Significance: Nuclear factor E2-related factor 2 (Nrf2) is a transcription factor that coordinates the basal and stress-inducible activation of a vast array of cytoprotective genes. Understanding the regulation of Nrf2 activity and downstream pathways has major implications for human health.Recent Advances: Nrf2 regulates the transcription of components of the glutathione and thioredoxin antioxidant systems, as well as enzymes involved in phase I and phase II detoxification of exogenous and endogenous products, NADPH regeneration, and heme metabolism. It therefore represents a crucial regulator of the cellular defense mechanisms against xenobiotic and oxidative stress. In addition to antioxidant responses, Nrf2 is involved in other cellular processes, such as autophagy, intermediary metabolism, stem cell quiescence, and unfolded protein response. Given the wide range of processes that Nrf2 controls, its activity is tightly regulated at multiple levels. Here, we review the different modes of regulation of Nrf2 activity and the current knowledge of Nrf2-mediated transcriptional control.Critical Issues: It is now clear that Nrf2 lies at the center of a complex regulatory network. A full comprehension of the Nrf2 program will require an integrated consideration of all the different factors determining Nrf2 activity.Future Directions: Additional computational and experimental studies are needed to obtain a more dynamic global view of Nrf2-mediated gene regulation. In particular, studies comparing how the Nrf2-dependent network changes from a physiological to a pathological condition can provide insight into mechanisms of disease and instruct new treatment strategies. In addition to these external stresses, free radicals and reactive oxygen species (ROS) are produced as byproducts of both physiological and pathological cellular processes occurring in the mitochondria, peroxisomes, and endoplasmic reticulum (ER). At high levels, these toxicants can cause damage to cellular components, including proteins, lipids, and DNA (Nfe2l2 gene name) (I and DNA . Cells nne name) . Initialne name) , 164. Nrne name) . In respne name) , 117. InNrf2 , 108 belKeap1 knockout mice express constitutively high levels of Nrf2 and Nrf2 target genes (via their Kelch-repeat domain)   166). K. KKeap1  domain) , 80, 192 domain) , 156. In domain) , 61, 92. domain) . Geneticpartners .cis-regulatory elements for NADPH quinone dehydrogenase 1 (Nqo1) and glutathione S-transferase (Gst) genes  in the promoter region of Nrf2 target genes. The AREs were initially identified as t) genes , 161. Sut) genes . Nfe2l2 ve genes , 164.c−  and thioredoxin (TXN) antioxidant system, as well as enzymes involved in NADPH regeneration, ROS and xenobiotic detoxification, heme metabolism, thus playing a fundamental role in maintaining the redox homeostasis of the cell  42). Nr. Nr42). c− , 22. Nrfstem xc− . In addistem xc− , 164. Thstem xc− . Throughstem xc− , thioredstem xc− , 170, anstem xc− .2+. Fe2+ catalyzes the Fenton reaction, which describes the conversion of H2O2 to the highly damaging OH• radical  and heavy polypeptides (Fth)  enzymes and the Nrf2 target Nqo1 . Nrf2 su radical . To preves (Fth) , 184. Threaction . Additioreaction , as wellreaction , 188. Inechanism . Of noteof Keap1 . Skinheaof Keap1 . This suNfe2l2 knockout MEFs  and phosphoglycerate dehydrogenase (PHGDH) to support GSH and nucleotide production and coordinately activates the pentose phosphate pathway (PPP) to supply ribose for nucleic acid biosynthesis , constitutive activation of cap ‘n’ collar isoform C (CncC), a homolog of Nrf2, sustains quiescence by maintaining low intracellular ROS levels  in response to polycyclic aromatic hydrocarbon exposure (Nfe2l2 and transactivates its transcription (NFE2L2 promoter contains a binding site for nuclear factor (NF)-κB, which allows it to be induced by inflammatory stimuli (NFE2L2 transcription is indeed activated by lipopolysaccharide (LPS) treatment in human monocytes (NFE2L2 gene and is believed to be one cause of resistance to chemotherapy in AML cells (NFE2L2 is induced by breast cancer susceptibility 1 (BRCA1) in human MCF10A mammary epithelial cells on xenobiotic stress  result in decreased Nrf2 expression; however, the significance of these alterations awaits further study  are short (20–22 nucleotides long), single-stranded, noncoding RNAs that regulate gene expression by sequence-specific binding with mRNA molecules and consequent inhibition of translation or degradation of the targets . miR-144NFE2L2 transcript variants missing exon 2, or exons 2 and 3, have been observed  phosphorylates Ser40 in the Neh2 domain of NRF2, disrupting KEAP1-NRF2 association and thus promoting NRF2 activation . This alKeap1-null MEFs  is a rare, invariably fatal genetic condition characterized by premature aging beginning in childhood . The disThe regulation of Nrf2 activity is not limited to the control of its abundance but can also be modulated by the availability of its binding partners. As mentioned earlier, Nrf2 forms heterodimers with the sMaf proteins and recognizes the AREs in the genome . The sMasMaf genes expression is detected in various tissues, but each sMaf gene has a distinct expression profile , then show severe growth retardation and liver hypoplasia and die around E13.5   , 71. Nrfthe AREs , 17. Howthe AREs . As CBP,the AREs . In addithe AREs . Mutatio16 cells . Togethe16 cells . The sub16 cells . MED16 benadione . In addienadione . Moreoveenadione , receptoenadione , and NAD (SIRT6) . HoweverDrosophila, cyclin-dependent kinase 12 (Cdk12) has been identified as the Pol II kinase responsible for the phosphorylation of Ser2 in Pol II CTD at Nrf2 target genes on exposure to oxidative stress. Under unstressed conditions, knockdown of Cdk12 in cell culture and in vivo does not affect cell viability or the expression of genes involved in basic housekeeping processes; however, in response to the oxidative stress inducer paraquat, it specifically impairs the expression of Nrf2 target genes and decreases the survival of the flies  at the promoters of target genes. Following PIC assembly, the carboxy-terminal domain (CTD) of Pol II has to be phosphorylated at Ser5 to initiate transcription of the gene and subsequently at Ser2 to promote productive transcription elongation . In Droshe flies . In humahe flies , 94. If Nfe2l2-deficient or Keap1-deficient mice to define the Nrf2 responsive genes. The first of these studies performed gene expression profiling by microarray of the small intestine from wild-type and Nfe2l2 knockout mice treated with vehicle or the Nrf2 inducer SFN and identified that the basal and inducible expression of several genes involved in ROS detoxification, GSH synthesis and NADPH regeneration is dependent on Nrf2  but only at the late time point (24 h), suggesting that the repression observed might be indirect  in wild-type mice, but not in indirect . In lineindirect , 52 (C.Tindirect , 15, 57.indirect . NRF2 Chindirect . An addiindirect .SCAL1 lncRNA in response to cigarette smoke in lung cancer cell lines (ROR in human MCF10A mammary epithelial cells (NFE2L2 and validated LINC00942 as a new Nrf2 direct target involved in modulating GCLC expression through an unclear mechanism (The global mapping of Nrf2 binding sites indicated that the majority of Nrf2 binding sites lie outside the promoter-proximal region , 52. Nrfll lines  and to ral cells . FurtherIn addition, distal Nrf2 binding sites could be located at enhancers. Enhancers are genomic domains that regulate transcription by functioning as binding platforms for transcription factors and are characterized by specific chromatin signatures of histone methylation and acetylation . The genThe systematic characterization of NRF2-bound regulatory elements becomes particularly relevant in light of evidence for positive selection of SNPs at specific NRF2 binding sites that could influence gene expression and, ultimately, disease risk , 178.Nfe2l2-null and wild-type MEFs showed that expression of Notch1 and AhR, as well as their target genes, is decreased in Nfe2l2-depleted cells (Nfe2l2-null MEFs revealed enhanced IκB kinase β activity, which phosphorylates IκB, the negative regulator of NF-κB, thus inducing IκB degradation and NF-κB activation (Analyses of transcriptional responses have revealed complex interactions between the Nrf2 regulatory network and other signaling cascades. As previously discussed, Nrf2 activity can be modulated in multiple ways by several signaling pathways, thereby affecting the expression of Nrf2 target genes. Conversely, Nrf2 can both positively and negatively influence the downstream pathways of other transcription factors and these interconnections can occur in various forms. For example, Nrf2 can regulate the expression or the stability of other transcriptional regulators. Gene expression microarray analyses comparing ed cells , 172. Intivation .Moreover, several Nrf2 inducing agents can concomitantly trigger the activation of other transcriptional regulators, thus creating complex interconnections between their signaling pathways , 70, 154Finally, Nrf2 activity can be modulated through cross-binding with other transcription factors. In cancer cells, mutant p53 has been shown to piggyback on NRF2 to regulate the expression of proteasome genes, leading to resistance to the proteasome inhibitor carfilzomib .A more comprehensive elucidation of the crosstalk between Nrf2 and other signaling pathways will help to decipher the complexity of Nrf2-driven cellular processes. In an attempt to dissect the Nrf2 interactome and regulome, Korcsmáros and colleagues developed NRF2-ome, an integrated web resource containing information on Nrf2 interacting factors, target genes, regulating transcription factors, and miRNAs , 168. ItSince the discovery of Nrf2 in 1994 , our und


