We trained a support vector machine classifier based on MPRA data to predict candidate silencers in over 100 human and mouse cell or tissue types. The predicted candidate silencers exhibit characteristics expected of silencers. Leveraging promoter-capture HiC data, we find that over 50% of silencers are interacting with gene promoters having very low to no expression. Our results suggest a general strategy for genome-wide identification and characterization of silencer elements.The study of gene regulation is dominated by a focus on the control of gene activation or increase in the level of expression. Just as critical is the process of gene repression or silencing. Chromatin signatures have identified enhancers, however, genome-wide identification of silencers by computational or experimental approaches are lacking. Here, we first define uncharacterized  cis-regulatory elements (CREs) in human and mouse genomes likely containing silencer elements, and test them in cells using massively parallel reporter assays to identify silencer elements that showed silencer activity.Identification of silencer elements by computational or experimental approaches in a genome-wide manner is still challenging. Here authors define uncharacterized  In 1985, the yeast mating-type loci revealed that distal silencer elements could control gene expression from afar5. The role for distal silencer elements in mammals was demonstrated shortly thereafter through a silencer element located several kilobases upstream of the rat insulin gene6. It would be a decade later before key experiments identifying a silencer in the intron of the mouse and human CD4 genes would revealed the important role that silencers can play in lineage specificity and cell fate determination, as this silencer represses CD4 expression in CD8+ T cells8. Later several studies identified genomic sequences with silencer properties, which are opposite of enhancers across many species15. While dozens of mammalian silencers have been identified, these elements are largely understudied, possibly due to our biased focus on gene upregulation and the poor understanding of those elements with non-promoter locations.It has long stood that repressive transcription factors can bind to the promoters of genes through silencer elements to inactivate gene expression4. Just as promoter elements can switch states from activating to repressive due to the presence of silencer elements and the factors bound, many distal cis-acting regulatory elements can have dual activity, depending on the set of conditions and cell type. Several previous studies showed this dual activity of enhancers switching to gene repression, or silencing, in another cell type and vice versa19. For example, GATA1 binding to a cis-acting element upstream of the Gata2 promoter displaces the activating GATA2-bound factor and represses Gata2 expression19. GATA switches at distal elements such as this are common during hematopoiesis18, and are context, cofactor, and concentration dependent22. Furthermore, the repressive activity of GATA1 can induce changes in chromatin looping. For example, during hematopoietic differentiation, an upstream enhancer is bound by GATA2 to activate expression of the Kit gene in multipotent cells. Subsequently during lineage commitment, GATA1 binds to inactivate the enhancer and also binds a downstream silencer to repress Kit expression, which results in loss of the enhancer loop and gain of a silencer loop with the promoter23.Classic studies of promoter silencers showed that these elements reside next to activating sequencescis-regulatory elements (CREs). These elements, and the factors that bind to them, are important for the nuanced output of RNA levels across cell types. Identification of distal CREs and our understanding of the regulation of gene expression in mammalian genomes has been greatly facilitated by the genome-wide mapping of element-specific histone modifications or transcription factors, e.g., histone H3 lysine 4 monomethylation (H3K4me1) distinguishing enhancers24 and CTCF binding to a subset of candidate insulators25. While the presence of repressor sequences within CREs such as promoters has recently become more evident by tiling millions of oligos across these regions and testing by massively parallel reporter assays (MPRA)26, distinct annotations for distal silencer elements in the human and mouse genomes are still missing from our cis-regulatory lexicon.Outside of promoter regions, silencers along with enhancers and insulators create a complex array of distal Our goal is to identify distinct silencer elements distal to the genes they regulate. In order to identify silencer elements, first we devised a simple subtractive analysis approach based on DNase hypersensitive sites (DHS) and other known CREs to find the DHS elements lacking known chromatin marks belonging to promoters, enhancers, and CTCF-bound insulators, and we term these elements as uncharacterized CREs. We tested ~7500 of these uncharacterized CREs for silencer activity in K562 cells using MPRA to identify silencer elements. Using MPRA data, we trained a support vector machine (SVM) classifier to predict potential candidate silencer elements from untested uncharacterized CREs in 82 human and 22 mouse cell or tissue types. This results in a catalog of >1.7 million candidate silencer elements in the human genome and a second catalog of ~1 million candidate silencer elements in the mouse genome. We find that candidate silencer elements are enriched for motifs of known repressive transcription factors, and de novo motifs for potential cognate transcriptional repressors. We are able to validate our predictions by direct silencer interactions with repressed target genes, and functional testing via reporter assays and CRISPR genome editing. Candidate silencer elements are often enriched for disease-associated variants in expected cell types or lineages. These catalogs, which will require more intensive study and validation from the field, should aid our understanding of gene expression through negative regulation of expression.cis-regulatory elements should be present within these regions of open chromatin. We can generate open-chromatin data either from DNase-seq (DNaseI hypersensitive sites sequencing) or from ATAC-seq (assay for transposase-accessible chromatin using sequencing) and other CREs data from ChIP-seq (chromatin immunoprecipitation coupled with sequencing) for any cell type or organism. In this SSA approach, we subtract enhancers (H3K4me1 peaks), promoters (2.5 kb window around TSS and H3K4me3 peaks), and potential insulators (CTCF sites) from open chromatin (DHS) in a cell-type specific manner, and assign the remaining DHS as uncharacterized CREs. We hypothesize that a subset of these uncharacterized CREs will contain silencer elements. Using this SSA approach, we determined 2,315,105 uncharacterized CREs in the human genome spanning across 82 cell types from the Roadmap27 and ENCODE consortia28, and 1,299,866 elements in the mouse genome for 22 cell types from the ENCODE consortium28  approach to determine DHS elements lacking known chromatin marks belonging to promoters and enhancers, or CTCF binding for insulators, and we term these elements as uncharacterized CREs Fig. . Other prs H3K4me peaks, p32. In addition to motif analysis, we also checked whether these uncharacterized CREs are enriched for experimentally bound repressor TF-binding sites (TFBS). We intersected uncharacterized CREs with known repressor TFBS within the same cell type using ChIP-seq data for REST, YY1, ZBTB33, SUZ12, and EZH2. We found that 73%, 61%, 49%, and 58% of total uncharacterized CREs in K562, H1, GM12878, and HEPG2 cells, respectively, are typically enriched for one of the above mentioned known repressor TFBS40  bind at CREs and regulate gene expression in response to external cues. Activator TFs bind at enhancers to enhance expression, and repressor TFs bind at silencer elements and repress gene expression. We hypothesized that uncharacterized CREs will contain silencer elements. To validate this hypothesis, we performed TF motif enrichment analysis on uncharacterized CREs across cell types and found a well-known transcriptional repressor, REST is consistently enriched across all tested cell types Fig. . Motifs S40 Fig. . This en41 approach in K562 cells  using the STARR-seqlls Fig. . The humons Fig. . We founons Fig. . Of unchons Fig. . We alsoons Fig. . Though ons Fig. .Fig. 2FuTo validate the silencer activity from MPRA experiments, we tested three silencer elements via traditional reporter assays by cloning the silencer elements upstream of SCP1 core promoter + luciferase gene construct in K562 cells. We noticed a significant decrease in reporter gene expression for three out of three tested silencer elements compared with two random controls Fig. .51. We targeted sgRNAs to these uncharacterized CREs regions, hypothesizing that deletion of these elements would result in increased gene expression of the target gene if acting as a silencer element. We observed a general trend of significant increases in gene expression upon CRISPR-Cas9 targeting of these uncharacterized CREs in three of the five elements tested 56, we trained the classifier based on MPRA functional screening data to find candidate silencer elements from untested uncharacterized CREs in K562 and other cell and tissue types  models can predict CREs from a given set of nucleotide sequencespes Fig. . We chospes Fig. . We thenpes Fig. .Fig. 3Ov64. The enriched motifs are distinct from all uncharacterized CREs  data from 17 cell types and found them to largely be in a hypermethylated state  lower methylation for enhancers and DHS than at candidate silencers. Candidate silencers are on average 71.5% methylated compared with 43% methylation in active enhancers, and 46% methylation in DHS elements across the 17 cell types   Fig. . This unes) Fig. . This an73 , and two in mouse ) cell types  to find their interacting genes. We exclusively focused on interactions that were separated by a distance of at least 10 kb between gene promoter and promoter-interacting fragments. This criterion removes the high frequency of close-proximity ligation events in the HiC data. In total, we find 12,321 candidate silencers from GM12878 are interacting with 17,250 genes in GM12878 cells and 5907 candidate silencers from CD34+ cells are interacting with 8599 genes in CD34+ cells. We find 4631 and 1621 inactive genes (with RPKM = 0) are interacting with 8329 and 1993 candidate silencers in GM12878 and CD34+ cells, respectively   studies reported that promoters of transcriptionally inactive or lowly expressed genes are interacting with uncharacterized regions of genome, which suggests that they may act as silencerspes Fig. 51,74. Weely Fig. . An addills Fig. . These rlls Fig. . Furtherlls Fig. . Next, wlls Fig. . The majlls Fig. .Fig. 53DWe repeated the p-CHiC analysis with the mouse data sets and found more profound results Fig. . Briefly77. We next investigated whether candidate silencer elements were also enriched for disease-associated variants, as this would alter how the functional impact of the variant is interpreted for distal CREs. First, we determined how many SNPs are present at candidate silencer elements. For this, we downloaded all disease SNPs from the NHGRI-EBI GWAS catalog. We included both lead SNPs and SNPs in linkage disequilibrium (LD) (r2 ≥ 0.8) for overlap with