Epstein-Barr virus (EBV) persists in human B-cells by maintaining its chromatinized episomes within the nucleus. We have previously shown that cellular factor Poly [ADP-ribose] polymerase 1 (PARP1) binds the EBV genome, stabilizes CTCF binding at specific loci, and that PARP1 enzymatic activity correlates with maintaining a transcriptionally active latency program. To better understand PARP1’s role in regulating EBV latency, here we functionally characterize the effect of PARP enzymatic inhibition on episomal structure through in situ HiC mapping, generating a complete 3D structure of the EBV genome. We also map intragenomic contact changes after PARP inhibition to global binding of chromatin looping factors CTCF and cohesin across the EBV genome. We find that PARP inhibition leads to fewer total unique intragenomic interactions within the EBV episome, yet new chromatin loops distinct from the untreated episome are also formed. This study also illustrates that PARP inhibition alters gene expression at the regions where chromatin looping is most effected. We observe that PARP1 inhibition does not alter cohesin binding sites but does increase its frequency of binding at those sites. Taken together, these findings demonstrate that PARP has an essential role in regulating global EBV chromatin structure and latent gene expression. Genome folding in the nucleus plays an important role in regulating gene expression. Here the authors show that 3D genome architecture also plays an important role for gene expression adaptability for the Epstein-Barr Virus (EBV), a virus that is associated with cancer. They also observe major changes in the fold of the EBV chromosome between silent and transcriptional active viral genomes. These circularized mini-chromosomes do not integrate into the host genome, making it necessary for EBV to organize its chromatin in a manner that allows access to genes essential for transcription and replication, yet structured enough to maintain genomic stability. As EBV has at least four established latency-associated transcriptional repertoires, the maintenance of a stable 3D chromatin structure for each program may explain the stability of latency-specific gene expression. In healthy adults, EBV exists in its proper latent form, “latency 0”, or the minimally transcriptionally active form “latency I” within resting memory B-cells1. During latency 0, only noncoding RNA is transcribed from the episome, including BART, BHRF1, and EBER microRNAs1. Type I latency only differs in the expression of one viral protein, EBV nuclear antigen protein 1 (EBNA1), which acts as a transcription factor to replicate the episome during host cell division1. There are numerous malignancies associated with type I EBV latency, which typically arises in immunocompetent individuals, including Burkitt’s lymphoma and gastric carcinoma2. An intermediate transcriptional profile, “latency II”, expressing EBNA1 and the noncoding RNAs in addition to latent membrane proteins  has been occasionally reported within the germinal center of healthy individuals. It is also associated with several malignancies, including nasopharyngeal carcinoma, Hodgkin’s disease, and nasal T/natural killer-cell lymphomas3. The final and most transcriptionally active latency program, “latency III”, expresses all previously mentioned genes in addition to various other latency transcripts1. Because 1–1.5% of all human cancers are associated with EBV infection6, there is an apparent need for EBV-specific treatments of these diseases. The current standard of care does not account for potential viral driving factors or etiology7. As each EBV latency program has a specific subset of associated malignancies, understanding how EBV maintains latency type may provide opportunities for novel, targeted therapeutics.Epstein-Barr virus establishes lifelong latency in human B-cells through maintenance of its chromatinized episomes replicating in synchrony with the host genome via cellular and viral factors8. Our group and others have previously shown that PARP1 and PARylation have an established role in latency type maintenance and lytic reactivation of EBV via regulation of the chromatin-binding protein CTCF12. We have also recently published that PARP1 colocalizes with CTCF across the EBV episome and that PARP1 enzymatic activity stabilizes CTCF binding and maintains a permissive chromatin status at the C-promoter (Cp) during type III latency10.The host enzyme poly [ADP-ribose] polymerase 1 (PARP1) catalyzes the addition of ADP-ribose polymers of varying lengths to histone H1 as well as various target proteins13. CTCF functions as both an insulator to prevent the spread of epigenetic marks between various chromatin loops and as an anchor to recruit cohesin, which physically forms chromatin loops via its ring-shaped SMC components holding two separate regions of chromatin together14. In the human genome, CTCF insulator function is known to be enhanced by PARP1 at specific loci, and CTCF, in turn, activates PARP1 enzymatic activity17. Previous work has established a role between both CTCF and cohesin21, and CTCF and PARP110 in regulating the epigenome of EBV. Because of these findings, in the present work, we aimed to investigate whether inhibition of PARP1 catalytic activity would disrupt the global EBV genomic structure, specifically by altering the binding of the chromatin looping factors CTCF and cohesin. As cohesin is necessary for CTCF to form long-range intragenomic interactions24, and there is evidence that CTCF