Igf2r, Mest, Zac1, Peg3, Snrpn and non-imprinted Myc loci. In terms of expression pattern, the alternative promoters are highly tissue-specific, which is in a stark contrast to the ubiquitous expression of the corresponding main promoters. The alternative promoters are associated with the histone modification mark H3K4me1, but not with H3K4me3, which is frequently associated with the main promoters. Phylogenetic analyses also indicated that the majority of the alternative promoters are unique to the mammalian lineage, further suggesting the recent formation of these promoters during mammalian evolution. Overall, this study suggests that the alternative promoters of imprinted loci may have been derived from enhancers in recent evolutionary times and co-evolved with the genomic imprinting mechanism.In the current study, we characterized the expression and histone modification profiles of the alternative promoters found within imprinted  In mammalian genomes, a subset of genes are expressed mainly from one allele due to an epigenetic mechanism termed genomic imprinting, by which one allele is repressed by DNA methylation and histone modifications , 2. Aboude novo DNA methyltransferase DNMT3A to ICRs may require one particular histone modification, H3K36me3, which is formed as part of the transcription elongation process by RNA Pol II [Imprinted genes also tend to be clustered in specific regions of chromosomes, forming imprinted domains. The imprinting of several genes in a given domain is controlled through small genomic regions, termed Imprinting Control Regions (ICRs) , 2. ICRsA Pol II .In the current study, we sought to identify additional alternative promoters from imprinted loci by performing a series of Next Generation Sequencing (NGS)-based 5' Rapid Amplification of cDNA Ends (RACE) experiments. With this series of experiments, we have identified several alternative promoters, and subsequently analyzed their expression and histone modification profiles. According to the results, these alternative promoters share several features with transcriptional enhancers, and are likely formed during recent mammalian evolution. Thus, we predict that these alternative promoters might have been derived from enhancers and subsequently co-evolved with the genomic imprinting mechanism.Snrpn, Zac1, Gtl2, Dlk1, Igf2r and non-imprinted Myc, which served as a control. These cDNAs were further modified with G-tailing, which were then amplified for NGS runs. We obtained total 2.5 million reads from this series of experiments. Inspection of these raw reads derived the following immediate conclusions. First, the majority of the sequence reads still belonged to the two categories of the transcripts: normal transcripts with a proper joining of two known exons, Exon1 (E1) and Exon2 (E2), and unspliced transcripts. For the majority of the six tested genes, these two categories of transcripts made up more than 90% of the mapped sequence reads from each of the four sequenced libraries  of Igf2r accounted for 7.2% of the transcripts from the ovary , while the alternative transcript (U1) of Myc made up 2.2% of the transcripts from the neonatal heads   . Thus, bIgf2r and Myc were further characterized in terms of their expression and histone modification profiles. The expression patterns of the identified alternative promoters were first determined through performing RT-PCR-based surveys  is well conserved among all the eutherian mammals in terms of gene orientation and distance  and Mest is also well conserved among all the mammals, including marsupials and platypus, and also even in the avian lineage  of Snrpn are believed to have been formed through genomic duplication only in the muroid genomes, including mouse, rat and Chinese hamster. This is based on the fact that the other mammals have only one alternative promoter U1, which is localized about 100-kb upstream of the main promoter , protocol #16–060.5’-GTCCCGTGGTGAGAGGCACAAG-3’) and mSry-R (5’-GCAGCTCTACTCCAGTCTTGCC-3’).The C57BL/6J strain from the Jackson Lab (Stock No. 000664) was used in the current study. All the mice were housed at the DLAM  of LSU on a regular 12–12 dark-light cycle under a constant temperature 70°F and 50% humidity. All animals were given ad libitum access to water and Rodent Diet 5001. The nursing females were with Mouse Diet 5015. The mice were euthanized by CO2 asphixation in accordance with the rules and regulations set forth by the IACUC. Genomic DNA was isolated from either clipped ears or tail snips by incubating the tissues overnight at 55°C in the lysis buffer . The isolated DNA was subsequently used for genotyping. The sex of the pups was determined through PCR using the following primer set: mSry-F  and internal gene-specific primers [5′-GGTTGTGAGCTCTTCTAGA-3′) and additional internal gene-specific primers to increase the possibility to detect low abundant transcripts. The PCR products were further purified, multiplexed, and sequenced according to a next generation sequencing (NGS) protocol [Tissues were collected from hypothalamus, testis, liver, heart, and kidney from an adult male mouse (WT); ovaries were collected from an adult female (WT); a whole head was used from a one-day-old neonate (WT). The tissues were subject to total RNA isolation using the Trizol RNA isolation kit (Invitrogen). The resulting total RNA (2.5–5 μg) was mixed with gene-specific primers corresponding to the second exons for  and Myc , and rev primers , 23. Theprotocol , 23. TheSeveral sets of cDNA panel were generated using the total RNA isolated from various mouse tissues. In brief, the total RNA was isolated from each tissue using the Trizol RNA isolation kit (Invitrogen). The isolated total RNA (2.5–5 μg) was mixed with random hexamers, and subsequently reverse-transcribed using the M-MuLV reverse transcriptase . The resulting cDNAs were used as templates for detecting the alternative and main transcripts for each imprinted gene. The information regarding the sequences of the primers used for these PCR are available .The histone modification profiles used for the current study was derived from the dataset of Release 3 (Aug 2012) of ENCODE/LICR, which contains a total of 130 ChIP-seq experiments on histone modifications. Mammalian conservation plots used for the current study was derived from the dataset of 30-Way Multiz Alignment and Conservation in the UCSC genome browser. This plot represents the results derived from comparison of genome sequences of 30 mammals, which was further refined with the phyloP program.S1 FileThis file contains the number of raw sequence reads for each gene, and also the summary of the mapping results showing the number of alternative transcripts in each library.(XLSX)Click here for additional data file.S2 FileIgf2r, Mest, Zac1, Peg3, Snrpn, and non-imprinted Myc. This file also contains the information regarding the sequences of all the primers used for RT-PCR analyses.This file contains the information regarding the sequences and exon structures of the alternative exons of the imprinted genes, including (DOCX)Click here for additional data file.S3 FileZac1. This file also contains the results from RT-PCR-based expression analyses of the U1 and E1 promoters.This file contains the exon structure and histone modification profiles of the U1 alternative and E1 main promoters of (TIF)Click here for additional data file.S4 FilePeg3. This file also contains the results from RT-PCR-based expression analyses of the U1 and E1 promoters.This file contains the exon structure and histone modification profiles of the U1 alternative and E1 main promoters of (TIF)Click here for additional data file.S5 FileSnrpn. This file also contains the results from RT-PCR-based expression analyses of the U1, U2, U3, and E1 promoters.This file contains the exon structure and histone modification profiles of the U1, U2, U3 alternative and E1 main promoters of (TIF)Click here for additional data file.S6 FileSnrpn derived from mouse, rat, Chinese hamster, and human.This file contains a set of dotplot results comparing the 450-kb genomic regions of (TIF)Click here for additional data file.


IDH1 or IDH2 result in reduced levels of α-KG as a result of increased formation of D-2-Hydroxyglutarate (2-HG). α-KG is an essential co-factor for certain histone and DNA demethylases, while 2-HG is a competitive inhibitor. These IDH1/2 mutations are thought to result in hypermethylated histones and DNA which in turn alters gene expression and drives cancer progression. While this model seems to be generally accepted in the field, the exact molecular mechanisms still remain elusive. How much of this model has been rigorously demonstrated and what is just being assumed? Are the effects genome-wide or focused on specific loci? This Perspective aims at elucidating the key questions that remain to be addressed, the experimental techniques that could be used to gain further insight into the molecular mechanisms involved and the additional consequences of these mutations beyond DNA and protein methylation.Mutations in genes encoding enzymes of the tricarboxylic acid cycle often contribute to cancer development and progression by disrupting cell metabolism and altering the epigenetic landscape. This is exemplified by the isoforms of isocitrate dehydrogenase (IDH1/2), which metabolize isocitrate to α-Ketoglutarate (α-KG). Gain of function mutations in  Metabolism and epigenetics are highly interconnected. Several proteins involved in metabolic pathways also participate to chromatin remodeling and gene regulation by producing co-factors or substrates used by epigenetic writers . One sucIDH mutations are common in several types of cancers, including ∼80% of glioblastomas, ∼40% chondrosarcomas, 20% of acute myeloid leukemias (AML), ∼55% sinonasal undifferentiated carcinoma, and 1% prostate cancer  , an oncoFumarate hydratase (FH) and Succinate dehydrogenase (SDH)  . These lIDH1 mutation, R132H, the group reports that high DNA methylation levels might prevent binding of insulator proteins to the DNA, thus destroying existing chromatin domains and promoting the formation of new TADs within the chromatin. This mechanism contributes to the dysregulation of an already compromised gene expression. Indeed, they propose that loss of a domain boundary between a constitutive enhancer upstream of the FIP1L1 gene and the gene encoding PDGF receptor alpha (PDGFRA) induces its aberrant expression   may have different consequences from increased insulator methylation. (G-CIMP)  and thattivation . There ativation . Given itivation , the preDnmt3A/B . Interesmutation . PotentiIDH1 mutation  protocol, a real time PCR is performed using specific primers to amplify a locus of interest. In more advanced methods, such as Hi-C, gaps in the DNA sequences are filled in with biotinylated nucleotides after restriction enzyme digestion. Hybrid sequences are then pulled down and used to prepare libraries, enabling whole-genome analysis of the interactions, as opposed to one locus at a time .IDH mutation, they find that in mutated gliomas, genes tend to correlate better with genes from a close, but separate, domain than with genes within their same domain. Finally, they scan these loci of interest for genes whose expression is higher in IDH1 mutant gliomas, obtaining a final list of genes. As a result of this key computational experiment, the authors select the locus on chromosome 4 between FIP1L1 and PDGFRA as an example of disrupted insulation. To validate their hypothesis, the authors use a series of 3C experiments around the FIP1L1-PDGFRA locus to show the altered domain structure.FIP1L1-PDGFRA locus in a series of primary glioma cells and a panel of glioblastoma cell lines. To confirm that loss of insulation, and the subsequent PDGFRA overexpression, is indeed due to loss of CTCF binding, FIP1L1 and PDGFRA using the CRISPR-Cas9 system. However, instead of measuring the actual formation of a novel interaction through a 3C experiment on such CRISPR-modified cells, their readout is based on PDGFRA expression levels obtained by qPCR, and the presence of PDGFRα on the cell membrane, measured by FACS. Thus, they do not actually show that the CTCF and methylation-dependent function of the insulator alters higher order chromatin loops.The way they formulated their hypothesis using a largely computational method raises an interesting problem. While the publicly available RNA sequencing datasets used derive from glioma samples, the Hi-C experiments had been carried out in a series of very different cell lines: IMR90 human lung fibroblasts, GM12878 lymphoblastoid cells, K562 bone marrow chronic myelogenous leukemia cells and NHEK normal epidermal keratinocytes. Publicly available datasets are a valuable resource to test out a hypothesis, especially given the complexity of carrying out a Hi-C experiment, and it could be argued that if the same conformations exist in different cell types they are likely to be widely conserved. On the other hand, is it valid to use 3C data from different cell types to make specific assumptions about glioma cells, when they might have a completely different