candidate silencer elements. We found that 57,961 SNPs belonging to 2214 disease traits are present at silencer elements across all cell types. Next, we examined significant enrichment of specific disease SNPs at silencers for each cell type. This analysis resulted in ~20% of these diseases (451 out of 2214) being significantly enriched  at silencers for one or more cell types  from genome-wide association studies (GWAS) are prevalent at noncoding regions of the genome, especially at CREs, and often in enhancers of cells thought to be associated with the diseasepes Fig. . Next, wpes Fig. . Indeed,+ cells. We then found enhancer interacting genes in CD34+ cells from p-CHiC data as was done for silencer interacting genes in GM12878 cells and compared the target gene expression levels in their respective cell types. The relative expression of silencer interacting genes in GM12878 cells is indeed significantly less compared with the expression of interacting genes in CD34+ cells when silencers are acting as enhancers  of the remaining cell types. Interestingly, we found that 22–62% of candidate silencer elements from one cell type can act as enhancers in at least one other cell type Fig. . For theers Fig. . Promote4, silencers lack a unique chromatin signature to aid their genome-wide identification. Here, we developed a simple computational (SSA) approach to first identify genome-wide uncharacterized CREs containing candidate silencer elements. Then, functional screening via MPRAs was performed on a select subset of 7500 uncharacterized CREs to identify the true silencer elements. MPRA assays confirmed silencer activity for 41.5% of tested uncharacterized CREs. This validation rate is within the range or above what was shown for enhancer functional validation studies78. However, it is susceptible to false negatives due to caveats of in vitro reporter assays. It is also feasible that weak silencers were not detected given that the origin of replication can act as a promoter and also need to be repressed79. As further evidence of silencer activity, we tested a handful of silencer elements via traditional reporter assays, mitigating any effect of being in the 3′ UTR, and also demonstrated via CRISPR-Cas9 experiments that silencer deletions trend toward increased target gene expression.The precise control of gene expression, either activation or repression, is essential for changes in cell fate and cellular response to external cues. That control is ultimately mediated by CREs in the genome and their cognate binding factors. Though silencers have shown to be analogs to enhancers in that they can be distal to the genes they regulate and often function in a position- or orientation-independent mannerBased on our MPRA data, we trained an SVM model to predict candidate silencer elements from untested uncharacterized CREs in K562 and other cell and tissue types. We find that candidate silencer elements are enriched with many known repressor TF motifs. P-CHiC interactions showed that inactive and lowly expressed genes are interacting with candidate silencer elements. These results strongly support the annotation of silencer elements in the human and mouse genomes. We acknowledge that this may still be an overestimate as the majority of these candidate silencer elements are model predicted, and large-scale functional validation screens are needed to obtain an accurate count of silencers. In addition, some cell-, tissue-, or species-specific silencers may be missed. Furthermore, these predictions also rely on the annotation of DHS in each genome, which are subject to some degree of both false positive and false negative calls.80. Similar to enhancers, we found disease-associated SNPs are also enriched at candidate silencer elements of relevant disease cell types or tissues27. Mutations within silencer elements may provide one means for genes to escape repression in a disease-specific manner and have implications for other diseases such as various cancers.The vast majority of disease-associated SNPs are known to occur outside of coding regions28 and Roadmap consortia27 catalogs for other CREs. This approach should also prove applicable to future data sets from other cell types and across several species.Overall, we find that the characteristics of the predicted candidate silencer elements are expected of silencer elements and are in contrast to that of active enhancers. The catalogs of candidate silencer elements presented here across many cell types and tissues for human and mouse may serve as a resource that complements the ENCODE27 . Similarly, we obtained the data for mouse (mm10) cell types and another 30 human cell types from the ENCODE consortium (https://www.encodeproject.org/matrix/?type=Experiment)28. We used the peak files generated by the uniform ENCODE Processing Pipeline. We merged the peaks files for each cell type if data are available from different sources. The CTCF TFBS are obtained from CTCFBSDB 2.0 (http://insulatordb.uthsc.edu/) for both human and mouse81. The gene TSS coordinates were obtained from GENCODE annotations for human (GRCh37.p13/hg19) and mouse (GRCm38.p5/mm10)82.We obtained uniformly processed consolidated epigenome data of ChIP-seq and DNase-seq from the Roadmap consortium for 52 human cell types. We used narrow peaks called by MACS2 program for DNase-seq and broad peaks called by MACS2 program for H3K4me1, H3K4me3, and H3K27ac histone modifications. We directly obtained the peaks files from NIH Epigenomics Roadmap project83. The large variations in the number of uncharacterized CREs across cell types is most likely due to the quality of available chromatin data.In the SSA approach, we used DNase-seq peaks as open chromatin (DHS), H3K4me3 peaks, and TSS plus 2000 bp upstream and 500 bp downstream of TSS of all GENCODE genes as promoter elements, H3K4me1 peaks as enhancer elements, and CTCF TFBS as insulator elements. We start with DHS peaks and removed DHS overlapping with any of enhancer elements or promoter elements or insulator elements in a cell-type specific manner, and the remaining non-overlapping DHS peaks are termed as uncharacterized CREs. We included CTCF TFBS from all cell types during subtractive analysis as these TFBS are largely shared across cell types. We used the BEDTools suite for genomic subtractive analysis2 in RPMI-1640 (Gibco) medium with 10% fetal bovine serum (Gibco) and 1% penicillin–streptomycin.All experiments are performed in K562 cells. Cells obtained from the ATCC (CCL-243) are grown at 37 °C and 5% CO41. To select control-random regions, first we downloaded histone marks and DHS data in terms of processed peaks for K562 cells from the Roadmap Consortium. Next, we randomly selected genomic regions as control elements not overlapping with any of those annotated regions or any of Refseq annotations  as to avoid CREs as controls, as these were selected separately. Control enhancer and control silencer elements were obtained from a previous study26. An oligonucleotide library was synthesized containing 200 nt of genomic regions with 15 nt of flanking sequence matching the Illumina primer sequence . The obtained library was PCR amplified using primer “Starr-seq_homology_forward: TAGAGCATGCACCGGAATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT” and “Starr-seq_homology_reverse: GGCCGAATTCGTCGACAAGCAGAAGACGGCATACGAGAT[6-base-barcode]GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC” to add Illumina primer sequence and 15 bp of sequence matching the STARR-seq backbone (Addgene: 71509) for In-Fusion cloning (Clontech). The library was amplified with KAPA Hi-fi 2×(Kapa biosystem) with the following thermal condition . The resulting product was purified using Ampure-XP beads at 1.8× beads: reaction ratio. The STARR-seq screening vector was digested for 6 h with SalI-HF and AgeI-HF and the linearized backbone was run on a gel  and purified with a gel purification kit (Qiagen). In total, 200 ng of backbone and 50 ng of pooled insert were cloned in four 10 μl Infusion-HD reactions incubating at 50 °C for 15 min (Clontech). Resulting products were then combined and purified using Ampure-XP beads with a 1× volume of beads and eluted in 8 μl of purified water and electroporated into NEB® 10-beta electrocompetent cells at a ratio of 2 μl of reaction to 20 μl of competent cells for a total of four electroporations using a Bio-Rad GenePulserR II electroporator, with the following electroporation conditions: 2.0 kV, 200 Ω, 25 μF. Transformations were recovered for 1 h in SOC medium while shaking , and then grown for 16 h in 500 mL of Luria Broth while shaking . The STARR-seq input library was then purified using the Qiagen Plasmid Maxi prep kit. K562 cells were electroporated with a Neon transfection kit and device (Invitrogen) with the following transfection parameters: pulse voltage: 1450; pulse width: 10; pulse number: 3. Five replicate transfections were performed. Cells were grown in antibiotic-free media for 24 h. Cell pellets were rinsed once with PBS, and then lysed in 2 ml of RLT buffer (Qiagen) with 2-mercaptoethanol. The total RNA was prepared using the QIAGEN RNeasy Plus kit. Poly-A RNA was isolated from 50 μg of the total RNA using the µMACS mRNA isolation kit. RNA was treated with turboDNase (4U) for 30 min at 37 °C. DNase-treated poly-A RNA was purified using the RNeasy kit. mRNA and RT primer “RT-primer: CAAACTCATCAATGTATCTTATCATG” were incubated at 65 °C for 5 min, and cDNA was synthesized using Superscript III by incubating for 1.5 h at 55 °C then inactivated at 80 °C for 15 min. Following synthesis, cDNA was treated with RNaseA (Sigma) at 37 °C for 30 min. cDNA was purified using Ampure beads in a 1.5:1 bead:cDNA ratio and then amplified and indexed for sequencing using a two-stage PCR as described previously41. The cDNA sample from each replicate was used as an input into the first round gene-specific PCR using primers “targeted_library_F: GGGCCAGCTGTTGGGGTG*T*C*C*A*C” and “targeted_R: CTTATCATGTCTGCTCGA*A*G*C”, and input sample from each replicate was amplified using primers “targeted_input_F: GGGCCAGCTGTTGGGGTG*A*G*T*A*C” and “targeted_R: CTTATCATGTCTGCTCGA*A*G*C” and KAPA Hi-fidelity polymerase. PCR conditions were: 98 °C for 2 min, amplification with 12 cycles of 98 °C for 20 s, 65 °C for 20 s, and 72 °C for 60 s; final extension at 72 °C for 2 min. * = Phosphorothioated DNA bases.To multiplex testing for silencer activity of uncharacterized CREs, we leveraged synthetic oligonucleotide array synthesis and adopted the STARR-seq (self-transcribing active regulatory region sequencing) method as described in ref. Samples were then purified using the Zymo PCR purification kit and eluted in 15 μl of nuclease-free water. The resulting products were used as templates for the second round of PCR, which used a standard Illumina TruSeq indexing primer on the p5 end of the library and custom indexing primers to barcode the samples for multiplexing prior to sequencing  at a 1.8× SPRI: PCR ratio. All libraries were sequenced on Illumina NextSeq 500 performing 1 × 75 cycles.