colocalizes with cohesin across the EBV genome19, we postulated that PARylation may not only regulate the binding of CTCF to the EBV genome, but also cohesin, and perhaps alter the interaction between these two proteins to influence gene expression18. Here, we report that PARP inhibition surprisingly stabilizes the binding of cohesin at CTCF/cohesin colocalization sites. We also found that isogenic type I and III EBV genomes have nearly identical CTCF and cohesin binding profiles. Despite this, the 3D conformation of the type I and type III genomes was strikingly different, with only seven DNA-DNA interactions in type I and 26 in type III. Inhibition of PARylation alters the EBV episome’s 3D conformation, with long-range chromatin looping lost in the type I genome and a distinct intragenomic binding profile observed in the type III genome. When PARylation is inhibited in type III latency, we witnessed the downregulation of viral genes typical of the type III latency repertoire and an increased expression of lytic genes. The data presented in this study illustrate a role for PARP1 in the regulation of 3D chromatin structures and cohesin-chromatin binding, as well as maintenance of EBV latency type.CTCF is a cellular factor that regulates chromatin composition and the three-dimensional structure of the genome by promoting chromatin loop formationsTo date, the effect of PARylation on cohesin-chromatin binding has yet to be studied in the context of EBV or the human genome. Nor has the effect of PARP inhibition on 3D chromatin remodeling, i.e., chromatin looping, been studied on a genome-wide level. Thus, our work investigating the impact of PARP inhibition in EBV-positive B-cells is of high significance not only for its therapeutic potential in the context of host/viral interaction but also for providing a better understanding of host chromatin modifiers on a small scale, offering higher resolution than is currently possible in the human genome.25. Since changes in viral gene expression are associated with different EBV latency programs, we investigated whether differences in CTCF occupancy across the viral genome exist between different latency types. To compare CTCF binding across the EBV genome in type I and type III latency programs, we performed chromatin-immunoprecipitation and global sequencing (ChIP-seq) on a human type I latency B-cell line (Mutu I) and compared it with a type III profile we previously generated that is isogenic with respect to the EBV genome10. When the type I CTCF binding profile was aligned to the published type III profile, we observed minimal differences  than the immunoevasive type I latencyo 4 Fig. , D and fo 4 Fig. . We alsoo 4 Fig. , D. For p value < 0.05) to compare changes in the expression of viral genes in EBV+ cells before and after olaparib treatment. In type I Mutu cells, which basally have a minimal transcriptional profile, inhibition of PARP only elicits the differential expression of three genes, including the two lytic genes BOLF1 and BPLF1, which code for the inner and larger tegument protein of EBV respectively30  using antibodies against the RAD21 component of the cohesin complex39. We normalized the RAD21 binding profile to input chromatin and used peak calling to illustrate cohesion occupancy across the EBV genome in both type I and type III cells lines  differs between latency types, (2) is dependent upon PARP1 enzymatic activity, and (3) can be correlated to latency-type specific gene expression. To answer these questions, we have generated a complete 3D model of the EBV episome in type I and type III latency states, as assayed through statistical modeling of in situ HiC matrices from two biological replicates. Interestingly, despite being isogenic with respect to the EBV genome, the two latency types displayed vastly different structures of viral episome, with type III having significantly more intragenomic contacts than type I. These results demonstrate that EBV episome can be differentially folded in the host nucleus between latency types.45, our results indicate that CTCF–chromatin binding cannot account for the distinct profiles of the EBV episome found via global HiC analysis. Since cohesin is known to colocalize with CTCF across the type III EBV genome19, we additionally examined the cohesin binding profiles of the same cell lines, with and without PARP inhibition. As with CTCF, we noted a nearly identical binding profile between latency types. However, cohesin occupancy was increased across the type III genome after PARP inhibition while CTCF binding was decreased. As no relationship has previously been published to the best of our knowledge regarding PARP1 regulation of cohesin-chromatin binding outside of double-strand break repair46, we were excited by this finding establishing a functional link between PARP activity and cohesin/CTCF interaction. Future experiments are needed to determine the mechanism by which PARP regulates cohesin-chromatin binding. Based on our data, numerous hypotheses can be considered including the direct PARylation of cohesin impeding ATPase-dependent motility or an indirect effect of PARP inhibition on other chromatin effector molecules.CTCF plays an important role in establishing chromatin loops. However, in the context of EBV, the distinct 3D structures we observed cannot be accounted for by differences in CTCF binding between latency types, as type I and type III CTCF profiles were found to be highly similar. Taken together, though CTCF has been shown to play a critical