Dear Editor,2. ChIP-seq experiments are now widely used to characterize the genome-wide binding of these proteins, and comparing ChIP-seq data from different cell types can provide valuable insight into understanding how cell type-specific transcriptional programs are established3. Specifically, epigenetic regulators often show dynamic chromatin binding during development and disease progressions2. However, most of them are broadly expressed across tissues, and their chromatin binding is thought to be mainly modulated by crosstalk with TFs, which could be considered as their cell type-specific co-factors4. Thus, identifying TFs that preferentially bind at the genomic regions differentially bound by a chromatin-associated protein between different cell types has become an important step toward deciphering the molecular mechanism modulating its chromatin binding4. Moreover, applying this analysis to histone modifications marking active regulatory elements such as H3K4me1-3 and H3K27ac is frequently used for discovering cell type-specific regulators5.Eukaryotic gene transcription is controlled by a large cohort of chromatin-associated proteins including transcription factors (TFs) and epigenetic regulators6. But, the cell type-specific peaks defined in this way often suffer from high false-positive rates, which can severely affect the accuracy of downstream analysis7. Recently, it has been demonstrated that quantitative comparison of ChIP-seq data using MAnorm or other statistical models can more precisely characterize the differential binding of proteins than arbitrarily classifying their peaks into cell type-specific and non-specific ones based on peak overlap, and thus can provide a better basis for the following analysis8. This is particularly important for identifying the cell type-specific co-factors of the protein under study, which highly relies on both the sensitivity and specificity of the detection of differential binding6. Therefore, developing computational tools that systematically incorporate quantitative comparison of ChIP-seq data based on appropriate statistical models into the identification of cell-type specific regulators can effectively facilitate the application of these models.A traditional way of this analysis is to first detect the cell type-specific ChIP-seq peaks of the protein of interest, which are typically defined as those that do not overlap with peaks identified from other cell types, and then search for TFs whose binding sites are significantly over-represented in these peaks9. More than 97% of the H3K4me3-associated genes (defined as genes with H3K4me3 peaks at promoters) are shared between adult and fetal stages, covering 93% of the genes differentially expressed between two stages  of IRF2 ChIP-seq peaks in adult proEs are located at gene promoters, we speculate IRF2’s promoter binding may also be important for adult proEs.Here, we present a practical toolkit, MAmotif, for this purpose. It can automatically perform quantitative comparison between ChIP-seq samples of the same protein but from different cell types, and identify TFs whose binding is significantly associated with the cell type-biased binding of this protein as its candidate co-factors Fig. . To asse2. By taking all 14,108 H3K4me3-associated genes of adult proEs as background, we found genes covered by the H3K4me3 promoter peaks containing IRF family motifs are significantly enriched in genes more highly expressed in adult proEs than fetal proEs  were covered by the H3K4me3 promoter peaks co-occupied by IRF2 than expected by chance (Fig. 2-ratios of H3K4me3 intensities and corresponding P-values, and confirmed that the IRF2 motif, but not GATA2 motif, is significantly enriched in adult-biased H3K4me3 promoter peaks compared to the fetal-biased ones (Supplementary Fig. 10.Next, we incorporated the gene expression profiles of adult and fetal proEs to test these predictions. The rationale is that if a TF does preferentially bind at the adult-biased H3K4me3 peaks, genes bound by it at promoters should be more likely to have adult-biased expression than other H3K4me3-associated genes, as H3K4me3 is a strong transcriptional activation marknce Fig. , indicatnce Fig. . Then, w9. With our new analysis, now it is clear that IRF2 preferentially regulates a considerable number of immune-related genes by directly binding at their promoters, and these genes tend to be more highly expressed in adult proEs (Supplementary Fig. We have shown that besides distal enhancers, IRF2 can also function as transcription activator at gene promoters. However, only a small fraction of IRF2 promoter-bound genes overlap with the genes associated with IRF2-bound enhancers Fig. , though https://github.com/shao-lab/.In summary, we present a new computational toolkit, MAmotif, for detecting co-factors associated with the differential chromatin binding of proteins, based on quantitative comparison of their ChIP-seq data and systematic integration with TF binding information from motif analysis or other resources. Applying it to real ChIP-seq data, we unveiled an important role of IRF2 as transcription activator at gene promoters through coupling with H3K4me3 mark, which clearly illustrates the power of quantitative integration of epigenomic variation and TF binding information at regulatory elements using MAmotif for detecting cell type-specific regulators. MAmotif toolkit is available at Supplementary Information


MGMT expression in human brain tumors, the associated regulatory elements, and linkages of these to its epigenetic silencing are not understood. Because the heightened expression or non-expression of MGMT plays a pivotal role in glioma therapeutics, we applied bioinformatics and experimental tools to identify the regulatory elements in the MGMT and neighboring EBF3 gene loci. Results: Extensive genome database analyses showed that the MGMT genomic space was rich in and harbored many undescribed RNA regulatory sequences and recognition motifs. We extended the MGMT’s exon-1 promoter to 2019 bp to include five overlapping alternate promoters. Consensus sequences in the revised promoter for (a) the transcriptional factors CTCF, NRF1/NRF2, GAF, (b) the genetic switch MYC/MAX/MAD, and (c) two well-defined p53 response elements in MGMT intron-1, were identified. A putative protein-coding or non-coding RNA sequence was located in the extended 3′ UTR of the MGMT transcript. Eleven non-coding RNA loci coding for miRNAs, antisense RNA, and lncRNAs were identified in the MGMT-EBF3 region and six of these showed validated potential for curtailing the expression of both MGMT and EBF3 genes. ChIP analysis verified the binding site in MGMT promoter for CTCF which regulates the genomic methylation and chromatin looping. CTCF depletion by a pool of specific siRNA and shRNAs led to a significant attenuation of MGMT expression in human GBM cell lines. Computational analysis of the ChIP sequence data in ENCODE showed the presence of NRF1 in the MGMT promoter and this occurred only in MGMT-proficient cell lines. Further, an enforced NRF2 expression markedly augmented the MGMT mRNA and protein levels in glioma cells. Conclusions: We provide the first evidence for several new regulatory components in the MGMT gene locus which predict complex transcriptional and posttranscriptional controls with potential for new therapeutic avenues.Background: The molecular regulation of increased  The samples were electrophoresed on 12% SDS-polyacrylamide gels along with the pre-stained molecular weight markers. After electrophoretic transfer of the proteins onto Immobilon-P membranes (Millipore Co.) and blocking in 4% non-fat dry milk, the blots incubated with anti-MGMT monoclonal antibodies (1 µg/mL) for 8 h followed by secondary antibody linked with horseradish peroxidase. Protein bands were visualized by enhanced chemiluminescence . The blots were photographed, and the bands quantitated by densitometry using a VersaDoc 5000 imaging system  and ImageJ software.CTCF transcript cloned in psiLv-U6 plasmids, also coding for puromycin resistance was obtained from Genecopoeia . Transient transfections of plasmid DNAs (1 µg) for 40 h were performed using the Roche X-tremeGENE HP reagent in 4 GBM cell lines. Cell extracts prepared from these experiments were processed for western blotting. A full-length expression vector of NRF2 cloned in pCDNA3.1 was a gift from Dr. Anil Jaiswal . After transfection of SF-188 glioblastoma cells by standard procedures and gentamycin selection, a clone expressing the highest level of NRF2 was isolated and cultured.OriGene’s siRNA-27 kit  containing 3 siRNAs targeting a specific CTCF transcript sequence was purchased. The siRNA pool or the scrambled counterparts was transfected at 10 nM using the Roche X-tremeGENE siRNA transfection reagent according to the manufacturer’s instructions in four GBM cell lines, namely, the SF188, T98G, GBM6, and GBM10. Following transfection for 24 h in the presence of DMEM with 10% FBS, the cells were trypsinized and processed for western blotting. A set of four shRNAs against the CTCF motif in MGMT promoter 1: forward primer: GCCTTACCTCTAGGTGCCA, reverse primer: TGGCGGAGGGAAAGCTG. For determining the GAPDH promoter in RNA-polymerase enriched DNA, the primers were, forward GATTACGGGATGGGTCTGAA and reverse GCTGCACCTCTGGTAACTCC. The fold enrichment method was used to analyze the ChIP-qPCR data. The 2−∆∆Ct is calculated from the outcome of (Ct IP)—(Ct mock). The normalization was done using the IgG Ct value.The EpiQuik ChIP assay kit  was used to determine the presence of a CTCF-binding site in the MGMT-E1 promoter. Briefly, the SF-188 GBM cells were exposed to 1% formaldehyde in the medium without serum for 10 min, followed by lysis, DNA shearing, and protein/DNA Immunoprecipitation using CTCF antibodies . The antibodies to RNA polymerase were used as a positive control and the normal mouse IgG as a negative control. The purified DNA obtained after reversal of protein-DNA crosslinks by heating was subjected to SYBR green real-time q-PCR. The following set of primers was used to identify the MGMT genomic setting, regulatory elements, and the genomic criteria of the MGMT-EBF3 genomic region, and MGMT protein sequence. The UTRdb research tool was used to identify the 5’-UTR and 3’-UTR regions of MGMT mRNA. The precise genomic map locations of the identified sequences were verified and updated to the hg38 version of the human genome sequence by using the BLAST tool/UCSC Genome Browser database. EMBOSS Needle tool was used for Pairwise Sequence Alignment of nucleotide sequences. The tools JASPAR and CTCFBSDB 2.0 [Genomics, promoter and protein databases, GenBank, Ensembl, EMBL-EBI, PrEESSTo/FANTOM5, UCSC genomic browser, TRED, EPD, and UniProt and Enzyme Portal were used to search for the BSDB 2.0 ,90 were CTCF, p53, MYC/MAX/MAD genetic switch, and GAGA factor (GAF). Transcriptional Regulatory Element Database (TRED), JASPAR database, CTCFBSDB 2.0 database, and the Bio-Web tools were used to search for the TFBSs. Identification and plotting of CpG Islands in the MGMT promoter were achieved by EMBOSS CPGplot tool . The following parameter sets used to identify CpG islands in nucleotide sequence of MGMT promoter: Observed/Expected ratio > 0.60, Percent C + Percent G > 55.00, Length > 200 bp [MGMT alternative promoters in NCBI/Nucleotide, PrESSTo/FANTOM, Ensembl 84, and EPD databases and to identify transcription factors binding sites (TFBSs) involved in the initiation of transcription and their setting in respect to CTCF motif in MGMT exon one revised promoter. The search included: TATA-8  and TATA-532 , INR (YYANWYY), DTIE (GBBRDNHGG), CCAAT and its inverted sequence TAACC, BRE (SSRCGCC), and DPE (RGWCGTG) binding sites [The transcription factor binding sites (TFBSs) were selected according to their unique functional properties that characterize the functional activity of the promoter, e.g., TATA, INF, DTIE, > 200 bp ,92. In-sng sites .MGMT gene transcripts and NRF1 occupancy at the MGMT promoter: GM12878 (Lymphoblastoid), HeLa S3 , K562 (chronic myelogenous leukemia), and H1 human embryonic stem cells (H1-hESC). Of these, the MGMT-proficiency of HeLa S3 cells [Also analyzed were various sequencing-generated data from the ENCODE Project . These iS3 cells  and its S3 cells  has beenMGMT and EBF3 transcripts, were analyzed and identified by RNA22 v2 microRNA target detection tool [p-value represents, the greater chance that the loci contain a valid MRE.Identification of un-reported mature sequences (miR) of ncRNA transcripts was performed by using the miRBase BLASTN search tool sequence option . The miRion tool . The lowp < 0.05.The statistical analyses of the obtained results were achieved using Excel software. T-test was used for the analysis of two Independent samples following ANOVA. One-way ANOVA was used to analyze the differences among group means of more than two samples. All statistical computations were calculated using GraphPad Prism 7. A significant difference was assessed at


