs. Additionally, we present evidence that NRF2 could modulate levels of specific miRNAs through target-directed miRNA degradation (TDMD), further expanding the functional toolkit of NRF2.https://genome.ucsc.edu/cgi-bin/hgTables) (accessed on 3 February 2023) containing the locations of every transcript and its RefSeq accession number in the genome. To identify regulatory elements, a combined segmentation BED file was generated by concatenating Combined Segmentations [NFE2L2, MAFF, MAFK, MAFG, and BACH1 or their mouse orthologs were downloaded and intersected with the extended transcripts of HDAC1, HDAC2, HDAC3, SIRT1, DNMT1, DNMT3A, DNMT3B, DROSHA, DGCR8, DICER1, and TARBP2 genes or their mouse orthologs. Then, the sequences of the ChIP-seq peaks were extracted using pybedtools from the FASTA file of the hg19 human genome  and are previously described [−/−Keap1 and +/+Keap1 MEFs were provided by Dr. Ken Itoh . Mouse embryonic fibroblasts (MEFs) and mouse hippocampus-derived HT22 cells were grown in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum  and 80 μg/mL gentamicin . All cell lines were mycoplasma-free, as ascertained through regular tests. Cells were changed to serum-free DMEM without antibiotics 16 h before the addition of dimethyl fumarate (DMF-20 μM) treatment .escribed ,25,26. K−ΔΔCT method, with normalization of the raw data based on the geometric mean of Actb and Gapdh (Applied Biosystems), encoding housekeeping proteins. All PCR amplifications were performed in triplicate.Total RNA extraction, reverse transcription, and quantitative polymerase chain reaction (qPCR) were performed as detailed in previous articles . Primer The expression vector pcDNA3.1/V5HisB-mNRF2ΔETGE was described in McMahon et al. . pEF-ΔNRTransient transfections of MEFs or HT22 cells and luciferase assays were performed as described in . pTK-Ren3VO4, 1 mM NaF, 1 μg/mL aprotinin, 1 μg/mL leupeptin, and 1 μg/mL pepstatin). Whole-cell lysates were loaded for SDS-PAGE. Immunoblots were performed as described in [Whole-cell lysates were prepared in RIPA-Buffer  and Nfe2l2 (eleven ASOs). Incubation of whole-cell lysates with biotinylated ASOs was followed by incubation with Streptavidin-coupled Dynabeads™ . RNAs were isolated from the pull-down materials, and qPCR was performed. Briefly, whole-cell lysates were prepared using RIPA buffer with a cocktail of protease and phosphatase inhibitors . One milligram of whole-cell extract was incubated with 1 μg of either Nfe2l2 ASO or control LacZ ASO for 16 h at 4 °C, whereupon RNA complexes were isolated with M-280 Streptavidin Dynabeads (Invitrogen) for 2 h at 25 °C. Total RNA was isolated with Trizol-Choloroform and cDNAs were synthesized with the Maxima Reverse Transcription kit following the manufacturer’s protocols ; real-time quantitative (q)PCR analysis was then performed using a PCR kit according to manufacturer’s instructions , and the mRNA abundance was calculated via the 2−ΔΔCT analysis method, using Gapdh mRNA levels as the control transcript for normalization. miRs were reverse-transcribed using the MiR-X kit  and quantified via qPCR analysis using U6 as the normalization control RNA.To identify ® Green PCR Kit . We employed hsa-miR-128-3p miRCURY LNA (YP00205995), mmu-miR-155-5p miRCURY LNA (YP02119303), and miR-103a-3p miRCURY LNA (YP00204063) PCR Assays . Data analyses were based on the ΔΔCT method, with normalization of the raw data based on UniSp6 . All PCR amplifications were performed from at least triplicate samples.Total RNA extraction was performed using the miRNeasy Mini Kit . For reverse transcription and qPCR, we used miRCURY LNA SYBRt-tests. A one-way ANOVA with post-hoc Newman–Keuls test was used.Data are presented as the mean ± SEM. To determine the statistical test to be used, we employed GraphPad Instat 3, which includes the analysis of the data to a normal distribution via the Kolmogorov–Smirnov test. In addition, statistical assessments of differences between groups were analyzed  by performing an unpaired Student’s The epigenome is comprised of modifications to chromatin, including histone modifications and DNA methylation. One of the main histone modifications is acetylation, which is often a necessary precursor to other modifications, such as phosphorylation, methylation, and ubiquitylation. Acetylation is controlled by the opposing functions of two families of enzymes: histone acetyltransferases (HATs) and histone deacetylases (HDACs). HDACs are involved in key biological functions, such as transcription, cell cycle, autophagy, DNA damage repair, stress responses, and senescence ,29. HDACHDAC1, HDAC2, HDAC3, SIRT1, DNMT1, DNMT3a, and