arrangement of TADs? To address this, the authors follow their computational analysis with local 3C experiments to assess the interactions at the IDH1 has been published. In their paper, IDH1 and silencing of both p53 and ATRX to model lower-grade glioma genetic lesions in human neural stem cells (HNSC). They propose that reduced CTCF binding around the SOX2 gene is associated with loss of a DNA domain or loop which normally positions the SOX2 promoter in close proximity to an enhancer, ∼0.5–1 Mb downstream from SOX2. Thus, altered CTCF binding is associated with downregulation of SOX2 expression, blocking differentiation, in contrast to the increase expression of PDGFRA in the Flavahan study.Recently, a second example of higher order chromatin structure alteration following mutation of SOX2 promoter, there were no striking differences between their three-hit cells and the empty-vector controls. Only when taking into account a much larger region around SOX2 (1.2 Mb) they were able to identify specific areas up- and down-stream of the gene that indeed showed increased methylation levels. When comparing these areas to the CTCF ChIP-Seq data from IDH1 wild-type and mutated cells, a thorough annotation of the H3K27ac mark to define enhancer sequences, an accurate portrayal of DNA methylation landscape and finally a method to validate the findings.Despite findings with similarities to the model proposed by Matching different -omics into a single picture of the epigenetic state of the cell will prove to be difficult. When starting from publicly available data, the main issue will be choosing the appropriate datasets. Data from the different -omics might not be available in the same cell line, or, at times, even the same cell type. Furthermore, sequences might have been analyzed or normalized according to different methods, and raw data is not always disclosed. All these might seem small details, but they add up introducing biases in the analysis, making it difficult for the scientist to draw clear conclusions.IDH1 or R132H IDH1 into glioma cell lines. While this might have been a good solution to initially study the alterations induced by the IDH1 R132H mutation, if the focus has now shifted to higher order chromatin structure then perhaps introducing a gene via transfection might cause some alterations to the DNA loops by itself. A possibility could be to selectively introduce the IDH1 R132H mutation in a wild-type IDH1 cell line using the CRISPR-Cas9 system.A second option is carrying out the -omics experiments in the lab. This would ensure consistency of the cellular model, and a better control over the technical biases that might be introduced in the experimental procedure. However, this would require designing a proper cellular model. Many papers have generated their own system by stably transfecting an empty vector, wild-type IDH1 mutations affect particularly sensitive loci containing potential oncogenes such as PDGFRA and SOX2, or whether this is a genome-wide mechanism.Obtaining the data will only solve part of the problem, as potential difficulties will lie in correctly mapping the TADs and understanding which CTCF binding sites are responsible for the disruption or the formation of new contacts between gene promoters and enhancers. Multiomics is an approach to data analysis that aims at integrating, rather than comparing, results from different -omics experiments, in an effort to model complex phenotypes. Despite this being a task that presents its own challenges, it could be the most appropriate way to move forward. The final challenge will be to define whether Another question that needs to be addressed is whether histone hypermethylation plays any role in remodeling higher order chromatin structures. 2HG production induces inhibition of Jumonji-C domain histone demethylases  (KDMs), Drosophila have described how H3K27me3 distribution seems to divide the genome into H3K27me3-enriched areas, corresponding to prominent TADs domains and delimited by CTCF binding sites, or H3K27me3–depleted areas, whose distribution correlates with TADs boundaries  the R132H IDH1 mutation without the risk of perturbing the DNA loops. It will be interesting to see whether cutting edge genome editing techniques will help in designing an adequate model. On the computational side, the multiomics approach of integrating different -omics into one comprehensive mapping of insulator binding sites, enhancer-associated chromatin marks and methylation patterns is required before attempting to find which interactions are lost and which are newly formed upon IDH1 mutation.Research in the field has made an important progress over the past few years, after the discovery that SR wrote the manuscript and designed the figures. JM critically reviewed and edited the manuscript.JM acts as an advisor to and holds stock in Oxford BioDynamics Plc., Chronos Therapeutics Ltd., and Sibelius Natural Products Ltd. SR is employed by Chronos Therapeutics Ltd.


Consistently with predictions, we show that chromosomal relaxation artificially induced by gyrase inhibitors selectively activates convergent genes in several enterobacteria, while conversely, an increase in DNA supercoiling naturally selected in a long-term evolution experiment with Escherichia coli favours divergent genes. Simulations show that these global expression responses to changes in DNA supercoiling result from fundamental mechanical constraints imposed by transcription, independently from more specific regulation of each promoter. These constraints underpin a significant and predictable contribution to the complex rules by which bacteria use DNA supercoiling as a global but fine-tuned transcriptional regulator.DNA supercoiling acts as a global transcriptional regulator in bacteria, that plays an important role in adapting their expression programme to environmental changes, but for which no quantitative or even qualitative regulatory model is available. Here, we focus on spatial supercoiling heterogeneities caused by the transcription process itself, which strongly contribute to this regulation mode. We propose a new mechanistic modeling of the transcription-supercoiling dynamical coupling along a genome, which allows simulating and quantitatively reproducing  The role of DNA supercoiling (SC) in transcriptional regulation has attracted considerable attention in recent years. Due to the helical nature of DNA, mechanical torsion affects transcription at both initiation and elongation steps, and can thereby be considered as a non-conventional transcriptional regulator in eukaryotes as well as bacteria . In the Mycoplasma or Buchnera  is a primary factor in this response, by dictating the distribution of SC at the gene’s promoter. Indeed, as recognized >30 years ago  precisely mimicking specific experimental assays through a small number of biologically relevant input parameters , (ii) directly comparing the results of the simulations to observations and (iii) inferring the underlying dynamics of transcription and propagation of supercoils. In this paper, we use this model to develop the first systematic comparison between TSC simulations and gene expression data.Several TSC models have been recently proposed ,21 in a in vitro or in vivo experimental systems on SC-sensitive model promoters. We show that our simplified description is able to reproduce the quantitative effect of TSC on gene expression on the chromosome, and demonstrate that it is largely dictated by local gene orientations. We then propose that the genomic context may be a strong determinant of the ‘supercoiling-sensitivity' of many bacterial genes, independently from any sequence specificity of their promoter. We analyse existing and new transcriptomic data obtained in conditions of gyrase inhibition by antibiotics causing chromosomal relaxation, and show that convergent genes are significantly more activated than divergent ones in several bacterial species. We then demonstrate that this behavior results from the basic mechanical constraints imposed by transcription, independently from species- or gene-specific properties. These constraints define how DNA topology, globally controlled by the cell physiology, affects the expression of genes according to their local orientation, promoter strength and distance. Finally, we ask if this form of genome-printed regulation can contribute to bacterial evolvability; we analyse global transcription profiles obtained from the longest-running evolution experiment, in which SC-modifying modifications have been selected. As predicted by our TSC modeling, we demonstrate that genes’ expression changes in the evolved strains with modified SC are related to their local orientation. This analysis suggests that the regulatory rules dictated by neighbor genes’ topological interactions likely constitute a robust and fundamental constraint governing the evolution and regulation of bacterial genomes.After presenting the model, we first simulate a series of f(σ) = exp (m U(σ)) where U(σ) is the SC-dependent promoter opening free energy. It follows a sigmoidal curve (U(σ) = 1/(1 + exp ((σ − σt)/ε)), where σt is the threshold of promoter opening, ε sets the width of the crossover, and 1/m is an effective thermal energy that sets the SC activation factor. Standard values shown on Figure t = −0.042, ε = 0.005, m = 2.5 .Our model describes the dynamic transcription-supercoiling coupling. Most hypotheses and components of the model are described in Results and Discussion; here, we provide equations and parameter values. The promoter response curve Figure  is compual curve : U(σ) = t(σ) .Topoisomerase activity curves ) Figure  follow s) Figure ,24: bact) Figure  D. Basaldt = 2 s. At each timestep, free RNAPs are stochastically assigned to the available promoters according to their instantaneous initiation rate, or stay unbound; elongating RNAPs are moved by one unit length (speed 30 nt/s), and unbind when they reach a terminator. The SC levels of all topological regions (separated by fixed proteins or RNAPs) are then updated. Each elongating RNAP adds a constant number ±Γ of supercoils , which is normalised by the region length to compute the increment in superhelical density. Γ represents the fraction of DNA coils that are converted into torsional supercoils by the elongating RNAP, and might depend on the environment ; a value of 0.2 per unit length was used in all simulations presented here. Topoisomerases then increment the SC level of each domain by topo tk(σ) dt. In absence of topoisomerases, we checked that the genome-averaged SC level is constant throughout the simulation. Simulations, as well as all data analyses, were carried in Python with the NumPy numerical package. Note that the effective initiation rate of a gene in a simulation depends not only on its basal rate, but also on the number of RNAPs in the simulation, SC levels, and initiation rates of the other promoters. In all simulations of in vivo systems, we used a concentration of 0.25 μM for gyrase, and 0.025 μM for topoisomerase I  sucrose as carbon source, with or without 0.2% (wt/vol) polygalacturonate . Cells were harvested in early exponential phase  or transition to stationary phase (OD600 = 1.5 in sucrose and OD600 = 1.8 in sucrose + PGA). For each condition, total RNAs were extracted using the frozen-phenol procedure (D. dadantii growth (http://www.vertis-biotech.com): rRNA depletion using the Illumina Ribo-Zero kit, RNA fragmentation, strand-specific cDNA library