Salmonella typhimurium (HKST) results in changes in nucleosome sensitivity to MNase. We show that the HKST response alters the sensitivity of -1 nucleosomes at highly expressed promoters. Finally, we correlate the increased sensitivity with response-specific transcription factor binding. These results indicate that nucleosome sensitivity dynamics reflect the cellular response to HKST and pave the way for further studies that will deepen our understanding of the specificity of genome response.The nucleosome is the primary unit of chromatin structure and commonly imputed as a regulator of nuclear events, although the exact mechanisms remain unclear. Recent studies have shown that certain nucleosomes can have different sensitivities to micrococcal nuclease (MNase) digestion, resulting in the release of populations of nucleosomes dependent on the concentration of MNase. Mapping MNase sensitivity of nucleosomes at transcription start sites genome-wide reveals an important functional nucleosome organization that correlates with gene expression levels and transcription factor binding. In order to understand nucleosome distribution and sensitivity dynamics during a robust genome response, we mapped nucleosome position and sensitivity using multiple concentrations of MNase. We used the innate immune response as a model system to understand chromatin-mediated regulation. Herein we demonstrate that stimulation of a human lymphoblastoid cell line (GM12878) with heat-killed  The fundamental subunit of chromatin is the nucleosome, composed of a histone octamer core and approximately 150bp of DNA . It is cSalmonella typhimurium (HKST). We mapped nucleosome distributions with two MNase digestion levels, heavy and light. The comparison of these different MNase digestion levels reveals important information about transcription factor binding, gene expression prediction, and sensitivity of chromatin to digestion  is associated with flanking nucleosomes sensitive to MNase digestion.In order to investigate the role of nucleosome dynamics during the innate immune response, we have mapped nucleosome position and sensitivity at all human promoters in GM12878 lymphoblastoid cells stimulated with heat killed igestion . The comigestion . Here we9 heat-killed Salmonella typhimurium  for 20 min, 40 min and 60 min and harvested at the end of each time point, in biological replicate .Cell GM12878 cells were grown at 37° in 15% FBS-supplemented RPMI medium. Cells were stimulated with 1.0 X 107 cells were harvested, cross-linked in 1% formaldehyde, and incubated for 10 min at room temperature. After the 10 min incubation, the cross-linking reaction was quenched with 125 mM glycine. Next, the nuclei were isolated in nucleus isolation buffer containing: 10 mM HEPES at pH 7.8, 2 mM MgOAc2, 0.3 M sucrose, 1 mM CaCl2, and 1% Nonidet P-40. The nuclei were then pelleted by centrifugation at 1000g for 5 min at 4°.Approximately 1 X 106 nuclei were treated with light  and heavy MNase-digestion conditions , see average fragment size distribution in Fig. S1B. Chromatin at each time point was digested separately with the light and heavy concentrations of MNase for five minutes at 37° and stopped with EDTA. Decrosslinked, protease-digested DNA from MNase-digested nuclei was isolated via phenol-chloroform extraction and mononucleosome sized bands resolved with a 2% TBE agarose gel. The ∼150bp mononucleosomal band was excised for each time point and MNase concentration. Additionally, two untreated control samples were harvested and processed as described, and have been referenced here as untreated samples.At each time point and biological replicate ∼2.5 X 10MNase sequencing libraries were prepared for each replicate using NEBNext Ultra DNA library Prep Kit for Illumina (NEB #E7370S); using 30ng of input mononucleosomal DNA from each digestion level and time point. Following end-prep and adaptor ligation, the libraries were purified with AMPure XP beads. Universal and index primers from NEBNext Multiplex Oligos for Illumina (NEB #E7335S) were incorporated by a 12 cycle PCR. Library size and quality was verified with the Agilent 2100 Bioanalyzer. Molar concentration of each indexed library was determined by KAPA quantitative PCR and size corrected using sizing information from the Bioanalyzer.et al., 2016; https://sequencing.roche.com/en/products-solutions/by-category/target-enrichment/hybridization/seqcap-ez-choice.html). By qPCR we observe ∼300 fold enrichment of sample target genes compared to off-target loci. Paired-end reads (see below) were aligned to the hg19 genome assembly (IHGSC 2001).Previously, we combined MNase-seq with in-solution targeted enrichment of 2 kb surrounding TSSs of 21,857 human genes . matHeatmap was used to plot normalized rpm mTSS-seq data for all RefSeq genes +/− 2kb surrounding the annotated TSSs. Average plots represent the average value of all promoters in a respective cluster in 10bp windows across the 2kb SeqCap region. Gene ontology analyses based on heatmap gene classifications were performed using two unranked sets of genes, the target set of genes and the total mTSS-seq gene list as background . Mononucleosome-sized fragments were used to infer nucleosome position. Nucleosome occupancy profiles were obtained by calculating the fragments per million that mapped at each base-pair in the SeqCap regions (bedtoolsCoverage). Midpoints for nucleosome distributions were determined through the calculation of center fragments in 60 bp windows at a 10 bp step in the 2kb surrounding each TSS. Data matrices were subsequently processed in R (https://doi.org/10.25387/g3.12290333.The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (MNase Transcription Start Site-enriched sequencing (mTSS-seq) allows for high quality nucleosome maps at all human promoters . mTSS-seq data are highly concordant with the preeminent nucleosome maps in lymphoblastoid cell lines . The differences we observe in MNase-sensitivity of well-positioned nucleosomes in promoters of expressed genes at the 20 min HKST time point are consistent with the time frame of a primary response to bacterial infection  from nucleosome-bound fragments that were recovered even after heavy digestion . To identify MSFs within our mTSS-seq experiment we calculated the log78 cells . We direromoters . In the romoters . These dnfection . AdditioκB, Pu1, and Ebf1 in the same gene order . Specifically, we observed a significant increase of sensitivity of the -1 positioned nucleosome 20 min after stimulation with HKST. Alternations in chromatin structure that result in nucleosome sensitivity to MNase may be the result of transcription machinery, chromatin remodelers, histone chaperones, or other regulatory factors. Our results are consistent with important regulatory functions of -1/+1 nucleosomes reported first in yeast and Drosophila, such as maintaining the structure of a nucleosome-free region at the TSS, interaction with RNA Pol II, transcription factor binding and access to underlying DNA . These results are consistent with the observation that changes in promoter nucleosome architecture is a transient event . Time course studies have shown early and late gene expression changes during B-cell activation, commensurate with the chromatin sensitivity changes found at promoters following HKST treatment (κB pathway in lymphomas and leukemias show that signal transduction occurs as quickly as 15 min with TNFα stimulation (In vitro studies have shown that TF binding to nucleosomal DNA requires nucleosome eviction or repositioning, however pioneer TFs can bind to DNA within the bounds of the nucleosome, which may result in nucleosome sensitivity to MNase (κB has been studied in depth regarding its ability to bind chromatin and induce immune gene transcription, and it appears to play a diverse role in promoter binding and activation of genes in hetero- and euchromatic regions of the genome (We have shown that sensitive nucleosomes are associated with active transcription. 20 min after HKST stimulation, we observe significantly greater -1/+1 nucleosome sensitivity that flanks a larger nucleosome-free region at the TSS . These rmulation . In vitre genome . The assA genomic response occurs through multiple regulatory layers, including signaling cascades, regulatory factors, and the resulting regulation of chromatin structure and gene expression. Our study adds to a comprehensive understanding of the induction of immune signaling pathways. Our results add important and complementary information to existing data showing the importance of the chromatin landscape at promoters including nucleosome position and sensitivity to MNase, localization of regulatory factors on promoters and histone post-translational modifications. The data we show here expounds upon the interplay between chromatin structure to function, which is consonant with models suggested by preeminent immunologists . In aggr


Chromatin regulatory landscape plays a critical role in many disease processes and embryo development. Epigenome sequencing technologies such as chromatin immunoprecipitation sequencing (ChIP-seq) and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) have enabled us to dissect the pan-genomic regulatory landscape of cells and tissues in both time and space dimensions by detecting specific chromatin state and its corresponding transcription factors. Pioneered by the advancement of chromatin immunoprecipitation-chip (ChIP-chip) technology, abundant epigenome profiling technologies have become available such as ChIP-seq, DNase I hypersensitive site sequencing (DNase-seq), ATAC-seq and so on. The advent of single-cell sequencing has revolutionized the next-generation sequencing, applications in single-cell epigenetics are enriched rapidly. Epigenome sequencing technologies have evolved from low-throughput to high-throughput and from bulk sample to the single-cell scope, which unprecedentedly benefits scientists to interpret life from different angles. In this review, after briefly introducing the background knowledge of epigenome biology, we discuss the development of epigenome sequencing technologies, especially ChIP-seq & ATAC-seq and their current applications in scientific research. Finally, we provide insights into future applications and challenges. The genome is packaged by histone proteins that are decorated with a wide variety of modifications. Histone acetylation is one of the best-characterized chromatin modifications and correlates with the opening of local chromatin structures and transcriptional activation  . CompareModifications on histone surfaces are often dynamic: some modifications can be added and erased in just a few minutes after a cell has been stimulated, so that the histone modifications detected in a population of cells under specific conditions at a given moment are in fact only partially representative of the types of potential modifications.Antibodies to detect histone modifications are essential for many epigenetic chromatin sequencing techniques: in ChIP-seq, for example, antibody specificity testing is required for both histones and transcription factors .The mechanisms of heterochromatin formation and diffusion, and the “memory” and “fading” of histone modifications, have been studied only to a minimal extent.The concept of histone code can not be widely used to accurately describe and predict a specific biological phenomenon in many cases , 11.Some histone modifications are active in some genomic regions. In contrast, repressive in others: for example, methylation of the H3K9 locus can be repressive in the promoter region and active in the coding region .More histone modifications also include phosphorylation, ubiquitination, sumoylation and ADP ribosylation. The current exploration of histone modifications still has the following problems:In contrast to single histone modifications, the chromatin regulatory landscape is a higher-level annotation on the biological function of chromatin that combines histone modifications, transcription factor binding, and the regulatory function of genomic elements. The renowned Roadmap Epigenomics Consortium has led large-scale human reference epigenomic studies that have provided detailed and accurate descriptions and classifications of the functional states of regulatory elements , 13. In Many studies of chromatin dynamics require, besides access to information on histone modifications and genomic regulatory elements, a proper understanding of the interactions between transcription factors and chromatin, which is often essential for understanding development and disease progression , 16. SomThe earliest technology applied to large-scale epigenetic mapping was chromatin immunoprecipitation (ChIP) followed by microarray hybridization (chip) (ChIP-chip), which allowed scientists to detect DNA-protein interactions on a genome-wide scale . ChIP-chCompared to Chip-chip, chromatin immunoprecipitation sequencing (ChIP-seq) provides higher resolution, less noise, and greater coverage , 21. WitFor DNA-binding proteins, ChIP-seq experiments are aimed at enriching DNAs bound to specific proteins. The procedure consists of multiple steps Fig. a: first 5 ∼ 107 cells due to a massive loss in the immunoprecipitation process, and the potential for epitope masking due to the formaldehyde crosslinking process .