CDKN2A/B and AICDA loci. Disruption of a CTCF site at AICDA locus increases AICDA expression. These data suggest that EBV controls lymphocyte growth by globally reorganizing host genome architecture to facilitate the expression of key oncogenes.Epstein-Barr virus (EBV) immortalization of resting B lymphocytes (RBLs) to lymphoblastoid cell lines (LCLs) models human DNA tumor virus oncogenesis. RBL and LCL chromatin interaction maps are compared to identify the spatial and temporal genome architectural changes during EBV B cell transformation. EBV induces global genome reorganization where contact domains frequently merge or subdivide during transformation. Repressed B compartments in RBLs frequently switch to active A compartments in LCLs. LCLs gain 40% new contact domain boundaries. Newly gained LCL boundaries have strong CTCF binding at their borders while in RBLs, the same sites have much less CTCF binding. Some LCL CTCF sites also have EBV nuclear antigen (EBNA) leader protein EBNALP binding. LCLs have more local interactions than RBLs at LCL dependency factors and super-enhancer targets. RNA Pol II HiChIP and FISH of RBL and LCL further validate the Hi-C results. EBNA3A inactivation globally alters LCL genome interactions. EBNA3A inactivation reduces CTCF and RAD21 DNA binding. EBNA3C inactivation rewires the looping at the  The dynamic and temporal changes of host genome architecture during Epstein-Barr virus (EBV) transformation are not well known. Here the authors transform human primary B lymphocyte into lymphoblastoid cell lines (LCLs) with EBV and show that the host 3D genome is rewired to facilitate expression of key oncogenes. DNA tumor viruses include Epstein-Barr virus EBV, human papilloma viruses (HPV), hepatitis B virus, and many others. These DNA tumor viruses cause a wide variety of different cancers, including B cell lymphomas, cervical cancer, head and neck cancers, and liver cancers, through expression of viral proteins or RNAs. Viral proteins activate or repress host transcription, leading to increased oncogene expression or reduced tumor suppressor expression2.Human tumor viruses and other microbes cause approximately 20% of human cancers each year3 and causes ~200,000 cases of cancers every year4. EBV causes Burkitt’s lymphoma, Hodgkin’s lymphoma, post transplantation lymphoproliferative disease (PTLD), AIDS CNS lymphoma, nasopharyngeal carcinoma, and 10% gastric cancers5. These EBV-associated cancers express different latent viral transcription programs that include EBV nuclear antigens (EBNAs) and latent membrane proteins (LMPs)6.EBV is the first human DNA tumor virus discovered more than 50 years ago7. These cells express the same viral latency genes as some EBV-associated cancers, including PTLD and AIDS CNS lymphomas. LCLs are therefore the ideal model system to study the molecular pathogenesis of EBV-associated cancers. 10 EBV encoded proteins are expressed in LCLs. EBNA1, EBNA2, LP, 3A, 3C, and LMP1 are required for EBV transformation5. After EBV infection, EBNA2 and EBNALP are the first EBV proteins expressed. EBNA2 is the major EBV transcription factor (TF) that activates the expression of other viral latency genes and many host genes13. EBNALP strongly co-activates EBNA2, partly through removal of transcription repressors and activation of EP30017. In addition, EBNA2 can also modulate the host TF DNA binding, such as RBPJ and EBF1, to enable combinatory TF interactions18. EBNA3A and EBNA3C repress CDKN2A/B expression to overcome virus-induced senescence24. EBNA3C also promotes cell cycle progression27. LMP1 activates the NF-κB transcriptional program through both the canonical and non-canonical pathways31.EBV transforms short-lived primary human resting B lymphocytes (RBLs) into continuously proliferating lymphoblastoid cell lines (LCLs), in vitro33. These enhancers are sometimes >500 kb away from the transcription start site (TSS), suggesting that a portion of viral-mediated chromatin interactions occur through long-range looping interactions33. Inactivation of the viral TFs EBNA2, EBNA3A, or EBNA3C can affect the looping at a number of host loci, mainly exemplified by MYC, CDKN2A/B, and BCL2L1113. However, it is not known if EBV infection of B cells causes global genome reorganization. Using POLR2A Chromatin interaction analysis followed by paired-end tag sequencing (ChIA-PET), we previously linked EBV enhancers to their direct target genes13, building the first virus-host 3D genome organization map. Deletion of MYC EBV enhancer sites by clustered regularly interspaced short palindromic repeats (CRISPR) lead to reduced MYC expression, showing the essential nature of many of these regulatory elements targeted by EBV13. An alternative silencing method using CRISPR interference (CRISPRi) also downregulated the expression of EBNALP enhancer targets17. Previous work characterizing EBV encoded TFs and LMP1-activated NF-κB subunits also show that they assemble EBV super-enhancers (ESEs)34. ESEs are also co-occupied by many host TFs and have extraordinary broad and high ChIP-seq signals for active enhancer marks, including the histone modification H3K27ac. ESEs are more sensitive to perturbation than typical enhancers, through both genetic and chemical means34. Combinatory analysis of ChIA-PET and CRISPR screen data in EBV-transformed LCLs also shows that ESEs control host genes that are important for LCL growth and survival35.Genome-wide analyses of EBV TFs using chromatin immune precipitation (ChIP) followed by deep sequencing (ChIP-seq) find EBV TFs and NF-κB subunits mostly bind to enhancer sites36. Host DNA is packaged in a way that remote enhancers and their direct target genes can communicate rapidly and efficiently, looping out many kilobases of DNA between them37. 3D genome interactions can be assessed using chromatin conformation capture followed by deep sequencing (Hi-C), and subsequently identifying the interaction frequencies between genomic loci41. Initial Hi-C studies indicate that the human genome is partitioned into A and B compartments. Genes within A compartment are frequently actively transcribed whereas genes within B compartment are generally repressed42. DNA tumor virus such as hepatitis B virus can preferentially position its genome at the active host chromatin, likely to be in the A compartments43. We investigated the effect of EBV infection on host genome organization to determine if EBV infection causes global genome architectural changes.To accommodate the small size of the nucleus, the host genome is packaged in extremely complex, yet ordered patterns39. We validate the Hi-C findings using POLR2A HiChIP in an EBV transformation time course experiment. We next extend our 4D studies by testing if specific EBV protein contributed to global host genome reorganization, using H3K27ac HiChIP in LCLs conditional for EBNA3A expression. In addition, we uncover a role for EBNA3C as a modulator of host genome organization. This represents a comprehensive study into how a DNA tumor virus rewires the host genome during infection, through viral transcription factors, to achieve immortal growth.In this manuscript, we seek to unravel the effects of EBV, and its viral TFs, on the host genome organization during infection and proliferation, in both space (3D) and time (4D). We generate RBL and LCL Hi-C maps, perform integrative analysis to compile the 4D nucleome of EBV infection of B cells45. However, little is known about the human RBL genome organization and how does it differ from LCL. To understand the dynamic and temporal changes in 3D genome organization between RBL and LCL, we generated Hi-C maps of healthy donor RBL and LCL from the same donor. Primary human B lymphocytes were purified through negative selection. LCLs were generated from these cells 4 weeks after EBV infection. RBLs and LCLs were crosslinked and DNA was cut by HindIII. The DNA ends were filled in with biotinylated dCTP and other nucleotides and ligated. After reverse crosslinking, purified DNA was sonicated. Streptavidin beads captured the ligation products. Purified DNA was paired-end sequenced to generate the Hi-C maps.LCL 3D genome organization is well studied. GM12878 LCL Hi-C, ChIA-PET, and HiChIP data all documented the high-resolution 3D genome architecture of these cells40. Hi-C contact frequency matrices of 100 kb chromosome bins were converted into eigenvectors for both RBL and LCL through eigenvector decomposition47. The signs of eigenvector values were determined based on Pearson correlation between the eigenvector and H3K4me3 signals. Positive eigenvector represents active chromatin (A compartment). Negative eigenvector represents repressed chromatin (B compartment). A and B compartment switch were evident comparing RBL with LCL  and transcriptionally repressed B (blue) compartments  method at 10 kb resolution were compared between RBL and LCL Fig. 49. In RBery Fig. .48. RBLs had 4187 contact domain boundaries while LCLs had 4915 contact domain boundaries  was evaluated at these genes42. Local interactions are defined as interactions between a genomic region and other genomic regions within a 3 Mb window. Distal interactions are defined as those between the same genomic region and other genomic regions outside the 3 Mb window. A negative DLR indicated more local interaction compared to distal interaction and enrichment in local looping. Cumulative DLR values at 25 kb resolution near TSSs were determined for LCL dependency factors in both RBLs and LCLs  correlated with poor prognosis in liver cancers55. During EBV transformation of RBL into LCLs, ATP1A1 and CD58 expression greatly increased by RNA-seq analyses  was used. RBLs and 4 week LCLs from the same donor were hybridized with BACmids targeting BUB3 and ESE. In RBLs, ~50% of the cells had monoallele colocalization and ~50% biallele non-colocaliztion. In LCLs, ~50% of the cells had biallele colocalization and ~50% monoallele colocalization  grow normally in the presence of 4-hydroxytamoxifen (4HT). In the absence of 4HT, LCLs enter growth arrest with increased p14ARF and p16 INK4A expression58. EBNA3A is tethered to DNA through interactions with host TFs33 and regulates host gene expression62. EBNA3A can bind to RBPJ, a Notch pathway protein, and USP46/USP12 or CtBP65. EBNA3A is also involved in long range enhancer-promoter interaction13. However, it is not known if EBNA3A can affect the global host 3D genome organization.EBNA3A is essential for EBV transformation of RBLs into LCLs. Recombinant EBV deleted for EBNA3A fails to immortalize RBLsTo evaluate the genome-wide effect of EBNA3A on host 3D genome organization, H3K27ac enriched enhancer interactions with their direct targets were analyzed using HiChIP assay. Conditional EBNA3AHT LCLs were grown under permissive or non-permissive conditions for 14 days followed by H3K27ac HiChIP.p < 0.05). In LCLs grown under non-permissive conditions, HiChIP identified much less loops, with 2508 significant H3K27ac loops between enhancer-enhancer, enhancer-promoter, CTCF site, enhancer-unannotated site, or promoter-promoter were identified (p < 0.05). In the EBNA3A on condition, 2.4% of the loops were between CTCF sites, 35.5% of the loops were enhancer-enhancer, 52% of the loops were enhancer-promoter, and 10% of the loops were enhancer-unannotated site. EBNA3A inactivation slightly increased the fractions of loops between CTCF site (5%) and greatly increased