preparation, and Illumina NextSeq500 paired-end sequencing (∼15 million paired reads per sample).Transcriptomes of escribed . D. dadarocedure , either i growth . Controli growth . FurtherEscherichia coli clones isolated from the Long-term Evolution Experiment (LTEE)  and 20 000  generations (Transcriptomes of t (LTEE) ,30 were t (LTEE) . Brieflyerations . For eacD. dadantii were mapped on the reference genome (NCBI NC_014500.1) with Bowtie2, counted with htseq-count .Sequenced reads from eq-count  against eq-count . A thresE. coli (10 208 probes per microarray) was cured to only retain data from Affymetrix probes that entirely match within one coding sequence of the REL606 reference genome (NCBI NC_012967.1) and with the highest score of sequence homology in case several probes target the same gene. The cured dataset (3839 probes per assay) was obtained for all three strains (six replicates each) and log2 transformed. Differential expression was analysed with the optimal discovery procedure , as often in microbiological literature.kon), transcribe the genes, and eventually unbind at terminators; and (B) the local distribution of SC is affected by transcription and by the action of the two major topoisomerases, DNA gyrase and topoisomerase I. These two aspects of the genome  affect each other in a reciprocal way: (i) elongating RNAPs act as topological motors that pump positive supercoils from back to front, resulting in heterogeneous SC distributions; (ii) the initiation rate of each promoter is modulated by the local level of SC, following a single response function  is complex and dynamic, and must therefore be simulated with a suitable biophysical model. As previously proposed , we simun Figure . Qualitan Figure ,18. For twist  contribution to SC is considered, rather than the complete (twist + writhe) level that involves 3D deformations of the double-helix . This approximation signifies that the generated writhe can be effectively dissipated by topoisomerases  so that the elongating RNAP is not stalled by positive supercoils. Since the latter effect occurs at highly positive SC levels, this hypothesis differs from that of a previous model based on such a stalling effect , the latagnitude . Based oagnitude , where tagnitude .i.e., seconds) along kilobase distances, until reaching another RNAP or a fixed topological barrier, which can represent H-NS or other DNA-bound proteins with the same ability to isolate supercoils .As previously proposed ,39, RNAPpercoils . The respercoils  B appearpercoils . FinallypelE promoter  gene, anchored to a surface that hampers rotation Figure ). TopoisEscherichia coli chromosome . When the inducer concentration was varied, the expression of both genes changed in a coordinated manner, which was attributed to an activation of the second (non-inducible) gene by SC. We simulated this construct, using its exact architecture and physiologically relevant parameters . Simulations reproduced a mutual activation when the basal level of tetP was progressively increased . As can be observed, the activation of tetP is only semi-quantitatively reproduced, as the simulated levels remain below experimental ones: this small discrepancy may be indicative of a different SC-sensitivity for the two promoters (weaker for tufB), in contrast to our unique response curve . Since we are investigating a mechanism relying on basal properties of transcription shared by many different bacterial species, we assessed the universality of this observation by conducting RNA-Seq experiments on Dickeya dadantii. Transcriptomes were collected 15 min after a novobiocin shock applied either in exponential phase . Altogether, in spite of strong differences in organism, experimental conditions and methods, a systematic and coherent relation between gene orientation and transcriptional response is observed in all data. But interestingly, while we intuitively expected convergent genes to be more hampered by gyrase inhibition (since the latter is more active in these regions), the reverse effect is observed, as they systematically appear more activated by chromosome relaxation than divergent genes. This effect was already noted by Sobetzko  which inhibit the DNA gyrase . Based ofloxacin   gene expression strength  and (ii) DNA gyrase concentration. The latter parameter actually also mimics the controlled variations of gyrase activity in the cell in response to environmental constraints, in particular through variations of the ATP/ADP ratio in exponentially growing vs stationary cells ,45. Figulocally experienced by these promoters on the chromosome may differ quite strongly from the global levels measured from plasmids, and even more so when considering other factors disregarded here, such as nucleoid-associated proteins . Simulations of gyrase inhibition  occurred before 20 000 generations, owing to the fixation of another beneficial mutation affecting fis among 45 mutations in total . However, in contrast to the latter case, here the alterations in gene expression are not caused by a fast change in SC, but rather by an inheritable SC change, the consequences of which can be balanced by other actors of the regulatory network, including transcription factors. Since the few identified genes with altered expression are those where the activation/repression is strongest, most of these changes are probably due to the strong and localised action of ‘digital' (on/off) transcription factors. Indeed, SC alone usually exhibits a more subtle and ‘analogue' (more/less) regulatory effect on a larger number of genes . This operation implies that these larger datasets likely contain many false positives which might blur out the investigated effects of TSC, but the latter may still emerge as a statistical feature. We therefore looked for a statistical relationship between gene orientation and response , and also significant for the 20K strain (P = 0.016) which exhibits more mutations that could contribute to rewire the regulatory network independently of SC effects. Note that the observed behaviour does not depend on the precise threshold chosen above and is robust with more stringent selection of differentially expressed genes, although with weaker statistical significance owing to their lower number (data not shown). To crosscheck if it is indeed reproduced by the model, we ran simulations involving decreased . Evolved strains exhibit differentially expressed genes with respect to the ancestor, yet their number is relatively low under standard statistical selection procedures compared to relaxation shock experiments  and Gene Expression Omnibus repository GSE126600 (Escherichia coli LTEE microarray dataset).Transcriptomes have been deposited at ArrayExpress repository E-MTAB-7650 (gkz300_Supplemental_FilesClick here for additional data file.


Arabidopsis thaliana homeobox 12 (ATHB12) promotes leaf growth especially during the cell expansion phase. In this study, we identify TCP13, a member of the TCP transcription factor family, as an upstream inhibitor of ATHB12. Yeast one-hybrid screening using a 1.2-kb upstream region of ATHB12 resulted in the isolation of TCP13 as well as other transcription factors. Transgenic plants constitutively expressing TCP13 displays a significant reduction in leaf cell size especially during the cell expansion period, while repression of TCP13 and its paralogs (TCP5 and TCP17) result in enlarged leaf cells, indicating that TCP13 and its paralogs inhibit leaf development, mainly at the cell expansion phase. Its expression pattern during leaf expansion phase is opposite to ATHB12 expression. Consistently, the expression of ATHB12 and its downstream genes decreases when TCP13 was overexpressed, and increases when the expression of TCP13 and its paralogs is repressed. In chromatin immunoprecipitation assays using TCP13-GFP plants, a fragment of the ATHB12 upstream region that contains the consensus sequence for TCP binding is strongly enriched. Taken together, these findings indicate that TCP13 and its paralogs inhibit leaf growth by repressing ATHB12 expression.Leaves grow by distinct phases controlled by gene regulatory networks including many transcription factors.  Leaves are plant organs essential for harvesting the light that provides energy for living organisms. The development of leaves involves complicated coordination of several factors including correct spatio-temporal transcriptional regulation of genes, hormonal control, and responses to environmental conditions ,2,3,4,5.Arabidopsis thaliana homeobox 2 (ATHB2) and ATHB4, HD-Zip II proteins, are induced by a low ratio of red to far-red light and contribute to the shade avoidance response, including hypocotyl elongation [ATHB2 expression is controlled by basic helix–loop–helix (bHLH) phytochrome-interacting factors PIF4 and PIF5, as Kunihiro et al. [ATHB2 promoter. HD-Zip II proteins are also involved in the regulation of adaxial–abaxial patterning through repression of miR165/166 expression, together with HD-Zip III proteins [ATHB12, an HD-Zip I gene, is primarily expressed in leaves and stems and is also inducible by osmotic stress and abscisic acid (ABA) [gibberellin 20 oxidase 1 (GA20ox1), thus inhibiting stem elongation [Homeobox genes affect plant development as well as the development of various animals ,9. Homeoongation ,16. ATHBo et al.  showed tproteins . ATHB12,id (ABA) ,19,20. Dongation . ATHB12 ongation . ATHB7 aongation ,23. How ATHB12 expression during leaf development, we use the yeast one-hybrid method to identify upstream regulators. One of candidate genes identified as binding to the promoter of ATHB12 in the assay, is TCP13, a member of TEOSINTE BRANCHED1/CYCLOIDEA/PCF (TCP) transcription factors, the role of which in coordinating cell division and cell differentiation during leaf development has been well established [TCP4, which belongs to the class II, results in reduced leaf size, indicating that CINCINNATA-like TCPs (CIN-TCPs) are negative regulators of leaf growth [To explore the regulation of ablished ,25,26. Cf growth ,28. In af growth . TCP13 bATHB12 promoter and negatively regulated ATHB12 expression. Its overexpression resulted in a reduction of leaf cell size, suggesting that it inhibits cell expansion during leaf growth. Our results in this study reveal that TCP13 bound to the Arabidopsis thaliana seeds were surface-sterilized and grown on half-strength Murashige and Skoog (MS) medium supplemented with 1% sucrose and 0.8% phytoagar. Seeds were incubated at 4 °C for 2 days and transferred to a growth chamber at 22 °C under long-day condition with a light intensity of 50 μmol m−2 s−1. TCP13, full-length TCP13 was fused with GFP under the cauliflower mosaic virus (CaMV) 35S promoter. An artificial microRNA (amiR) against three TCPs  was made according to Efroni et al. [TCP13 expression, an upstream segment  of TCP13 was amplified by PCR and inserted in front of GUS in pBI121. All the resulting constructs were introduced into Agrobacterium tumefaciens strain GV3101 and transgenic plants were obtained by the floral dip method [To generate transgenic plants overexpressing i et al. . To examp method .ATHB12 in pHISi-1 (ATHB12::HIS3P) and pLacZi (ATHB12::LacZP) was constructed and integrated into S. cerevisae strain YM4271 . A yeast strain harboring ATHB12::HIS3P was transformed with a cDNA library of 1500 Arabidopsis transcription factors [ATHB12 in yeast, a full-length cDNA of TCP13 was cloned into pGAD424 and the resulting plasmid was introduced