–48.46–48ATAC-seq can measure samples with as low as 500 cells, but it has not been able to decipher cell-to-cell variability at the single-cell level. The advent of single-cell sequencing technology has enabled us to understand life at a finer single-cell level , 51. TheNot all chromatin regulators have a corresponding motif, regulators such as chromatin remodeling proteins do not have a DNA sequence preference. In contrast, the interaction between chromatin remodeling proteins and nucleosomes is often an essential factor in early embryonic development as well as cell fate decisions . Open chSome motifs have the potential to be bound by multiple sequence-specific transcription factors .Some of the homologous transcription factors bind in similar motif patterns, in many cases, the direct assignment of open chromatin peaks to specific individual transcription factors is less reliable. Results obtained from direct detection of interactions between specific transcription factors and DNA are often more reliable.ChIP-seq alone may be challenging to find meaningful regulatory elements when comparing two or more samples. In this regard, ATAC-seq has the advantage of high resolution and high signal-to-noise ratio, which allows researchers to identify differentially regulated sequences, such as enhancers. Combining ChIP-seq with ATAC-seq makes it easier to determine significant peaks. ChIP-seq methods are still irreplaceable although methods for detecting open chromatin, such as DNase-seq and ATAC-seq can help to infer genomic features. All those techniques are indirect for detecting the binding sites of transcription factors to DNA, which means that the transcription factors mapping inferred from the enriched motifs still needs to be further validated , 59. TheThe genome-wide chromatin accessibility profile detected using ATAC-seq represents another level of the chromatin regulatory landscape compared to ChIP-seq, which directly determines specific DNA-protein interactions Table . TherefoMammalian embryo development involves genome-wide epigenetic changes, including DNA methylation, histone modifications, open chromatin and chromatin conformation . CrucialEpigenome sequencing technology plays an irreplaceable role for experimental sytem to track the developmental trajectories and explore the cell fate determination mechanisms Fig. c. ChIP-sIn addition to neurobiology studies, chromatin dynamics analysis has shed light on muscle development , mammaryThere is currently limited understanding of the highly heterogeneous tumor tissues, including differences in the tumor microenvironment, differences between original primary cancer and metastases, and the evolution of tumor subclones. Immune cells in the tumor microenvironment are often critical for the immune escape and infiltration processes of cancer cells , 79. scAExtended technologies of ATAC-seq can also provide new insights into tumor heterogeneity. Assay of transposase-accessible chromatin with visualization (ATAC-see) helps to image open chromatin in situ by fluorescently labeling accessible loci . For exacis-regulatory elements and thus differ between individual cells. Based on this premise, how these heterogeneous cells act in coordination to construct a comprehensive network of cell communication is intriguing.To construct a complete regulatory network, RNA-seq, ATAC-seq and ChIP-seq often need to be combined. Although many algorithms have been developed to integrate multi-omics data, it is difficult to assess the performance of these algorithms and whether they fully preserve the biological variance. A growing number of studies in recent years have demonstrated the potential of parallel analysis of multiple modalities in the same cells –90. SingCompared to open chromatin data derived from populations of cells, scATAC-seq signals are binary and sparse, so a new analytic framework to account for fundamental differences from bulk data is needed. A feasible approach is to aggregate information from many single cells to identify determinants of cell-to-cell chromatin variation . As the Tn5 transposase is widely used for chromatin accessibility sequencing, linear amplification via transposon insertion  and evenNot only the spatial location and migration of cells are required, but also the spatial dynamics on the molecular level is crucial. Genome-wide chromatin conformational changes within cells are important mechanisms for regulating cell behavior . The HiC


Artemisia annua. Several critical genes involved in artemisinin biosynthesis are specifically expressed in GT. However, the molecular mechanism of differential gene expression between GT and other tissue types remains elusive. Chromatin accessibility, defined as the degree to which nuclear molecules are able to interact with chromatin DNA, reflects gene expression capacity to a certain extent. Here, we investigated and compared the landscape of chromatin accessibility in Artemisia annua leaf and GT using the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) technique. We identified 5413 GT high accessible and 4045 GT low accessible regions, and these GT high accessible regions may contribute to GT-specific biological functions. Several GT-specific artemisinin biosynthetic genes, such as DBR2 and CYP71AV1, showed higher accessible regions in GT compared to that in leaf, implying that they might be regulated by chromatin accessibility. In addition, transcription factor binding motifs for MYB, bZIP, C2H2, and AP2 were overrepresented in the highly accessible chromatin regions associated with artemisinin biosynthetic genes in glandular trichomes. Finally, we proposed a working model illustrating the chromatin accessibility dynamics in regulating artemisinin biosynthetic gene expression. This work provided new insights into epigenetic regulation of gene expression in GT.Glandular trichome (GT) is the dominant site for artemisinin production in  Artemisia annua, is a potent drug against malaria [Artemisia annua has gained increasing attention as a potential source as a drug against COVID-19 [Artemisia annua is too low (about 0.1–1.0% dry weight) to meet the increasing demands [Artemisia annua plants still remain the major commercial source for artemisinin production. There are also multiple breeding efforts for high-yielding Artemisia annua. For example, artemisinin-related traits were investigated and improved through population genetics [Malaria, as one of the most-deadly diseases, threatens the lives of nearly half of the world population. According to the World Health Organization (WHO), more than 200 million malaria cases and 405,000 deaths were reported in 2018 . Artemisinin, a sesquiterpene metabolite discovered from Chinese herb  malaria . More re demands ,7. Althogenetics ,11 and mgenetics ,13. A coArtemisia annua is a specialized 10-celled biseriate structure, composed of two non-photosynthetic apical cells, four photosynthetic subapical cells, two stalk cells, and two basal cells. The apical cells are surrounded by subcuticular space for the storage of secondary metabolites [Glandular trichome (GT) is the dominant site for artemisinin biosynthesis and sequestration ,15,16, aabolites . The artabolites . CYP71AVabolites ,22,23,24abolites .ADS and CYP71AV1 promoters in response to jasmonates [Artemisinin biosynthesis is subject to a sophisticated network incorporating regulatory transcription factors (TFs) that mainly belong to the MYB (myeloblastosis) family ,26, WRKYsmonates . LikewisChromatin accessibility is the degree to which nuclear macromolecules are able to make physical access to DNA, reflecting the gene regulatory capacity . The lanChromatin accessibility is generally detected by profiling the susceptibility of chromatin to either enzymatic methylation or cleavage of its constituent DNA . There aArtemisia annua remains largely unknown. In this study, we analyzed and compared the chromatin accessibility of Artemisia annua GT and leaf using ATAC-seq [Previous investigations on artemisinin biosynthetic regulation were mostly focused on TFs that were specifically expressed in GT, whereas the epigenetic regulation of gene expression in ATAC-seq , aiming Artemisia annua. Glandular trichomes from the young leaves of six-month-old plants were isolated with glass beads and manually collected under a microscope. Isolated GTs contained intact apical and sub-apical cells  and little contamination  with high and low accessibility in GT, compared to leaf  that were upregulated and downregulated in GT respectively, and we designated them as GT-up and GT-down DEGs , DXS (AA422860), DXR (AA444110), CYP71AV1 (AA566140), and DBR2 (AA049700), showed higher DNA accessibility in GT compared to that in leaf (HDR (AA477650), HMGS (AA036900 and AA288120), and HMGR (AA271980), did not show GT high-accessible DARs or associated ACRs. Also, several GT-up DEG genes showed no difference in chromatin accessibility between leaf and GT. For example, a GT-up DEG CYP71AV1 , a homolog of CYP71AV1  [Artemisia annua, artemisinin analogs include artemisinic acid, dihydroartemisinic aldehyde, and arteannuin B. The three all share the same biosynthetic pathway with artemisinin, therefore, the regulation of accessible regions on genes associated with artemisinin analogs’ biosynthesis is consistent with artemisinin.Next, we investigated the possible association between artemisinin biosynthesis and chromatin accessibility. Notably, several GT-specific artemisinin biosynthesis genes:  in leaf A. The ge in leaf B. HMGR, , DXS AA4860, DXR p < 1×10−10 for the enrichment analysis. The top four overrepresented TF families were MYB, AP2, bZIP, and C2H2 family (Putative cis-responsive elements (CREs) and TF families that may play a role in modulating the GT-specific transcriptome were identified from the GT high DARs . We focu2 family A, which 2 family B. The fi2 family A harbore2 family C. Transc2 family D. From t2 family . These TArtemisia annua (CYP71AV1 and DBR2) provide access for TF binding, which consequently promotes gene expression. Contrarily, the counterpart regions in leaf were less accessible, resulting in low gene expression.Based on these findings, chromatin accessibility seems to play an important role in regulating gene function in GT. We further proposed a working model to describe the possible association between chromatin accessibility and tissue-specific artemisinin biosynthesis gene expression in ia annua E. In GT,Tetrahymena thermophila, ChIP-Seq (chromatin immunoprecipitation assays with sequencing) analysis of a bromodomain-containing protein indicated that it primarily binds to highly expressed genes during growth [CYP71AV1 (AA566140), are more likely subject to the regulation of chromatin remodeling and thus show greater DNA accessibility plasticity between different tissues.Glandular trichome and leaf cells exhibit different morphology and functions, which is a readout of cell-specific gene expression pattern during differentiation. According to our analysis results, one-sixth of the total genes were differentially expressed in GT compared to leaf. These genes are likely responsible for the establishment and maintenance of cellular identity and function of glandular trichome, which is the main site for secondary metabolite biosynthesis D. Plant g growth . Likewisg growth . Anotherg growth . It is rArtemisia annua GTs contain photosynthetically active chloroplast, and photosynthesis-related proteins are among the top 10 most highly expressed transcripts [2 is released by the active specialized metabolism [Interestingly, photosynthesis and primary metabolites were also highly enriched in GT-up DEG and GT high DAR overlapped genes in GO analysis. nscripts ,49. Phottabolism . We prestabolism , responstabolism , and flotabolism , etc. TaArabidopsis thaliana also reported that only 851 out of 3282 root hair-up DEGs were overlapped with root hair high DARs [Arabidopsis thaliana and early embryo development in Oryzias latipes [Artemisia annua genome assembly needs further improvement, despite the efforts dedicated to accomplishing the current version  [Our study pointed out a possible link between chromatin accessibility and gene expression in GT. However, among these GT high DARs, only 728/4869 were associated with GT-up DEGs. Similarly, another study in igh DARs . In our  latipes . One pos latipes . Due to affolds) , which maffolds) .Artemisia annua. Our results further validated the complex gene expression regulation in artemisinin biosynthesis and provided new insights into the regulatory mechanism of GT-specific biological function from an epigenetic perspective.In