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and trimmed using TrimGalore package . Trimmed reads were mapped to human genome (hg19) using the Bowtie2 aligner84. The mapped reads were quantified against all tested sequences using “featureCounts” function from the Rsubread package85. The biological replicates were checked for similarity. We retained tested sequences for downstream analysis only if it contain reads at minimum three biological replicates at both RNA and plasmid libraries. The RNA and plasmid read counts were normalized by respective library size, and the normalized silencer element activity (fold change) is defined as ratio of RNA to plasmid counts averaged over biological replicates divided by mean ratio of RNA to plasmid counts of random control regions for each tested sequence. One-tailed t test was used to find significant differences in tested silencer activity over control-random regions activity. The computed P-values are adjusted with Benjamini–Hochberg method to control for false discovery rate. Then, the potential silencers were identified as sequences with fold change less than one and adjusted P-value < 0.05. We compared the relative activity of tested uncharacterized CREs with that of control enhancers and control silencers and statistical significance were assessed using two-tailed t test.We generated data in five biological replicate measurements. Sequencing raw reads from RNA and plasmid libraries are checked for adapter sequences and low quality reads (q-score < 20) using FASTQC  and treated with Calf Intestine Alkaline Phosphatase (CIP). In total, 100 ng of backbone and 20 ng of gel-purified PCR product of candidate regions having 15 bp flanking for vector insert were cloned using Infusion-HD reactions by incubating at 50 °C for 15 min (Clontech). Resulting products were electroporated into NEB® 10-beta electrocompetent cells at a ratio of 2 μl of reaction to 20 μl of competent cells using a Bio-Rad GenePulserR II electroporator, with the following electroporation conditions: 2.0 kV, 200 Ω, 25 μF. Transformations were recovered for 1 h in SOC medium while shaking  and then grown under ampicillin selection. To avoid false positives, colony PCR was performed for the selected colonies using site-specific primers sets. The PCR-confirmed colonies were then grown for 16 h in 50 mL of Luria Broth with ampicillin selection while shaking . The input libraries were then purified using the Qiagen Plasmid Midiprep kit.Reporter libraries: The STARR-seq luciferase validation vector_SCP1_empty (Addgene: 99299)6 K562 cells were electroporated with 7.2 µg of the STARR-seq luciferase validation (firefly luciferase) reporter plasmid and 800 ng of Renilla luciferase control plasmid (pGL4.74 ([hRluc/TK]). A total of six silencer element STARR-seq plasmids, two random control plasmids, and an empty vector plasmid were tested in three independent transfections. qPCR to quantify firefly luciferase transcripts normalized to Renilla luciferase transcripts was performed 24 h after transfection. Cells were lysed using QIAshredder columns, and the total RNA was extracted using the RNeasy miniprep kit, with beta-mercaptoethanol supplemented RLT buffer. In all, 1 µg of the total RNA was treated with recombinant DNaseI for 30 min at 37 °C followed by the removal of rDNaseI using a DNase inactivation reagent. The DNaseI-treated RNA was reverse transcribed using Invitrogen′s Superscript IV Vilo kit , followed by qPCR on 2 µl of diluted (1:5) cDNA using Go Tag SYBR Green qPCR Master Mix in a total volume of 10 µl with 0.5 µM gene-specific qPCR primers 79.qPCR method: In all, 1 × 10Table for qPCR primers:FF_fwd: GTGGTGTGCAGCGAGAATAGFF_rev: CGCTCGTTGTAGATGTCGTTAGRL_fwd: CAACTACAACGCCTACCTTCGRL_rev: CGGTGTTAGGGAACTTCTTAGCTC86. Delta-delta-Ct values were calculated between silencer elements and random controls were displayed as average (2−∆∆ct).qPCR analysis for reporter assay: Firefly luciferase Ct values for each candidate region were normalized to Renilla firefly Ct values using delta-Ct method87, (https://crispr.cos.uni-heidelberg.de/index.html). CCTOP predicted target sgRNA were selected according to their best hit/least off-target parameters . Each gRNA template was ordered in the form of Gblocks from IDT and cloned into the lenti Guide puro vector (Addgene: #73795). gRNA plasmids along with SpCas9-HF1 (Addgene: #72247) were transfected using the Neon electroporator. Twenty-four hours post transfection, puromycin selection was applied for 48 h. Cells were further transduced using adenovirus vector expressing Cas9 (kindly provided by Andre Lieber’s laboratory) at 2000 MOI. Ninety-six hours post transfection, RNA isolation was performed with TRIzol™ according to the manufacturer’s protocol. cDNA synthesis were carried out using SuperScript™ IV VILO™ Master Mix with the ezDNase kit. To determine the effect on the expression of candidate target genes, primers were selected from primer bank (https://pga.mgh.harvard.edu/primerbank/). See Supplementary Data t test. Our current experimental design is limited in that we are examining the effects of deletions in a highly heterogeneous  population and used only a single non-targeting control gRNA targeting lambda-phage DNA.The CRISPR-Cas9 system was used to edit potential silencer regions in K562 cells. Briefly, three gRNAs spanning each silencer region and one non-targeting control gRNA (targeting lambda-phage DNA) were designed using CCTop56. We chose the top 2000 candidate silencer sequences with lowest activity as a positive set and bottom 2000 silencers with highest activity as a negative set for SVM model training with default settings from the package. We used 80% of data for training, and the remaining 20% data for testing the model. We evaluated the performance of the model on test data by generating the ROC curve by plotting true positive rate versus false positive rate and the precision recall curves (PRC). We then used this model for predicting candidate silencers across all cell types from the list of uncharacterized CREs. We chose the threshold for the gkmSVM score where the model’s accuracy is maximum in order to classify the positive uncharacterized CREs, i.e., candidate silencer elements from negative ones.We used the R package “gkmSVM” for SVM model training and candidate silencer element predictions from untested uncharacterized CREs in K562 cells and other cell types88 (annotatePeaks.pl).The genomic annotations of predicted candidate silencers in human and mouse are performed using the HOMER suite83.The well-known repressor TFBS such as REST, YY1, ZBTB33, SUZ12, and EZH2 for cell types GM12878, H1, K562, and HEPG2 are directly downloaded from ENCODE project as processed peak files. We computed the enrichment (number of overlaps) of repressor TFBS at candidate silencer elements using BedTools suiteP-values. Briefly, we randomly selected the same number of DHS elements or enhancers as silencer elements and overlapped with repressor TFBS and compared these overlaps with silencer overlaps. Then, P-value is computed as follows:n is the number of permutations where expected overlaps (random DHS or enhancer overlaps with repressor TFBS) are greater than observed overlaps (silencer elements overlaps with repressor TFBS) and N  is the total number of permutations. The data in figures are represented as mean ± s.d.To compute the statistical significance for enrichment of candidate silencer elements at repressor TFBS relative to random DHS and random enhancers, we performed 10,000 random permutation tests and computed findMotifsGenome.pl -size given). In addition, we also identified enriched motifs at silencer elements relative to all DHS elements and enhancers within the same cell type as backgrounds. The enrichment of P-values are computed using binomial distribution. Enriched motifs are defined at adjusted P-value < 0.001. Heatmap visualizations are created using R package “pheatmap” and “ggplot2”.Motif analysis to find enriched TF motifs at candidate silencer elements relative to matched random genomic background were performed with HOMER suite (bigWigToBedGraph), and we computed the average methylation levels across candidate silencer elements, DHS peaks and active enhancers.The methylation value for each cytosine from WGBS data for 17 cell types were obtained from NIH Epigenomics Roadmap project as bigwig files. The bigwig files were converted to bedgraph files via UCSC file conversion tool  and mouse fetal liver cells (FLCs) from ref. 74. The processed significant promoter—other end-fragment interactions were directly obtained from the authors’ supplementary data. The corresponding promoter gene expression data for GM12878 and CD34+ cells are obtained from NIH Epigenomics Roadmap project, and for mESC and FLC are obtained from the ENCODE project. To find the target genes interacting with candidate silencer elements, we overlapped silencer elements with other end fragments. Then, we looked at the expression distribution of those target genes in the respective cell type. Similarly, we overlapped active enhancers with other end fragments to find enhancer interacting genes. We defined active enhancers as DHS overlapping both H3K4me1 and H3K27ac (DHS + H3K4me1 + H3K27ac).We obtained promoter-capture HiC data for GM12878 and CD34https://www.ebi.ac.uk/gwas/docs/file-downloads) dated March 2019, and SNP coordinates are converted from GRCh38 to hg19 using UCSC liftOver tool. We included both lead SNPs and SNPs in LD block (r2 > = 0.8) for overlap with silencer elements. The SNPs in LD (proxy SNPs) were obtained from SNAP web server (http://archive.broadinstitute.org/mpg/snap/ldsearch.php)90. The enrichment of disease SNPs to candidate silencer elements for each cell type are obtained by comparing the relative enrichment of particular disease SNPs against the rest of disease SNPs using a hypergeometric distribution.Disease-associated SNPs were obtained from NHGRI GWAS catalog (https://www.r-project.org/). Graphs and visualizations are prepared using “ggplot2” and “gplots” R packages.All the statistical tests are performed in the R environment (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Drosophila lineage, potentially explaining the inconsistently characterized function of Isw2-like proteins. Altogether, these data suggest an ‘interacting barrier model,’ where Isw2 interacts with a sequence-specific factor to accurately and reproducibly position a single, targeted nucleosome to define the precise border of phased chromatin arrays.Eukaryotic genomes are organized dynamically through the repositioning of nucleosomes. Isw2 is an enzyme that has been previously defined as a genome-wide, nonspecific nucleosome spacing factor. Here, we show that Isw2 instead acts as an obligately targeted nucleosome remodeler in vivo through physical interactions with sequence-specific factors. We demonstrate that Isw2-recruiting factors use small and previously uncharacterized epitopes, which direct Isw2 activity through highly conserved acidic residues in the Isw2 accessory protein Itc1. This interaction orients Isw2 on target nucleosomes, allowing for precise nucleosome positioning at targeted loci. Finally, we show that these critical acidic residues have been lost in the  DNA encodes the genetic instructions for life in a long, flexible molecular chain that is packaged up neatly to fit inside cells. Short sections of DNA are wound around proteins to form bundles called nucleosomes, and then spun into chromatin fibres, a more compact form of DNA. While nucleosomes are a fundamental part of this space-saving packaging process, they also play a key regulatory role in gene expression, which is where genes are decoded into working proteins.Placing nucleosomes at regular intervals along DNA invariably controls which parts of the DNA – and which genes – the cell’s machinery can