role in the establishment and maintenance of latency repertoires47. Cohesin is also known to bind chromatin independent of CTCF in a cell-type-specific and highly conserved pattern, generally in promoter and enhancer regions of active genes48. Most work studying post-translational modifications of cohesin and their impacts on DNA-binding affinity is in the context of sister-chromatid cohesion and DNA damage. In these instances, SUMOylation of RAD21 enhances cohesin-chromatin binding affinity49, while phosphorylation of SA1 yeast homolog Scc1 induces dissociation from DNA50. Taken together, there is precedence that not only is cohesin found to bind independently of CTCF, but its chromatin binding is also known to be influenced by post-translational modifications. For these reasons, we believe that our data demonstrating that PARylation increases cohesin stability on the EBV episome despite lower CTCF binding would be an important contribution to the field. However, one potential caveat of our observations is the limited size of the EBV genome (~170 kb) which may not fully recapitulate the interactions between CTCF and cohesin observed in mammalian genomes at megabase scales38. Nevertheless, our results indicate that in the context of relatively small loops, PARP1 activity may be critical to the regulation of cohesin binding to DNA.The future mechanistic examination could also expand the current view of cohesin–CTCF dynamics, as the widely accepted loop-extrusion model implies that lower CTCF binding frequency would prevent the accumulation of cohesin at those sites. With this in mind, increased cohesin binding after olaparib treatment seems contradictory. However, work in CTCF-depleted fibroblasts has demonstrated that cohesin-chromatin binding is not prevented by the absence of CTCF, but its localization is rather centralized to active transcriptional start sites52 including EBV42, we performed an additional HiC assay on the same cell lines after PARP inhibition and found that both the type I and type III genome had a decrease in total unique intragenomic interactions. However, while type I exclusively lost chromatin loops, type III gained new chromatin contacts not seen in the control genome  and in situ HiC. HiC provides a global, unbiased view of all intragenomic interactions within the episome, but only at an average 5 kb resolution in this study. Because of this, the relatively local interaction previously identified in Mutu-LCL between OriP and Cp may be too close to be identified in HiC analysis. Our analysis did agree with another prior publication, however, showing strong intragenomic interactions between the region just before OriP and the LMP1/LMP2a transcript region25. In type I latency, the chromatin loop previously observed between the OriP and Qp was also not detected in our HiC analysis. As 3C requires longer crosslinking time, predetermined primer sets, and many more PCR cycles than are required for HiC analysis, it is possible that prior studies may have reported chromatin loops occurring at very low frequencies. It is also possible that the process of lysing the nuclei and incubating the contents together allow for chromatin binding events that would not be possible using an in situ approach.Unexpectedly, we observed differences between our HiC analysis and the chromatin loops identified in a previous study using the same isogenic type I and type III cell lines53. In LCLs, genes indicative of type III latency, namely the EBNAs, were significantly downregulated, corroborated by earlier work illustrating the downregulation of EBNA2 in the same cell line after PARP inhibition10. These data together with observed changes in the viral 3D chromatin structure after PARP inhibition indicate that PARP1 aids in the maintenance of latency programs and prevention of lytic gene expression, especially in type III latency.This work also establishes the functional effect of altered chromatin looping after PARP inhibition on EBV gene expression. The RNA-seq analysis performed with or without PARP enzymatic inhibition on the same cell lines shows that viral genes expression is altered after olaparib treatment. Type I cells displayed significant dysregulation of only three viral genes while type III showed altered expression of ten viral genes. For both cell types, lytic genes were upregulated, consistent with prior work demonstrating that PARP1 knockdown increases viral copy number and lytic reactivation58, KSHV63, HCMV64, HPV67, and HTLV-168. In these viruses, it could be interesting to determine whether PARP1 similarly regulates the viral epigenome through CTCF or cohesin. Outside of virology, this work reports PARP1’s role as a regulator of 3D chromatin structure on a global scale utilizing the EBV episome as a model.We were also interested in the correlation between increased cohesin binding and increased RNA reads at the same regions. While demonstrating a direct causal relationship between gene expression and particular chromatin interactions would require the mutation/deletion of cohesin binding sites, we are excited that this global, correlational study provides the rationale for future studies of EBV genome structure and maintenance of latency repertoires. Our findings are of particular interest for other DNA viruses as previous work has shown that CTCF/cohesin has a major role in the regulation of transcription and latency maintenance in HSV171. Because of this, we do wonder whether different EBV strains exhibiting the same latency type would have similar