conclusion, we comprehensively investigated and compared the landscape of chromatin accessibility in GT and leaf of the important medicinal plant Artemisia annua seeds  were purchased from Youyang, Sichuan, China. Artemisia annua plants were grown under natural light condition in the greenhouse of the Chinese University of Hong Kong.Young and just expended leaves (the fourth to sixth leaf from the top on the stem) of 6-month-old plants were collected into 50 mL sterile Falcon tubes containing 20 mL PBS  with 0.1% TritonX-100 and 5 mL 0.5 mm glass beads . The tube was shaken by hand vertically and vigorously more than 300 times to abrade GT off the leaves to obtain crude GTs . Then, iFor the leaf sample, leaves at the same age as leaves used for GT preparation were collected. As many GTs as possible were removed by gently brushing with a writing brush. After brushing, leaves were checked under a fluorescence microscope to confirm the removal of trichomes. Purified GTs and leaves were directly used for ATAC-seq and RNA-seq library construction.Artemisia annua since the GT sample could not meet the input requirements for other methods. ATAC-seq was performed as previously described with minor modifications [2). The nuclei were incubated with 2 μL Tn5 transposase  in 40 μL tag buffer at 37 °C for 30 min without rotation. The integration products were purified using DNA clean and concentration column  and then amplified using non-hot-start DNA polymerase for 10–13 cycles. Amplified libraries were purified using VAHTS DNA clean beads  to remove adapters.There are several methods for DNA accessibility measurement, such as DNase-seq , ATAC-seications ,43. GlanArtemisia annua reference genome using bowtie2 with the following parameters: “-p 10 -X 1000” [ATAC-seq libraries were pooled and sequenced using the Illumina Hiseq X Ten platform. Raw reads were trimmed using trim_galore with parameter: “-q 25 --phred33 --length 40 --paired”. Trimmed reads were aligned to -X 1000” . Aligned-X 1000” .Artemisia annua genome annotation file [ACRs were identified using MACS2  with parameter:“ --nomodel --shift 100 --extsize 200” . Peak fiion file .Artemisia annua genome annotation file, BLAST  was used to search Artemisia annua ortholog genes from sunflower Helianthus annuus genome. DAR-associated genes were transferred to their ortholog genes in sunflower Helianthus annuus to perform GO analysis with online tools .Since we did not find GO numbers in the Artemisia annua reference genome using Hisat2 [Total RNA of GT and leaves were extracted by TRIzol  and mRNA was purified by the Dynabeads mRNA DIRECT kit (Invitrogen). First-strand DNA was synthesized with reverse transcription with oligo dT as a primer and second-strand DNA was synthesized by random hexamer and followed by Tn5 tagmentation. DNA fragments with adapters were amplified and pooled for sequencing. Raw reads were trimmed using trim_galore with parameter: “-q 25 --phred33 --length 40 --paired”. Trimmed reads were aligned to the g Hisat2 . Alignedg Hisat2  and cleag Hisat2 .RNA was extracted with TRIzol reagent , followed by reverse transcription with SuperScript IV reverse transcriptase (Invitrogen). cDNA was then used for RT-qPCR (Vazyme). Beta-actin was used as an internal control. Primers used for RT-qPCR are listed in ATAC-qPCR was performed using the ATAC-seq library with 1 ng DNA as a DNA sample. Values was first normalized by ACT1 (ACTIN1) and then by genomic DNA. ACT1 was selected for its low variation among different samples and high accessibility. Primers used for ATAC-qPCR are listed in Motif enrichment was performed with Homer using default parameters . Motif d


CTCF is a key insulator-binding protein, and mammalian genomes contain numerous CTCF sites, many of which are organized in tandem.Pcdh) and β-globin clusters, function as an enhancer-blocking insulator by forming distinct directional chromatin loops, regardless whether enhancers contain CTCF sites or not. Moreover, computational simulation in silico and genetic deletions in vivo as well as dCas9 blocking in vitro revealed balanced promoter usage in cell populations and stochastic monoallelic expression in single cells by large arrays of tandem CTCF sites in the Pcdh and immunoglobulin heavy chain (Igh) clusters. Furthermore, CTCF insulators promote, counter-intuitively, long-range chromatin interactions with distal directional CTCF sites, consistent with the cohesin “loop extrusion” model. Finally, gene expression levels are negatively correlated with CTCF insulators located between enhancers and promoters on a genome-wide scale. Thus, single CTCF insulators ensure proper enhancer insulation and promoter activation while tandem CTCF topological insulators determine balanced spatial contacts and promoter choice.Using CRISPR DNA-fragment editing, in conjunction with chromosome conformation capture, we find that CTCF sites, if located between enhancers and promoters in the protocadherin (These findings have interesting implications on the role of topological chromatin insulators in 3D genome folding and developmental gene regulation. In the case of a polynomial of degree 2:Locally estimated scatterplot smoothing (Loess) locally fits the response L ≔ X(XTWX)−1XTW. Then, the covariance matrix of the errors i for each data point.Denote Z scores  of each 5C chromatin interaction. First, we calculated the global average relationship Z score α = 0.01. In this equation, yi and xi are the interaction frequency and genome distance of pair i, respectively. In addition, SDi is the standard deviation of We performed data correction using locally estimated scatterplot smoothing (Loess) to calculate ed Loess  implemendatasets –67. UsinZ score for each 5C interaction. If the Z score of a 5C singleton is larger than 12, the singleton is removed  forms Ji valid pairs  for j ∈ . Let uij be the contact frequency of pair . We choose ki for i ∈  and α minimizing the geometric standard deviation of β is the mean of α and logki solve the linear algebra:Unlike Hi-C and 5C data, 4C data with different viewpoints, even for the same cell types, cannot be compared directly because of their inconsistent scales. Assume viewpoint ower law )\documek1 = 1 to remove the redundancy among the equations of logki for i ∈ . Finally, divide uij by ki to obtain comparable 4C contact frequencies.Without loss of generality, fix Since there are data of two biological replicates available, both mean and variance of contact frequencies are calculated for each pair. Finally, pairs with 0 mean contact frequency are excluded from the fitting of 4C simulation by the approach of relative maximum entropy.In statistical mechanics, the 3D conformations of the polymer are microstates, which cannot be observed directly in experiments. In single-cell experiments, some macroscopic variables, such as contact strength between monomers, can be observed for single microstates. In multiple-cell experiments, only the mean of macroscopic variables over an ensemble of microstates can be observed. As an inverse problem, inferring the distribution of microstates from the macroscopic variables can be achieved in two different ways. The first is the maximum entropy approach. One searches for the best in all microstate distributions which coincide with the observed macroscopic variables, and choose the distribution with the maximum entropy. The justification for this is that one should introduce as little information as possible other than that from the direct observation. The second is the model-based simulation. One sets up a computation model, such as the cohesin loop extrusion, and simulates many microstates. The difficulty comes from the parameter choice. Generally, novel methods are used to optimize parameters by minimizing the differences between the macroscopic variables calculated from the simulated microstates and those observed from experiments. Depending on the problem, the optimization process can be extremely hard and achieve very limited improvements.The advantage of the maximum entropy approach is that the predicted distribution of microstates resulting in the same macroscopic variables as the observations. The disadvantage is that it abandons all known central mechanisms, such as the cohesin loop extrusion. On the contrary, the model-based simulation includes known mechanisms to set up the model but predicts the macroscopic variables usually deviating from the observations. We use the relative maximum entropy approach  that comℂ (the distance at which two monomers are determined to be in contact). Let pij is the contact probabilities of simulations, and Pcdh locus. We subtract L = 1266 by a compact conformation . It forms long-distance chromatin contacts, measured as loop counts lij in the capture Hi-C experiments, with a putative enhancer fragment . The enhancer strength ej of the fragment  is defined as its total H3K27ac signals from ChIP-seq experiments. The insulator strength uij of the loop is defined as the total CTCF ChIP signals in the interval  (a = 500 bp to exclude the rare cases that promoters or enhancers themselves contain CTCF binding sites) if min + a ≤ max − a, and zero otherwise. Let si, ti]. The total enhancer strength for the bait promoter  is defined as si, ti] in days 0, 3, and 6, respectively, with Bayesian networks are a powerful and widely used probabilistic model to infer the underlying conditional dependency of factors shared by a group of instances. In our case, each instance is a promoter, which has four factors: the enhancer strength, the insulator strength, the loop strength, and the promoter activity. Bayesian networks are a non-cyclic directed graph which uses nodes to represent factors and arrows to connect them. The networks are learned by maximizing the posterior likelihood. An arrow from the insulator strength to the promoter activity means it is a direct dependence. We analyzed 207,663 enhancer-promoter contacts of the capture Hi-C data for the genome-wide relationship between insulators, enhancers, and promoters . For eacX ≔ {X1, X2, ⋯, Xn} be the set of n discrete random variables. x ≔ {x1, x2, ⋯, xn} is the specific value of X. k ≤ ri are the ri possible values of Xi. Given a network structure S, Xi, and Let Assume D ≔ {dl| 1 ≤ l ≤ m} are m samples. dli and i and its parents in sample l, respectively. Define:Dl with variable i taking the kth value jth value Thus:S, such that S, where p = p. For simplicity, we assume αx≡α (uniform priors) and α = 1 (limited prior information). The best structure is defined as S∗ ≔ argmaxSp = argmaxSpp(S)/p(D| α). For simplicity, assume that p(S) is uniformly distributed over all possible structures. To find S∗, we first transform the data into an all-dimensions tree and then apply the max-min hill-climbing (MMHC) algorithm [The independence assumption is valid by assuming prior modularity, marginal likelihood equivalence, and Dirichlet More importantly, there exists lgorithm .p values are provided as exact values where possible and otherwise are reported as a range. All of QHR-4C, 5C, and RNA-seq experiments were performed with at least two biological replicates. Single-cell CRISPR CBS insertion clones and their corresponding mutant clones were screened for at least two clones for each genotype.All statistical tests used were performed using R 3.5 and Microsoft Excel. All of the statistical tests used are described in the relevant text. Additional file 1: Figure S1. Stochastic and monoallelic expression of the Pcdhα genes in single cells. Figure S2. A sensitive QHR-4C method for one-to-all capture of chromosome conformations. Figure S3. Both forward and reverse CBS elements inserted between the Pcdhα cluster and its downstream HS5-1 enhancer function as insulators. Figure S4. Reverse CBS elements inserted between Pcdh α13 and αc1 function as an insulator for the upstream genes. Figure S5. Reverse-forward CBS pair as an insulator for the Pcdhα genes. Figure S6. Reverse-forward tandem CBS pairs as an insulator for the Pcdhα genes. Figure S7. Forward-reverse convergent CTCF sites do not compromise their insulation activity. Figure S8. Polymer simulations of the chromatin looping interaction profiles upon CBS insertions or their mutations in the Pcdh and Igh clusters. Figure S9. Tandem CTCF sites ensure stochastic and balanced Pcdh gene expression. Figure S10. Topology of spatial chromatin contacts between the Pcdh β and γ clusters and the downstream super-enhancer. Figure S11. Topology of spatial chromatin contacts between the Pcdhγ clusters and the downstream super-enhancer. Figure S12. Genotyping of the mouse lines of various HS5-1 CBS deletions and inversions. Figure S13. Tandem CTCF sites function as insulators for enhancers with no CBS.Additional file 2: Table S1. Oligonucleotides in this study. Table S2. CRISPR deletion and inversion mice. Table S3. CRISPR single-cell clones. Table S4. Mapping statistics of the 5C data. Table S5. Pearson correlations between 5C replicates.Additional file 3: Review history.