enhancer-unannotated site (40.7%), but greatly decreased enhancer-promoter loops (20.5%), while enhancer-enhancer interactions remained similar (33.5%)  Fig. .Fig. 5EBRBPJ and CCND2  was performed in EBNA3C conditional LCLs. Conditional EBNA3C LCLs grown under permissive or nonpermissive conditions were crosslinked and lyzed. DNA was first cut with HindIII and ligated. DNA was purified after reverse crosslinking and cut again with Dpn II. The DNA fragments were circularized by ligation and inverse PCR was done to amplify the DNA ligated to viewpoint followed by deep sequencing. The viewpoint . These results suggest to a model in which EBNA3C represses local chromatin interactions, particularly enhancer-promoter interactions, at the CDKN2A/B locus to repress genes activated as part of the cellular senescence during EBV infection and LCL establishment.EBNA3C can regulate long-range looping at several genes essential for LCL growth, including AICDA encodes for a vital B cell protein, AID, which is important in regulating class switch recombination and somatic hypermutation67. AID-induced chromatin breaks at the MYC locus are required for chromosome translocations that result in the MYC and immunoglobulin enhancer fusions prevalent in Burkitt’s lymphomas68. Interestingly, EBNA3C up-regulates AICDA expression, which plays a role in increasing global mutational burdens in LCLs69. A detailed analysis of the AICDA locus identified a key CTCF site downstream of AICDA, looped towards multiple sites upstream shown by LCL CTCF ChIA-PET . With 4C-seq analysis, we speculate that the local chromatin conformation maybe accessible upon EBNA3C activation and condensed upon EBNA3C inactivation.eam Fig. . To undeAICDA expression. To reduce potential off-target effects through long-termed expression of the Cas9 protein and its associated sgRNAs, we nucleofected LCLs with ribonucleoprotein (RNP) complexes of Cas9 and sgRNAs . Single cells were cloned out from nucleofected LCLs through serial dilution, and the expression of AICDA quantified for clones that successfully underwent deletion (100% of motif deleted), or clones that received RNPs but did not have an observable deletion (0% of motif deleted) . Taken together, EBNA3C disrupts local CTCF interactions at the AICDA locus to increase the expression of this key B cell protein, thereby increasing the mutational burden of LCLs.CRISPR/Cas9 deletions was performed find a causal role of the CTCF motif on ed) Fig. . DisruptHere we report that the host genome undergoes global reorganization during a prototypical human DNA tumor virus infection and transformation of host cells into cancer-like cells. Defining the nuclear spatial and temporal changes during viral infection is a crucial discovery step towards understanding the molecular pathogenesis of virus infection. Understanding how these changes occur during immortalization will not only provide insight into EBV oncogenesis, but also elucidate transcriptional regulation during normal B cell activation.13. This map highlighted the key components governing the regulation of key oncogene expression, through validation studies using CRISPR deletion of enhancers, or via essential genes identified through a companion CRISPR screen in LCLs35. However, little is known about the temporal genome organizational changes during EBV infection and subsequent growth transformation, and how these changes drive gene expression. Therefore, it is important to track the 3D genome organizational changes between RBLs and LCLs systematically.Previous incorporation of LCL POLR2A ChIA-PET data with viral and host TF ChIP-seq data allowed the generation of the LCL 3D genome landscape, linking LCL enhancers to their direct target genes44. However, the large number of cell input required for a ChIA-PET experiment presented a technical hurdle to study primary RBLs, and the infection process. The lower cell number input of the Hi-C assay also allows a robust construction of a high resolution LCL 3D genome interaction map, albeit at the cost of a higher depth of sequencing39. In this study, we therefore first used Hi-C to define the RBL genome organization map and compared it with LCLs, to delineate the temporal changes following viral infection. We then used a combination of H3K27ac HiChIP and 4C-seq to further determine the contributions of individual EBV oncogenes on host 3D genome organization.POLR2A ChIA-PET selectively enriches for enhancer-enhancer and enhancer-promoter interactions linked by POLR2A70. Similarly, we discovered that human RBLs and LCLs also significantly differed in their chromatin contact domain numbers. Furthermore, we found that in comparison to uninfected RBLs, LCLs have increased local chromatin interactions at ESEs and their associated genes, indicating that EBV is involved in SE assembly to alter host genome organization. LPS and IL4 signaling activate a cascade of transcription factors, such as NF-kB and STAT6; similarly, LCLs have high NF-kB activity and express 4 essential EBNAs. EBNAs can modulate host histone modifying enzymes to alter the epigenetic landscape, which may lead to differential binding of host TFs and looping factors on DNA72. In parallel, EBV infection can also affect global host DNA methylation, which can affect the specific binding of specific TFs, notably CTCF75. CTCF can function as insulator, blocking the spread of chromatin modifications from one region to the next region76, while cohesin subunits form rings to extrude out DNAs between enhancers and their regulated genes. Activated mouse B cells have much higher RAD21 ChIP-seq signals at the induced loops, but only modestly higher CTCF ChIP-seq signals;70 further work will be needed to understand how B cell activation can affect the DNA occupancy of these looping factors. We elucidated in this study, that DNA occupancy of looping factors CTCF, SMC1, and RAD21 can be affected by EBNA2, 3A, and 3C in LCLs. Furthermore, EBNALP frequently co-localizes with looping factors, although the EBNALP-specific interactions may be difficult to tease out at the moment, due to the lack of a conditional genetic system16. Future work should focus on the detailed molecular mechanism through which EBV alters looping factor DNA binding during infection and transformation.Mouse resting B cells undergo dramatic genome organizational changes when stimulated by LPS and IL4, resulting in vastly increased numbers in contact domains77. Reduced expression of ESE key EBV components  significantly reduced the looping. The assembly of ESE may lead to the formation of new contact domains, as ESEs can interact with multiple downstream target genes through long ranged chromatin interactions. In addition to looping factors studied here, ZNF143 and YY1 are also key factors whose roles are yet to be fully elucidated79. It has not escaped our notice that previous EBNA ChIP-seq studies identified a significantly enriched of these motifs near EBNA binding sites80. A thorough, global analysis of how EBV usurps the B cell transcription program through host TFs will be paramount to better understand the intricacies of both viral and host processes.SEs play critical roles in cell growth and differentiation58. All LCLs were grown in RPMI (Gibco) supplemented with 1% L-glutamate, 1% Pen/Strep, and 10% FBS. B958 ZHT, P3HR1 ZHT cells, and virus induction were previously described82. 1μg anti- CTCF (Abcam Cat: #ab70303) antibody was used for CUT & Run; 1μg anti-H3K27ac (Abcam Cat: #ab4729) antibody was used for CUT & Run; 12μg anti-RAD21 (Abcam Cat: #ab992) antibody was used for ChIP-seq. 8ug Anti-RNA Polymerase II RPB1 (Biolegend Cat: #664906) antibody was used for HiChIP.The GM12878, EBNA3A-HT, and EBNA3C-HT LCLs were previously describedDe-identified blood cells were obtained from the Gulf Coast Regional Blood Center, following institutional guidelines. The Epstein-Barr virus studies described in this paper were approved by the Brigham & Women’s Hospital Institutional Review Board. B cells were purified via negative selection, with RosetteSep Human B Cell Enrichment Cocktail and EasySep Human B Cell Enrichment Kits (StemCell Technologies), according to the manufacturer protocols. LCLs were generated from RBLs infected with B95.8 strain EBV for 28 days.Hi-C was done following the Arima-HiC Kit protocol . 10 million cells were crosslinked with 2% formaldehyde for 10 min at room temperature. The reaction was quenched using 200 mM glycine for 5 min. Samples were washed in PBS once, then resuspended in 1 ml of cold Hi-C lysis buffer  and kept on ice for 15 min. Nuclei were spun down at 2500 x g for 5 min and the supernatant were discarded. Pelleted Nuclei were washed once with 1 ml of cold Hi-C lysis buffer then resuspended in 450ul dH2O containing 15ul 10% SDS shaking at 900 rpm for 1 h in 37 °C. 75ul 20% Triton X-100 was added to quench the SDS. The chromatin was digested with 600 units HindIII (New England Biolabs) at 37 °C overnight with shaking. Restriction enzyme was inactivated by incubating 30 min at 65 °C. The single-strand overhangs were filled in with 37.5 ul of 0.4 mM biotin-14-dCTP (Life Technologies) by 10ul of 5U/ul Klenow DNA polymerase (New England Biolabs) for 1 h at 37 °C. The biotinylated DNA was suspended in 6.6 ml ligation mix . DNA was ligated overnight at 16 °C by slow rotating. Proteins were removed by adding 30ul of 10 mg/ml proteinase K (New England Biolabs) and incubated at 55 °C for 30 min. Crosslinking was reversed with incubation at 65 °C overnight. DNA was purified with Phenol-Chloroform extraction and followed by ethanol precipitation. Biotin dCTP at non-ligated DNA was removed with 5 units T4 DNA polymerase. 5ug of Hi-C DNA pellets were dissolved in 130ul 1xTris buffer and sonicated. Covaris LE220 with parameter  sheared DNA to 300–700 bp fragments. Hi-C library was prepared using size selection with Ampure XP beads. Biotinylated DNA was recaptured by 100ul Dynabeads MyOne C1 Streptavidin beads (Life Technologies). Sequencing libraries were directly amplified on C1 beads with 8 cycles of PCR using illumine primers and protocol. After PCR, solutions were placed on a magnet and libraries were eluted into new tubes. The libraries were then purified with DNA Clean and Concentrator columns to a volume of 10ul. The sequencing libraries were checked using an Agilent Bioanalyzer 2100 and quantified using a Qubit (Life Technologies). Libraries were sequenced on an Illumina NextSeq 500 with 75 cycles of paired-end reads.8. In brief, 1 × 107 cells were fixed with formaldehyde and lyzed. Cell lysates were sonicated and diluted. The lysates were precleared with protein A beads and the incubated with antibody overnight at 4 °C, rotating. The protein-DNA complexes were captured by protein A beads. After extensive wash, the protein-DNA complexes were eluted and reverse crosslinked. Qiagen PCR purification kits were used to purify the DNA. Libraries were prepared using Illumina DNA library prep kit (E7645S).ChIP-seq assays were done as previously describedCut and run was done following the protocol from CUTANA™ ChIC/CUT&RUN Kit . In brief, 0.5 million cells per sample were harvested and washed with PBS once. Nuclei were isolated with nuclear extraction buffer and then captured with activated ConA beads. 