into the yeast strain with ATHB12::HIS3P. For Y2H assays, the Matchmaker two-hybrid system  was used as previously reported [For yeast one-hybrid screening, a dual reporter consisting of the upstream region of  factors  and growreported .TCP13, TCP5, TCP17 and TCP7 were subcloned into p326-YFPC vector, and those of ATHB2, ATHB4, HAT3 and ATHB53 were inserted into p326-YFPN vector. Pairs of TCPs-YFPC and ATHBs-YFPN plasmids were co-introduced into Arabidopsis protoplasts by the PEG-method [The coding regions of G-method , and nucArabidopsis leaves were fixed in formaldehyde-acetic acid-alcohol (FAA) and cleared in chloral hydrate solution [http://rsb.info.nih.gov/ij). solution ,34. Cellsolution  were takUBQ5 and PP2A. Some of the gene-specific primers used in real-time PCR were reported previously [RNA was extracted from leaves or whole seedlings using RNeasy plant mini kit . cDNA was synthesized using the isolated RNA by M-MLV reverse transcriptase . Real-time quantitative PCR was performed with qPCRBIO SyGreen Blue Mix  using a LightCycler 96 . mRNA levels were normalized with eviously  and otheATHB12 expression, plasmids containing GFP, TCP13-GFP or ATHB2-GFP under the control of the CaMV 35S promoter were introduced into protoplasts from leaves of ATHB12::GUSP plants, and proteins from the transfected protoplasts were prepared in GUS extraction buffer . GUS activity was measured using 1 mM 4-methylumbellyferyl-β-D-glucuronide (4-MUG) in GUS extraction buffer. GUS activity was normalized with protein concentrations measured by the Bradford assay. GUS staining was performed as described previously . Seedlin35S::GFPP and 35S::TCP13-GFPP were used for ChIP-qPCR. After crosslinking in 1% formaldehyde, sheared chromatin complexes were obtained by sonication . Protein-DNA complexes were immunoprecipitated with GFP antibody , and real-time PCR was performed using the primers listed in Ten-day-old seedlings of 35S::GFPP or 35S::TCP13-GFPP or 35S::ATHB2-GFPP were used as effector plasmids, ATHB12::LUCP or mutated ATHB12::LUCP as reporter genes, and Renilla luciferase (35S::RLUCP) as internal control. Arabidopsis protoplasts were isolated and transfected by the polyethylene glycol (PEG)-method, as previously described [Renilla luciferase (RLUC) activity.The escribed . FireflyATHB12-overexpressing plants had enlarged leaves with enlarged and endoreduplicated cells [ATHB12 expression is regulated. To identify a direct upstream regulator(s) of ATHB12 during leaf development, we used a yeast one-hybrid (Y1H) screen involving a cDNA library of 1500 Arabidopsis transcription factors [ATHB12 was cloned into pHISi-1 and pLacZi vectors, and the resulting reporter plasmids were used to screen the cDNA library. Twelve genes were isolated from the screen and regarded as putative upstream regulators of ATHB12  selection . TCP13 d plants C. These TCP13 under the cauliflower mosaic virus (CaMV) 35S promoter (TCP13-GFP) or expressing a construct down-regulating TCP13 and its homologs. For the latter purpose, since a T-DNA insertional mutant of TCP13 did not have a distinctive phenotype and in addition, it has been reported that the sequences of TCP5 and TCP17 are highly homologous to that of TCP13 [TCP5, TCP13, and TCP17 under the control of CaMV 35S promoter . We obse leaves A,B. BothTCP13 overexpressor had the most clear-cut effect in the cell expansion phases (10 and 14 DAS); the leaf areas of TCP13 overexpressor were about half of those of the wild type (0.56-fold at 10 DAS and 0.51-fold at 14 DAS) whereas the amiR-3TCP seedlings had leaves that were about 1.2-fold larger than those of the wild type at the same stages  at different phases of leaf growth: leaf primordia , cell proliferation (8 DAS) and cell expansion (10 and 14 DAS) phases [e stages C. These TCP13 overexpressors and amiR-3TCP plants at different phases of leaf development using differential interference contrast (DIC) microscopy. The palisade cells of the TCP13 overexpressor were slightly smaller than those of the wild-type (0.86-fold) at 8 DAS, while those of the amiR-3TCP plants were slightly larger  in the presence of cycloheximide for 2 h , exactly the same results were also obtained . A specific binding of ATHB2 to the promoter of ATHB12 under high stringency selection condition (60 mM 3-AT) was observed, confirming ATHB2 as another transcription factor participating in the regulation of ATHB12 expression possibly through direct binding to the promoter GGNCCC) [ATHB12 contains two potential TCP binding sites (TBSs) at -254 bp  and -158 bp , respectively, from the translational start site GGNCCC) ,43. Sequart site A. Chromaequences B and monATHB12. Leaf development requires a controlled cascade of activities of diverse transcription factors. Previously, ATHB12, a homeodomain transcription factor, was reported to promote leaf growth, especially in the cell expansion phase, by activating several genes related to cell expansion . HoweverATHB12  of leaf development including the cell expansion stage. However, given the significant differences in cell area of 8 DAS transgenic plants  did not fully recover their expression levels in the in amiR-3TCP transgenic plants. A possible explanation for this discrepancy could be that many of these are indirect targets of ATHB12, and perhaps other additional factors as well as ATHB12 could be required for their full activation. Or the restored ATHB12 expression in the amiR-3TCP plants was not completely linked to the full activation of the ATHB12 protein for some reasons. At this point, the exact explanation remains unanswered.The expressions of c plants B. On theATHB12 or other genes, considering the highly conserved structural similarity in helices and the loop domain in the CIN-like TCPs [Interaction between TCP13 and ATHB2 was confirmed by yeast two-hybrid assays and BiFC analysis . In addiike TCPs . TCP4, aike TCPs , so thatATHB12 (NGA resulted in reduced leaf cell numbers. In addition, HB51, also called LMI1, is expressed in expanding leaves and affects leaf morphology [ATHB12 expression/repression, thus influencing particular phases of leaf development. Further research on them in relevance of ATHB12 should clarify their roles in leaf development.We isolated several transcription factors in addition to TCP13 from the yeast one-hybrid screen for proteins that bind to the 1.2-kb upstream region of ATHB12 . Interesrphology . It is q


In yeast, Hda1 histone deacetylase complex (Hda1C) preferentially deacetylates histones H3 and H2B, and functionally interacts with Tup1 to repress transcription. However, previous studies identified global increases in histone H4 acetylation in cells lacking Hda1, a component of Hda1C. Here, we find that Hda1C binds to hyperactive genes, likely via the interaction between the Arb2 domain of Hda1 and RNA polymerase II. Additionally, we report that Hda1C specifically deacetylates H4, but not H3, at hyperactive genes to partially inhibit elongation. This role is contrast to that of the Set2–Rpd3S pathway deacetylating histones at infrequently transcribed genes. We also find that Hda1C deacetylates H3 at inactive genes to delay the kinetics of gene induction. Therefore, in addition to fine-tuning of transcriptional response via H3-specific deacetylation, Hda1C may modulate elongation by specifically deacetylating H4 at highly transcribed regions. In yeast, Hda1 histone deacetylase complex (Hda1C) preferentially deacetylates H3 and H2B, but has also been implicated in global H4 deacetylation. Here, the authors provide evidence that Hda1C binds to hyperactive genes, where it specifically deacetylates H4 to partially inhibit elongation, suggesting a role for Hda1C in elongation by specifically deacetylating H4 at highly transcribed regions. Histone acetylation directly activates RNA polymerase II (RNA Pol II) transcription by disrupting the interaction between histones and DNA, thereby opening the chromatin structure, and/or recruiting factors that affect local chromatin structure and transcription. By contrast, histone deacetylation directly represses transcription.Eukaryotic transcription is regulated by covalent modifications of histones, including acetylation, methylation, phosphorylation, and ubiquitination3, whereas histone deacetylation is mainly mediated by the Rpd3 large (Rpd3L) HDAC5. Histone acetylation/deacetylation within coding regions affects RNA Pol II elongation, transcription initiation from cryptic promoters, and histone exchange2. Furthermore, co-transcriptional histone methylations at H3K4 and H3K36 play important roles in the regulation of histone acetylation. H3K4me3 at promoter regions acts as a binding site for multiple HATs and HDACs3. In addition, H3K4me2 targets the Set3 HDAC to deacetylate histones located at the 5′ ends of genes6. In 3′-transcribed regions, H3K36me2/3 enhances deacetylation by recruiting the Rpd3 small (Rpd3S) HDAC8.The opposing activities of histone acetyltransferases (HATs) and histone deacetylases (HDACs) dynamically regulate histone acetylation and transcription. In yeast, multiple HATs, including SAGA, NuA3, and NuA4, are responsible for acetylation at promoter regions10). Hda1 is a class II HDAC that contains a HDAC domain and an Arb2 domain at its N terminus and C terminus, respectively11. Hda2 and Hda3 are structurally similar and form a heterodimeric complex; both subunits contain a potential DNA-binding domain and a coiled-coil domain at their N terminus and C terminus, respectively11. The coiled-coil domains of Hda2 and Hda3 are essential for both the interaction with Hda1 and the HDAC activity of the complex11. Notably, the Arb2 domain of Hda1 and the DNA-binding domains of Hda2 and Hda3 are not required for HDAC activity, suggesting that they may have a role in targeting of the complex to chromatin. Indeed, a recent study revealed that the Arb2 domain of Hda1 binds directly to nucleosomes in vitro12. Furthermore, the DNA-binding domains of Hda2 and Hda3 are able to bind to double- or single-stranded DNA in vitro11; however, their functions in vivo remain unknown.Hda1C includes three subunits: Hda1, Hda2, and Hda3 . In addition, HDAC activity of the Hda1C complex toward histone H4 has also been reported in vitro, suggesting that Hda1C may play important roles in H4 deacetylation9.Hda1C deacetylates histone H3 and H2B and interacts with the general corepressor Tup1 to repress transcriptionIn this study, we show that Hda1C binds to hyperactive genes, likely through an interaction with RNA Pol II. Surprisingly, we find that, unlike at inactive genes, Hda1C specifically deacetylates histone H4 within hyperactive coding regions. Furthermore, loss of Hda1C slightly suppresses the growth defect caused by loss of Bur1, suggesting that Hda1C may play a negative role in elongation. A comparative analysis of acetylation changes suggests that Hda1C and the Set2–Rpd3S pathway have overlapping but distinct functions in the deacetylation of histone H4 within coding regions. Finally, we find that Hda1C preferentially deacetylates histone H3 at less active genes to delay the kinetics of gene induction upon environmental changes. We propose that Hda1C has two distinct functions in transcription: (1) H4 deacetylation at hyperactive coding regions to regulate elongation, and (2) H3 deacetylation at inducible genes to fine-tune the kinetics of gene activation.GAL genes, chromatin was prepared from wild-type and hda1∆ cells grown in medium containing glucose or galactose. Levels of histone