access and ‘read’ to make proteins. But the nucleosomes’ positions are not fixed, and gene expression is a dynamic process. The cell often uncoils and repackages its DNA while molecular motors called chromatin remodelling proteins move nucleosomes up and down the DNA, exposing some genes and obstructing others.One group of chromatin remodelling proteins are called Imitation Switch (ISWI) complexes. It has long been thought that these complexes position nucleosomes with little regard to the underlying DNA sequence or the genes encoded, that is to say in a non-specific way. However, this theory has not been thoroughly tested. It is possible that ISWI complexes actually place nucleosomes on certain parts of DNA at particular times in an organism’s development, or in response to other environmental factors. Except how such precision is achieved remains unknown.To test this alternative theory of nucleosome positioning, Donovan et al. studied ISWI proteins and nucleosomes in common baker’s yeast. This involved systematically removing sections of ISWI proteins to see whether the complexes could still position nucleosomes, and which parts of the proteins where essential for the job. By doing so, Donovan et al. identified multiple ‘targeting’ proteins that bind to ISWI proteins and deliver the complexes to specific target sequences of DNA. From there, the complex remodels the nucleosome, positioning it at a specific distance from its landing site on DNA, as further experiments showed.This research provides a new model for explaining how nucleosomes are positioned to package DNA and control gene expression. Donovan et al. have identified a new mechanism of interaction between nucleosomes and chromatin remodelling proteins of the ISWI variety. It is possible that more interactions of this kind will be discovered with further research. Chromatin consists of the nucleic acids and proteins that make up the functional genome of all eukaryotic organisms. The most basic regulatory and structural unit of chromatin is the nucleosome. Each nucleosome is defined as an octamer of histone proteins, which is wrapped by approximately 147 base pairs of genomic DNA . The spechromodomain-helicase-DNA binding (CHD) and imitation switch (ISWI) families of ChRPs have been characterized as nonspecific nucleosome sliding and spacing factors in vitro  . Extensiin vitro . In yeasin vitro . HoweverA widely accepted model is that ChRPs pack nucleosome arrays against a noninteracting barrier, such as an unrelated DNA binding protein or another nucleosome . In thisunscheduled meiotic gene expression (Ume6), which is found in yeasts. It has been previously demonstrated that Ume6 and Isw2, an ISWI-containing ChRP complex in Saccharomyces cerevisiae (homologous to the ATP-dependent chromatin assembly factor [ACF] complex in humans and flies), share genetic targets of repression and likely interact physically  and Swi4/Swi6 (SBF) targets. We have also determined that the transcription factor-interacting interface of Isw2/ACF-like remodeling complexes contains a few key and highly conserved residues within the WAC (WTSF/Acf1/cbp146) domain. Finally, we show that these residues, which are essential for directional, sequence-specific remodeling, were lost in the evolution of the Drosophila lineage, where extensive biochemical, genetic, and genomic characterization has been performed on the ITC1 ortholog ACF.In this work, we have successfully identified the mechanism of interaction between Isw2 and Ume6 in Drosophila melanogaster ACF, an Isw2 ortholog, can organize nucleosomes into evenly spaced arrays , it is evident that Isw2 activity is specialized at only a subset of target sites d arrays . To firset sites . As seenet sites , no globet sites . Close iet sites . Importaisw1/chd1 background in the presence and absence of Isw2 to determine whether Isw2 is similarly restricted at known target sites. Again, we determined that Isw2 is efficient at positioning the Ume6-proximal nucleosome but positioning of nucleosomes decays rapidly as the distance from the proximal nucleosome increases, suggesting that Isw2 may only position single nucleosomes at target sites  Isw2 is solely required to move single nucleosomes at target sites, (2) Isw2 does not have global nucleosome spacing/organizing activity, and (3) Isw2 moves nucleosomes within 100 nucleotides of bound Ume6 suggest that Isw2 behavior in cells is distinct from our understanding of Isw2 activity from decades of biochemical characterization. Similarly, these specific movements toward Ume6 (a barrier) are inconsistent with previous biophysical studies, where ISWI proteins were shown to move nucleosomes away from inert DNA-bound factors . Becauseleosomes  and 5, wleosomes , with Umleosomes . We suspleosomes .To determine which region(s) on Ume6 are required for specific nucleosome positioning by Isw2, we initially created a panel of N-terminal Ume6 truncations to determine when nucleosome positioning by Isw2 is lost . This inRPD3+ compared to Ume6(∆2–479)/RPD3+ /∆rpd3 strain displayed extreme induction of Ume6-regulated genes, suggesting that both Isw2 and Rpd3 activity are absent /RPD3+ , which we absent .Finally, we wanted to know if the predicted helix consisting of Ume6 residues 479–508 was sufficient to bring Isw2 nucleosome positioning activity to Ume6 target sites. To test this, we employed the SpyCatcher/SpyTag system , which cMBP1 gene resulted in ectopic nucleosome positioning at a subset of Mbp1 target loci, which was identical to mispositioned nucleosomes in a ∆isw2 strain. Mbp1 is a conserved cell cycle regulator that complexes with Swi6 to form the MBF complex  produced identically ectopic nucleosome positions compared to ∆isw2 at target loci  deletion. Isw2 containing Itc1(∆2–374) did not display any defects in nucleosome sliding using a gel mobility shift assay that detects nucleosome centering by Isw2 . Surprise genome . Since pget loci  and genoget loci . We concTo confirm that the WAC domain can interact with Isw2 targets throughout the genome, we created Itc1(1-73)-FLAG and Itc1(1-132)-FLAG constructs based on two differentially conserved regions within the full WAC domain . NeitherWe noticed that the Itc1 signal and Isw2 signal were offset at target genes such that Itc1(1–73) or Itc1(1–132) was upstream and Isw2 was closer to the nucleosome that was selected for repositioning . Genome-Drosophila ACF complex, the Isw2 ortholog, is a nonspecific nucleosome spacing and assembly factor that evenly spaces phased nucleosome arrays against defined genomic barriers . The other two (E43 and D70 in Itc1) are more loosely conserved, though they are strictly positive charge in Drosophila. We made charge-reversal mutations in S. cerevisiae Itc1 to recapitulate the D. melanogaster residues at each of these positions either pairwise  or simultaneously  to reverse all charges to the D. melanogaster sequence. We assessed whether charge reversal was sufficient to abrogate targeted nucleosome positioning at Isw2 targets across the yeast genome  N-terminus, extranucleosomal DNA, and the histone H4 tail . In thisOur results indicate that the N-terminus of Itc1 does not have a primary cellular function of length sensing and nucleosome spacing. If the Itc1 N-terminus can bind H4 tail and transcription factor epitopes similarly to extranucleosomal DNA and H4 tail, the Hwang et al. model  can mechWhat advantage may an interacting barrier provide that a general barrier cannot, particularly since a noninteracting barrier can still phase nucleosome arrays? We envision at least two major advantages of the interacting barrier model. First, an interacting barrier can behave directionally while a non-interacting barrier cannot. Indeed, Isw2 seems to be positioned on a specific barrier-proximal nucleosome through interactions between the WAC domain of Itc1 and the epitope on Ume6 or Swi6. Directionality allows for more refined establishment of transcriptionally relevant chromatin arrays. Second, an interacting barrier can be modulated in a condition-specific manner through post-translational modification of the small epitope on the Isw2-recruitment factor. For example, one of the proteins that we identified as containing an Isw2- recruitment helix in yeast is Swi6, a critical regulator of the cell cycle in the G1/S transition. Interestingly, only three Swi6-regulated genes were identified as Swi6-mediated Isw2-recruitment sites. It is thus likely that the Swi6–Isw2 interaction can be tuned by cellular context, which is not possible for noninteracting barriers. Importantly, a tunable interacting barrier allows for continuous expression of the barrier and the ability to alter its barrier activity. This is a versatile mechanism through which chromatin structure may be spatiotemporally regulated in a dynamic fashion through these ChRP-recruitment factor interactions. We predict that other targeting factors likely exist, which might recruit the Isw2 complex to specific sites in the genome through a variety of different domains, and that this creates a mechanism by which remodeling occurs at specific subsets of genomic loci in response to the presence of these recruitment factors. The expression level of transcription factors is controlled by cellular context, such as cell cycle progression or stress response, and in this way the recruitment of Isw2 to specific sites might also be linked to changes in cell conditions.S. cerevisiae W303 RAD5+. Gene deletions were made by replacing the gene of interest with antibiotic resistance markers amplified from pAG vectors. C-terminal deletions of genes were also made by replacing the region to be deleted with antibiotic resistance markers. N-terminal gene deletions were made by first replacing the region to be deleted with a URA3 marker, and then counterselecting with 5-fluoroorotic acid (FOA) to delete the URA3. Ume6-helix was introduced to yeast through plasmid transformation of a p416 vector containing the Ume6 helix fused to the SpyCatcher protein  were grown in SD (-)Ura overnight, diluted to OD600 = 0.2 in YPD and grown to OD600 = 0.6–0.8 for chromatin analysis. Cells were then fixed with 1% formaldehyde and harvested for chromatin analysis.Cells were grown at 30°C and 160 rpm in yeast extract–peptone–2% glucose (YPD) medium unless otherwise indicated. Strains were streaked from glycerol stocks onto 2% agar YPD plates and grown at 30°C for 2–3 days. An isolated colony was then grown overnight in 25 ml of YPD. This pre-culture was used to inoculate 25 ml of YPD at an OD600 of ~1. Yeast were pelleted, washed with binding buffer -N,N,N',N'-tetraacetic acid], 0.1 mM EDTA [ethylenediamine tetraacetic acid], 2 mM MgCl2, 20% glycerol, 0.02% NP-40, 2 mM beta-mercaptoethanol, 1 mM PMSF [phenylmethylsulfonyl fluoride], 1× protease inhibitor cocktail ), and then lysed via cryogrinding. Yeast powder was incubated with binding buffer for 90 min before the addition of 200 μl bed volume anti-FLAG magnetic beads . After 3 hr incubation at 4°C, beads were collected with magnets and washed three times with binding buffer and three times with elution buffer . A 0.5 mg/ml solution of FLAG peptide in 100 ml elution buffer was then added to the beads and allowed to incubate for 30 min. This process was repeated three more times for a total of four elutions. Elutions were analyzed by silver staining and combined by estimated purity for aliquoting and storage at −80°.Yeast strains containing the Isw2 variants