1ug antibody targeting protein of interest was added to nuclei solution and incubate at 4°C with shaking overnight. DNA was cleaved with PAG-MNASE and released to solution. DNA was then purified. The library was prepared using Illumina DNA library prep kit (E7645S).45 using H3K27ac antibody (Abcam). Briefly, 1 × 107 cells for each biological replicate were collected and cross-linked by 1% formaldehyde for 10 min. Chromatin was digested using MboI restriction enzyme (New England Biolabs). DNA ends were filled in with Biotin-14-dATP (Thermo Fisher) and other nucleotides and then ligated. After sonication, sheared chromatin was pre-cleared and 3-fold diluted as described in ChIP method and then incubated with 4ug anti-H3K27ac antibody at 4 °C for overnight. Chromatin-antibody complex was captured by Dynabead Protein-A bead, followed by capture with Streptavidin C-1 bead (Thermo Fisher). Libraries were generated using Tn5 followed by PCR. HiChIP samples were size selected by PAGE purification (300–700 bp). All libraries were sequenced on the Illumina NextSeq 500. Each sample has an average depth of ~20 million reads.H3K27ac HiChIPs from LCLs conditional for EBNA3A expression were performed as previously describedRBLs and 4-week LCLs from the same donor were swelled in 0.075 M KCl at room temperature for 20 minutes and fixed with 3:1 Methanol: Acetic Acid. BACmids RP11-773A8 and CTD-2117B10 were labeled with green or orange dUTP (Abbott Molecular). Fixed cells on the slides were hybridized with the labeled BACmid probes. The nuclei were stained by DAPI.83 to obtain putative interactions with default parameters under genome build hg19. Contact maps were generated with bins under different resolutions  in order to fit downstream analysis at different scales. Contact maps were normalized with iterative correction and eigenvector decomposition (ICE)47 using the version implemented within the HiC-Pro pipeline. Normalized contact maps with necessary re-formatting were then used to generate visualization files (.hic) and Pearson correlation of contact matrix using Juicer tools (version 1.5)84 with ‘pre’ command.Raw reads for LCLs and RBLs were processed with HiC-Pro pipeline first (version v3.1.0)pca’ function implemented inside R mixOmics package85. Eigenvector was then smoothed by a moving average with a five-bin window. To correctly identify A from B compartments, Pearson correlation was calculated between Eigenvector and H3K4Me3 ChIP-seq data signals downloaded from ENCODE project (Supplementary Table https://www.encodeproject.org/). For each chromosome, positive correlation resulted no changes of Eigenvector while negative correlation resulted sign flipping of the whole Eigenvector.Eigenvector (A/B compartments) of RBLs and LCLs was estimated with 100 kb bin contact maps using ‘86 with parameters ‘-is 500000 -ids 200000 -im mean -bmoe 1 -nt 0.1’ based on normalized contact matrix under 25 kb resolution. Overlaps of domain boundaries were determined using ‘findOverlaps’ function in R GenomicRanges package. Starting from the first boundary on every chromosome, regions between two consecutive boundaries after merging of overlapped boundaries were defined as contact domains.Continuous putative contact domain boundaries in RBLs and LCLs were identified using insulation score42 with minor changes to avoid infinities:35, DLRs surrounding transcription starting sites of genes were aggregated together to show changes between RBLs and LCLs. DLR scores are zero center normalized to avoid potential sequencing depth biases in comparison. DLR of all genes were calculated as control.Estimation of distal to local ratios (DLR) of genome-wide 25 kb bins were accepted from recently reported protocol21. For ENCODE ChIP-seq, proper datasets were selected to minimize potential biases if multiple labs producing the same sequencing library87. ChIP-seq data for histone modification and CTCF in RBLs and LCLs was used to visualize chromatin status changes associated to Hi-C interaction changes in Fig. ChIP-seq and RNA-seq data in Hi-C analysis was downloaded from ENCODE project (Supplementary Table We postulated that transcription factor (TF) occupancy, as quantified by ChIP-seq reads, around the top 1000 statistically differential loops identified from H3K27ac HiChIP in EBNA3A On and Off conditional LCLs would allow us to infer potential TFs that play a role in loop formation and transcriptional activation in each state of EBNA3A. Input normalized TF ChIP-seq occupancy data was downloaded and processed for GM12878 LCLs from ENCODE. The signal of each TF was computed within +/−2kb around each of the top 1000 differential HiChIP loops from (1) EBNA3A On and (2) EBNA3A Off. A Wilcoxon test was performed to test the signal of each TF in the EBNA3A On versus Off condition, and the log2 fold-enrichment of TF signal in On/Off was plotted.88. The CDKN2A 4C-seq library was prepared using HindIII as the first restriction enzyme, followed by DpnII. The AICDA 4C-seq library was prepared using NlaIII as the first restriction enzyme, followed by Csp6I.EBNA3C-HT growth, withdrawal, and formaldehyde crosslinking were performed as described above. The 4C-seq was done as previously describedhttps://benchling.com).sgRNAs against the CTCF motif at the AICDA loci were designed with Benchling  in SF media (Lonza), using the DN-100 program. Cells were incubated for 24–48 hours at 37 C, before dilution seeding into 96 well plates to obtain cells from single deletion clones. An eGFP vector (Lonza) was co-trans nucleofected to assess nucleofection efficiency.Purified Cas9 protein and sgRNA were obtained from Synthego. A detailed protocol for the assembly and nucleofection of ribonucleoprotein RNP complexes was previously described89. PCR sequencing was performed (Eton Biosciences) for the PCR product from both control or CTCF motif-targeted cells. The sequencing output was parsed using the Tracking of Indels by Decomposition (TIDE) software .Genomic DNA from Cas9 RNP nucleofected cells (pooled or single-cell clones) was extracted and PCR was performed to amplify the deletion, as previously described90. In brief, a reduced hg19 genome was first constructed with a in silico digestion using HindIII or NlaIII. Illumina sequencing barcodes and primer sequences are trimmed and the resulting reads mapped onto the reduced hg19 genome with bowtie v2 (-N 0 −5 0)91. Self-ligated and undigested fragments are removed, and subsequent differential 4C-seq interactions identified using the 4C-ker package90. The cis-interacting regions were determined by a Hidden Markov Model, using the “nearBaitAnalysis” function. Differential interactions between the EBNA3C On and Off conditions were determined using DESeq2 through the “differentialAnalysis” function with a p-value cutoff of 0.05.4C-seq data analysis was performed as previously described92 with default settings and parameter --SPMR to generate sequencing coverage tracks.Sequencing reads for H3K27Ac, CTCF and Rad21 in EBNA3A on and off conditions were aligned to human genome hg19 using Bowtie v2 . Uniquely aligned reads were merged from replicate samples and filtered to remove those located in blacklist regions. Peaks were called using MACS v2.2.7https://www.encodeproject.org) and all other data were previously generated as described13.All GM12878 ChIP-seq data sets were downloaded from the ENCODE project portal  with the HiC-Pro pipeline (version v3.1.0) against a MboI digested hg19 genome build. Long-ranged interactions were identified with hichipper v0.7.3 using the parameters for peaks . Read depth normalization for long-ranged interactions identified was performed based off the number of valid PET counts per library, as determined by HiC-Pro, for visualization purposes via the WashU Epigenome Browserp < 0.05). Input normalized ChIP-seq reads were identified +/− 2 kb around loop anchors to calculate TF occupancy around anchors.The diffloops R package was used to read in long-ranged interactions called from above for downstream filtering and analysis of loops. Loops with FDR < 0.01, width > 5 kb, and in addition did not have more than 4 PETs in one biological replicates and 0 PETs in the other, were retained. Differential loops were identified in diffloops using edgeR (83 are converted to a bedpe file with R package HiCcompare94. Then miniMDS (default parameters) is used to inferring genome 3D structure based on a multidimensional scaling method49. Finally, the output structural files from miniMDS were converted to g3d format with g3dtools95 and visualized using WashU genome browser95.To illustrate the difference of LCL and RBL at a genome structure level. A 3D genome structural visualization is generated based on Hi-C data at a 100k resolution. Firstly, ICE normalized matrix and related bed file generated from HiC-ProFurther information on research design is available in the Supplementary InformationReporting Summary


Escherichia coli promoter is explored by combining multiple deep learning models. First, “DRSAdesign,” which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, “Ndesign,” which relies on generating random sequences carrying functional −35 and −10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6‐mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL‐assisted metabolic engineering.Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active  In this study, deep learning tools are developed to design promoters and predict their activity. For “DRSAdesign,” promoters are generated using a diffusion model, followed by promoter recognition and strength prediction. For “Ndesign,” constraint‐based promoters are generated and their strength was predicted. Controlling gene expression levels is important for recombinant protein expression and engineering of microbial cell factories. However, there is a limited number of naturally existing strong promoters to facilitate high protein expression. Current techniques mainly rely on mutagenesis or shuffling of key elements for screening novel promoters, which is labor‐intensive. Controllable design of stronger promoters is beneficial but remains challenging. Previously, a generative deep learning (DL) model was implemented for the de novo design of highly diversified promoters, suggesting combined DL models may provide solutions for controllable design of active promoters. However, it is still necessary to further explore the possibility of using DL models for de novo promoter design, in order to address previous limitations and increase the number of available strong promoters.Promoters are DNA sequences that regulate gene transcription.1 The  RNA polymerase (RNAP) occupancy, and the distribution of sequential fragments. The activity of constitutive promoters is also related to the concentration of free RNAP and their minimum requisite on RNAP concentration. Several bioinformatics tools have been developed to analyze the sequential composition rules for regulating promoter activity. Recognition of the critical components that affect promoter activity is pivotal for promoter design. Bacterial promoters contain key functional motifs known as −10 and −35 motifs that are recognized by RNAP to facilitate RNA transcription, which affects protein expression levels. Therefore, in several studies promoters were engineered by shuffling the −35 and −10 motifs. Joseph et al. suggested that the combination of TTTACG (−35 motif) and TATAAT (−10 motif) displayed higher promoter activity in Escherichia coli (E. coli) than the other 6‐mer combinations. In addition, the spacer length between the −35 and −10 motifs can also