acetylation were normalized to total histone content, as determined by H3 occupancy. Consistent with previous reports, H3 acetylation, but not H4 acetylation, was higher at GAL3 promoters in hda1∆ cells than wild-type cells under inactive conditions (YP-Glucose)  assays using antibodies recognizing di-acetyl H3 (K9 and K14) and tetra-acetyl H4, respectively. To determine whether Hda1C differentially affects histone acetylation depending on the transcriptional status of se) Fig. 13,14. Unose Fig. . Howeverons Fig. .Fig. 1HdYEF3 and PMA1. Whereas H4 acetylation within the coding regions of these genes was strongly increased in the hda1∆ strain, deletion of HDA1 did not strongly affect H4 acetylation at the promoter regions or H3 acetylation at the promoter or coding regions  to monitor H3 and H4 acetylation in the wild-type and ncy Fig. . By contnes Fig. . We furtnes Fig. . This fiHDA1 . Unlike the untagged control, Hda1-myc was highly enriched at the promoter and coding regions of the active genes YEF3 and PMA1  (n = 3)  Fig. . SurprisMA1 Fig. . Consistlls Fig. . These rS. pombe spike-in controls to confirm Hda1C crosslinking to highly active genes and the role of the Arb2 domain in this process. Hda1 was highly enriched at actively transcribed genes and this binding was observed throughout all yeast chromosomes  at various positions of the ium Fig. . Similarium Fig. . Consistda1 Fig. . However8. A previous study proposed that the Set2–Rpd3S pathway acts primarily on infrequently transcribed long genes17. This function contrasts with that of Hda1C, which appears to preferentially deacetylate histone H4 within the coding regions of hyperactive genes . NuA3 HAT is known to have at least two subunits, Nto1 and Pdp3, that directly recognize H3K36me319. Although a strong interaction between Nto1 and H3K36me3 was seen, Hda1C did not recognize H3K36me3 even in low salt conditions  and first-strand cDNA was prepared with 1 μg total RNA, ReverTra Ace qPCR RT kit (TOYOBO), and gene-specific primers. cDNA was analyzed by real-time qPCR using SYBR Green Supermix and CFX96 cycler (Bio-Rad).The sequences of oligonucleotides used in this study are listed in Supplementary Table m Tris-HCl (pH 7.5), 0.1% NP-40) containing 150 mm, 200 mm, or 300 mm NaCl and protease inhibitors. In all, 1 mg of total extracts was incubated with 1μg of biotinylated histone peptides (Anaspec) and 25 μl of streptavidin coupled Dynabeads (Invitrogen) at 4°C overnight. Beads were washed five times with 1.5 ml of binding buffer and precipitates were resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by immunoblot analysis.Whole cell extracts were prepared with binding buffer , and 300 mm Sodium phosphate buffer (pH 7.2). The membranes were washed with 2× SSC and 0.1% SDS for 20 min and exposed to PhosphoImager. Uncropped Northern-blots can be found in Supplementary Fig. Total RNA was isolated from cells with hot phenol and 10 μg of total RNA was separated on an agarose gel and then transferred to nylon membrane. Northern blot analysis was done as previously described8/ml and subjected to threefold serial dilutions in synthetic complete (SC) media lacking any carbon sources, and 3 μl of aliquot of each dilution was spotted on the indicated plates. Growth was assayed at 2 days (SC/Glu as a positive control) or 8 days (SC/5-FOA to select against the BUR1/URA3 plasmid).For spotting analysis, cells were resuspended at 2.5 × 10oC to mid-log phase. Cells were lysed using lysis buffer  with protease inhibitors  and glass beads. Protein concentration was quantitated by Bradford assay. For SDS-PAGE and western blot analyses, 15~30 μg of whole cell extracts was used. Proteins were separated in SDS-PAGE and transferred onto a PVDF membrane (Millipore). The blots were visualized on film with SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific). Uncropped Western-blots can be found in Supplementary Figs. Cells expressing TAP-tagged or myc-tagged proteins were grown in YPD at 30 m Tris-HCl (pH 7.5), 0.1% NP-40) containing 150 mm NaCl and protease inhibitors . In all, 1.5 mg of total extracts was incubated with 25 μl of protein G sepharose  and with or without 4 μl of α-Rpb3 antibody (BioLegend) at 4 oC for 4 h. Beads were washed five times with 1.5 ml of binding buffer and precipitates were resolved by SDS-PAGE followed by immunoblot analysis.Whole cell extracts were prepared with binding buffer  and CFX96 cycler (Bio-Rad). The DNA libraries for ChIP-seq were prepared using Accel-NGS 2 S Plus DNA Library Kit (Swift Biosciences) and sequenced on the HiSeq2500 platform (Illumina) following the manufacturer’s instructions.S. pombe were used as a spike-in control. The sequence reads were aligned to the S. cerevisiae genome (R61–1–1) or S. pombe genome (ASM294v2) using Bowtie2 program (ver. 2.2.5)35 and ones exclusively assigned to each genome were counted to remove ambiguity. Data filtering, conversion, and visualization were performed by Picard-tools-2.6 (http://broadinstitute.github.io/picard), BEDTools-2.26.036 and IGV-2.3.91, respectively37. Assuming that spike-ins should contain the constant amount of DNA in each experiment, the total sequence read numbers were adjusted and the ratio of the read numbers from treatment (H4ac or H3ac) to the ones from control (H3) was calculated as the enrichment factor in S. pombe. Considering this ratio, the relative enrichment of modified histones (H4ac or H3ac) to the control (H3) in S. cerevisiae was calculated by MACS2 (ver. 2.1.0) program with parameter ‘–ratio'38 and deepTools-2.3.539. The gene ontology (GO) enrichment was assessed by a Bioconductor package, GOstats (ver. 2.40.0)40. The R packages (ver. 3.4.1) (http://www.r-project.org) were used to plot a heatmap, compute the Pearson’s correlation coefficients and P-values, and group the data by K-means clustering. Significantly enriched ChIP-seq regions, called peaks, were identified by MACS2 program (ver. 2.1.0)38 with minimum false discovery rate  cutoff of 0.05. The peak overlapping between samples was examined by DiffBind-2.2.341. Gviz-1.18.242 visualized the location of the peaks on chromosomes.To ensure an accurate comparison between experimental conditions, genomic DNA fragments of S. cerevisiae transcriptome using HISAT2–2.0.443. The sequence reads were counted by StringTie-1.3.044 and fragments per kilobase of transcript per million mapped reads (FPKM) was used as a normalized expression level. The downstream analysis such as drawing heatmap and violin plot, and permutation test, was done by using R packages (ver. 3.4.1).Total RNA was isolated by hot-phenol method. The mRNA-seq libraries were prepared using NEXTflex Rapid Directional mRNA-Seq Kit (Bioo Scientific) and sequenced on the HiSeq2500 sequencer (Illumina). The sequences were aligned to the Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Tfap2c and Bmp7 encompasses two contact domains separated by a region between the two genes, termed transition zone (TZ), characterized by two arrays of CTCF sites in divergent configuration. In this study, we created deletion and inversion alleles of these and other regions across the locus and investigated how they impinge on the conformation.Contact domains of chromatin serve as a fundamental unit to regulate action of enhancers for target genes. Looping between a pair of CCCTC-binding factor (CTCF)-binding sites in convergent orientations underlies the formation of contact domains, while those in divergent orientations establish domain boundaries. However, every CTCF site is not necessarily engaged in loop or boundary structures, leaving functions of CTCF in varied genomic contexts still elusive. The locus containing Deletion of the whole two CTCF arrays with the CRISPR/Cas9 system resulted in impairment of blocking of chromatin contacts by the TZ, as assessed by the circular chromatin conformation capture assay (4C-seq). Deletion and inversion of either of the two arrays similarly, but less pronouncedly, led to reduction in the blocking activity. Thus, the divergent configuration provides the TZ with the strong boundary activity. Uniquely, we show the TZ harbors a 50-kb region within one of the two arrays that contacts broadly with the both flanking intervals, regardless of the presence or orientation of the other CTCF array. Further, we show the boundary CTCF array has little impact on intra-domain folding; instead, locally associating CTCF sites greatly affect it.Our results show that the TZ not only separates the two domains, but also bears a wide interval that shows isotropic behavior of chromatin folding, indicating a potentially complex nature of actual boundaries in the genome. We also show that CTCF-binding sites inside a domain greatly contribute to the intra-domain folding of chromatin. Thus, the study reveals diverse and context-dependent roles of CTCF in organizing chromatin conformation at different levels.The online version of this article (10.1186/s13072-018-0221-1) contains supplementary material, which is available to authorized users. Therefore, control of the chromatin folding is a fundamental step in gene regulation [The chromatin is highly folded in the nucleus of the eukaryotic cells. A pattern of the chromatin folding either facilitates or prevents interactions between genes and gulation . The gengulation  have revgulation –6. Contagulation . Enhancegulation –16. Genogulation , 17, 18.The underlying mechanism for formation and partitioning of the contact domains is currently explained by the extrusion model, in which cohesin complex plays a major role together with CCCTC-binding factors (CTCF) , 20. AccThough the extrusion model still awaits direct evidence to prove the validity, it well corroborates experimental observations presented so far: bridging via cohesin complexes , 21–24 eHowever, it is still uncertain how various patterns of CTCF bindings on the genome impact on the chromatin conformation. Both in silico modeling and experimental validation showed that a CTCF site orienting one side is sometimes enough to establish a boundary between domains . This isTfap2c and Bmp7 are adjacent with each other, intervened by a ca. 310-kb noncoding region in mice. A previous study showed that the locus is partitioned into two topological domains by action of a discrete region termed transition zone (TZ) [The two developmental genes one (TZ)   . The TZ one (TZ) . Enhanceone (TZ) . Thus, tIn this study, we investigated how the arrangement of CTCF-binding sites around the locus organizes the chromatin conformation. Using CRISPR/Cas9 genome-editing system efficiently, we produced comprehensively deletion and inversion alleles of CTCF-binding sites around the TZ as well as those within a domain that do not constitute domain boundaries in the mouse embryonic stem (ES) cells. The TZ largely consists of two arrays of CTCF-binding sites in divergent orientations, which we show is the more effective configuration in blocking chromatin contacts than those arrayed in one direction. Moreover, we show that directionality by CTCF-binding sites inside a domain is critical for the intra-domain contact pattern. Thus, our results highlight differential contribution of those CTCF sites to the higher-order chromatin conformation of the locus.Tfap2c–Bmp7 locus, one encompassing Tfap2c, and the other Bmp7, each hereafter referred to as Tfap2c domain and Bmp7 domain, respectively [Tfap2c, referred to as TZ-L1, L2, L3, L4 in this order from the centromeric side and collectively as TZ-L, and the other