of interest appended with a FLAG tag were grown at 30°C to an ODSliding assays were performed at least three independent times with reproducible results.2, 0.1 mM EDTA, 5% sucrose, 0.2 mg/ml bovine serum albumin, with or without 5 mM ATP) by incubating 1 ml or 1.5 ml of purified Isw2 with 12.5 nM reconstituted mononucleosomes for 40 min in 6 ml reaction volume. Reactions were quenched by diluting 1:2 with solution containing 3 mM competitor DNA and 5% sucrose. Native PAGE (6%) was used to separate the positioning of the mononucleosomes, with Cy5.5-labeled nucleosomal DNA detected by a LiCor Odyssey FC imager .Recombinant yeast histones were purified as previously described  and dialMicrococcal nuclease digestions were performed with a minimum of two biological replicates as previously described . BrieflyChromatin immunoprecipitation was performed with biological replicates as previously described . BrieflyS. cerevisiae reference genome , RNA was purified by hot acid phenol extraction followed by polyA selection and strand-specific library construction using the NuGEN Universal Plus mRNA Kit according to the manufacturer’s instructions. Libraries were sequenced on an Illumina NextSeq500 on the 37 cycle, paired-end, high-output setting. Paired-end reads were quality filtered for adapter contamination and low-quality ends using trimmomatic . After qe genome  using STe genome . Gene coe genome . Differee genome , and expe genome .S. cerevisiae reference genome . PIC locations were obtained from S. cerevisiae reference genome with Bowtie 2 and total read coverage was normalized such that the average read at a genomic location was 1.0. ChIP peaks were called using a 400 bp sliding window with a threshold average enrichment within the window of 3.0. Data were visualized using Integrated Genome Browser .Thank you for submitting your article "Basis of Specificity for a Conserved and Promiscuous Chromatin Remodeling Protein" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Chromatin remodelers use the energy derived from ATP hydrolysis to reposition or evict nucleosomes, thus shaping the chromatin landscape of the cell. In this study, the McKnight lab use creative genetic and genomic approaches to understand how the apparently nonspecific biochemical activity of one such chromatin remodeler, Isw2, is targeted to specific nucleosomes in the budding yeast genome. The use of an isw1/chd1 mutant is a nice approach to remove the effects of spacing factors, and the SpyTag/SpyCatcher approach is a novel idea for artificial recruitment of factors. The bottom line of the study is that small, conserved epitopes in transcription factors act as recruiting elements for Isw2, allowing precise targeting of a nonspecific biochemical activity to specific genomic loci. From a larger perspective, the results lend support to an interacting barrier model of nucleosome positioning, wherein positioning of specific nucleosomes defines the borders of nucleosomal arrays. The data appear to be of high quality and soundly interpreted, and I believe that the results will be of great interest to those interested in chromatin and transcription. There are many questions raised by the results that I believe will drive further investigation into specificity in chromatin remodeling. My one major criticism (not that major in the scheme of things) is that the authors analyze the interesting subsets of their sites, as detailed below. One example is the analysis of the Isw2/Itc2 co-bound sites to the exclusion of the Isw2-alone sites. I think some exploration of these sites would be warranted, as discussed below.Major comments:1) In Figure S1C, there is nice correspondence between strong Isw2 K215R binding and Isw2-dependent nucleosome remodeling. However, at PICs where there is no apparent Isw2 remodeling, there does seem to be some Isw2 K215R ChIP-seq signal, albeit at a lower level. Does this potentially represent capture of transient sampling-type interactions, or something else?2) In Figure 1—figure supplement 3, Ume6 ChIP  is shown at 202 intergenic Ume6 motifs. It is stated that the rows are linked with Figure 1B – it would be nice to see the nucleosome data next to the ChIP data in this panel, as it appears that Ume6 is bound to at some level to the majority of these 202 sites, while Isw2 seems only to be active at the 58 sites of cluster 1. Germane to this point, I of course understand why the authors focused on the cluster 1 sites, but it would be nice to have some speculation on why Isw2 only seems to function at a fraction of Ume6-bound loci. Also, the lengths of the cluster-denoting bars appear to be off here relative to Figure 1B.3) In Figure 5C, it appears that only a subset of Isw2 sites are bound by Itc1 as well. Again, as with the selection of the 58 Ume6 sites, I understand why the Isw2/Itc1 co-bound sites are selected for further analysis, but the Isw2 sites without Itc1 could be discussed as well. Are these sites non-functional? How does Itc1 ChIP-seq data compare to the Isw2 remodeling activity shown in Figure 1A? How does it compare to Ume6 binding? Does it specify the Isw2-remodeled nucleosomes?4) Did the authors perform western blots to ensure that their various truncation constructs were stable? This is important for interpretation of the results vs deletions.5) To summarize the above points, a major thing missing from the discussion is why only subsets of TF binding sites recruit Isw2. For instance, as mentioned above, 58 Ume6 sites seem to specific Isw2 remodeling – what is special about those sites versus the other ~150 sites that appear to be bound by Ume6? It's mentioned briefly in the discussion that only three Swi6 sites were identified as Isw2-recruiting and that this may be tuned by cellular context, but this is quite vague and superficial. More speculation on what differentiates these sites from the TF-bound but non-Isw2 recruiting sites could be included. Major comments:1) In Figure S1C, there is nice correspondence between strong Isw2 K215R binding and Isw2-dependent nucleosome remodeling. However, at PICs where there is no apparent Isw2 remodeling, there does seem to be some Isw2 K215R ChIP-seq signal, albeit at a lower level. Does this potentially represent capture of transient sampling-type interactions, or something else?We believe that this is mostly noise in the ChIP signal, which is exacerbated by the fact that remodelers are notoriously difficult to ChIP. As you have suggested, it is possible that crosslinking in the ChIP experiment captures transient interactions, indicative of a sampling process. We do believe that this sampling likely occurs, as we have observed that Isw2 is able to nonspecifically bind histones and DNA, but that remodeling only occurs when there is a specific/stable interaction. However, we are reluctant to draw specific conclusions from the ChIP data due to the noise in the sample.2) In Figure 1—figure supplement 3, Ume6 ChIP  is shown at 202 intergenic Ume6 motifs. It is stated that the rows are linked with Figure 1B – it would be nice to see the nucleosome data next to the ChIP data in this panel, as it appears that Ume6 is bound to at some level to the majority of these 202 sites, while Isw2 seems only to be active at the 58 sites of cluster 1. Germane to this point, I of course understand why the authors focused on the cluster 1 sites, but it would be nice to have some speculation on why Isw2 only seems to function at a fraction of Ume6-bound loci. Also, the lengths of the cluster-denoting bars appear to be off here relative to Figure 1B.We have added the nucleosome position heatmap from Figure 1B to Figure 1—figure supplement 3 so that it is easier to compare to the Ume6 ChIP data. Figure 1B actually shows that both Cluster 1 and Cluster 3 have Isw2-dependent nucleosome shifts; those in Cluster 3 are shifted in the opposite direction, but the Ume6-proximal nucleosomes are consistently more distal to the URS in the absence of Isw2 than in the presence of Isw2. We have updated the language in the second paragraph of the Results section of the text to make this more clear.We believe that there are several reasons that Isw2 is not active at cluster 2 URS sites. Many of these sites show reduced Ume6 binding, according to our ChIP data, which could in turn lead to the inability to recruit the Isw2 complex. We also suspect that, at least for a subset of these sites, Isw2 is recruited to the motif but the nearest nucleosome is out of reach of the remodeling complex, resulting in an unproductive binding event; we have added this idea to the end of paragraph two of the Results section. We would like to explore this concept further in the future but we feel it is outside the scope of this publication.We have also adjusted the bar for cluster 1 in Figure 1—figure supplement 3, which was incorrect, to reflect the appropriate clustering as shown in Figure 1B. Thank you for pointing out this discrepancy.3) In Figure 5C, it appears that only a subset of Isw2 sites are bound by Itc1 as well. Again, as with the selection of the 58 Ume6 sites, I understand why the Isw2/Itc1 co-bound sites are selected for further analysis, but the Isw2 sites without Itc1 could be discussed as well. Are these sites non-functional? How does Itc1 ChIP-seq data compare to the Isw2 remodeling activity shown in Figure 1A? How does it compare to Ume6 binding? Does it specify the Isw2-remodeled nucleosomes?Due to the difficulty in ChIP-ing Isw2, we aren’t convinced that Isw2 is not actually present at some of the Itc1 sites. We attempted to analyze the Itc1-FLAG ChIP-seq data, but unfortunately we were unable to replicate the exact processing that was used for the original heatmaps, so we could not link the rows to the prior data; therefore it is difficult to draw conclusions from this analysis. However, as we have demonstrated, there are factors other than Ume6 that interact with the Isw2 complex, and we predict that there are many more factors that target Isw2 to specific subsets of sites in the genome.4) Did the authors perform western blots to ensure that their various truncation constructs were stable? This is important for interpretation of the results vs deletions.We understand that Western blots are generally standard to show evidence of functional protein, but full-length Ume6 is largely unstructured, making it very unstable outside of the cell so that it degrades rapidly, thus we have been unable to get a satisfactory Western blot of full-length Ume6 or the truncations. However, the ChIP-seq data for the truncated Ume6 constructs recapitulates the data from wild type, which we believe is strong evidence that these truncations are stable, since they occupy the same sites in the genome as full-length Ume6. We have added heatmaps of this data to Figure 1—figure supplement 3 to further strengthen this evidence.We have purified the Isw2 complex with a more truncated Itc1 construct (∆9-374) for use in some in vitro sliding assays, and the remodeler complex with that truncated Itc1 functions in the same way as the wild type complex; we predict that constructs with fewer amino acids deleted would be less disruptive, and therefore expect all of our truncations are stably folded. We believe the ChIP data for Itc1(1-73) and Itc1(1-132) shown in Figure 5B is also good evidence that these constructs are stable, as they show similar localization to wild type Itc1. There are differences, which we highlight, but it seems unlikely that these differences are the result in protein stability as one would expect a more global change in that case.5) To summarize the above points, a major thing missing from the discussion is why only subsets of TF binding sites recruit Isw2. For instance, as mentioned above, 58 Ume6 sites seem to specific Isw2 remodeling – what is special about those sites versus the other ~150 sites that appear to be bound by Ume6? It's mentioned briefly in the discussion that only three Swi6 sites were identified as Isw2-recruiting and that this may be tuned by cellular context, but this is quite vague and superficial. More speculation on what differentiates these sites from the TF-bound but non-Isw2 recruiting sites could be included.You are correct to point out that we did not address why some Ume6 or Swi6 sites don’t exhibit Isw2-remodeling. We have been reluctant to speculate without more concrete data, but we have added language in the final paragraph of the Discussion section of the manuscript to indicate that this is something we have thought about and suggest our ideas on the matter. It is a complex tertiary interaction, which requires assembly of all the necessary elements  at the same time. It is likely that there are simply few genomic loci which have all the right pieces of the puzzle in the right place at the right time.