critically affect promoter activity. Excessively long and short spacers can both contribute to weak promoter activity, while 17‐bp spacers are most likely to form high‐strength promoters. Previous studies illustrated several subtypes of E. coli promoters with distinct functions, such as inducing transcription at a certain growth phase or under heat shock. Therefore, accurate prediction of promoter activity across different subtypes remains challenging, despite the availability of several tools displaying high accuracy in predicting strong and weak promoters.Understanding of the mechanism behind promoter activity has contributed to promoter research and design. Fundamental studies have suggested that promoter activity is mainly affected by the accommodation of functional motifs,7 RNA  A growing number of DL methods have been developed for predicting promoter strength and generating novel promoters. However, many of the developed models were not adopted for practical engineering of active promoters. Wang et al. adopted a supervised model to predict promoter transcription levels and generate promoters using a generative adversarial network (GAN). The generated promoters with predicted high activity were experimentally validated and aligned with a reported strong constitutive promoter, J23119. However, many of the generated promoters displayed very low activity, suggesting the need for further exploration of a better generative model or other tools to assist in the promoter design task. GAN was also implemented for the architecting of deep exploration networks (DENs) by Seelig et al., which optimized their capability for engineering desired promoter sequences. Generative models combined with supervised models provide possibilities for artificially designing promoters. However, further research is needed to determine to what extent DL methods can contribute to the generation of functional promoters.Bioinformatics provides potential solutions for accurate and controllable design of promoters compared with classic methods relying on mutagenesis or engineering of functional motifs.23 A gFigure We integrated a diffusion model‐based generative network into DRSAdesign, which aims to address several limitations of previous generative models. For example, GAN is unstable during training and it is hard for variational autoencoders to generate high‐quality samples. Taking advantage of promoter prediction models, we developed tools to adopt a promoter length of 50 bp, rather than 81 bp in a previous study, to fit the promoter‐generating model. These tools are used to predict whether promoters are real or fake as well as their strength. Notably, DRSAdesign is based on a top‐down process, in which generated promoters are classified as real or fake, followed by prediction of their strength. This process was developed because single models may not provide sophisticated results. Simultaneously, constraint‐based generation of sigma70 promoters was conducted to train our developed supervised model, and the model was further used to predict the strength of novel generated promoters; this method is named Ndesign , and the combined methods 31 ands Figure .E. coli. Another model, PromoA, was created and trained using promoter transcription level data. The dataset, which we named NDB, contains E. coli native promoters obtained from global TSS maps created by Thomason et al. The database contains 11884 non‐redundant promoters, of which 3098 are not constitutive. Regarding sample diversity and complexity, PromoA achieved a Pearson correlation coefficient (PCC) of 0.31 by 10‐fold validation . The 11884 non‐promoters were obtained by randomly retrieving 50 bp from the 81 bp of non‐promoter sequences in RegulonDB. PromoR trained with the combined dataset achieved an accuracy of 0.8861 using five‐fold cross‐validation  by a series of steps (Z), and the model learned to reverse the noising process which can recover the sample to the denoised state  PromoR predicted the real promoter portion (RPP); and 2) a sequence logo (SL)39 wass Figure . Generat In the present study, we used enhanced GFP (EGFP) to evaluate the strength of generated promoters. We selected promoters with PromoS scores above 0.9 and the top 50 PromoA scores for inducing egfp expression. Promoter activity was calculated as fluorescence intensity (FI) divided by the optical density at 600 nm (OD600). Five promoters showed a less than 20% increase in FI/OD600 compared with the negative control (a non‐promoter sequence) and were hence considered as non‐active promoters  from the generated promoters showed that unexpected logos were significantly minimized  and −10 (TATAAT) motifs of the sigma70 promoter and the spacer length varied from 16 to 18 bp. To explore the sequential content, we generated 1 500 000 promoters and selected those with a PromoS score of 1 and the top 200 PromoA scores at different spacer lengths for inducing egfp expression.The weak correlation between predicted and real promoter strength has inspired our further research to simplify the design protocol by learning from data with distinctive features. cription.42 Amo600 values than the negative control. By contrast, two promoters displayed only 17.1% and 11.5% higher FI/OD600 values than the negative control and were hence considered as inactive promoters  to investigate possible 6‐mers that can comprise the −10 motif. Random promoters were generated using an optimal spacer length of 17 bp according to the previous study43 . This time, all generated random promoters (including random 6‐mers) with TATAAT at the −35 position were predicted as real promoters, and only 8.54% of TATAAT in combination with other possible 6‐mers were predicted as weak promoters  for generating novel promoters in this study. Even though 90% of the generated promoters were active, their activity varied by more than 150‐fold; regardless, they were all predicted as strong promoters  had a 90% chance to form real promoters . Intriguingly, all of the generated strong promoters had a spacer length of 16 or 17 bp, indicating that: 1) 17 bp is not the only proper spacer length and 2) a length of 18 bp is not favorable for strong promoters. Many natural E. coli promoters have a spacer length of 18 bp, which may serve as an evolutionary strategy to control downstream gene expression. Even though PromoNet trained with constraint‐based promoters gave a higher PCC, the low amount of training data may limit the performance of PromoNet for the exploration of other sequences than the −35 and −10 motifs.The models trained with NDB showed less correlation between predicted and real promoter activity,5 whic4Our study shows that DL tools can be implemented for characterizing promoter functions and guiding promoter design. DL tools are more robust for promoter recognition than promoter strength prediction. We highlight the importance of data regularity for model accuracy. The DL tools designed in this study can assist in the design of novel promoters and provide guidance for the development of other networks.5E. coli strain BL21 was used as a recombinant protein expression platform. egfp was synthesized and cloned into the NcoI and XhoI sites . The cells were collected by centrifuging fermentation samples at 12 000 g for 5 min and resuspended with Tris‐HCl (pH 8.0). The cells were sonicated using ultrasonic oscillation . The obtained cell lysate was centrifuged at 12 000 g for 5 min and the supernatant was collected to determine the intracellular eGFP levels. FI was measured at an excitation wavelength of 395 nm and an emission wavelength of 509 nm using a microplate reader . Promoter activity was calculated as FI divided by OD600.Cell density was determined by measuring OD This dataset also contains 3382 fake promoter sequences. The promoters in RegulonDB were originally 81 bp in length, but they were reduced to 50 bp according to their −35 and −10 motifs distribution  represents the output from the layer, Wi represents the parameters given to the CNN layer, and Ws represents certain convolution configurations to make the dimensions of input and output identical.The networks for strong and weak promoter classification (PromoS), transcription level prediction (PromoA), and promoter recognition (PromoR) were all built by integrating ResNet31 andd Figure . The forQ, K, and V are vectors of queries, keys, and values of dimension kd, where kd is the size of the attention keys.The self‐attention mechanism is used to link the significance of the partial information to the overall information. The equation is given below, where Due to all three networks being shared with the same sample length and type, initial network optimization was carried out based on PromoS, and the other two networks adopted the same architecture with minor differences. For network optimization, the prediction accuracy of PromoS was used to provide feedback. CNN, ResNet, self‐attention, MaxPool, and dropout modules were integrated in the model. Promoter features extracted using one‐hot encoding and pseDNC were independently or combined used as input, and an output value of 1 was considered as a strong promoter and an output value of 0 was considered as a weak promoter. The mean squared error (MSE) was used for optimization. the features of an input promoter were extracted using the one‐hot encoding method. The data point was denoted as X0. The forward diffusion process was based on gradually adding Gaussian noise to X0 through T steps, and the Gaussian noise with variance can produce a new latent variable Xt. Then the developed network was trained to recover the original data by reversing the noising process. The reverse diffusion process can generate new data which highly mimic the original ones.PromoDiff was a diffusion model with UNet integrated. UNet was created for semantic segmentation. It contained an encoder and decoder block for feature extraction and sample size recovery. To learn features of promoters, ResNet and self‐attention were used on the encoder side, and to recover the promoter size we adopted upsampling on the decoder side. For the diffusion model,29 theThe accuracies of PromoS and PromoR for strong and weak promoter prediction and real and fake classification were calculated by dividing the number of correct predictions by the total number of samples. The accuracy of PromoA was the PCC of predicted and real promoter activity. The performance of PromoS was evaluated using sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathew's correlation coefficient (MCC), and the receiver operating characteristic (ROC) curve as previously reported.20(4The authors declare no conflict of interest.X.W. and J.Z. conceived the project and wrote the manuscript. X.W., K.X.,Y.T., S.Y., and X.Z. designed and performed all the experiments. X.W. and J.Z. analyzed the results.The peer review history for this article is available in the Supporting Information for this article.Supporting InformationClick here for additional data file.Supplementary Data S1Click here for additional data file.Supplementary Data S2Click here for additional data file.Supplementary Data S3Click here for additional data file.Supplementary Data S4Click here for additional data file.Supplementary Data S5Click here for additional data file.Supplementary Data S6Click here for additional data file.Supplementary Information: Record of Transparent Peer ReviewClick here for additional data file.