episomal conformation as those we have elucidated in this project. In addition, it is interesting to speculate if the same strain of EBV in a different donor B-cell background would display the same chromatin conformation. On a technical level, the in situ HiC method utilized in this paper can only generate an inferred 3D chromatin structure from a population dataset, meaning these results cannot determine if individual episomes within the same human B-cell display different chromatin conformations. The models generated here can only illustrate the most frequently occurring DNA-DNA interactions as a generalized chromatin conformation for the population. While the limitations outlined here should certainly be taken into consideration, we are still excited to report that isogenic EBV genomes displaying different latency types have vastly different chromatin conformations that are regulated by PARP1 enzymatic activity.Finally, we would be remiss to not include the major limitations of this study as points of consideration. The major conclusions of this project were outlined by our H–C analysis and models made from EBV chromatin extracted from populations of five-million cells per treatment group. It has long been established that EBV episome copy number varies not only by strain and viral lifecycle but also by genetic variation between host B-cells2 in RPMI 1640 medium supplemented with 1% penicillin/streptomycin and 15% fetal bovine serum. Treatment with PARP inhibitor olaparib  was given 72 h before collection at a concentration of 2.5 µM. PARP inhibition was validated via PAR ELISA as per the manufacturer’s protocol . The relative luminescence was measured on the EnVision 2104 Multilabel Reader (PerkinElmer). PDD00017273 treatment  started 72 h before collection, with redosing every 24 h, at concentrations varying from 0 to 50 µM. PARG inhibition was validated by western blot against PAR . For chromatin immunoprecipitation assay cells were treated with 2.5 µM PDD00017273 for 72 h.The type I latency “Mutu I” Burkitt Lymphoma cell line and the type III latency lymphoblastoid cell line “Mutu-LCL” were maintained in a humidified incubator at 37 °C and 5% COThe following antibodies at the indicated dilutions or concentrations were used: RAD21: Abcam Product No. ab992, WB: 1:1000, IP: 2.5 μg, ChIP-seq, 5 μg; STAG1: Abcam Product No. ab4457, WB: 1:1000, IP: 2.5 μg; SMC1: Abcam Product No. ab9262, WB: 1:1000, IP: 2.5 μg; SMC3: Abcam Product No. ab9263, WB: 1:1000, IP: 2.5 μg, ChIP 2.5 μg; CTCF: Active Motif Catalog No. 61311, WB: 1:2000, ChIP-seq: 5 μg; IgG: Jackson ImmunoResearch, Product No. 111-005-003, IP: 2.5 μg; βtubulin: Abcam ab6046, WB: 1:1000; Lamin B1: Abcam ab16048, WB: 1:1000; Histone H3: Abcam ab1791, WB: 1:1:000; PARP1: Abcam ab227244, WB: 1:1000; IP: 2.5 μg; PARP1 Active Motif, No: 39561, ChIP: 10 μl per ChIP; PAR: Abcam Product No. ab14459 WB: 1:200; PARG: Abcam Product No. ab169639 WB: 1:2000; Actin: Sigma-Aldrich, No. A2066, WB: 1:200.Qsonica sonicator  to generate chromatin fragments roughly 200–500 bp in size. Chromatin was centrifuged to clear debris and a sample of this cleared chromatin  was kept as standard input for comparison against immunoprecipitations. Chromatin was incubated rotating at 4° overnight with 25 µg of antibody against RAD21 . Chromatin–antibody complexes were precipitated using 50 µL of Dynabeads Protein A  incubated rotating at 4° overnight. Beads were collected on a magnetic rack and resuspended in 1 mL modified RIPA buffer and were rotated for one minute at room temperature. Beads were collected on the magnetic rack, the wash was repeated four additional times, with the final wash in TE buffer. After the final wash step, beads were resuspended in 60 µL elution buffer and incubated for 15 min in a 65° thermomixer at max RPM. Eluant was collected and 0.2 M NaCl was added to each sample (including input chromatin). All samples were incubated in 65° water bath overnight to reverse crosslink. DNA was purified using Promega Wizard SV Gel and PCR Clean-up Kit (Product No. A9285). Libraries for sequencing were made NEBNext® Ultra™ II DNA Library Prep Kit  and sequenced on the hiseq2500 (Illumina).After 72 h incubation with or without olaparib, 25 million cells per immunoprecipitation were collected and fixed with 1% formaldehyde to preserve all DNA-protein interactions for 15 min and then quenched with 0.25 M glycine for 5 min on ice. Pellet was resuspended after centrifugation in 10 mL each of a series of three lysis buffers before fragmentation in 26. We used MACS273 software packages to call reads enrichment in pull-down samples compared to input samples as peaks. Analysis of peak distribution under differentiated conditions was performed with the bedtools software package for genome arithmetic74, and for data visualization we used deepTools75. RNA-seq and ChIP-seq data were deposited for public access at Gene Expression Omnibus (GEO accession number: GSE159837).Reads were mapped against the human gammaherpesvirus 4 (HHV4) NC_007605.1 genome assembly using bowtie2After 72 h incubation with or without olaparib, 1 million cells per immunoprecipitation were collected and fixed with 1% formaldehyde to preserve all DNA–protein interactions for 15 min and then quenched with 0.125 M glycine for 5 min on ice. After centrifugation, the pellet was resuspended in 120 µL lysis buffer and sonicated using Covaris S2  to generate chromatin fragments roughly 200–500 bp in size. A sample of “input chromatin” was collected at this point as a