The vast majority of methods available to characterize genome-wide chromatin structure exploit differences in DNA accessibility to nucleases or chemical crosslinking. We developed a novel method to gauge genome-wide accessibility of histone protein surfaces within nucleosomes by assessing reactivity of engineered cysteine residues with a thiol-specific reagent, biotin-maleimide (BM).S. cerevisiae genome, including heterochromatic regions, suggesting limited, higher-order chromatin structures in which this surface is obstructed by tight nucleosome packing. However, we find that nucleosomes within 500 bp of transcription start sites exhibit the greatest range of accessibility, which correlates with the density of chromatin remodelers. Interestingly, accessibility is not well correlated with RNA polymerase density and thus the level of gene expression. We also investigated the accessibility of cysteine mutations designed to detect exposure of histone surfaces internal to the nucleosome thought to be accessible in actively transcribed genes: H3 102, is at the H2A–H2B dimer/H3–H4 tetramer interface, and H3 A110C, resides at the H3–H3 interface. However, in contrast to the external surface site, we find that neither of these internal sites were found to be appreciably exposed.Yeast nuclei were obtained from cells expressing the histone mutant H2B S116C, in which a cysteine resides near the center of the external flat protein surface of the nucleosome. BM modification revealed that nucleosomes are generally equivalently accessible throughout the S. cerevisiae chromatin are equivalently accessible genome-wide is consistent with a globally uncompacted chromatin structure lacking substantial higher-order organization. However, we find modest differences in accessibility that correlate with chromatin remodelers but not transcription, suggesting chromatin poised for transcription is more accessible than actively transcribed or intergenic regions. In contrast, we find that two internal sites remain inaccessible, suggesting that such non-canonical nucleosome species generated during transcription are rapidly and efficiently converted to canonical nucleosome structure and thus not widely present in native chromatin.Overall, our finding that nucleosomes surfaces within  The vast majority of eukaryotic genomic DNA is assembled into nucleosomes, the basic repeating subunit of chromatin. In solutions containing physiological ionic strengths long chains of nucleosomes condense into higher order chromatin structures, facilitated by inter-nucleosomal interactions mediated by the core histone ‘tail’ domains –3. WhileNumerous non-histone chromatin factors govern the accessibility of genomic DNA within chromatin to regulate crucial biological processes, including gene expression. Some of these factors act by covalently adding or removing posttranslational modifications such as acetylation and phosphorylation on the core histones which alter electrostatic interactions that directly or indirectly alter chromatin structure –18. AlteEfforts to map accessible, open regions of chromatin associated with active genes have relied on determining locations of differential accessibility of genomic DNA to nucleases such as DNase I, micrococcal nuclease, or chemical reagents. For example, DNA regions hypersensitive to DNase I were found to indicate open chromatin regions associated with active genes . FormaldS. cerevisiae nuclei. This method involves installation of a rationally selected cysteine substitution within a core histone and limited reaction with the thiol-specific reagent biotin-maleimide to determine the genome-wide accessibility of selected external and internal histone surfaces in nucleosomes. Measurements of histone surface accessibility provide insights complementary to chromatin accessibility derived from nucleases, such as MNase-Seq [S. cerevisiae genome. In stark contrast, we find little evidence of noncanonical nucleosome structures with exposed internal surfaces.We previously described a method to probe the accessibility of surfaces of the core histones in the chromatin of Nase-Seq . Here weHTA–HTB locus (HTA2–HTB2 deleted) is transformed with an integration plasmid bearing the HTA1–HTB1 locus. The mutation of interest is made via site-directed mutagenesis. In addition to the HTA1–HTB1 locus, the plasmid also contains a functional copy of HIS3 which permits transformants to grow in histidine deficient media. Confirmation of proper integration and maintenance of mutation was performed by isolating the genomic DNA and sequencing the region of interest. For the H3 experiments, we used ROY1281 , treated with 6000 units of lyticase (Sigma Aldrich L2524), nuclei isolated with a Ficoll 400 cushion, then resuspended in MNase digestion buffer lacking β-mercapoethanol (BME) and CaCl2. Nuclei were modified with biotin-maleimide  as described in [2 was then added to final concentration of ~ 0.5 mM and the samples split into two aliquots for digestion with either 480 or 960 units of MNase  at room temperature for 3 min. The amount of enzyme was empirically determined to yield predominantly mononucleosomes. Digestion was stopped by the addition of 100 µL MNase stop solution  and held on ice for 2 min. Samples are then treated as in [Nuclei from control and cells expressing histone mutants were prepared as in . Brieflyribed in  with mined as in  with 100A detailed protocol has previously been reported . BrieflyLibrary preparation was carried out with NEBNext® Ultra™ DNA Library Prep Kit for Illumina® (New England BioLabs E7370) and NEBNext® Mutiplex Oligos for Illumina® (New England BioLabs E7335). In addition to yeast input and AP DNAs, we added 1 ng or 10 ng of mouse nucleosome core particle DNA to each sample prior to DNA repair step. This allowed us to standardize fold-enrichment in our experiments by analyzing the ratio of yeast reads to mouse reads between input and AP. A size selection step was not used in the library preparation, and it was found that 5 µL of the adaptor ligated DNA coupled with ten cycles of the PCR conditions yielded sufficient material for sequencing.Fastq files were aligned to reference genomes (SacCer2 or mm10) using the default parameters of Bowtie2 to generate SAM files . SAMtoolXenopus H2B (S112C) is more accessible in expanded oligonucleosome arrays than condensed arrays (results not shown). Freshly prepared nuclei from yeast cells expressing H2B S116C as the sole source of H2B were incubated with BM, digested with micrococcal nuclease, and BM modified nucleosomes affinity-purified (AP) and sequenced [We first sought to determine the extent to which the accessibility of an external histone protein surface on nucleosomes varies genome-wide among genes in yeast cells. We employed a new method in which modifiability of an engineered cysteine residue is probed with the thiol-specific reagent biotin maleimide (BM) . The hisequenced   according to average accessibility scores for the promoter proximal (TSS to 0.5 kb) region. For all time points, the average of quintile 1 genes was ~ 1.45 while that for quintile 5 genes was ~ 0.9  , 50. To 47 bp , 5Similar to our investigation of the relationship between accessibility and RNAPII (Rpb3 ChIP), we investigated the relationship between nucleosome accessibility and RSC, SWI/SNF and ISW1a chromatin remodelers by sorting genes according to their density of Rsc8, Snf2, and Ioc3, respectively , then plS. cerevisiae genome is largely euchromatic, however there are heterochromatic regions where gene expression is repressed. Genes within 20 kb of the telomeres have been found to be less transcriptionally active and in some instances are bound by the SIR complex which induces heterochromatin formation in S. cerevisiae [S. cerevisiae heterochromatin does not silence gene expression via steric occlusion [S. cerevisiae is not binary , but rather that changes in accessibility are more subtle, possibly as a consequence of nucleosome remodeling and positioning. To ensure that our standard nuclei resuspension conditions (buffer containing 75 mM NaCl and 0.5 MgCl2) maintained folded structures, we repeated this experiment for the 1 min modification time point, using a nuclear resuspension buffer containing spermidine  as polyamines have been shown to stabilize higher-order chromatin structures [It is well known that the revisiae –62. Althcclusion –65. It sructures –69. We fS. cerevisiae at least transiently expose histone surfaces normally buried within the canonical nucleosome. For example, when present, the thiol group of H3 C110, which is normally buried within the 4-helix bundle comprising the H3–H3 interface at the center of the nucleosome, was found to be accessible to organomercural-agarose columns in nucleosomes derived from transcriptionally active genes in yeast [2 hexamers and (H3/H4)2 tetramers may transiently exist in genes being transcribed by RNAPII [Previous reports indicated that nucleosomes isolated from transcriptionally active chromatin in in yeast , 71. In y RNAPII , 72, 73.S. cerevisiae. We anticipated that this site would be more exposed in open, transcribed euchromatic regions of the genome, but also exhibit reduced exposure, especially within known regions of heterochromatin and inactive genes. However, we find similar average accessibilities for nucleosomes within 0.5 kb upstream to 1.5 kb downstream with respect to the TSS of all yeast genes, including those within sub-temomeric heterochromatin regions. Somewhat greater variability in accessibility was found in ‘promoter proximal’ nucleosomes located within the first 500 bp of genes, but even in this case the vast majority of genes exhibited accessibilities that differed by less than twofold. We designed our conditions to maintain the integrity of yeast chromatin in isolated nuclei, consistent with in vitro work demonstrating maximally folded chromatin states at salt concentrations commensurate with those used in this study [S. cerevisiae nuclei. Our findings are consistent with studies indicating minimal long-range inter-nucleosomal interactions in S. cerevisiae and that regulation of S. cerevisiae chromatin is regulated largely by local interactions [We employ a new method  to probeis study . Moreoveis study , and fouractions , 75, 76.Our data also indicate that diffusion of BM through the nucleus occurs much more rapidly than cysteine modification, because accessibility differences are not affected by extent of modification. We find a high degree of correlation between AP data sets from 10 s and 10 min of modification (Pearson correlation 0.86), despite the large difference in the extent of modification between these two experiments  and more directly, the level of chromatin remodeling during these two states.Given the similar role to RSC in promoting gene expression, it was somewhat surprising to observe a negative correlation between SWI/SNF and nucleosome surface accessibility, similar to that observed between accessibility and ISWa. We note that RSC and SWI/SNF exhibit a negative correlation among themselves (− 0.337), and that ISW1 and SWI/SNF occupancies are well correlated (0.682). Because SWI/SNF is much less