The placenta is an essential organ that supports the growth and development of the fetus during pregnancy. However, cell type-specific enhancers and transcription factors (TFs), and the mechanisms underlying the maintenance and differentiation of trophoblast stem cell (TSC) populations in the human placenta remain elusive. Here, using human TSCs as a model system, we identify 31,362 enhancers that are enriched with the motifs of previously reported TSC-pivotal TFs, including TEAD4, GATA2/3 and TFAP2C. Subsequently, we identify 580 super-enhancers (SEs) and 549 SE-associated genes. These genes are robustly expressed in the human placenta and include numerous TFs, implying that SE-associated TFs (SE-TFs) may play crucial roles in placental development. Additionally, we identify the global binding sites of five TSC-pivotal SE-TFs , revealing that they preferentially co-occupy enhancers, regulate each other and form a trophoblast-active gene regulatory network. Loss-of-function studies unveil that the five TFs promote self-renewal of TSCs by activating proliferation-associated genes while repressing developmental genes. We further reveal that the five TFs exert conserved and unique functions on placental development between humans and mice. Our study provides important insights into the roles of human TSC-pivotal TFs in regulating placenta-specific gene expression programs. The placenta is a short-lived but vital organ that sustains the growth of the fetus during pregnancy. It originates from the outer part of the blastocyst, which is called the trophectoderm, and plays versatile roles, including hormone production, oxygen and nutrient supply to the fetus, fetal waste removal and fetal protection . The humin vitro counterparts of CTs, since they can self-renew and exhibit multipotency that gives rise to STs and EVTs. As human TSCs can faithfully recapitulate the in vivo differentiation of CTs toward STs and EVTs, they are considered reliable in vitro models for studying human placental development and placenta-associated pregnancy complications.Recently, human trophoblast stem cells (TSCs) were derived from the blastocyst and the first trimester placenta . TSCs arGene expression programs are precisely controlled by orchestrated actions of cell type-specific transcription factors (TFs) and enhancers that function as hubs to integrate chromatin state and TF binding . Combinacis-regulatory elements have been reported. In addition, their functions in human TSCs have not been thoroughly addressed. Importantly, it is largely unknown to what extent key human TSC-active TFs share their targets and exert conserved or distinct functions between humans and mice. Even though human and mouse placentas play equivalent roles and have numerous functionally conserved genes, there are some discrepancies in their morphology, cellular organization and gene expression patterns  of the enhancer-associated protein EP300 and subsequently defined SEs and SE-associated genes. SE-associated genes include many TFs that are highly expressed in the human placenta. To gain insights into the molecular mechanism by which TSC-pivotal TFs modulate placenta-specific gene expression, we also mapped the global binding sites of five TSC-pivotal SE-TFs in human TSCs, revealing that they collaboratively regulate placenta-specific gene expression programs.l-ascorbic acid (Sigma-Aldrich). When the cells reached 70–80% confluency, they were trypsinized with TrypLE (Gibco) for 8 min at 37°C and the detached cells were seeded on Collagen IV-coated dishes at a 1:3 split ratio. TSCs were cultured in a 37°C and 5% CO2 incubator. The medium was replaced every day with a fresh medium.Human TSCs were obtained from Dr. Takahiro Arima and cultured as previously described . Briefly6 HEK293T cells were transfected with 2.5 μg of the pLKO construct with a specific shRNA, 1.7 μg of Δ8.9 and 0.8 μg of VSVG using 15 μl of a GenJet transfection reagent (SignaGen) to generate lentiviruses expressing a specific shRNA. After 24 h, the medium was replaced with the TSC medium. After cultivating at 37°C for 24 h, the viral supernatant was collected, filtered with a syringe filter (0.45 μm) and used to infect TSCs. To knock down individual TFs, 2.5 × 105 TSCs were infected with pLKO-shRNA viral particles in a well of a 12-well plate. Infected cells were incubated overnight, and the medium was replaced with fresh TSC medium supplemented with puromycin to select out the infected cells. To determine the KD efficiency of each TF and profile global gene expression, we harvested the cells after 3 days of selection to get complete KD of an individual TF.Short hairpin RNAs (shRNAs) against FOS, GATA2, MAFK, TEAD4, TFAP2C, NCOR2, RCOR1 and ZNF362 were purchased from Sigma-Aldrich . A total4 cells/ml in 96-well plates. Proliferation was measured using the cell counting kit-8  following the manufacturer's instructions at four time points beginning on the day of seeding. Briefly, the medium was aspirated from each well and replaced with 100 μl of TSC medium with 10 μl of CCK-8 reagent. The plates were incubated at 37°C for 2 h and their absorbance was measured with the Tecan M1000 Plate Reader at 450 nm.TSCs were infected with pLKO-shRNA viral particles for 24 h, and then cultured in TSC medium supplemented with puromycin for 2 days. The infected cells were seeded at a density of 2.1 × 10TSCs were infected with pLKO-shRNA viral particles for 24 h, and then cultured in TSC medium supplemented with puromycin for 3 days. Apoptotic cell populations were measured using the NucView Dual Apoptosis Assay Kit for Live Cells . The infected cells were dissociated into single cells and washed with phosphate-buffered saline (PBS). The cells were resuspended in 200 μl of 1× Annexin V binding buffer with 5 μl of Sulforhodamine 101–Annexin V (Texas Red) stock solution and incubated for 30 min at room temperature, protected from light. Cells were washed once with 1× Annexin V binding buffer and filtered using a cell strainer . Flow cytometry was performed on a BD LSRFortessa SORP Flow Cytometer (BD Biosciences) and the data were analyzed with FlowJo .http://bioinfo.ut.ee/primer3/). All primer sequences are listed in –ΔΔCT method. The relative expression of a gene was calculated from an average of three replicates.Complementary DNA (cDNAs) were generated from 500 ng of total RNA using qScript (Quanta) and then diluted 20 times with DNase-free water. A 2 μl aliquot of cDNA was used for one RT–qPCR using a PerfeCTa SYBR Green FastMix (Quanta). PCR primers were designed to amplify the junction between two exons using a web-based primer design program, Primer3  >| 1 |.Global gene expression was profiled using RNA-seq in human TSCs. Total RNAs were isolated using the RNeasy Plus Mini Kit . RNA-seq libraries were generated with 500 ng of total RNA using the NEB Next Ultra II RNA Library prep kit  following the manufacturer's protocol. Briefly, mRNAs were enriched from total RNAs with the Magnetic mRNA Isolation Kit [oligo(dT) beads] , and then cDNAs were synthesized using random primers and purified by NEBNext Sample Purification Beads . The ends of purified double-stranded cDNAs were repaired, ligated with a barcoded adaptor and amplified with PCR. RNA-seq libraries were sequenced using an Illumina NovaSeq 6000 platform. Paired-end reads were aligned to human transcript (hg38) using the Salmon mapper (v1.4.0) . ExpressChIP experiments were conducted as previously described . Brieflyhttps://genome.ucsc.edu/cgi-bin/hgTables). Peaks found in simple redundant regions of the genome were further filtered out.Single reads (of 50 bp) from ChIP-seq were mapped onto the human genome assembly (hg38) using the Bowtie2 mapper , and lowhttp://younglab.wi.mit.edu/super_enhancer_code.html). We first identified the binding sites of EP300 using the MACS3 peak caller, and then the defined peaks were transformed into general feature format (GFF) files to meet the criteria of input files of the ROSE program. We ran the ROSE with options of a stitching distance (12.5 kb) and transcription start site (TSS) exclusion zone size (2.5 kb).SEs were defined using the ROSE program downloaded from the website of the Young lab (A region of ±3 kb from the center of a peak was binned (100 bp), and all reads from ChIP-seq were mapped into each bin. The score in each bin was calculated by summing up the number of reads assigned into a bin. The score was also normalized with a sequencing depth. Average bin scores across the regions were plotted to generate averaged read density across the regions.To identify the target genes of a TF, peaks were mapped to the region surrounding ±20 kb from the TSS of all RefSeq genes in the RefFat file downloaded from the UCSC genome browser. To assign a binding site to one genomic feature, we used the following hierarchy: promoter > intron > exon > intergenic regions. A promoter was a region within ±2 kb from the TSS. Binding sites except promoters, introns or exons were considered as intergenic regions.http://homer.ucsd.edu/homer/motif/ HOMER). Central motif enrichment was further examined by annotatePeaks.pl with -hist option in Homer.Enriched motifs of each TF were searched using findMotifsGenome.pl in the Homer motif analysis tool  terms, the Genomic Regions Enrichment of Annotation Tool (GREAT) was used for ChIP-seq data . To avoiA pair-wise Pearson correlation coefficient between the target genes of two TFs was calculated for each pair of TFs. Clustering analysis and visualization of the data were done with Cluster 3.0  and Javahttp://www.cytoscape.org).To assign a binding site to a gene, TF-binding sites were mapped to the region surrounding 20 kb upstream and downstream of the TSS as well as the gene body of all RefSeq genes from the RefFlat file obtained from the UCSC genome browser. When a binding site was not found within ±20 kb including the gene body, the nearest gene was assigned as the target of the binding site. TFs and target pairs were visualized using Cytoscape  with accession number GSE208539. Processed human TSC, ST and EVT RNA-seq data were obtained from the published paper of Okae et al. . For PE To investigate the global landscape of enhancers and understand enhancer usages in human TSC, we performed ChIP-seq of an enhancer-associated protein EP300. As shown in Figure Since multiple TFs can co-occupy an enhancer in close proximity and form an enhanceosome to stimulate gene activation , we explSince SEs determine cell type-specific gene expression , we sougTo examine the significance of SE-associated genes in TSCs, we performed GO analysis, revealing that SE-associated genes are highly enriched for placental development and molecular functions of TF binding, suggesting that SE-TFs play important roles in placental development Figure . In highTo interrogate the molecular mechanisms by which TSC-pivotal TFs modulate human placental development, we selected five SE-TFs  that are robustly expressed in both TSCs and CTs Figure  and perfSince human TSC-pivotal TFs co-occupy many enhancers of TSCs Figure , we explTo assess whether these five TFs co-occupy the enhancers in a cooperative manner, we investigated the occupancy of TFs along with the number of TFs co-bound. Stronger occupancy of a TF was observed in the regions with a higher number of TFs co-bound , implyinWe also interrogate the biological relevance of co-occupancy of TFs by comparing the co-occupancy with the expression of G1 targets in self-renewing TSCs and differentiated cells, including STs and EVTs. As expected, G1 targets presented higher expression in TSCs and CTs than STs and EVTs Figure , indicatTo gain insights into the molecular functions of the five TFs in TSCs, we knocked down the TFs with shRNAs. After confirming a >70% reduction in the level of each TF using RT–qPCR , we profTo test whether co-occupancy of TFs affects the activation or repression of their target genes, we compared the gene activity of five TF-bound genes with no more than two TF-bound genes when a TF is depleted. Intriguingly, depletion of either one of these TFs  preferentially down-regulated the five TF-bound genes but up-regulated no more than two TF-bound genes. Conversely, KD of TFAP2C showed an opposite pattern Figure . These rPreviously, we identified mouse TSC-pivotal SE-TFs and the targets of a subset of the TFs . We idenTo investigate to what extent these five TFs influence the species-specific gene expression, first we classified the genes into two groups (human-enriched and mouse-enriched gene groups) with a criterion of 2-fold higher gene expression by comparing the transcriptomes of human TSCs with those of mouse TSCs. Next, we examined the number of TFs associated with a gene. The number of co-bound TFs on genes enriched in humans was significantly higher in human TSCs than in mouse TSCs, whereas mouse TSCs hold a higher number of TFs binding on the genes enriched in mice Figure . MoreoveDevelopmental processes are regulated by the collaborative action of tissue-specific enhancers and TFs. In this study, we identified tens of thousands of enhancers, and several hundred SEs as well as SE-associated genes across the genome in human TSCs. Among the SE-associated genes, we found multiple previously known trophoblast-pivotal TFs, including MSX2 that was recently characterized as a repressor of ST differentiation . ImportaIn accordance with a previous mouse TSC study , SE-assoA loss-of-function study of the five TFs revealed that they could function as both activators and repressors. Nevertheless, depletion of TFAP2C tended to activate multiple TFs-bound genes, unlike others, suggesting that TFAP2C can function as a repressor in a subset of genes. Since multiple TF-bound targets are likely to be activated , it is pThe mouse and human placenta differ in structure, cell types, endocrine functions and the length of gestation . AlthougRaw and processed ChIP-seq and RNA-seq data have been deposited to the public server GEO database under the accession number GSE208539.gkad215_Supplemental_FilesClick here for additional data file.