standard for comparison against immunoprecipitations . Chromatin was then incubated overnight rotating at 4° with 2.5 µg of antibody against each cohesin component, CTCF, or IgG, respectively . Chromatin–antibody complexes were precipitated via 2-h incubation with 50 µL of Dynabeads Protein A rotating at 4°. Beads were washed with 1 mL of a series of three wash buffers, followed by two washes with TE buffer. Washed beads were resuspended in 150 µL SDS-TE and incubated for 15 min in a 65° thermomixer at max RPM. Beads were cleared on a magnetic rack and eluant was incubated in a 65° water bath overnight to reverse DNA–protein crosslink. Proteinase K  was then added at 20 mg/mL and incubated in a 55° water bath for 3 h. DNA was then recovered using Promega Wizard SV Gel and PCR Clean-up Kit (Product No. A9285). Real-time PCR was performed with a master mix containing 1× Maxima SYBR Green  0.25 μM primers and 1% of ChIP or input DNA per well. Data were analyzed by the ΔΔCT method relative to DNA input and normalized to the IgG control. Primer sequences are provided in Supplementary Data 28, with minor modifications. Libraries of total ligation products were produced using Ultralow Library Systems V2  as per manufacturer’s protocol with minor modifications. Purified libraries were then enriched for only EBV genome ligation products using myBaits enrichment kit as per manufacturer’s protocol. Enriched libraries were sequenced using hiseq2500 (Illumina) with paired-end 75 bp read length. Complete protocol with all minor alterations will be happily supplied by the corresponding author per request.After 72 h incubation with or without olaparib, five million cells per condition were collected for in situ HiCProcessing HiC data. HiC data were processed as described previously76. Briefly, 75-bp paired reads were separately aligned to the EBV genome (NC_007605.1) using Bowtie2 (version 2.2.9) with iterative alignment strategy76. Redundant paired reads derived from a PCR bias reads aligned to repetitive sequences, and reads with low mapping quality (MapQ < 30) were removed. Reads potentially derived from self-ligation and undigested products were also discarded. EBV genome was divided into 5 kb windows with 1 kb sliding. Raw contact matrices were constructed by counting paired reads assigned to two 5 kb windows. HiC biases in contact matrices were corrected using the ICE method77. The ICE normalization was repeated 30 times. Significant associations were determined based on the distance between two 5 kb windows, all combinations were categorized into 20 groups. We assumed the HiC score as Poisson distribution with a parameter λ matching the mean score. We then assigned a p values for each group and applied an FDR correction for multiply hypotheses78. FDR < 0.05 were defined as significant associations. Significant associations were plotted as circos graph using the circlize package (version 0.3.3) of R (version 3.6.1).™ III First-Strand Synthesis System . Real-time PCR was then performed with a master mix containing 1X Maxima SYBR Green  0.25 μM primers and 25% cDNA. Data were analyzed by the ΔCT method relative to 18S-ribosomal subunit control. Primer sequences are provided in Supplementary Data Five million cells were collected for three biological replicates of both Mutu-LCL untreated and Mutu-LCL 2.5 μM olaparib treated for 72 h. Cells were centrifuged, washed twice with PBS, and resuspended in trizol. RNA was extracted using chloroform and isopropanol. After DNAse treatment, cDNA was prepared using SuperScriptThree biological replicates of 10 million Mutu-LCL per treatment group (with or without 2.5 μM olaparib for 72 h) were prepared using the Subcellular Protein Fractionation Kit for Cultured Cells from Thermo Scientific . Respective proteins of interest were probed via western blot using antibodies listed previously for chromatin immunoprecipitations. Samples were normalized to the following loading controls: β-tubulin (Abcam ab6046) for cytosolic extracts, lamin B1 (Abcam ab16048) for nuclear soluble extracts, and histone H3 (Abcam ab1791) for chromatin-bound extracts.For SMC3-immunoprecipitation (IP) assays, 10 million LCLs, with or without 72-h 2.5 μM olaparib treatment, were collected for each IP and resuspended in 1 mL of RIPA buffer with protease/phosphatase inhibitor cocktail (Thermo Scientific). Before the addition of 10 μg of either SMC3  or normal rabbit IgG , 50 μL of cell lysate was collected and kept as input material. Cell lysates were incubated with respective antibodies for 1 h at room temperature, rotating, after which 30uL of protein A magnetic beads  were added. The mixture was left to incubate overnight at 4°, rotating. The beads were then separated with a magnetic rack and washed three times in RIPA buffer with protease/phosphatase inhibitor, each for 10 min in a 4° thermomixer at 1000 rpm. The beads were then boiled at 95° for 8 min in 50 μL 2× laemmli buffer, with half of the volume ran on an immunoblot for SMC3 and half for PARP1  as described above. Densitometry analysis was performed on Invitrogen iBright Analysis Software, with signal density/area from IgG control lanes subtracted from IP lanes. IgG normalized IP signal was then normalized to input signal density/area. Data shown are representative of three independent co-IP assays, averaged.