abundant than RSC and associated with a distinct cohort of genes compared to RSC, it is possible that the negative correlation is due to a lack of correspondence between the most accessible genes and those with greatest SWI/SNF density. SWI/SNF is an important component for transcriptional activation –97. TherIn stark contrast to the H2B S116C external surface site, we find little evidence of accessibility of two internal nucleosome sites installed on H3. Two residues on H3, H3 102 and H3 110, were selected to investigate the accessibility of internal nucleosome surfaces. Interestingly, prior studies have suggested that the H3–H3 interface in nucleosomes from transcriptionally active regions of chromatin is highly accessible in yeast, as well as chicken and HeLa cells , 101. ThWe also probed for H3 102, which is located at the H2A–H2B/H3–H4 interface and is exposed following removal of H2A–H2B dimer. RNA polymerase II transcription occurs with transient disruption of the canonical nucleosome structure, resulting in H2A–H2B dimer loss and, in the case of very high densities of RNAPII, even octamer eviction , 102–105In addition, such noncanonical nucleosome structures are expected to survive nuclei preparation and subsequent steps including washing of MNase digested chromatin fragments bound to the streptavidin-agarose resin, as H3–H4 tetramer dissociation from DNA occurs at ~ 1.2 M NaCl –120. TheS. cerevisiae genome, including sub-telomeric regions, suggesting limited inter-nucleosomal interactions mediated by these surfaces. Furthermore, we find that the accessibility is modestly correlated with chromatin remodelers: RSC, ISW1a and SWI/SNF. RSC and accessibility exhibit a positive correlation whereas ISW1a and SWI/SNF exhibits a negative correlation. These results may indicate that for yeast, proper nucleosome spacing may be a primary driver of yeast chromatin structure and gene regulation. Consistent with this idea, we find that the accessibility of nucleosomes closest to the promoter is more dynamic than nucleosomes found upstream of the promoter and further downstream within gene bodies. Conversely, we find no evidence of widespread exposure of internal nucleosome surfaces suggesting that disruption of the canonical nucleosome structure and subsequent exposure of these sites is an extremely transient process.Overall, we find that an external nucleosome surface site, H2B S116C, is widely accessible throughout the Additional file 1: Figure S1. Differences in accessibility scores (AP/input) seem to be influenced more by differences in AP reads (right) rather than input (left). Input and AP reads were plotted for each quintile. The range of AP scores is greater than that of the inputs which do not differ much between quintiles. Figure S2. Weak correlation between H2B S116C accessibility and nascent transcript (NET-seq) density. Genes were sorted according to NET-seq score and grouped into quintiles where quintile 1 contained the genes with the greatest nascent transcript density and quintile 5 the least. AP/input scores were then plotted for genes preserving the NET-seq sorting and grouping. All time points exhibit similar ordering and profiles and the difference between quintiles is minor. Figure S3. H2B S116C accessibility and RNAPII density are not correlated for subnucleosome length fragments (90–135 bp). Genes were sorted according to rpb3 density and grouped into quintiles where quintile 1 contained the genes with the greatest rpb3 density and quintile 5 the least as in Fig. Figure S4. Nucleosome external surface accessibility does not exhibit obvious correlation with genes most affected by Rsc8 depletion. Genes were sorted according to change in NDR width following Rsc8 depletion  to ensure folded chromatin structures were maintained. Accessibility scores were plotted according to remodeler density as in Fig. Figure S6. Extent of desired MNase digestion for internal H3 mutants. To preserve noncanonical nucleosome structures, accessibility of H3 mutants were investigated using less digested chromatin where the predominant species was greater than core particle size (~ 147 bp). In S6A, digestion yielded predominantly mononucleosomes with ~ 20 bp of total linker DNA with the major peak being ~ 170 bp. In S6B, digestion conditions were used generating core particle length species with the major peak at ~ 150 bp, similar to digestions for the H2B surface mutant. Table S1. Sequencing summary statistics for H2B S116C biotin-maleimide time course experiment. The total number of aligned reads for all samples is on the same order of magnitude [Table S2. Fold enrichment and fraction of modified nucleosomes from WT and H2B S116C yeast nuclei. Fold enrichment was calculated by dividing AP/input of the mutant by that of the wild type. The AP/input ratio is determined by calculating the ratio of yeast:mouse aligned reads for each sample. The percentage of modified nucleosomes was determined by dividing the amount of AP DNA by the corrected input as determined in Tables S1, S2. All time points reveal signal above background with 1 min and 10 min time points averaging a fold enrichment of 43. Percent of modified nucleosomes is corrected by subtracting the average apparent enrichment in the WT samples (0.08). Rep indicates replicate experiments. Bolded numbers are plotted in Fig. Table S3. Sequencing summary statistics for H2B S116C biotin-maleimide time course replicate experiments. The total number of aligned reads for all samples is on the same order of magnitude [Table S4. Promoter-proximal nucleosomes exhibit greater differences in accessibility as compared to nucleosomes across genes. AP/input accessibility scores were calculated for genes in two different windows, − 500 to + 1500 (A) and TSS + 500 (B). Accessibility scores are more dynamic for promoter-proximal nucleosomes as compared to the nucleosomes throughout the region − 0.5 kb to + 1.5 kb with respect to the TSS. Table S5. Pearson correlation coefficients for accessibility scores within 500 bp of TSS. The average AP/input scores for each gene were determined from TSS to 500 bp and the Pearson correlation was determined between each data set. Overall, these values are similar to those found from TSS to TTS . Additionally, genes were grouped into quintiles based on remodeler density. Pearson correlations were then determined for accessibility and remodeler density scores for quintiles 1 and 5 (B) and quintiles 2–4 (C). Table S7. Sequencing summary statistics for internal nucleosome surface mutant sites. Yeast nuclei were modified and input and AP-selected nucleosome libraries prepared and sequenced as described in the text. 1 ng of mouse DNA was added as a normalization control to all samples. Note the yeast/mouse DNA ratio in wild-type samples exceeds that of mutants suggesting no enrichment above background.on (ref. ). This gagnitude . Howeveragnitude . However


CD69 gene encodes a C-type lectin glycoprotein with immune regulatory properties which is expressed on the cell surfaces of all activated hematopoietic cells. CD69 activation kinetics differ by developmental stage, cell linage and activating conditions, and these differences have been attributed to the participation of complex gene regulatory networks. An evolutionarily conserved regulatory element, CNS2, located 4kb upstream of the CD69 gene transcriptional start site, has been proposed as the major candidate governing the gene transcriptional activation program. To investigate the function of human CNS2, we studied the effect of its endogenous elimination via CRISPR-Cas9 on CD69 protein and mRNA expression levels in various immune cell lines. Even when the entire promoter region was maintained, CNS2-/- cells did not express CD69, thus indicating that CNS2 has promoter-like characteristics. However, like enhancers, inverted CNS2 sustained transcription, although at a diminished levels, thereby suggesting that it has dual promoter and enhancer functions. Episomal luciferase assays further suggested that both functions are combined within the CNS2 regulatory element. In addition, CNS2 directs its own bidirectional transcription into two different enhancer-derived RNAs molecules (eRNAs) which are transcribed from two independent transcriptional start sites in opposite directions. This eRNA transcription is dependent on only the enhancer sequence itself, because in the absence of the CD69 promoter, sufficient RNA polymerase II levels are maintained at CNS2 to drive eRNA expression. Here, we describe a regulatory element with overlapping promoter and enhancer functions, which is essential for CD69 gene transcriptional regulation.The  Gene expression is coordinated by an interplay among different regulatory regions, which work together to determine precisely when, where, and how they are transcribed. The orchestration of gene transcriptional programs relies on two main players: promoters and distal regulatory elements. Whereas promoters are located immediately upstream of gene transcriptional start sites (TSS), distal regulatory elements act over long genomic distances in an orientation-independent manner. Promoters are thought to be sufficient to recruited RNA polymerase II (POLR2A) and establish the start site and directionality of transcription. Basal expression from promoters is modulated by a combination of signals from distal regulatory elements, which determine the final transcriptional output.Traditionally, enhancers and promoters have been considered to be two different regulatory elements on the basis of chromatin characteristics, such as DNase hypersensitivity and chemical modification of histone tail residues. Promoters are characterized by open chromatin regions bearing H3K4me3 whereas H3K27ac, H3K4me1 and P300 are often associated with enhancers . High-thcis or trans    in regulsomally) , severalsomally) .In vivo studies in CD69 KO mouse models have demonstrated an important role of CD69 in the pathogenesis of different inflammatory and autoimmune diseases  relative to the promoter. These four elements display DNase hypersensitivity and bear regulatory cell-type specific histone marks that undergo changes during development and activation .Jurkat T cells and C1R B cells were cultured in RPMI medium supplemented with 10% fetal calf serum, 1% L-glutamine and 100 U/ml of penicillin-streptomycin (Gibco). Cells were cultured under standard conditions  for 20 min at 4oC and analyzed with a FACSCanto flow cytometer  and FACSDiva software . Data were analyzed in FlowJo .To determine the CD69 surface expression levels in different assayed conditions, we cultured 10CD69 promoter and CNS2 region were PCR amplified with NZYTaq II 2x Colorless Master Mix . The primer sequences used are listed in CD69 promoter or CNS2 amplification products were cloned into the pGL3b plasmid  upstream of the luciferase ORF with Gibson Assembly 2x Master Mix , according to the manufacturer’s instructions. NcoI-digested pGL3b plasmid and PCR-amplified fragments containing vector-overlapping ends, were mixed 1:1 with Gibson Assembly Master Mix and incubated for 10 min at 50°C. The CNS2 region was cloned into the pGL3b-CD69 promoter-containing