Histones are the main structural proteins of eukaryotic chromatin. Histone acetylation/ deacetylation are the epigenetic mechanisms of the regulation of gene expression and are catalyzed by histone acetyltransferases (HAT) and histone deacetylases (HDAC). These epigenetic alterations of DNA structure influence the action of transcription factors which can induce or repress gene transcription. The HATs catalyze acetylation and the events related to gene transcription and are also responsible for transporting newly synthesized histones from the cytoplasm to the nucleus. The activity of HDACs is mainly involved in silencing gene expression and according to their specialized functions are divided into classes I, II, III and IV. The disturbance of the expression and mutations of HDAC genes causes the aberrant transcription of key genes regulating important cancer pathways such as cell proliferation, cell-cycle regulation and apoptosis. In view of their role in cancer pathways, HDACs are considered promising therapeutic targets and the development of HDAC inhibitors is a hot topic in the search for new anticancer drugs. The present review will focus on HDACs I, II and IV, the best known inhibitors and potential alternative inhibitors derived from natural and synthetic products which can be used to influence HDAC activity and the development of new cancer therapies. The mechanisms regulating gene expression involve a series of molecular modifications in DNA and chromatin and are responsible for the response to any type of physiological signaling in the organism . This coDifferent forms of HAT and HDAC have been identified, including coactivators that can interact with transcription regulators . DeacetyThe HATs are divided into two classes (type A and type B) according to their localization and function in the cell. Their activity can be modulated by several protein–protein interactions, protein cofactors and autoacetylation. Type A HATs are nuclear enzymes which contain acetyl-CoA binding sites. This class of HATs probably catalyzes acetylation and events related to gene transcription. Type B HATs are believed to have a maintenance role in cell functioning as cytoplasmic enzymes that modify free histones in the cytoplasm after their synthesis and transport them to the nucleus, where they can be deacetylated and incorporated into chromatin . In addi3) from an ε-N-acetyl lysine in a histone, an event that confers to histones the capacity to compact DNA -(E)-3-(4-hydroxy-3-methoxyphenyl)prop-2-enoate) is a curcumin analog that contains a ferulic acid ester bond [Another potential HDACi is curcumin (diferuloylmethane), a polyphenol and the active component of turmeric (a longa) , which ia longa) ,254,255.a longa) . Curcumia longa) ,258,259.a longa) . This coa longa) . Severala longa) . A reduca longa) . Furthera longa) ,265. Calter bond  . A major emphasis has been placed on various inhibitors of HDACs and how they exert their bioactivity. Although neoplasms are currently the main clinical indication for these compounds, future applications may include autoimmune diseases, neurological indications and even parasitic diseases. However, improvements in the therapeutic index of these drugs should be made, as they present high toxicity, inducing symptoms from fatigue, nausea and vomiting to thrombocytopenia, neutropenia and some cardiac toxicity. One path to this improvement may come from more specific inhibitors of individual HDAC isoforms that are critically involved in particular indications. By targeting the most relevant HDAC isoform in a specific indication, it may be possible to significantly improve their efficacy by removing certain toxicities that may be associated with the inhibition of multiple isoforms .


The fusion of two transcriptionally silent gametes, egg and sperm, generates a totipotent zygote that activates zygotic transcription to support further development. Although the molecular details of zygotic genome activation (ZGA) are not well understood in most species, an emerging concept is that one or more pioneer transcription factors trigger zygotic transcription. Concomitantly, extensive changes in 3D chromatin organization occur during development. In this review, we discuss recent advances in understanding when and how genome architecture emerges in early metazoan embryos, how the zygotic genome is activated, and how these events might be coordinated. We also highlight some of the unknowns that may be critical to address in the future. Mus musculus) embryonic development and draw connections to other species, such as fruit fly (Drosophila melanogaster) and zebrafish (Danio rerio). In these species, the proteins that trigger ZGA have been identified, whereas the drivers of 3D chromatin organization remain unclear, revealing that there are major gaps in our knowledge of genome architecture and ZGA in any one organism.At the earliest stage of development, the single-cell embryo inherits the genomes of two terminally differentiated germ cells, the sperm, and the oocyte. The newly formed zygote has the developmental potential to generate all cell types and a complete organism, which is the strictest definition of totipotency. It is thought that achieving a state of totipotency requires extensive epigenetic reprogramming  and actin vitro [The emergence of 3D chromatin organization during embryonic development has recently been investigated using chromosome conformation capture–based Hi-C methods . Brieflin vitro ,18, provin vitro , 16,19 bGiven these different aspects of genome architecture, it is important to consider these separately when investigating the timing of their establishment and function during embryonic development. The idea that the function of TADs is to regulate gene expression by facilitating enhancer–promoter interactions may have to be revised and could be relevant to specific loci but not others , 21, 22.de novo during embryonic development or is inherited from gametes, it is necessary to know the chromatin folding states of the oocyte, egg, and sperm. Mammalian oocytes arrest after meiotic recombination in prophase I, which is a G2 phase–like interphase state. The chromatin of germinal vesicle (GV) stage oocytes is organized into loops, TADs, and compartments [Studies from different organisms have yielded surprisingly diverse timings of genome architecture establishment during embryonic development. To know whether 3D chromatin organization arises artments . After tartments , ∗26. Thartments ,72. In cartments ,29. In cartments .Recent studies have improved our understanding of 3D chromatin reorganization that occurs after fertilization. Single-nucleus Hi-C (snHi-C) of isolated maternal and paternal nuclei from zygotes revealed that chromatin is organized into loops and weak TADs in G1 phase zygotes . These wRecently, two additional compartment-like domains have been investigated in mouse gametes and embryos. Lamina-associated domains (LADs) can be considered as a subtype of B compartments with a subcellular localization at the nuclear envelope ,32. LADsBulk and snHi-C studies of the first stages of embryonic development appear to yield conflicting results with regard to the timing of TAD formation. Bulk Hi-C heat maps of embryos do not reveal TAD profiles until embryos progress to the 8-cell morula stage . On the Drosophila, CTCF does not grossly coincide with TAD borders, suggesting that these are generated by other proteins and/or mechanisms [Oryzias latipes), a preprint reports that TADs and compartments are not detected before ZGA [The timing of TAD establishment is radically different in other species, in which it has not been experimentally addressed whether these domains are generated by cohesin-dependent loop extrusion. In chanisms , 36, 37.chanisms . In the fore ZGA . In contfore ZGA . TherefoThe activation of zygotic gene expression is crucial for the development of an organism. Despite its importance, remarkably little is known about the mechanism of ZGA in most species, with some exceptions. In mammals, ZGA is characterized by a transient expression of transposable elements . For insin vivo. Therefore, the master regulators of genome activation are largely unknown in mammals.An emerging concept is that one or more pioneer transcription factors bind to closed chromatin, remodel its accessibility, and either directly initiate transcription or prime chromatin for other transcription factors to trigger transcription . In mousDrosophila [. Zelda is maternally deposited as mRNA in the egg and translated into a six CH2H2 zinc finger-binding domain protein after fertilization [in vitro [The pioneer transcription factor Zelda is essential for ZGA in osophila . Zelda ilization ,55. Zeldlization , 57. In lization , Zelda hin vitro . Interesin vitro . TherefoAdditional insights into how ZGA might be orchestrated in vertebrates come from zebrafish studies. The combined loss of the transcription factors Nanog, Sox19b, and Pou5f3 (Oct4) prevents transcription of >75% of genes activated at ZGA and causes gastrulation failure ,62. All The key question is how genome architecture and ZGA are mechanistically related. Is chromatin organization a prerequisite for ZGA? In zebrafish, cohesin depletion in early embryos leads to a delay in ZGA many cell cycles later , which cA related question is whether transcription is required for higher order chromatin structures. With the exception of human embryos, transcription is not required for loop and TAD formation in embryos ,26,30,38However, loops are detectable in transcriptionally silent mouse zygotes and TADs are detectable pre-ZGA in zebrafish ,12,40, sFinally, the most striking change in chromatin organization associated with ZGA is an increase in TAD boundary insulation. Interestingly, the pioneer transcription factor Zelda  is requiRapid progress has been made in the last two years in describing the emergence of genome architecture during development by genomic methods in several organisms. By comparison, ZGA was first described decades years ago in vertebrates ,71, but Both authors A.V. and K.T. wrote and edited the manuscript.Nothing declared.