of three sites orienting toward Bmp7, referred to as TZ-R1, R2, R3 in this order from the centromeric side and collectively as TZ-R  in variectively . We examectively   encompassing the whole locus with the CRISPR/Cas9 system targeting the both ends of the 1.2-Mb region as described before  in the “Hap” ES cells. Since N-ChIP only detects direct binding sites , this shThe TZ was first identified as a genomic region that autonomously partitions chromatin topology upon translocation to different genomic contexts by artificially introduced inversion . In thisTfap2c and Bmp7 of the Hap clone: One is the deletion of the two CTCF arrays, TZ-L and TZ-R (del2), the others deleting the intervals either between Tfap2c and TZ-L (del1) or between TZ-R and Bmp7 (del3)  and Spo11 (VP-Spo11) well extended to the intergenic region between Tfap2c and Bmp7, but mostly up to the TZ  and Bmp7 (VP-Bmp7), the contact barely extends beyond the TZ to the neighboring domain  and inverted (inv-L and inv-R), respectively Fig. a. StrikiTo understand how the chromatin conformation is organized in the different configurations of the CTCF-binding sites, we further performed 4C-seq from five viewpoints around the TZ: VP-Tdom to the centromeric side of the TZ-L within the Tfap2c domain; VP-TZL between the TZ-L1 and L2; VP-TZmid at the middle of the TZ near the TZ-L4; VP-TZR between the TZ-R1 and R2; VP-Bdom to the telomeric side of the TZ-R in the Bmp7 domain , although certain degree of directionality was still maintained Fig. g, m. IntIt should be noted that in all the mutant configurations as well as the wild-type allele, the contact profiles from the TZmid were equally distributed toward both centromeric and telomeric sides Fig. i, l–n. STfap2c, which harbors CTCF binding toward the telomeric  side, may form stable loops with TZ-L. Therefore, the folding directionality of Tfap2c might be affected by the presence of TZ-L. To test this, we compared the contact distribution of VP-Tfap2c between the mutant alleles around the TZ , which together altered the orientation of the two associating CTCF sites, greatly and significantly increased the proportion of the Tfap2c-cen reads over the Tfap2c-tel reads , and performed the 4C-seq. Although this strongly affected the contact profile within the inverted region , picked up the grown colonies and propagated them. To identify correctly edited clones, we performed PCR against the genomic DNA extracted from each clone using primer sets in Additional file Tfap2c and Bmp7 genic regions by qPCR, normalized the values with that of Gapdh and compared it to normal diploid wild-type cells. The primers used for the qPCR are listed in Additional file To perform the genome editing, we cloned the target sequences of CRISPR into the cloning site of pSpCas9(BB)-2A-Puro (PX459), which was gifted from Dr. Feng Zhang (Addgene plasmid # 48139), with n enzyme . The CRIn enzyme , 36, as 2 and protease inhibitors, placed on ice for 10 min and briefly incubated at 37 °C for 2 min. Then, 0.3 μl of micrococcal nuclease  was added per 1.5 million cells. After incubation at 37 °C for 10 min, EDTA and EGTA were added to the final concentrations of 10 mM and 20 mM, respectively, to stop the digestion reaction. The chromatin was solubilized by sonication using Ultrasonic Homogenizer UH-50  and incubated at 4 °C for 1 h. The cell debris was pelleted and removed by centrifugation. 3.6 μl of anti-CTCF antibody , or 5 μg of normal rabbit IgG control  was then incubated with solubilized chromatin from the 1.5 million cells. After 4 h of incubation, the chromatin with the antibodies was incubated with 20 μl of Dynabeads Protein G  for 1 h. Then, the beads were washed for five times with ChIP dilution buffer supplemented with 0.05% SDS. The immunoprecipitated chromatin was treated with RNaseA (50 ng/μl) at 37 °C for 15 min and then with Proteinase K (100 ng/μl) at 55 °C for 1 h in ChIP extraction buffer . After removal of beads, the DNA was precipitated with ethanol and eluted in 10 mM Tris–HCl pH 8.0. Then, enrichment of DNA was quantified by qPCR. The primers used are listed in Additional file We basically followed the protocol of N-ChIP for CTCF binding that was developed and described in a previous report . After hNlaIII restriction enzyme  at 37 °C for overnight. After heat inactivation of NlaIII, the digested chromatin was self-ligated in the presence of 50–100 units of T4 DNA ligase . After de-cross-linking and purification, we carried out second digestion with 50 units of DpnII restriction enzyme . Then, the chromatin was again self-ligated with 50–100 units of T4 DNA ligase. After purification with NucleoSpin Gel and PCR purification kit , we measured the concentration of the library using QUBIT 2.0 (Thermo Fisher Scientific). Typically, we obtained 30–40 μg of DNA library from one preparation. We then performed two steps of PCR amplification. The primers used for the first and second PCR are listed in Additional file To prepare a 4C-seq library, we basically followed a protocol described before , with slhttp://ggplot2.org) for R (https://www.r-project.org) to produce plots based on the counts of the 4C-seq reads. The one-sided permutation test was performed with the coin package (https://cran.r-project.org/web/packages/coin/index.html) for R to test the significance of the increase in inter-domain contacts in the mutation alleles from the wild-type allele. One-way ANOVA with Tukey’s multiple-comparison post hoc test was carried out with programs included in R as default. The numbers of replicates were two or three for each group in these statistical tests.We first combined the separately produced fastq files of same libraries from different lanes. Then, we removed the sequences of the target fragment up to the restriction site with FASTX-Toolkit and mapped the remaining sequences against the mouse genome (mm9) using Bowtie2 software with its default setting . We convAdditional file 1. Figure S1. 4C-seq plots of Hap, del1, del2 and del3. Figure S2. 4C-seq plots of mutant alleles around the TZ from the VP-Fam209 and VP-Spo11. Figure S3. Inter-species comparison of contact domains, CTCF binding pattern and non-coding sequences. Figure S4. CRISPR target IDs used to produce the deletion and inversion alleles analyzed in the study.Additional file 2. Table S1. List of ENCODE files of the CTCF binding peaks used in the study. Table S2. List of the CRISPR target sequences and their genomic coordinates, using the mm9 assembly. Table S3. Combination of CRISPR targets used to introduce mutations in this study. The IDs of the targets are given in Additional File Table S4. Primer pairs used to confirm the CRISPR mutations. Table S5. Primer pairs used for the qPCR assay. Table S6. Primers used to prepare the 4C-seq libraries. Table S7. List of the 4C-seq libraries prepared and analyzed in this study. The IDs of primers used for the 1st and 2nd PCR are given in Additional File Table S8. List of intervals utilized for counting mapped reads, and their coordinates using the mm9 assembly.


Telomeres, the protective structures of chromosome ends are gradually shortened by each cell division, eventually leading to senescence or apoptosis. Cancer cells maintain the telomere length for unlimited growth by telomerase reactivation or a recombination-based mechanism. Recent genome-wide analyses have unveiled genetic and epigenetic alterations of the telomere maintenance machinery in cancer. While telomerase inhibition reveals that longer telomeres are more advantageous for cell survival, cancer cells often have paradoxically shorter telomeres compared with those found in the normal tissues. In this review, we summarize the latest knowledge about telomere length alterations in cancer and revisit its rationality. Finally, we discuss the potential utility of telomere length as a prognostic biomarker. Telomeres are protective structures of chromosome ends containing repetitive DNA sequences (TTAGGG in vertebrates) and associated proteins. In human somatic cells, telomeres are gradually shortened by each cell division by semi-conservative DNA replication, and cells with extremely shortened telomeres reach senescence or apoptosis. However, self-renewing cells, such as embryonic stem cells and most cancer cells, can maintain telomeres to overcome the cell senescence or apoptosis caused by telomere shortening and acquire immortality. Two mechanisms for telomere maintenance have been discovered in previous studies: Transcriptional activation of a telomere reverse transcriptase called telomerase and the activation of alternative lengthening of telomeres (ALT), which is a telomerase-independent telomere maintenance mechanism utilizing the DNA homologous recombination repair pathway. Of all cancer cells, 85–95% expresses telomerase, whereas ~5–15% exhibit ALT pathway activation .TERT expression is epigenetically silenced [Telomerase consists of two essential components, telomerase RNA (TERC/TR) and telomere reverse transcriptase (TERT), and some accessory proteins, such as DKC1, NHP2, NOP10, pontin/reptin and TCAB1 . Telomersilenced ,7. In adsilenced .TERT transcription has been studied for many years. In 1999, three independent groups isolated the 5’ promoter region of the TERT gene [TERT transcription, transcription factors C-MYC and SP1 bind to the E-box (5′-CACGTG-3′) at −165 and +44 bp and five GC boxes (5’-GGGCGG-3′), respectively, to induce TERT mRNA expression [TERT transcriptional activation [The mechanism for the regulation of ERT gene ,11. In tpression . The bintivation . Anothertivation ,14, has tivation ,16. The tivation ,18,19. TTelomeres have two major functions: Genomic sacrifice zones for the end-replication problem  and chromosome end protection from DNA damage response. These functions are mainly regulated by the telomere binding protein complex, called shelterin, which is composed of six proteins: TRF1, TRF2, RAP1, TIN2, TPP1 and POT1 . TelomerIn this review, we summarize the latest knowledge obtained via whole genome analysis regarding telomere length regulation, mainly focusing on TERT point mutations and the regulatory mechanism of TERT expression. Furthermore, we summarize the rationality for the maintenance of shortened telomeres in cancer and discuss the potential utility of telomere length as a prognostic biomarker.TERT promoter in melanoma. Horn’s group and Huang’s group discovered point mutation in the promoter at −124 (C > T) and −146 base pairs (C > T) from the transcription start site (TSS)  in sporadic melanoma [TERT in familial melanoma [TERT promoter, leading to upregulation of TERT mRNA expression. In ETS family proteins, ETS1 and GA-binding protein transcription factor α (GABPA) and β1 (GABPB1) dimers are specifically recruited to the de novo ETS binding motifs in the TERT promoter, which increases telomerase enzymatic activity and telomere elongation and is correlated with poor prognosis in urothelial cancer [TERT promoter mutations are currently the most common non-coding somatic mutations in cancer and are present in many types of cancers, including melanoma (67%), glioma , myxoid liposarcoma (79%), osteosarcoma (4.3%), hepatocellular carcinoma (44%), urothelial carcinoma (50.8%), squamous cell carcinoma (14.4%), medulloblastomas (21%), ovarian clear cell carcinoma (15.9%), thyroid cancer (10%), and bladder cancer (59%) [Employing advanced genome sequencing technology, two different groups unraveled non-coding mutations in melanoma ,41. Furtmelanoma . These