vector via the HindIII restriction site. Cloning products were transformed into DH5α competent E. coli cells . Vector integrity and fragment directionality were confirmed by Sanger sequencing. The plasmids with the correct sequence were purified with Qiagen Mini Kit and used for transfection.DNA was isolated from Jurkat T cells with 10% SDS-containing buffer followed by ethanol purification. The Jurkat T cells were transfected with 100 ng of each construct and 2 ng of the Renilla plasmid , which was used as an internal control for transfection efficiency. Electroporation was performed with the Neon transfection System (Thermo Fisher) according to the manufacturer’s instructions. One day after electroporation, cells were stimulated with 10 ng/ml phorbol 12-myristate 13-acetate (PMA) and 500 ng/ml ionomycin for 24 h. The cells were then lysed and luciferase activity was measured with a Dual luciferase kit  and an Orion II microplate luminometer . The activity of each vector was expressed as the ratio between firefly and Renilla luciferase luminescence. Data are presented as mean ± SEM of three different experiments, each with two technical duplicates.https://benchling.com/. The selected sequences and their genomic positions are listed in The CRISPR-Cas9 system was used to eliminate the promoter and the CNS2 sequences by deletion. Two simultaneous double strand breaks resulted in a loss of the intervening DNA sequence without large insertions/deletions via non-homologous end-joining repair . Sequenc5 cells were co-transfected with Prom 3′sgRNA and Prom 5′sgRNA or CNS2 3′sgRNA and CNS2 5′sgRNA with the Neon Transfection System . cDNA synthesis was performed with a combination of random hexamers and oligodT primers with an NZY First Strand cDNA Synthesis Kit (MB125). Gene expression was evaluated by probe-based qPCR analysis  on QuantStudio3 (Thermo Fisher) by using a predesigned qPCR assay from Integrated DNA Technologies (IDT) (Assay ID: Hs.PT.58.20340459). Human β2microglobulin (assay ID: Hs.PT.58v.18759587) mRNA expression was used for normalization. Relative gene expression was calculated with the ΔCt method.Total RNA was obtained with Trizol . After extraction, the RNA was treated with RQ1 RNase-free DNAse  for 1 h at 37°C with 1 μl of DNase per μg RNA in a total volume of 50 μl. The RNA was then re-purified with phenol/chloroform and precipitated with 70% ethanol. A total of 3–5 μg of RNA was reverse-transcribed in a total volume of 20 μl with random primers and SuperScript II reverse transcriptase (Thermo Fisher) for 1 h. Because eRNAs do not undergo splicing, an identical reaction of each sample, with the same quantity of RNA, but without RT enzyme (-RT control), was used to evaluate DNA contamination. The cDNA was diluted to 100 ng/μl on the basis of the initial RNA input. A volume of 1 μl of diluted cDNA was used in a 10 μl qPCR reaction with NZY qPCR Green Master Mix 2x (MB221). Quantification was performed on QuantStudio3 (Thermo Fisher) with the primers listed in 7 cells, treated for 30 min with PMA + IO, washed and fixed with 37% paraformaldehyde for 10 min. The cross-linking reaction was quenched with 1,375 M glycine before cell lysis. For chromatin sonication, 20 pulses of 30 s were applied with Bioruptor Next Generation instrument (Diagenode). Sheared chromatin was incubated with 10 μg of the following antibodies: anti-RNA polymerase II CTD repeat YSPTSPS (phospho S2) ; antiH3K4me3  and anti-IgG ; at 4°C overnight. Chromatrap Spin Columns were used for immunoprecipitation. DNA from the eluted immunocomplexes was purified with a QIAquick PCR Purification Kit (Qiagen) columns. Real-time PCR analysis was performed with QuantStudio3 (Thermo Fisher) and NZY qPCR Green Master Mix, ROX plus (NZY MB222). The Ct values of IgG controls were used for normalization. Primer sequences , according to the manufacturer’s recommendations. Briefly, 1 × 10equences  were desCharacterization of CNS2 non-coding transcripts was performed with a GeneRacer RACE kit from Thermo Fisher Scientific according to the manufacturer’s instructions. In brief, 5 μg of total RNA from 30 min stimulated Jurkat T cells was treated with tobacco acid phosphatase to remove 5′cap and expose 5′ phosphates, thus enabling the ligation of the GeneRacer RNA Oligo with a known specific sequence. cDNA was generated by reverse transcription with SuperScript II RT and either random hexamers or GeneRacer Oligo dT primer. A 1 μl volume from a 1/5 dilution of the RACE product was used in PCRs with GeneRacer 5′ Primer annealing to 5′ GeneRacer RNA Oligo and different gene specific 3′ primers in a 20 μl total reaction. To avoid non-specific bands, we re-amplified 1μl of PCR product with GeneRacer 5′ Nested Primer and a second inner specific primer. Only cDNA containing the GeneRacer RNA oligo was amplified. A similar procedure was followed for 3′ end characterization, with GeneRacer 3′ primer and GeneRacer 3′ Nested primer. Amplification bands were analyzed by Sanger sequencing. Specific primer sequences used for sense and antisense-RNA characterization are listed in To determine the 3′ end of the different eRNA species transcribed from the CNS2 region, we used a previously described RNA circularization method . Brieflyp < 0.05 were considered as statistically significant: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ****p < 0.0001. Non-significant differences are indicated as ns.All data was represented and analyzed with GraphPad Prism 7 software. Significant differences between the different assayed conditions were tested with an unpaired two-tailed t-test. Differences with On the bases of transient transfection experiments and transgenesis assays, we previously defined four non-coding regions upstream of the CD69 promoter as key regulatory elements required for proper gene transcriptional regulation during T cell development and activation . ENCODE To further investigate CNS2 function, we applied CRISPR-Cas9 technology to delete 1432 bp of DNA containing the entire conserved CNS2 region . We obtained several cell lines with homozygous deletion of the CNS2 element (CNS2-/-) and also produced cell lines with a promoter deletion  as a negative control for CD69 expression .To assess the effects of CNS2 deletion on CD69 expression, we measured surface protein levels in basal conditions and at their peak after activation with PMA/IO for 24 h. For Jurkat T cells , very loTogether, these results indicate that CNS2 acts as part of the promoter and theirs cooperative function is necessary for transcription, since none of them can act on their own to induce CD69 expression.To further investigate the role of CNS2 in CD69 transcriptional activation we assayed its ability to work independently of its orientation, as traditionally described for distal regulatory elements. Thus, we examined whether an inverted CNS2 might enhance CD69 promoter activity in a luciferase reporter assay . The CD6We also measured the effect of CNS2 inversion in its original genomic context, by analyzing CD69 expression in different CNS2-/ + edited clone, which were found to contain the remaining CNS2 copy in an inverted position . We deteTherefore, a combination of experiments in genomic and episomal contexts supports the convergence of traditional promoter and enhancer properties in the CNS2 regulatory element.High-throughput analysis of gene regulatory regions have revealed that distal elements, preferentially those marked with H3K4me1 and H3K27ac, are commonly transcribed bidirectionally into a newly described class of RNA molecules.In agreement with a putative promoter function of CNS2, publicly available data show overlapping RNA-seq peaks, H3K4me1 and H3K27ac marks and POLR2A binding within this region, which may indicate transcriptional activity . In addiTogether, these data suggest that the CNS2 element might be transcribed into at least three eRNAs molecules. To explore this possibility we performed 5′ and 3′ Rapid Amplification of cDNA Ends (RACE) reactions on total RNA from 30 min-stimulated Jurkat T cells. 3′ RACE reactions did not reveal transcript 3′ ends, thus suggesting that none of the potential transcripts were polyadenylated, in agreement with evidence indicating a lack of polyadenylation in this type of RNA molecules . HoweverQuantification and kinetics analysis of CNS2 transcription was assayed with RT-qPCR. DNase-treated RNA was reverse-transcribed with random primers and quantification was performed with the primer pairs highlighted in red at Because occupancy by POLR2A and H3K4me3 has been described as a feature of regulatory elements with promoter ability, we analyzed these parameters at the CNS2 region, as well as how they affected by promoter deletion.Chip experiments of POLR2A binding kinetic at CD69 promoter and CNS2 regulatory element showed a low occupancy at both regions before 30 min of stimulation . Chip exNext, to explore whether the absence of the promoter affected eRNA transcription at CNS2 region, we measured the forward and reverse eRNA levels in the basal state and after a 30 min induction with PMA/IO in WT, PROM-/- and CNS2-/- cell lines. The detection was performed with qPCR using primer pairs located outside the deleted CNS2 region , has rapidly become a widely accepted feature of all active enhancers , with a CNS2 transcription occurs bidirectionally from two independent TSS at similar levels, with no gene direction bias. We demonstrated that the induction of eRNAs precedes CD69 mRNA expression, and their production is not dependent on the presence of the promoter region or its activity. Thus, as with the α-globin locus , the abiThe original contributions presented in the study are included in the article/PL and JR-A designed the study. JR-A performed the experiments. MF, EL, and LN provided assistance with experimental design and data analysis. SR-P and RT-R helped with CRISPR-Cas9 experiments. JR-A and PL wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Elongation factor 1), NusA (N-utilization substance A), TFS4, RIP and Eta, and (ii) their evolution and phylogenetic distribution across the expanding tree of Archaea.The archaeal RNA polymerase (RNAP) is a double-psi β-barrel enzyme closely related to eukaryotic RNAPII in terms of subunit composition and architecture, promoter elements and basal transcription factors required for the initiation and elongation phase of transcription. Understanding archaeal transcription is, therefore, key to delineate the universally conserved fundamental mechanisms of transcription as well as the evolution of the archaeo-eukaryotic transcription machineries. The dynamic interplay between RNAP subunits, transcription factors and nucleic acids dictates the activity of RNAP and ultimately gene expression. This review focusses on recent progress in our understanding of (i) the structure, function and molecular mechanisms of known and less characterized factors including Elf1 ( Transcription — the DNA template-dependent synthesis of RNA — is essential to life. The overall structure of the molecular machine that drives transcription, RNA polymerase (RNAP), is universally conserved in all domains of life, including