™ Directional RNA Library Prep Kit for RNAseq (Cat #E4720L) and sequenced on the hiseq2500 (Illumina).Five million cells each for two biological replicates were collected per control and olaparib treatment groups for RNA sequencing. RNA was purified using the PureLink RNA Mini Kit from Thermofisher (cat no. 12183018 A). Libraries for sequencing were made using NEBNext UltraRNA-seq analysis. Sequenced reads were aligned to the human gammaherpesvirus 4 (HHV4) NC_007605.1 genome assembly using the STAR software suite79. To determine differential gene expression, we used the EdgeR software suite43. Genes with 0 reads across all samples were excluded. Genes with 2-fold change and q < 0.01 after correction for multiple testing (FDR) were considered as significantly differentially expressed. We used R software packages (CRAN project and Bioconductor) for downstream analysis of RNA-seq such as hierarchical clustering and PCA. Ingenuity Pathway Analysis (Qiagen) was used to determine functional gene enrichment.Further information on research design is available in the Supplemental InformationDescription of additional Supplementary FileSupplementary Data 1Supplementary data 2Supplementary data 3Reporting Summary


Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer–promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein–protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer–promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer–promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression. First, the resolution of interacting genomic anchors profiled by Hi-C and Capture Hi-C is relatively low (∼5–10 kb genomic fragments) ,31, whicBecause of these limitations, computational models are needed to predict cell-type specific long-range enhancer regulation, based on integration of multi-omics signatures, e.g. genomics, transcriptomics, and epigenomics. Large-scale multi-omics data resources collected by the ENCODE and Roadmap Epigenomics projects contain the multi-view information of gene regulation , includiInterestingly, as shown by recent experimental studies ,53–58, iThese observations strongly support the mechanistic hypothesis that specific TF PPIs, except intratypic dimerizations where TFs can only co-bind locally to DNA instead of across long-range distances, may mediate long-range enhancer regulation. Therefore, incorporation of TF PPIs as a new set of features into a machine learning model is expected to improve the accuracy of long-range enhancer–promoter interaction predictions. Moreover, the prioritized TF PPIs from the predictive model can further indicate the important transcription factors that facilitate long-range enhancer regulation, leading to novel understandings of enhancer biology. However, unlike basic enrichment analysis of candidate TF–TF pairs that are over-represented in enhancer–promoter interactions , buildintrans-eQTLs, leading to new mechanistic interpretations of trans-genetic effects propagated through the combined regulatory pathways of TF bindings, TF PPIs and long-range chromatin interactions.In this study, we developed a new predictive model, ProTECT, to infer long-range enhancer–promoter interactions with substantially improved accuracy. A unique novelty of the model is designing a graph-based dimension reduction algorithm, which can efficiently incorporate combinatorial TF PPI features into the model and, in the meantime, control the overfitting risks. By setting rigorous genomic bin-split cross-validations and controlling various confounding factors, we systematically demonstrated the superior performance of our model compared to existing algorithms. Furthermore, we analyzed the relative importance of TF PPI features in different cell-types and prioritized the key TF PPIs that may participate in the regulation of long-range enhancer–promoter interactions, leading to new mechanistic insights on enhancer regulation. Accordingly, we further classified genes into specific subsets, where enhancer-gene interactions are predicted to be mediated by different TF PPIs. Interestingly, genes in different subsets are enriched with distinct biological pathways, suggesting the specific functional impacts of TF PPIs. Genome-wide implementation of ProTECT in human GM12878 and K562 cell-lines results in 134 792 long-range enhancer–promoter interactions, which are significantly enriched with cis-eQTLs. In addition, by analyzing enhancer–promoter interactions mediated by different TF PPIs, we were able to assign specific TFs as upstream trans-factors to downstream target genes through distal enhancers. Strikingly, the prioritized TF–gene pairs are significantly supported by https://github.com/wangjr03/PPI-based_prediction_enh_gene_links) further integrates the information of PPIs between transcription factors as new features, because TF PPIs have been found to be functionally associated with the regulation of chromatin loops . In addition to cell-type specific multi-omics data, ProTECT  bindings in GM12878 and K562 are collected from ENCODE separately . For eacThe protein–protein interaction dataset is collected from the STRING database v11 . To remoIn a specific cell-type, enhancer–promoter pairs that overlap with significant Hi-C interactions , i.e. thTo avoid biased model training and inflated performance evaluations, we generate a balanced negative set of training samples by randomly selecting the same number of enhancer–promoter pairs that do not overlap with Hi-C interactions. In addition, as pointed out by recent benchmark studies , predictBased on the cell-type specific multi-omics datasets, the matrix of features are then constructed for enhancer–promoter pairs in the training dataset Figure . There aa–TFb represents the PPI