Employing an advanced version of (nascent elongating transcript sequencing) NET-seq (RNET-seq), we found that the universally conserved transcription elongation factor NusG is a robust suppressor of backtracked and nonbacktracked pauses in Escherichia coli. Our in vivo work revealed a similarity of NusG to Spt5, its archaeal and eukaryotic homolog, which regulates the elongation rate of many eukaryotic genes.Pausing by RNA polymerase is essential for transcription in all domains of life. Little is known about how pausing is regulated by  Escherichia coli. We employed RNET-seq for genome-wide mapping of RNAP pause sites in wild-type and NusG-depleted cells. We demonstrate that NusG is a major antipausing factor that suppresses thousands of backtracked and nonbacktracked pauses across the E. coli genome. The NusG-suppressed pauses were enriched immediately downstream from the translation start codon but were also abundant elsewhere in open reading frames, small RNA genes, and antisense transcription units. This finding revealed a strong similarity of NusG to Spt5, which stimulates the elongation rate of many eukaryotic genes. We propose a model in which promoting forward translocation and/or stabilization of RNAP in the posttranslocation register by NusG results in suppression of pausing in E. coli.Transcription elongation by multi-subunit RNA polymerases (RNAPs) is regulated by auxiliary factors in all organisms. NusG/Spt5 is the only universally conserved transcription elongation factor shared by all domains of life. NusG is a component of antitermination complexes controlling ribosomal RNA operons, an essential antipausing factor, and a transcription-translation coupling factor in  Escherichia coli RNAP at a few sites from the above categories was characterized in detail in vitro, but only recent studies addressed pausing genome-wide by nascent elongating transcript sequencing (NET-seq). Genome-wide mapping of the nascent RNA 3′ ends at the pause sites revealed the consensus pause sequence in E. coli and enrichment of the pauses at translation initiation codons (Transcription by multi-subunit RNA polymerases (RNAPs) is interrupted by random and programmed pauses . Numerou1n codons , 7. Moren codons . RNET-sen codons A. Similan codons . However70 of RNAP, and the nascent RNA cleavage factors GreA and GreB dramatically affect RNAP pausing (E. coli RNAP holoenzyme (σ70) was shown to negatively regulate transcription elongation. Interaction of σ70 with a promoter-like sequence in the nontemplate DNA (ntDNA) strand during elongation promotes RNAP pausing  operons  both in vivo and in vitro by interaction with a sequence-specific motif in the ntDNA strand within the paused transcription bubble, a mechanism that shares some similarity with σ70-dependent pausing in E. coli . Using RNET-seq, we found that NusG is a general pause-suppressing factor in E. coli responsible for strong suppression of thousands of pauses genome-wide. We also found that the previously identified consensus pauses enriched at translation start codons are targets for NusG. NusG depletion reduces expression of genes for rRNA and ribosomal proteins, but increases expression of genes involved in the DNA damage response, suggesting that the role of NusG in genome stability occurs through resolution of conflicts between replication and transcription. In contrast to the numerous in vitro observations, NusG only mildly suppresses RNAP backtracking in vivo. We propose a model in which NusG suppresses pausing by promoting forward translocation or stabilizing RNAP in the posttranslocation register.We assessed the genome-wide role of B. subtilis NusG (E. coli.We previously developed combination of RNase footprinting of the transcripts with native elongating transcript sequencing (RNET-seq), an advanced version of NET-seq that enables mapping of paused RNAP molecules throughout bacterial genomes with single base pair resolution . In contlis NusG . In thisSI Appendix. Pause score is a metric that was developed to quantify the in vivo strength of a pause site based on RNET-seq data  and generally increased the pause score .Our bioinformatic approaches for analysis of RNET-seq data are described in seq data . NusG dese score B and C, sG cells C. Intere2FC-Pause-TPM) was calculated as fold change in pause score values normalized to transcript abundance  upon NusG depletion for all pauses. Of note, 1971 pauses with log2FC-Pause-TPM values above 2 (fourfold increase) were considered NusG suppressed, whereas 1662 pauses with log2FC-Pause-TPM values between −1.5 and 1.5 (less than threefold impact) were considered NusG independent or unaffected  were considered NusG stimulated. Due to their low number, strength, poor reproducibility among the biological replicates of WT RNET-seq data, and our inability to show stimulation by NusG in vitro, we did not characterize these pauses further . The average log2FC-Pause-TPM value was 2.4, indicating that NusG depletion resulted in more than a fivefold increase in pause score genome-wide  and NusG-suppressed pauses (87%) were in open reading frames (ORFs) . Eight percent of the NusG-suppressed pauses were located in antisense transcription units . The average strength of pauses in antisense transcription units increased about twofold upon NusG depletion . NusG-suppressed pauses were substantially enriched at the start codons, which matched the previously reported sharp increase of pausing in this region in general  . The density of pauses increased at the beginning of ORFs in WT cells, whereas pauses were more evenly distributed along the ORFs in dNusG cells .Representative NusG-suppressed pause sites from our RNET-seq data are shown in ig. S5A) . In contE. coli “elemental” or “consensus” pause site . However, the quantitative effect of NusG in vitro did not match the log2FC values calculated from the in vivo data. For example, at the intensively characterized hisL pause site, NusG suppressed pausing fivefold in vivo (log2FC = 2.3) but only threefold in vitro .Inhibition of pausing by NusG was confirmed by in vitro transcription of several NusG-suppressed pause sites identified in vivo. Most frequently, NusG decreased both the pause efficiency (fraction of RNAP that pauses) and pause half-life (duration of pause) . Presumably, NusG-stimulated pausing was an indirect consequence of NusG depletion and/or additional factors participate in NusG-mediated pause stimulation in vivo that are absent in vitro. In combination with poor reproducibility of the effect of NusG depletion on pausing at these sites in vivo, this class of pause site was not examined further.We could not reproduce stimulation of pausing by NusG observed in vivo at several representative pause sites that we tested in vitro. Instead, we observed suppression of pausing at these sites in vitro . Therefore, we were unable to evaluate the effect of NusG on backtracking at these sites. In contrast, the effect of NusG depletion on backtracking at the pauses shared by WT and dNusG cells was minimal. In fact, the average strength of the shared pauses increased more than twofold upon NusG depletion  with only a small 2% increase of backtracking . Thus, the fraction of backtracked pauses was nearly identical in WT and dNusG cells . Principal component analysis (PCA) revealed a different expression pattern in RNET-seq and RNA-seq data for WT and dNusG cells. Three biological replicates of RNET-seq and four biological replicates of RNA-seq formed 4 separate PCA clusters. Interestingly, a 70% variance in PC1 reflected the difference between RNA-seq and RNET-seq data, whereas a 16% variance in PC2 reflected the difference between WT and dNusG cells . Genes that increased or decreased expression upon NusG depletion at the greatest level in the RNA-seq and RNET-seq data are listed separately in SI Appendix, Tables S3 and S4. Both RNA-seq and RNET-seq data indicate that NusG depletion strongly increases expression of a subset of SOS regulon genes such as recA and recN. These genes are under the control of the LexA repressor and are typically up-regulated in response to DNA damage . The expression of genes involved in motility and saccharide transport decreased upon NusG depletion . Although the expression of many genes involved in biofilm formation were increased upon NusG depletion, expression of the stimulator of autoaggregation and biofilm formation Ag43 (gene flu) was decreased in these cells . According to RNET-seq data, NusG depletion increased the pause score in antisense transcripts and the relative abundance of antisense transcripts . Notably, changes in the abundance of released (terminated) RNA upon NusG depletion generally exceeded changes in the abundance of nascent RNA. For example, the average expression of nascent and total RNA increased 17-fold and 60-fold, respectively, among the top 50 genes whose expression increased in each of these groups. Similarly, the average expression of nascent (RNET-seq) and total (RNA-seq) RNA decreased 10-fold and 15-fold, respectively, among the top 50 genes whose expression decreased in each of these groups (Dataset S7). Note that rac prophage genes were excluded from this analysis since construction of the NusG depletion strain apparently resulted in prophage excision. Changes in gene expression in response to NusG depletion exhibited a low negative correlation with changes of the relative density of NusG-suppressed pauses; the correlation coefficient of linear regression was equal to −0.25 . This result indicates that NusG-regulated pausing has only a minor contribution on the relative mRNA abundance in E. coli.A combination of RNET-seq and RNA-seq data indicated that NusG depletion affected expression of numerous protein coding genes in SI Appendix, Table S5) . It is worth noting that the hisL pause site, which is the prototypical hairpin-stimulated pause, has a shorter than average hairpin to the 3′ end distance of 11 nt . We conclude that pause-stimulating RNA hairpins are components of some NusG-suppressed sites, but that their impact varies depending on the site.Nascent transcripts can form a secondary structure 10 to 13 nt upstream from the 3′ end of the RNA that dramatically stimulates pausing of bacterial RNAP both in vitro and in vivo , 26, 28.able S5) , 17, 29.E. coli hisL and the B. subtilis trp U144, ribD and tlrB pauses, contain pause hairpins that end with a G residue . Note that the 17 nt reads were, in part, the products of Gre factor–stimulated 3′ RNA cleavage of longer reads derived from backtracked complexes . Sequence logos for each group revealed the presence of an upstream G-rich tract corresponding to the 3′ end of putative hairpins. Notably, this tract started at the −15 position for the sites with the positive correlation but was shifted to the −14 position for those with negligible or negative correlation between pausing and backtracking . This observation suggested that the single nucleotide difference in position of the hairpin relative to the 3′ end of the RNA distinguished between backtracked and nonbacktracked pauses, such that hairpins with a hairpin to the 3′ end distance of 13 nt inhibited backtracking, while those with a distance of 14 nt did not. Thus, the proximity of the hairpin to the 3′ end dictated whether paused RNAP was capable of backtracking. As such, pause sites with a hairpin to 3′ end distance ≥14 nt might constitute a specific group of pauses in which NusG prevents backtracking by suppressing the initial pause.To test a role of the hairpins in the inhibition of backtracking, we selected three groups of 50 NusG-suppressed pause sites from 2FC-Pause-TPM >3) (Dataset S9). The number of predicted hairpins was plotted as a function of the distance from the 3′ RNA end for the 458 pauses and for 458 scrambled derivatives of these sequences . This analysis revealed the presence of a palindromic sequence capable of forming an RNA hairpin at most of these sites. Importantly, the predicted hairpins were primarily positioned in a narrow 11 to 13 nt window upstream from the 3′ RNA end of the 458 pause sequences, but not of their scrambled counterparts. This result suggests that the NusG-suppressed pauses contain an RNA hairpin with a hairpin to 3′ end distance of 11 to 13 nt. We concluded that these hairpins contributed to the pausing efficiency and were responsible for inhibiting RNAP backtracking at these sites in WT and NusG-depleted cells.We used CLC Genomics Workbench 21.0.5 software to predict hairpins at 458 pause sequences that exhibit the strongest NusG-suppressed pausing (logprfB and rho) that are repressed by their own gene products .  