ml cancer ,43. Thesarcinoma %, urotheer (59%) ,46.TERT promoter mutation is low in sarcomas except for myxoid liposarcoma [TERT promoter mutations are mutually exclusive with the mutations in α-thalassemia/mental retardation syndrome X-linked (ATRX), death domain associated protein (DAXX) or switch/sucrose non-fermentable (SWI/SNF) related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 1 (SMARCA1), which are chromatin remodeling proteins associated with ALT pathway activation [TERT promoter mutations in ovarian clear cell carcinoma or glioma [TERT expression by binding to TSS via the transcriptional repressor SIN3A and accumulates histone H3 lysine 9 trimethylation (H3K9me3) [TERT transcription activation. The PIK3CA/AKT/NF-kB pathway activates TERT activity at the protein level via TERT phosphorylation by AKT in breast and ovarian cancers [The percentage of osarcoma ,47 becauosarcoma . In accotivation ,50,51,52r glioma ,54. ThesH3K9me3) . Therefo cancers ,18,19.TERT promoter mutations. BRAF V600E mutations often co-occur in melanoma and thyroid cancer with TERT point mutations [TERT promoter mutations. In some cases, further enhancement of telomerase activity by other gene mutations occurs in the process of tumorigenesis [In contrast to genes with mutual exclusivity, whole genome sequencing analysis has also revealed a simultaneous gene mutation with utations ,41,56. Tutations ,58. In mutations  or phospigenesis .TERT expression is regulated not only by transcription factors but also by epigenetic status. DNA methylation in the TERT promoter region has been studied for two decades [TERT promoter region are correlated with TERT expression levels [TERT promoter region are altered between specific positions in the region. For example, the position between approximately −600 and −200 bp from TSS is hypermethylated, whereas the area from −200 to +150 bp is relatively methylated at lower levels [TERT transcription. A CCCTC binding factor (CTCF) binding motif exists in TSS (+1 to +100 region) and also in an enhancer region at approximately 4.5 kb upstream from TSS sites [TERT mRNA expression, the formation of loop structure at TSS suppresses downstream TERT transcription [TERT mRNA expression [TERT expression is positively correlated with methylation levels at the TERT promoter region and is negatively correlated with gene body methylation level [ decades ,62. In cn levels ,63. DNA r levels ,65. AmonSS sites . Whereascription ,14,16. Gon level .TERT transcription. In fact, DNA methylation levels on the TERT promoter are different from each allele because monoallelic TERT promoter mutations occur in some cancer cells [TERT expression exclusively from the mutated allele [On the other hand, Stern et al. demonstrated that the methylation level at the −600 position is inversely correlated with er cells ,68. In cd allele .TERT promoter via point mutations also contributes to changes in epigenetic histone marks on the promoter region. Akincilar et al. reported that the introduction of TERT promoter mutations at −146C (−146C > T) in colorectal cancer HCT116 cells with the wild-type TERT promoter by CRISPR/Cas9 alters the chromatin status of the TERT promoter to an active conformation. In contrast, restoration of the promoter mutation to the wild-type sequence or depletion of GABPA in melanoma and glioblastoma cell lines enables silencing of the histone mark, suggesting that GABPA binding to TERT promoter is a key step to change the chromatin status [TERT promoter induces long-range chromosome interaction with a region 300-kb upstream from TERT promoter called T-INT. Long-loop formation occurs in telomeres as well. Telomere position effect-over long distances (TPE-OLD) is another example of transcriptional regulation via the interaction of telomeres with chromosomal regions far from telomeres (see below) [TERT is also regulated by this TPE-OLD. In this case, TERT expression is suppressed by loop formation with telomere through the accumulation of silencing chromatin marks [GABPA1 recruitment to the n status . Interese below) . TERT isin marks . The detin marks ).Although cancers with telomerase activation acquire the ability for telomere elongation, it has been reported that the telomere length of prostate cancers is shorter compared with normal tissues ,74. ReceIn the well-established tumorigenesis model, telomeres in human somatic cells gradually become shortened with each cell division. After 50 to 60 cell cycles, cells with shortened telomeres provoke replicative senescence by chromosomal instability and p53 activation, which is induced by the DNA damage response according to telomere shortening ,77,78. HTERT point mutants to maintain telomeres through cell division using human embryonic stem (ES) cells [TERT promoter mutation in ES cells, differentiated these cells into fibroblasts and inactivated checkpoint proteins, such as P53 and CDKN2A. As a result, ES cells with both TERT point mutation and checkpoint inactivation acquired an immortalization phenotype. Of note, despite TERT expression, telomere length was shortened in approximately 70 cell cycles, which is near the crisis phase. Thereafter, TERT expression and telomerase activity emerged for an unknown reason, which stabilized telomere length. Indeed, TERT promoter mutations increase gene expression by only two- to four-fold [TERT mRNA copy number revealed that there are less than 5 copies/cell in most telomerase-positive cancers [Chiba et al. examined the ability of S) cells . They inour-fold ,81. In a cancers .TERT promoter mutations are quite low to elongate all telomeres, and telomerase needs to choose the shortest telomere to elongate, resulting in gradual shortening of bulk telomere length. Then, after telomeres are shortened, TERT activity increases to sufficient levels to stabilize telomere length.Xi et al. also reported that TERT is co-localized only at 5–7% of telomeres by TERT immunofluorescence, in which an amino terminal Flag-SNAP-tag sequence is integrated by CRISPR/Cas9 genome editing . This obSaccharomyces cerevisiae, telomerase is activated by extremely shortened telomeres depending on the activation of TEL1, which is a human ataxia telangiectasia mutated (ATM) orthologue [In thologue . Althougthologue . Given tTERT gene consists of 16 exons in 42 kb of gene body at chromosome 5p15.33 and 22 splicing variants containing well-examined variants, α- and β-splicing variants, have been identified up to date [TERT mRNA regulates the expression level of the full length TERT and regulate telomerase activity.TERT expression and telomerase activity are regulated by an indirect way as well.  to date . None of to date  and α-,  to date ,92,93. α to date ,94. In b to date . Interes to date ,97. ThesWe also should mention the expression levels of shelterin proteins in cancer. Previous clinical studies have reported that expression levels of shelterin genes, such as TRF1, TRF2, TIN2, and, in some cases, POT1, are elevated in many types of cancer as compared to the noncancerous tissues ,99. AlsoIn the process of tumor development, various mutations are introduced in the cancer cell population, causing intratumor heterogeneity. Recent single cell analyses by NGS have shown that the genetic variance is generated among the cells in a tumor ,106,107.Given that telomerase-mediated telomere elongation is important for the infinite proliferation of TERT-positive cancer cells, genetic or pharmacological inhibition of telomerase activity in cancer cells induces gradual shortening of telomeres and eventual cell senescence or apoptosis ,110,111.TERT transcripts may also inhibit telomerase function because the splicing variants work as dominant negative proteins, as described above. The second interpretation is that the length does not matter if the capping function by t-loop formation is intact. Third, moderate genomic instability elicited by shortened telomeres might be advantageous to cancer evolution [As a well-established mechanism for limited telomere elongation, telomere-bound shelterin complexes exert a negative feedback effect on telomerase access to telomeres  ,116. TERvolution . In factvolution .We previously addressed whether it is advantageous for cancer cells to maintain short telomeres . First, TERT promoter mutation. One of the glioblastoma subtypes, proneural subtype, has features of high expression of platelet derived growth factor receptor alpha (PDGFRA) or isocitrate dehydrogenase 1 (IDH1) gene mutation [TERT promoter mutation and partly associated with activation of the ALT pathway [TERT promoter mutation although there are some glioblastoma with longer telomeres by ALT activation. Given that alteration of telomere length affects the tissue morphology of the tumors and expression of cancer-associated genes, shortened telomeres might contribute to tumor malignancy through reprograming gene expression signatures.ISGs expression is regulated by signal transducer and activator of transcription 1 (STAT1), which is a transcription factor activated by IFN. This IFN/STAT1 pathway exerts an antiviral and antitumor activity by inducing cell cycle arrest or apoptosis ,121,122,mutation . WGS ana pathway ,140,141. pathway . Thus, aSaccharomyces cerevisiae by Gottschling et al., who found that expression of RNA polymerase II gene inserted immediately adjacent to telomere locus was suppressed [DSP (desmoplakin), which is located 7.5 Mb away from telomeres, interact with the subtelomeric region by chromosome loop formation in a telomere length-dependent manner [TERT gene and form a TRF2-dependent chromosome loop with the telomere to suppress TERT gene expression [Canonical telomere function involves the protection of chromosome ends and acts as sacrifice sequences for the end replication problem as mentioned above. In addition, telomere length affects the expression of genes adjacent to the telomeric region, which is called the telomere position effect (TPE). TPE was first identified in ppressed . This phppressed . Robin eppressed . This att manner . As anotpression . Given tpression , TPE-OLDpression . TRF2 ispression .Telomere length-dependent regulation of gene expression may also involve telomeric non-coding RNA called TERRA, which is associated with telomere end protection, maintenance of chromatin structure and telomere length regulation ,149,150.TERT promoter mutations has triggered a remarkable improvement of our understanding of the mechanism for TERT transcriptional activation and the relationship between TERT promoter mutations and other gene mutation to regulate TERT expression and telomere length in a cooperative or mutually exclusive manner. These observations have established the importance of telomere maintenance for carcinogenesis and its malignant progression. Nevertheless, most cancers exhibit shorter telomeres compared with normal tissues despite the risk of crisis, apoptosis or senescence, due to excessive telomere shortening. One rational answer might be that maintaining shortened telomeres causes activation of specific genes, such as ISGs, which are required for cancer progression (Identification of gression .Accumulating evidence has demonstrated that short telomeres correlate with increased cancer malignancy, and telomere length in cancer would be useful as a prognostic biomarker or a risk predictor. Some reports have shown the association of telomere length with a poor prognosis of cancer by monitoring telomere length in blood cell DNA as a surrogate biomarker ,155,156.