DNMT3b.First, to define comprehensively the role of NRF2 in the transcriptional regulation of HDACs and DNMTs, we searched for putative ARE sequences in the ChIP-Atlas database, an integrative database covering almost all public data archived in the Sequence Read Archive of NCBI, EBI, and DDBJ, using ChIP-seq data  of the hMAFG, MAFF, MAFK, BACH1, and NFE2L2. We used Python-based bioinformatic analysis to scan this binding region for the consensus ARE, as established in the JASPAR database [TGACNNNGCN) described by [The ChIP-Atlas database includes experimental data from chromatin immunoprecipitation (ChIP) analysis of the ARE-binding transcription factors database ,35. Depedatabase ,37. Thesribed by . As show+/+Nfe2l2 and −/−Nfe2l2 mice  derived from −/− mice A. These −/− mice B, furthe−/− mice . We trea−/− mice C. These −/− mice D. Taken Dnmt1, Dnmt3a, and Dnmt3b mRNAs in NRF2-deficient MEFs. We observed that the levels of Dnmt1 and Dnmt3b mRNAs were significantly reduced, while the changes in Dnmt3a mRNA were more modest (p = 0.07)  A. These  = 0.07) B. We the = 0.07) C,D; furtBesides histone acetylation and DNA methylation, miRNAs  reduce the stability and translation of target mRNAs and are also epigenetic regulators. ROS can modulate miRNA biogenesis at many levels, and several enzymes and components of the miRNA processing machinery can be affected by ROS . TherefoDGCR8, DROSHA, DICER1, and TARBP2 genes. We observed that, especially in the human genome  of messenger RNAs (mRNAs) govern their stability, localization, and expression . miRNAs Nfe2l2 mRNA, by using the Targetscan (119 miRNAs) and miR DB (55 miRNAs) databases (Nfe2l2 mRNA (NM_010902.4) were designed in order to pull down the endogenous Nfe2l2 mRNA. Whole-cell lysates from HT22 cells were then prepared and incubated with either Nfe2l2 ASOs or control LacZ ASO, whereupon RNA complexes were isolated with streptavidin Dynabeads (Invitrogen). As shown in Nfe2l2 mRNA was highly enriched in Nfe2l2 ASO samples compared to that in LacZ ASO samples. We then measured the levels of all the predicted miRNAs  and SIRT1, DNMTs, and proteins involved in miRNA biogenesis, DROSHA, DGCR8, DICER1, and TARBP2. Our data further suggest that NRF2 may be involved in the regulation of the levels of certain miRNAs through TDMD. These new data are of great potential relevance to the pharmacological regulation of NRF2 as a therapeutic strategy for various pathologies since epigenetic changes triggered by NRF2 will also have to be weighed.NRF2 is a pleiotropic transcription factor capable of regulating the expression of genes involved in different processes, including xenobiotic, redox, and carbohydrate metabolism, inflammation, and proteostasis  Figure . TherefoOur studies suggest that when NRF2 levels are decreased in pathologies, this reduction may lead to decreased expression of Type-I HDACs and increased expression of genes that were repressed under physiologic conditions. Similarly, NRF2 deficiency would also lower DNMT levels, in turn inducing the expression of genes that were previously repressed. On the other hand, NRF2 inducers, such as sulforaphane, were found to induce NRF2 regulation at the epigenetic level, mainly associated with DNA methylation . Epigene, and neurodegenerative diseases [NFE2L2 promoter have been documented [NFE2L2, reduced the translation of NRF2, and increased the levels of intracellular ROS [The results presented in this study can be potentially relevant to a wide range of pathologies where epigenetic mechanisms are of particular importance, such as cancer ,53, allediseases ,56. In ccumented ,57,58, wcumented . Along tcumented . In the ular ROS . Howeverular ROS . Therefoular ROS .Outside of cancer, type I and II HDAC inhibition mediated by Trichostatin A (TSA) activated transcription factor NRF2 and protected against cerebral ischemic damage. On the other hand, SIRT1 activation was also found to induce the NRF2 signaling pathway, with beneficial effects in focal cerebral ischemia , indicatNfe2l2  or have a more general functions, as well as whether there are differences between the various brain cell types.Oxidative stress is one of the main inducers of the NRF2 pathway, and thus, its activation is linked to the induction of oxidative stress-associated miRNAs, the so-called “redoximiRs”, such as miR-27a-3p and miR-155-5p ,78. In H2l2 mRNA B. There 2l2 mRNA B. Here, icroglia ,84. Furticroglia , supportThis study demonstrates for the first time that the molecules involved in epigenetic gene regulation, namely HDACs, DNMTs, and miRNA biogenesis factors, are under the control by NRF2, expanding the field of action of this transcription factor.