HoweveprfB transcript is targeted by the prfB gene product RF2, resulting in termination of translation inside its own gene in E. coli and other bacteria . Instead, an NusG-suppressed pause site is located immediately upstream of the regulatory stop codon . This arrangement is typical of regulatory pause sites in 5′ UTRs that provide additional time for sensing environmental signals and/or the recruitment of additional regulators. As such, specific sequences downstream of in-frame stop codons may dramatically affect transcriptional polarity. NusG depletion has a minor effect on genome-wide reduction of RNAP traffic in 5′-to-3′ direction for all E. coli ORFs, which we interpreted as a genome-wide manifestation of transcription polarity . This result does not support global role of NusG in suppression of transcription polarity by coupling transcription and translation. It also argues against translation-mediated indirect impact of NusG depletion on transcription pausing.The natural in-frame stop codon in the bacteria . HoweverB. subtilis NusG . We found that NusG-dependent pause suppression was stronger in vivo than in vitro, suggesting that additional protein factors and cellular conditions contribute to the antipausing activity of NusG in vivo. Matching these conditions in vitro is a major challenge considering that NusG is a component of multiprotein complexes associated with RNAP during elongation. These complexes can include NusA, NusB, NusE, SuhB, Rho, ribosomes, and/or other specialized transcription factors such as Mfd and UvrD that are transiently recruited to RNAP under certain conditions . This finding is consistent with a previous ChIP-chip study showing that the occupancy of NusG on DNA is higher during transcription of ORFs compared to UTRs .Enrichment of pauses in UTRs of WT cells suggests that NusG could participate in regulating expression of target genes A. On the to UTRs . EnrichmNusG-suppressed pause sites share sequence similarity to the previously identified consensus pause sequence that is enriched in pyrimidine residues at the 3′ end of the paused transcript, followed by a guanine residue in the ntDNA strand at position +1 (−1Y +1G motif). Another hallmark of the consensus sequence is a −10G residue , which iE. coli in vivo , suggesting that the pause-suppression activity of NusG is generally unrelated to backtracking. This apparent disagreement between the in vitro and in vivo results could indicate that pauses detected by RNET-seq are derived primarily from delayed or incomplete forward translocation as opposed to backtracking . This enrichment suggested the presence of a conserved hairpin upstream that includes this G residue at or near its base. The in vitro transcription analysis using antisense oligonucleotides confirmed the presence of pause hairpins for several NusG-suppressed pause sites . Our genome-wide analysis using CLC Genomics Workbench 21.0.5 software is consistent with a hairpin to 3′ end distance of 11 to 13 nt being a common component of NusG-suppressed pause sites . Notably, the conserved G residues ending at −15 in the logo of backtracked pauses are shifted 1 nt upstream relative to the logo of nonbacktracked pauses . An RNA hairpin that forms within the RNA exit channel of RNAP interferes with backtracking at the hisL pause site .The massive increase in RNAP pausing in dNusG cells likely results in frequent collisions between paused RNAP and the replication fork as was previously suggested , 52. TheSI Appendix, Fig. S15A). Genes for ribosomal proteins are negatively regulated by transcription attenuation or translation repression feedback loops when transcription of rRNA operons decreases .NusG is a component of a transcription antitermination complex during transcription of rRNA operons, and disruption of this complex is likely responsible for the observed decrease in rRNA transcription in dNusG cells , 35 . Expression of genes involved in flagellum-dependent cell motility and the stimulator of autoaggregation and biofilm formation Ag43 (gene flu) is reduced in dNusG cells . Thus, natural repression of Ag43 by RfaH, an NusG paralog and competitor for RNAP occupancy, is likely exacerbated in dNusG cells . Therefore, the prfB gene that contains a natural in-frame stop codon that we used to exemplify this phenomenon may not be a general model for transcriptional polarity.We showed that NusG is a global inhibitor of RNAP pausing in perature , 20. OurnusG knockdown strain, plasmids, oligonucleotides, and the bioinformatic pipelines for analysis of RNET-seq data are described in SI Appendix.Construction of the dcas9 for depletion of NusG. Cells were harvested during midexponential phase growth when the culture reached an OD600 value between 0.4 and 0.5.All strains were grown at 37 °C in LB medium supplemented with 25 µg/mL of kanamycin. Cells from a frozen stock culture were streaked on LB agar plates and incubated overnight. A single colony was used to inoculate 6 mL of LB, which was grown overnight in a rotary shaker. Two milliliters of an overnight culture of NB1246 (WT strain) were used to inoculate a 250-mL LB culture. Six milliliters of an overnight culture of NB1247 (NusG depletion strain) were used to inoculate a 250-mL LB culture supplemented with 25 µg/mL of kanamycin. NB1247 was grown at 37 °C for 1 h, and arabinose was added to a final concentration 0.4% to induce expression of 2+-NTA agarose, recovery of nascent RNA, and preparation of RNET-seq libraries were described in detail in our previous publication , 5 mM MgCl2, 0.1 mM EDTA, 4% trehalose, and 4 mM DTT as described previously . RNAP was added from a 20× stock solution containing 1.5 mg/mL RNAP in 50% glycerol. The halted complexes were split in two 3.2 µL aliquots and diluted twofold with 1× transcription buffer containing 200 µg/mL acetylated BSA, 400 µg/mL heparin, and 200 mM KCl ±4 µM NusG. Elongation was resumed at 23 °C by the addition of 4.2 µL of all four NTPs in transcription buffer together with 50 µg/mL heparin and 100 mM KCl. The final NTP concentrations were 150 µM of ATP, CTP, and UTP and 10 µM of GTP. Performing transcription elongation at 23 °C and low GTP concentration slows down the transcription elongation rate and allows reliable collection of short-lived pause complexes manually. Two µM aliquots of the transcription elongation reaction were removed at various times. Transcription of the last aliquot (chase reaction) was continued for 10 min at 37 °C with 0.5 mM of each NTP. Transcription was stopped by the addition of an equal volume of the gel loading solution . Samples were fractionated through standard 5% sequencing gels. RNA bands were visualized with a phosphorimager and quantified using ImageQuant software .DNA templates and proteins used for in vitro transcription are described in eviously . In the Appendix 01 (PDF)Click here for additional data file.Dataset S01 (XLSX)Click here for additional data file.Dataset S02 (XLSX)Click here for additional data file.Dataset S03 (XLSX)Click here for additional data file.Dataset S04 (XLSX)Click here for additional data file.Dataset S05 (XLSX)Click here for additional data file.Dataset S06 (XLSX)Click here for additional data file.Dataset S07 (XLSX)Click here for additional data file.Dataset S08 (XLSX)Click here for additional data file.Dataset S09 (XLSX)Click here for additional data file.


E. coli Dam methyltransferase incombination with a fluorescent cofactor analogue to generate fluorescenttags in accessible DNA regions within the cell nucleus. The accessibleportions of the genome are then detected by single-molecule opticalgenome mapping in nanochannel arrays. This method allowed us to characterizelong-range structural variations and their associated chromatin structure.We show the ability to create whole-genome, allele-specific chromatinaccessibility maps composed of long DNA molecules extended in siliconnanochannels.Proteinsand enzymes in the cell nucleus require physical accessto their DNA target sites in order to perform genomic tasks such asgene activation and transcription. Hence, chromatin accessibilityis a central regulator of gene expression, and its genomic profileholds essential information on the cell type and state. We utilizedthe  Gene expression patternsin complex multicellular organisms are highly regulated by the physicaland structural properties of chromatin.7 Thus, genomic chromatin patterns are characteristic of specificcell types and states. As one of the key players in the epigeneticcontrol mechanism of gene expression, chromatin accessibility hasbecome a main target of research for better understanding genomicprocesses in health and diseases.10Chromatinis the packed formof eukaryotic genomes. It is a complex of macromolecules consistingof DNA, proteins, and RNA that enables genomic organization into microscopicnuclei. The basic structural unit of chromatin is the nucleosome,a complex consisting of DNA wrapped around assemblies of histone proteins.12 Dnase,13 MNase,14 and FAIRE-seq15 arebased on short-readnext generation sequencing (NGS) and suffer from the inherent limitationsof short-reads. Specifically, short-read sequencing is limited inits capacity to detect genomic structural variations (SVs) and copynumber variations (CNVs) such as those in large repetitive elements.21 Such variations have a direct impact on genetic disorders and cancer,with established structural aberrations associated with a specificdisease.26 The need to resolve large scale genetic structure has driven developmentsin cytogenetic technologies such as karyotype, fluorescence in situ hybridization (FISH), and arrays for comparativegenomic hybridization (CGH).27 Optical genome mapping (OGM)is the most advanced cytogenetic technology, enabling full characterizationof SVs and CNVs at high resolution.31 OGM is based on fluorescentlylabeling specific sequence motifs on high molecular weight DNA moleculesextracted from cells or tissue. Single molecules are stretched insilicon nanochannels and imaged in their extended form to reveal anoptical genetic pattern. The fluorescent patterns are then alignedto the human genome reference, identifying the chromosomal originof the genomic fragment.33Common methods for assessing chromatin accessibility suchas ATAC,41 Here, we present DAFCA (Dam Assisted Fluorescent tagging of ChromatinAccessibility), the latest addition to the epigenetic OGM toolbox and other DNAmethyltransferases (MTases) have already been shown to be suitabletools for reporting on chromatin accessibility by the detection ofspecific methylated sites with NGS and nanopore sequencing.4546 When EcoDam is incubatedwith a fluorescent cofactor in permeabilized nuclei, only the accessibleGATC sites within the chromatin are tagged and a fluorescent accessibilitypattern is created .52 Dueto the resolution of OGM, smoothing was not required for downstreamanalysis and is shown here for presentation only.54 Despite the similar profile alonggenes between ATAC and DAFCA, the higher resolution of short-readsequencing is reflected in the narrower features. The error bars inthe DAFCA plot display the variability in chromatin accessibilityin the 5000 gene groups.To furtherestablish the reliability of DAFCA, we performed a seriesof meta-analyses that examine the DAFCA signal around features characterizedby the other methods. In 49 For the two histone modifications that correlatewith open chromatin (H3K4Me3 and H3K27Ac), we show a clear correlationbetween DAFCA and the regions of open chromatin, while no DAFCA signalis generated around histones associated with closed chromatin (H3K27Me3).In We note that all DAFCA plots display noisy data at highly condensedchromatin regions, this is due to the low labeling ability of EcoDamin these inaccessible regions, and thus lower fluorescence levels.de novo assembly.47 Using the Bionano Solve 3.6algorithm, we generated a unique