O-linked β-N-acetylglucosaminetransferase (OGT), which monoglycosylates multiple chromatin factors at serine and threonine hydroxyls. This modification both antagonizes protein phosphorylation at those hydroxyls and induces structural transitions in multiple chromatin factors that modify or enhance their repressive activities so as to consolidate the repressed state.Methylated mammalian promoters are transcriptionally silenced by nuclear factors, but the identity of these factors and the molecular mechanism of methylation-induced repression have long been elusive. We show here that methylated promoters recruit  O-linked β-N-acetylglucosamine transferase (OGT) only in cells that had normal genomic methylation patterns. OGT is the only glycosyltransferase that modifies cytoplasmic and nuclear protein by transfer of N-acetylglucosamine (O-GlcNAc) to serine and threonine hydroxyls. Whole-genome analysis showed that O-glycosylated proteins and TRIM28 were specifically bound to promoters of active retrotransposons and to imprinting control regions, the two major regulatory sequences controlled by DNA methylation. Furthermore, genome-wide loss of DNA methylation caused a loss of O-GlcNAc from multiple transcriptional repressor proteins associated with TRIM28. A newly developed Cas9-based editing method for targeted removal of O-GlcNAc was directed against retrotransposon promoters. Local chromatin de-GlcNAcylation specifically reactivated the expression of the targeted retrotransposon family without loss of DNA methylation. These data revealed that O-linked glycosylation of chromatin factors is essential for the transcriptional repression of methylated retrotransposons.The mechanisms by which methylated mammalian promoters are transcriptionally silenced even in the presence of all of the factors required for their expression have long been a major unresolved issue in the field of epigenetics. Repression requires the assembly of a methylation-dependent silencing complex that contains the TRIM28 protein , a scaffolding protein without intrinsic repressive or DNA-binding properties. The identity of the key effector within this complex that represses transcription is unknown. We developed a methylation-sensitized interaction screen which revealed that TRIM28 was complexed with  It has been known for many years that the methylation of mammalian promoters induces heritable transcriptional repression –3. GenomBiochemical studies identified proteins that bind to methylated DNA in vitro and had the properties expected of methylation-dependent transcriptional repressors. However, ablation of the genes that encode MeCP2 and other methylation-dependent DNA-binding proteins singly or in combination did not reactivate methylated promoters in vivo . AblatioO-linked β-N-acetylglucosamine transferase (OGT), the sole protein glycosyltransferase that is active in the nucleus and cytoplasm. OGT has important regulatory functions in multiple pathways  , as doespathways , but hadTrim28 in mouse embryos does not cause appreciable demethylation of DNA  but phenocopies the reactivation of intracisternal A-type particles (IAP) retrotransposons induced by genome-wide demethylation . These data identify TRIM28 as an essential mediator of methylation-dependent silencing of transposons and methylation-dependent monoallelic expression of imprinted genes. However, TRIM28 does not bind to DNA directly nor does it possess intrinsic repressive activity and cannot be the ultimate effector protein that represses methylated promoters , which implicates an unknown factor in the repression of methylated promoters.Homozygosity for a strongly hypomorphic allele of hylation , as had  . OGT showed a methylation-dependent association with TRIM28 that was >2-fold greater than any other protein. This result was unexpected, as there had been no prior connection between DNA methylation and protein glycosylation  of IAP retrotransposons (the most actively proliferating retrotransposon in the mouse genome (O-GlcNAcylated proteins (O-GlcNAc and TRIM28 showed that LTRs are cooccupied by TRIM28 and O-GlcNAcylated proteins (O-GlcNAcylated proteins (O-GlcNAcylated (O-GlcNAc (Whole-genome chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) using an antibody against e genome ) are denproteins A. Comparproteins B. In conproteins B. While Acylated A. All teO-GlcNAc B.O-GlcNAc. As shown in O-GlcNAc. All ICRs tested were enriched in either TRIM28 or O-GlcNAcylated proteins; the large majority was enriched in both (Imprinting control regions (ICRs), which depend on DNA methylation for allele-specific expression , were in in both D.O-GlcNAcylation were isolated from nuclear extracts of Dnmt1−/− and Dnmt1+/+ ES cells by immunoprecipitation with antibodies to TRIM28 followed by collection by the GlcNAc-specific lectin Wheat Germ Agglutinin (WGA) and identification by mass spectrometry. As shown in Dnmt1−/− ES cells caused a loss of O-GlcNAc from multiple proteins complexed with TRIM28. The proteins showing the greatest degree of methylation-dependent O-GlcNAcylation are shown in SI Appendix, Table S2.Proteins subject to methylation-dependent O-GlcNAcylation. Many of these proteins had been previously reported to interact with each other directly or indirectly . All DNA methyltransferases and all tested histones and histone variants were also O-GlcNAcylated. TRIM28 itself was the only silencing factor found to lack detectable O-GlcNAc. The number of factors subject to O-GlcNAcylation was larger than expected; GlcNAcylation has important roles in the regulation of transcription  from Bacteroides thetaiotamicron . The inactive dCas9-OGAD242A fusion protein had no detectable effect, which indicates that reactivation was the result of deGlcNAcylation and not an effect of the binding of the dCas9-OGA-sgRNA complex. The RNA blot data were confirmed and quantitated by the RNA-seq data shown in O-GlcNAcylation is required for both mechanisms of repression. However, the fact that methylated IAP retrotransposon promoters was reanimated by targeting the dCas9-OGA fusion protein to IAP promoters provides strong evidence that O-glycosylation mediates transcriptional repression.The dCas9-OGA or dCas9-OGAiral DNA D, but thiral DNA E. This sO-glycosylation in the silencing of retrotransposon comes from studies of a liver-specific deletion of Ogt in mice  of the large subunit of RNA Pol II inhibits phosphorylation of the CTD and transcriptional elongation (Drosophila is dependent on the single Ogt gene (super sex combs or sxc) in the fly genome, even though Polycomb factors are bound to their normal sites in the sxc mutant . We propose that subsequent O-GlcNAcylation induces structural transitions in multiple chromatin factors that modify or enhance their repressive activities to impose transcriptional repression on methylated promoters and to mediate monoallelic expression of imprinted genes.The targeting of the repressive complex that contains TRIM28 and OGT to methylated promoters and imprinting control regions is likely to involve the very large and rapidly evolving group of KRAB-Zinc finger proteins that are restricted to tetrapod vertebrates and are especially numerous and diverse in mammals . We propgf2 gene . The datDnmt1 (Dnmt1−/−) was described previously  and incubated for 15 min on ice. Cells were treated with a Dounce homogenizer (25 strokes with tight pestle). Nuclei were recovered by centrifugation (10 min at 300 g at 4 °C), washed twice in buffer A , centrifuged , and resuspended in buffer B . NaCl concentration was increased to 300 mM, and extraction of the soluble protein complexes was allowed to proceed under gentle agitation for 3 h at 4 °C. Nuclei were pelleted by centrifugation , and the supernatant was collected as the nuclear soluble extract. Protein concentration was measured by bicinchoninic acid assay.Ten micrograms of anti-TRIM28 monoclonal antibody  bound to 50 μL Dynabeads Protein G magnetic beads (Thermo Fisher Scientific) was incubated with 8 mg of ES cell nuclear soluble extract for 14–16 h at 4 °C. Bound material was eluted by incubating the beads at 95 °C for 5 min in a buffer containing 10 mM Hepes pH 7.65, 0.1% sodium dodecyl sulfate (SDS), 1% Nonidet P-40, 1 mM DTT, 300 mM NaCl. Complexes were resolved by SDS/PAGE, stained by SYPRO Ruby (Thermo Fisher Scientific), and identified by mass spectrometry at the Taplin Biological Mass Spectrometry Facility .O-GlcNAc  bound to 10 μL Dynabeads conjugated with protein G (Life Technologies). Beads were washed and chromatin eluted as described previously  using bowtie2 (v2.2.2) and default parameters. Duplicate reads were removed using samtools rmdups (v1.3.1). The Macs2 (v2.0.10) callpeaks module was used to call peaks using -g 1.87e9, –SPMR, and -B flags and using the input as background . TRIM28 O-GlcNAcylated proteins were isolated with WGA conjugated to magnetic beads (O-GlcNAcylated proteins were isolated either from fractionated nuclei or from isolated TRIM28 complexes. The O-GlcNAase inhibitor PUGNAc (Tocris