between enhancer-binding TFa and promoter-binding TFb, while TFb–TFa represents the PPI between enhancer-binding TFb and promoter-binding TFa. Thus, a set of directional TF PPI features is generated. Because the features are generated only for TFs with cell-type specific ChIP-seq signals, PPIs between TFs that are not active in the specific cell-type do not participate in the predictions. Enhancer-promoter pairs are scanned for TF binding peaks in enhancers and promoters. For each enhancer–promoter pair, if TFa binds to the enhancer and TFb binds to the promoter, then the directional PPI feature TFa–TFb is labeled as 1. Therefore, a matrix of TF PPI features is constructed for all enhancer–promoter pairs. Combining with the activity-based features and genomic distances, the full matrix of features is then built .To address the over-fitting problem, we substantially reduce the feature dimensions by hierarchically grouping individual TF PPIs into TF PPI modules based on the topology of the PPI network, while maintaining the predictability of the model Figure . TF PPI TF PPI modules are computationally identified from the PPI network  using a n graphs , the 1-ss Figure . ‘wald’ s Figure . By tests Figure , \documei.e. S-modules, with the maximum size of S-modules represent densely connected TFs in the PPI network, corresponding to candidate protein complexes. S-modules are further merged to form large-size PPI modules, i.e. L-modules, with the maximum size of L-modules represent larger PPI network components that cover multiple densely connected S-modules. Biologically, L-modules represent candidate groups of highly interacting protein complexes. The maximum sizes for S-modules  inter-module features, which include inter S-module features and inter L-module features. The inter-module features cover PPIs linking TFs from two different modules. Given a pair of S-modules, e.g. S-module S-module S-module S-modules S-module PPI feature. The inter L-module PPI features are defined in the same way by checking PPIs of TF members from two L-modules. Each inter-module feature is further split into two directional features, depending on the binding sites of TF members in enhancers and promoters. Using this approach, the PPI features are substantially reduced. For example, the 1,888 individual TF PPI features are reduced to only 78 module-level PPI features in GM12878 and the 7066 individual TF PPI features are reduced to only 238 module-level PPI features in K562 cells.Based on the TF PPI module assignments, individual TF PPI features (i.e. direct TF–TF PPIs) are merged into module-level PPI features, and, therefore, the feature matrix of TF PPIs are restructured accordingly Figure . There aThe training set of enhancer–promoter pairs are then scanned for module-level PPI features. For each specific enhancer–promoter pair, based on the counts of individual TF PPI features calculated in the previous step, the counts of module-level PPI features are generated depending on the module memberships of TFs Figure . For eacRandom forest model is used to predict cell-type specific long-range enhancer–promoter interactions based on the feature matrix constructed above, after module-based dimension reduction Figure . Random Additionally, to quantitatively demonstrate the contributions from TF PPIs, we train random forest models based on two versions of input features: (i) the model is trained using only activity-based features and genomic distances; and (ii) the full set of features including module-level TF PPI features. The Area Under Curve (AUC) values of cross-validations are calculated for the two versions. The increased AUC from version 2 is the quantitative measurement of the additional information contributed from TF PPIs that is not encoded in activity-based or genomic distance features.module–modulemodulemodulemodule–modulemodulemoduleIn the random forest model, the backward feature elimination approach is used to select useful module-level TF PPI features, where the features with the minimum importance are recursively eliminated from the model. Furthermore, the statistical significance of the directions of TF PPI features are evaluated. As described in the previous section, every module-level PPI feature is split into a pair of two directional features, based on the binding sites of TFs in enhancers or promoters. For example, the feature e.g. the binary labels for enhancer–promoter interactions. And it is defined as The determination of whether a pair of directional TF PPI features to be merged into an un-directional feature is a model selection problem. While Akaike Information Criterion (AIC) has been a widely used metric for parametric models, it can not be applied to random forest models, which are non-parametric. Instead, we use the Generalized Degrees of Freedom (GDF) method to calculate a relaxed AIC  for the  studies , to calcanalyses , \documeFor each pair of directional TF PPI features, the relaxed AIC metrics are calculated before and after they are merged into an un-directional feature. If a smaller AIC is observed by merging the two directional features, the model with the merged un-directional feature is then selected, because the reduced AIC suggests the directions of the pair are not statistically important. This procedure is conducted for all pairs of directional TF PPI features, and a final random forest model with the selected features is built. In GM12878 cells, the number of module-level TF PPI features is reduced to 53 from 78. In K562 cells, the number