Bacteria, Archaea and Eukarya. But whereas bacteria and archaea use a single RNAP to transcribe all genes, eukaryotes have compartmentalized the transcription space into distinct subsets of genes that are transcribed by three and five different enzymes in animals and plants, respectively. Most features of archaeal transcription — including the RNAP, general transcription factors that govern its activities and the DNA sequence elements with which they interact — are closely related to the eukaryotic RNAPII system. The archaeal transcription apparatus is likely to resemble the ancestral version of eukaryotic RNAPII and thus worthy of our attention not only because it is interesting in its own right, but also because it serves as highly tractable and thus extremely valuable model system.Diapherotrites, Parv-, Aenigm-, Nano-, Nanohaloarchaeota, and others), TACK  and ASGARD  .,30.29,30derstood . Recent derstood ,43.To load the DNA TS into the RNAP active centre, the DNA strands are locally melted in a region 12 bp upstream of the TSS. This process is accompanied by large-scale conformational changes of the PIC that are referred as closed (CC) to open complex (OC) transition ,44–46. TS. solfataricus, unlike the remaining general transcription factors. Since TFE is a general factor and its activation of transcription varies considerably between promoters, the depletion of TFEβ has the potential to alter the RNA synthesis globally in S. solfataricus. In essence, modulation of OC formation provides an opportunity for the regulation of transcription, a mechanism which has previously been shown to operate in bacterial and eukaryotic transcription systems [H. volcanii TFEβ and indeed all haloarchaeal homologues lack the [4Fe–4S] cluster that is essential for S. solfataricus TFEβ function. Nevertheless, in line with TFEβ being a bona fide general transcription factor, the deletion of the H. volcanii TFEβ results in the misregulation of approximately one-third of all transcription units [The TFEβ subunit has a patchy phylogenetic distribution and is present in most group I euryarchaeota (with the exception of Thermoplasmata that lack both α- and β-subunits), TACK (missing in Korarchaeota) and ASGARD superphyla, but is absent from species from group II Euryarchaeota and DPANN ,51. TFEβ systems . Intereson units . The groon units ,36,48. Ton units ,52.Pyrococcus have shown that once the nascent RNA exceeds 5 nt in length, it collides with the TFB B-reader and B-linker domains, disrupting the interaction with and displacing TFB from the active site of RNAP [in vitro [in vivo determined using chromatin immunoprecipitation (ChIP-seq) [All DPBB RNAPs face similar mechanical engineering problems when entering the early elongation phase of the transcription cycle. A network of high affinity interactions between DNA-bound initiation factors  and RNAP are important to enable efficient recruitment to the promoter. However, these interactions need to be disrupted for RNAP to escape the promoter and enter processive transcription elongation. Structures of the initially transcribing complex of yeast RNAPII as well as recent cross-linking studies in  of RNAP ,53,54. P of RNAP . Once thin vitro  and it ihIP-seq) . The asshIP-seq) . The glohIP-seq) .NusGN-terminal) domain and the template DNA enhances the annealing of the TS and NTS at the upstream edge of the transcription bubble and thereby suppress backtracking and pausing, which overall improves processivity and increases the elongation rate [Kyrpides–Ouzounis–Woese) domains. In bacteria and probably in archaea, the KOW domain interacts with the ribosomal protein S10 thereby physically coupling RNAP and the first co-translating ribosome, coordinating transcription and translation [A subset of evolutionarily conserved regulatory factors assist RNAP during transcription elongation by modulating the elongation rate and/or by improving the processivity (defined as polymerized nucleotides per initiation). Elongation factors belonging to the Spt4/5 family  stimulate transcription by binding to the RNAP clamp-CC on one side of the DNA-binding channel and to the RNAP gate loop on the other ,55–58. Tion rate . Bacteriion rate ,59. In anslation .Elongation factor 1) is a transcription elongation factor conserved in eukaryotes and several archaeal species [in vitro transcription assays showed that Elf1 inhibits transcription elongation, possibly by interacting with downstream DNA via its N-terminal tail [Structural insight into complete yeast transcription ECs encompassing RNAP, DNA, TFIIS, Spt4/5 and Elf1 reveals a striking reoccurring theme, by which the latter two elongation factors form entry and exit tunnels for the DNA and RNA strands ,59. Elf1 species . Homolog species ,63,64. E species . ChIP-Se species , and in N-utilization substance A) interacts with the RNAP via the NusA N-terminal domain (NTD), and with RNA in at least two distinct ways that have different effects on transcription elongation. Interactions of the NusA S1 domain with an RNA hairpin enhances transcription pausing. In contrast, interactions between the NusA KH1 and -2 domains and the RNA nut (N-utilization target) sequence promote antitermination on ribosomal RNA operons [The origin of the RNAP stalk — a hallmark feature of archaeal and eukaryotic RNAPs — is opaque, but a combination of recent structural and functional studies has revealed a striking resemblance to a bacterial elongation factor. The bacterial NusA  or by roadblocks such as DNA-bound proteins (e.g. chromatin proteins) or DNA lesions –85. UponMethanopyrus kandleri, TFS is conserved in all archaeal species [While archaea and eukaryotes utilize evolutionary related factors, TFS and TFIIS, respectively, the non-homologous bacterial Gre factors carry out the same function while providing a stunning case of convergent evolution  Table 1Table 1.  species . Both TF species . All tra species –91.S. solfataricus includes four apparent TFS paralogues (TFS1 to 4). While TFS1 carries out the canonical transcript cleavage function, TFS4 has evolved into a highly potent RNAP inhibitor [tfs4 gene is not detectable under standard growth conditions. However, infection with STIV  leads both to a dramatic increase in TFS4 expression, and induces a dormant state in the infected cell [Several archaeal species encode more than one TFS paralogue, e.g. the genome of the crenarchaeon nhibitor . TFS4 shnhibitor ,93. In ated cell   and S. solfataricus are witnessed by the presence of several ATV genome-derived CRISPR spacers in the hosts’ CRISPR arrays, including sequences mapping to a small gene called ORF145 [RNAP inhibitory protein), binds tightly to the inside of the DNA-binding channel of the host RNAP, thereby locking the RNAP clamp into a fixed position [TFS4 is a host encoded archaeal transcription factor that inhibits transcription on a global level in response to viral infection . Surprisd ORF145 . ORF145,position . This coThe interaction of RIP with RNAP differs in a fundamental way from TFS4, but the outcome is surprisingly similar. RNAP-nucleic acid complexes are destabilized and transcription initiation and elongation are inhibited. Because RIP, like TFS4, binds directly to RNAP, both host and virus promoters are inhibited in a global fashion. While the regulatory rationale behind this mechanism is still unclear, it is likely that the inhibition of transcription attenuates or even prevents the activation of the type IIIb CRISPR–Cas system and expression of anti-ATV CRISPR RNAs, while still enabling transcription on viral genes required for virus proliferation .In vitro and in vivo studies have shown that euryarchaeal RNAPs are capable of terminating transcription directed by short poly-U stretches and unaided by exogenous factors, a property reminiscent of the eukaryotic RNAPIII system  and crenarchaeal  species using Term-seq has provided an overview of RNA 3′-ends in vivo on a genome-wide scale [M. mazei and S. acidocaldarius that are not accounted for in current models of transcription termination [An unbiased mapping of RNA 3′-ends in a euryarchaeal-  is a DEAD box helicase that is recruited to stalled transcription complexes via interactions with the DNA immediately upstream of the arrested RNAP [Little is known about factor-dependent transcription termination mechanisms in archaea. In ted RNAP ,112. Etated RNAP ,114.in vitro, a thorough understanding of how these factors influence transcription in vivo just starts to emerge. An integrated, genome-wide view of transcription in archaea shows promise to bring to light more sophisticated mechanisms of transcription regulation beyond the initial recruitment, probably involving promoter escape and transcription processivity during the elongation phase of the transcription cycle.RNAPs are among the most well-studied molecular machines of life. The initiation phase of transcription has been characterized over the last two decades. These studies have elucidated the structure, function and detailed mechanisms that govern the archaeal PIC. Many studies have identified positive and negative transcription factors that enhance or prevent its recruitment of the PIC. While the structure and mechanisms of elongation factors like TFS and Spt4/5 are reasonably well understood Recent discoveries of virus and host encoded global inhibitors of RNAP transcription have shed light on novel molecular mechanisms and regulatory strategies that seemingly play a key role in the host–virus arms race. Finally, our field is coming to terms with the fact that the chromatin structure, histone-based or otherwise, plays an important role in gene regulation in archaea. Novel approaches, including high-throughput sequencing techniques, live cell imaging, as well atomic-resolution cryo-electron microscopy, will lead to key discoveries and a new dawn of archaeal gene expression, with an ever more detailed understanding of transcription from the molecular to the systems level.The catalytic centre or the archaeal RNAP is formed between two DPBB.in vitro.Combined ChIP-seq and RNA-seq analyses reveal the genome-wide organization of transcription and generate new mechanistic hypotheses that can be tested The general transcription initiation factor TFEβ has the potential to regulate transcription globally in response to environmental stresses.Transcription elongation is modulated by RNAP subunits (Rpo4/7) and transcription factors .The RNAP stalk subunit Rpo7 shows an intriguing structural and functional similarity to the bacterial pausing/antitermination factor NusA.S. solfataricus.The expression of the transcript cleavage factor paralogue TFS4 is induced by STIV virus infection and acts as a powerful global inhibitor of RNAP in The ATV virus-encoded regulator RIP binds directly to RNAP and results in the global inhibition of transcription; thus both host- and virus-encoded RNAP-binding transcription factors globally inhibit or attenuate total RNA synthesis.Genome-wide mapping of transcript 3′-ends changes our view on the sequence context of archaeal transcription terminators.RNAPs stalled by DNA-damage can be efficiently removed by the termination-like factor Eta.