haplotype phased consensus map forthe GM12878 cell line. We utilized this aspect of the OGM technologyand created a specific reference for each allele . Interestingly, whencompared to the overall distribution of genetic features, we see aclear enrichment of differential accessibility at promoter regions. Clinicallyrelevant thresholding of regional chromatin accessibility may be establishedupon accumulation of statistically significant data.For chromatin accessibilityhaplotype phasing, we aligned our chromatinmaps to the different alleles using the green genetic labels, thusgenerating an accessibility map for each allele. We characterizedallele specific accessibility maps for the whole genome and presentdata from chromosome X. The female identity of the GM12878 cell lineis expected to display large variations in chromatin structure betweenthe two copies of chromosome X.16 Specifically, 47 Furthermore, DAFCA provides the accessibilityprofile along this repeat array, recognizing a distinct drop in chromatinaccessibility along GAGE12.Parts B and C of Thiswork establishes a chromatin accessibility assessment methodthat relies on optical investigation of individual DNA molecules confinedin silicon nanochannels. Utilizing a chemo-enzymatic protocol, ourassay integrates into the commercially available OGM technology fromBionano Genomics Inc. The combination of OGM long reads with chromatinaccessibility labeling gives access to complex genomic structuralvariants and their unique chromatin status. Additionally, allele-specificchromatin structure is resolved based on detecting heterozygous SVswith OGM.49 Greenleaf and co. have suggestedthat these differences arise from the physical size of the chromatinsampling enzyme used by each method.7 Ofparticular relevance is the higher peak density of DAFCA comparedto ATAC, as seen in 57 which can access smallergaps between compact chromatin regions compared to the larger Tn5Transposase (53 kDa)58 used for ATAC.Several methods for assessing chromatin accessibilityhave beendeveloped in the last 15 years. The different methods usually agreefor large portions of the genome but differ in the fine details theyprovide, displaying differences in resolution and peak density asshown in 59 Aninteresting recent finding links the density of chromatin to the expressionof GAGE 12. High expression of GAGE 12 results in relaxing of thechromatin and promotes resistance to radiation therapy in cervicalcancer.60 Due to the relation between chromatin densityand gene expression  were centrifuged for5 min at 1000g in two 50 mL conical tubes, followedby two 50 mL washes in cold PBS  for 5 minat 1000 RCF for both falcons. Final pellets were each suspended in500 μL of cold RSB by gentle flicking, transferred to two 15mL conical tubes, and kept on ice. Each of the 50 mL tubes were washedone more time with 500 μL of cold RSB that was added to thecorresponding 15 mL conical tube to a final volume of 1 mL of cellsin RSB.GM12878 cells were purchased from theCorriel Institute. Cells were grown in clonal populations under therecommended conditions: medium, RPMI 1640, 2 mM g for 15 min at 4 °C to pellet nuclei. The supernatantwas removed, and the tube was kept upside down to dry for a few minutes.Cell pellets were resuspended in 60 μL of EcoDam buffer ; at this point, both pelletswere untied to give 120 μL that was calculated to give ∼20× 106 nuclei per mL of concentration. Half of thevolume was directly used for the chromatin accessibility labeling,and the other half served for the naked control sample that continuedstraight to high-molecular-weight DNA extraction.Each of the cell-containingfalcons was gently lysed with cold RSB + 0.1% NP40 .The tubes were inverted 5–10 times and centrifuged at 100063 The expression protocol for EcoDam is presented in the Supporting Information. This labeling step wasperformed on isolated and permeabilized nuclei. Labeling was appliedin three separate reactions to maintain individual reaction volumesof 20 μL each: 17 μL (2 μg DNA) of nuclei in EcoDambuffer with 0.25 μL of EcoDam and fluorescent cofactor (AdoYnCF640R) at a final concentration of50 μM in a final volume of 20 μL . Samples were incubated for 1 h at 37 °Cand combined to a final volume of 60 μL.Synthesis of the AdoYnCF640Rlabeling reagent was previously reported.35 The threesamples were combined, and excess fluorophore cleanup was done bytwo subsequent drop dialysis steps using 0.1 μm dialysis membrane(Millipore) floating on 15 mL of TE in a 6 cm Petri dish. The mixturewas mixed 5 times with a commercial wide-bore tip and added as a singledrop on the center of the dialysis membrane. The Petri dish was coveredand protected from light, while the sample was dialyzed for 1 h atroom temperature; then, the drop was transferred to a different locationon the membrane for another 0.5 h. The collected 40 μL DNA mixturewas gently pipetted and incubated at RT overnight to achieve homogeneityof the high molecular weight DNA. The following day, EcoDam labelednaked DNA concentration was determined as 142 ng/μL by the QubitBR dsDNA assay (Thermo Fisher).Labeling of this sample wasperformed under the same conditions as the chromatin accessibilitysample, directly after the high molecular weight DNA extraction step.After labeling of the naked control DNA, 5 μL of proteinaseK (Qiagen) was added and the reactions were incubated at 50 °Cfor 30 min and then heated to 80 °C for 20 min for the red cofactor(AdoYnCF640R) deactivation.High moleculeweight (HMW) DNA was extracted from 60 μL of the chromatin accessibilitylabeling reaction using a Bionano Prep Blood and Cell Culture DNAIsolation kit according to the supplied protocol (Bionano Genomics).DNA concentration was determined to be 107 ng/μL for the chromatinsample by the Qubit BR dsDNA assay.For the naked control sample,this step was done directly after nuclei permeabilization and isolation;DNA concentration was 70.3 ng/μL.https://bionanogenomics.com/support-page/dna-labeling-kit-dls/).The chromatin and naked sampleswere labeled by Direct Label andStain (DLS) chemistry , creating a geneticbarcode according to a protocol by Bionano Genomics . Nanochannels were automatically imaged to acquire 728 Gbp ofindividual DNA molecules longer than 150 Kbp for the chromatin sampleand 350 Gbp for the naked control sample. Three layers of data werecollected from each field of view, DNA backbone in blue, genetic (DLE-1)barcode in green, and chromatin accessibility (EcoDam) profile inred, as seen in Foranalysis of the high-throughput nanochannel array data, raw imageswere processed, and DNA molecules were detected and digitized by customimage-processing and analysis software . Briefly, a set of coordinates along the molecules was assignedto genetic (DLE-1) labels. The accessibility labeling (EcoDam) wasdigitized as a continuous intensity profile along the DNA molecules.in silico human genome reference GRCh38.p13(Hg38 reference), based on DLE-1 recognition sites (hg38_DLE1_0kb_0labels.cmap), with default parameters matching the following combination ofarguments: haplotype, human, DLE-1, Saphyr. Only molecules with analignment confidence equal to or higher than 17 (P ≤ 10–17) and that at least 60% of theirlength was aligned to the reference were used for downstream analysis.Alignment outputs were converted to global fluorescent profiles (bedgraphfiles).35 The full pipeline and parameters used canbe found in the Supporting Information.Using Bionano Access (v1.6.1) and Bionano Solve (v3.6.1), the digitizedmolecules were aligned to the reference, according to the coordinatesof their genetic pattern. Molecules spanning over 150 Kbp were alignedto the 64 The smoothingparameters were chosen to provide the best fit between the experimentaland theoretical DAM site distribution. These parameters were appliedonly on the theoretical track, while the raw data was used as-is forfurther analyses. Further details on the establishment of these parametersare given in the Supporting Information. We used Bedtools makewindow (v2.26.0) followedby Bedtools map (v2.26.0) to bin all data sets into500 bp nonoverlapping genomic windows of hg38.65 Using Python data sciences tools,66 we found that the naked and predicted data fit a linear regressionmodel with the formula of: signal = 58.93 × (sites) + 216.18;this formula connects the number of predicted EcoDam sites with therecorded fluorescence intensity for each bin . We applied the reverse equation in order tonormalize the chromatin accessibility track: Figure S1C) regardless of the local site distribution.Due to the nonhomogeneous distributionof EcoDam sites in the genome, a normalization step based on thisdistribution was necessary. For example, two open chromatin regionsmay have a different number of EcoDam sites, resulting in differentfluorescent signals. This may be corrected by comparing the controlnaked sample  to the theoreticalEcoDam site distribution in the genome reference. By correlating thenumber of theoretical EcoDam sites per bin with the fluorescence intensityfor that bin, we get a conversion factor for normalizing the fluorescencedata. Bionano Genomics Access was used to generate a predicted genomictrack of EcoDam binding site motifs (GATC) on the Hg38 reference.We used the python SciPy smooth package “SciPy 1.0”with parameters set to a window size of 4000 bp, STD value of 2000bp, and overlapping of 500 bp.https://www.encodeproject.org/)68 with the following identifiers: alignmenttracks, ENCFF240YRV, ENCFF000THZ, ENCFF287HAO, ENCFF469WVA, and ENCFF486WAD.Data was smoothed as described above. The following peak tracks wereused for meta-analysis: ENCFF118WLS, ENCFF000THW, ENCSR057BWO, ENCFF835NLI,and ENCFF485JZA.Sequencing-basedaccessibility profiling data sets for GM12878 using ATAC-seq, FAIRE-seq,and ChIP-seq  were downloaded from theENCODE portal (69 gene bodies were definedas spanning from the transcription start site (TSS) to the transcriptionend site (TES).Transcription startand end sites (TSSand TES) of protein-coding genes were defined according to GENCODEannotation (v34);68 Protein-coding genes were dividedinto two groups based on their average normalized TPM score. The top25% with the highest score of transcripts per million (TPM) valueswas 27902: 23 (4909 genes) expressed genes and unexpressed genes withTPM value of 0 (5030 genes). Mean DAFCA/ATAC (ENCFF240YRV) signalsalong genes were calculated using DeepTools computeMatrix (v3.4.1)70 in scale-regions mode, whereeach gene was scaled to 15 Kbp and divided into 300 bp bins.RNA-seq for GM12878 was download from the ENCODEproject (ENCFF853VUK).computeMatrix (v3.4.1)70 parameters, and the length was set to 300bp.Mean chromatin signals around accessibility peaks generatedby three other methods were calculated. The calculation was performedwith default DeepTools de novo assembly of the GM12878 genome.The parameters used were ‘haplotype with extend and split’and “cut CMPR”. The human genome GRCh38.p13 (hg38_DLE1_0kb_0labels.cmap) was used as the reference. Theanalytical pipeline outputs contiguous maps (contigs) of the sample’sspecific genomic structure. It is haplotype sensitive and displaysdifferent contig maps for different alleles. These maps are ultimatelycompared to the Hg38 reference in order to call for SV and CNV eventsat regions where the genetic pattern differs from the reference.Opticalmapping data for the GM12878 cell-line samples was merged to a singledata set using Bionano Access (v1.6.1) and Bionano Solve (v3.6.1)and used for de novo constructedgenetic map. We then aligned the chromatin accessibility patternsrecorded in 100 Kbp regions around the SV to generate the allele specificchromatin maps. The signal intensity in these maps was summed in 1Kbp bins. The pairwise ratio between corresponding bins of each allele-specificaccessibility map was calculated and plotted as a scatter plot usingpython scatter plot function from the seaborn library.71 Bins presenting over 1.5-fold changewere defined as highly differential and are shown in blue in We selected134 contiguous maps larger than 300 Kbp that displayed at least oneallele-specific SV event and constructed separate genetic maps forthe two alleles. Using the list of detected SVs, we identified 350heterozygous SVs present only in our