Biosciences) was added at 2 mM in hypotonic lysis buffer, buffer A and B in order to preserve physiological O-GlcNAc levels during the cellular fractionation procedure. Nuclei were lysed with 1% SDS, cleared of nucleic acids by treatment with Universal Nuclease (Pierce), and denatured by heating to 100 °C for 2 min in 1% SDS. Denatured proteins were incubated for 2 h at 4 °C with 200 μL of Dynabeads Streptavidin C1 (Thermo Fisher Scientific) bound to 200 μg of biotin-conjugate wheat germ agglutinin (Sigma). Beads were washed six times with 20 mM Hepes pH 7.65, 250 mM NaCl, 5 mM CaCl2, 1 mM MgCl2, 0.2% Nonidet P-40. GlcNAcylated proteins were eluted from the beads at 95 °C. The specificity of binding was controlled by competitive inhibition with 0.75 M N-acetylglucosamine.ic beads . O-GlcNAStreptococcus pyogenes  fused to an N-terminal SV40 nuclear localization signal sequence, and a FLAG tag epitope was amplified by polymerase chain reaction (PCR). The mammalian codon-optimized OGA (Bacteroides thetaiotaomicron GH84 (UniProtKB - Q89ZI2) fused to a C-terminal SV40 nuclear localization sequence and a FLAG tag epitope was synthesized using IDT gBlocks gene fragments (Integrated DNA Technologies). The DNA fragments encoding dCas9 and OGA were ligated into the SalI and the NotI sites of the P2lox vector using Gibson cloning . The sgRNAs homologous to the IAP LTRs  gene expression system PLox-AinV15, which is designed to insert a circular Plox plasmid by cre/lox recombination into a recombinant doxycycline-inducible locus. The AinV15 cell line carries the reverse tetracycline transactivator (rtTA) integrated into the ubiquitously expressed ROSA26 locus . The comized OGA  from BacIAP LTRs B were clo GlcNAc  and was Three million AinV15 ES cells were nucleofected with 10 μg of P2Lox plasmid containing the dCas9-OGA cDNA and four U6 promoter-driven sgRNAs and 10 μg of a plasmid-expressing Cre recombisase (Adgene #11543). Recombinant cells were selected by treatment with 350 μg/mL G418 and genotyped for proper integration by PCR as previously described . Express6 ES cells. RNA was cleared of potential contaminating genomic DNA by two rounds of digestion with DNase  and quantified using Qubit Fluorometric Quantitation (Thermo Fisher Scientific). Ten micrograms of total RNA was denatured and subjected to electrophoresis in a 1% agarose gel containing 1.9% formaldehyde prior to transfer to a nitrocellulose membrane. After ultraviolet cross-linking, the membrane was hybridized with a radiolabeled IAP probe as described  from a pool of six embryos of same genotype dissected at embryonic day E8.5 or from 1 × 10escribed . The GapDnmt1−/− ES cells; and 40,282,098, 50,062,039, and 36,948,128 reads for three replicates of wild-type ES cells. Further, 38,798,921 and 38,840,713 reads were obtained for dCas9-OGAWT-expressing cells and dCa9-OGAD242A-expressing ES cells, respectively.Total RNA was extracted, and traces of contaminating genomic DNA were eliminated by two successive treatments with DNase . The integrity of the RNA was verified using the Bioanalyzer RNA 2100 Nano Assay (Agilent Technologies). RNA-seq libraries were prepared with the TruSeq Stranded mRNA LT (Illumina), and massive parallel sequencing was performed in single-end reads using an Illumina HiSeq 4000 and Next-seq instruments. We obtained 38,676,845, 40,766,737, and 36,497,140 reads for three replicates of For IAPEz expression analysis, reads were mapped to the mouse reference genome (mm10) using bowtie2  database (accession no. GSE93539).Supplementary File


UPF1 has been mostly studied for its role in nonsense mediated mRNA decay (NMD) and other translation-dependent RNA quality-control pathways in the cytoplasm . However, we have recently reported that UPF1 associates, genome wide, with nascent transcripts at most Pol II transcription sites in Drosophila . The association of UPF1 with nascent transcripts appears to be necessary for the release of processed polyadenylated mRNAs from their transcription sites and also for their export from nucleus to cytoplasm .UPF1 is an RNA helicase that scans RNA to unwind secondary structures and to displace associated factors  that there is significantly more unphosphorylated Pol II associated with the start of genes in cells depleted of UPF1 . This difference is most apparent 20 to 60 base pair (bp) downstream of the transcription start site (TSS), as shown in the expanded depiction in Figure 1B which is based on many more individual TSSs. This is measured by quantifying the aggregate Pol II signal over a +/- 100 bp span at each of 25440 TSSs (P = 0.011). A 1.2-fold or greater increase of unphosphorylated Pol II in UPF1-depleted cells was detected at 1990 TSSs. The 20-60 bp downstream region corresponds to TSS-proximal Pol II pausing sites in Drosophila . Conversely, the amount of Ser2 Pol II associated with these TSSs was unchanged or marginally reduced in UPF1-depleted cells . The increase in unphosphorylated Pol II loading downstream of the TSS in UPF1-depleted cells, alongside fairly constant Ser2 Pol II loading, is illustrated here by Xrp1 and RpS3A , two genes that are highly transcribed and show strong UPF1 association in normal cells .Here we considered whether the association of UPF1 with nascent transcripts influences Pol II transcription, and tested it by ChIP-seq of Pol II in Figure 1F, compare control lanes 9 and 10 with DRB-treated lanes 1 and 2). Ser2 Pol II levels began to recover soon after DRB removal and were similar to those of untreated control cells within 10 minutes . However, the recovery in UPF1-depleted cells seemed slower during the first 2.5 minutes and to a lesser extent after 5 minutes . Similar observations were made in another independent replicate of the experiment. A comparable blunted recovery of gene-associated Ser2 Pol II in UPF1-depleted cells was detected by ChIP-PCR at the two representative highly active genes, Socs36E and Xrp1 , both of which show strong association with UPF1 . RpS3A was not examined because it is too short (about 2 kb) for this assay.We considered whether this increase in unphosphorylated Pol II at pausing sites might be a consequence of reduced Ser2 phosphorylation, resulting in the slow release of Pol II from pausing sites and abnormal transcription elongation. We tested this hypothesis by examining the reappearance of newly phosphorylated Ser2 Pol II following withdrawal of 5, 6-Dichlorobenzimidazole 1-β-D-ribofuranoside (DRB), which is a transcription inhibitor that blocks Ser2 phosphorylation and should lead to rapid depletion of Ser2 Pol II. As expected, DRB treatment of S2 cells causes a drastic depletion of Ser2 Pol II and an increase of unphosphorylated Pol II  .These observations indicate that UPF1 might, by associating with nascent transcripts, influence Ser2 phosphorylation of Pol II at TSS-proximal pausing sites and hence transcription elongation at several Cell culture and RNA interference6cells/well in serum-free media and mixed with 15 µg of dsRNA/well. Following 1 hour incubation at room temperature, 2 mL of complete media was added to each well and the cells were incubated for the next three days to knockdown UPF1 before harvesting.S2 cells  were cultured in Insect–XPRESS media (Lonza) supplemented with 10% Fetal Bovine Serum (FBS) and 1% Penicillin-Streptomycin-Glutamine mix  at 27°C. To make the RNAi constructs for UPF1, the specific sequences were PCR amplified from S2 cell genomic DNA by using corresponding primer pairs. Along with the desired gene sequence, the primer pair carried the T7 promoter sequence at their 5’ end. The amplified PCR fragments were purified using Monarch® PCR and DNA Cleanup Kit  and dsRNA was synthesized using the T7 RiboMAX express RNAi system . To induce RNAi, a six-well culture dish was seeded with 10Western blotting and antibodieset al., 2019). The antibodies used in western blotting were diluted as follow: Rabbit anti-Ser2 Pol II , Mouse anti-Pol II , 1:5000) and mouse anti-β-tubulin .Western blotting was performed as described previously  for 1 hour at room temperature. Ser2-Pol II ChIP was performed as described below and quantified by real-time PCR using ChIP-seq and data analysiset al., 2019). For each ChIP, typically we used 5 µg of antibody including Rabbit anti-Ser2 Pol II  and Mouse anti-Pol II . ChIP-signal quantification and metagene analysis were performed as previously described . All new ChIP-seq raw sequencing FASTQ data and Bedgraph files were deposited in the GEO repository (Accession No GSE157612), all other datasets used in this study were previously deposited (Accession No GSE16808).Chromatin immunoprecipitation, sequencing and data analysis was done as described previously (Singh Primers List:TTAATACGACTCACTATAGGGGAGAGGAGAAGCCAGGCATTGA-3’UPF1RNAi-FP – 5’-TTAATACGACTCACTATAGGGGAGAGACCGTGGCCCAACAGG-3’UPF1RNAi-RP – 5’-(Bold is T7 promoter sequence)Socs36E-FP – 5’-CAGAAAACCGCACACAGACA -3’Socs36E-RP – 5’-CACACATCGGACTAACAGCG-3’Xrp1-FP – 5’-TCATAATGCTTGTGGGGCCT -3’Xrp1-RP – 5’-AGGGTCCCTCTAAACAAGCT-3’See Methods