is reduced to 139 from 238. This feature selection process further boosts the generalizability of our model and improves the biological interpretations of the learned TF PPI features .To evaluate the performance of our model, i.e. area under curve (AUC), we designed a stringent strategy of 5-fold cross-validation. As highlighted by previous studies ,51, multThe performance of our model, ProTECT, is compared with two most recent supervised methods that also leverage TF information: IM-PET  and Targhttps://github.com/tanlabcode/IM-PET) is applied to the same dataset. Since IM-PET automatically makes predictions for all enhancer-gene pairs with distances <2 Mb, only the enhancer-gene pairs overlapping with the dataset are used for performance evaluation, leading to a fair comparison for IM-PET. The TargetFinder software  is also implemented to the same training and testing dataset. The same set of TF ChIP-seq peaks are used to generate the window related features for TargetFinder. 5-fold cross-validation with the same genomic bin-split strategy is applied to remove the potential issues of inflated performance evaluations.The stand-alone package of IM-PET . Using the fractions, the top-ranking TF-level PPI features are thus identified for each important module-level PPI feature. The prioritized features, both module-level and TF-level, shed light on new biological insights on long-range enhancer regulation.q-values, and the top 1000 genes regulated by these interactions are selected. In this way, the prioritized subset of genes represent strong targets of long-range enhancer regulation mediated by the important TF PPIs. Gene Ontology enrichment analyses are performed on different gene sets using DAVID , if the enhancer contains the SNP and the promoter matches with the gene. For each eQTL dataset, the fraction of predicted enhancer–promoter interactions that are supported by cis-eQTLs is calculated, and is compared to two versions of negative controls. The first version of negative control is based on random pairing enhancers with promoters that are within 2 Mb distances. The second version of negative control further requires the genomic distances of random enhancer–promoter pairs follow the same distribution from our predicted enhancer–promoter interactions. Therefore, the second version is a more stringent control. For each version, 1000 random samples are generated. And the statistical significance, i.e. P-values, of the observed overlapping fractions from our predictions is calculated as the portion of random samples showing a higher overlapping fraction than the real observed one.As the orthogonal information to validate the accuracy of genome-wide predictions made by ProTECT, datasets  which wecis-hQTLs, i.e. histone QTLs, to evaluate the accuracy of our predictions. The hQTL dataset was also profiled from the human GM12878 cells .Using the trans-eQTLs from the published database , we identrans-eQTL if the SNP is located within the TF’s gene body and the gene is the same as the trans-eQTL’s target gene. As comparisons, two versions of controls are generated based on the same set of TFs and enhancers. The first version uses the nearest genes to the enhancers as target genes, instead of using ProTECT’s predictions. The second version randomly selects genes within 2 Mb distances as target genes. In each version, the same number of enhancer–promoter interactions are generated as seen from the foreground for each sample, and totally 1000 random samples are created, along with the hypergeometric P-values.Hypergeometric test is used to statistically test whether the enhancer-mediated TF–gene pairs significantly overlap with the subset of trans-eQTLs described above. A TF–gene pair is considered to overlap with a P-value = 10–3), suggesting that the TFs within each pair can establish interactions at the protein level. Figure As discovered by recent experimental studies ,58,59, tDue to the large number of TF PPI features, i.e. PPIs between enhancer-binding TFs and promoter-binding TFs, basic predictive models significantly suffer from overfitting problems, as shown in As a new predictive model, here we highlight a series of key novelties of ProTECT . First, a rigorous method of controlling confounding factors, such as TAD domains, genomic separation distances and gene expression levels, is designed in the steps of data and feature generations. This method efficiently removes the impacts of confounding factors, which are fundamentally important to control as discussed by recent benchmark analyses ,51. SecoWe trained ProTECT using the high-resolution Hi-C datasets from the human GM12878 and K562 cell-lines separately . The balUsing the genomic bin-split cross-validation strategy , we rigorously tested the accuracy of ProTECT and compared with the other two supervised methods, i.e. IM-PET(45) and TargetFinder . In bothde novo discoveries used for experimental studies in practice, this observation further exemplifies the superior precision of ProTECT.In addition to the overall AUC metrics, to demonstrate that ProTECT has better capabilities of pinpointing true enhancer–promoter interactions in top-ranking predictions, we calculated the cumulative odds ratio (OR) of true positives along the ranked list of predictions. As shown in Figure Moreover, we further evaluated the robustness of ProTECT’s superior performance with respect to different settings of input features and data. As shown in Figure To further justify that the superior performance of ProTECT is indeed due to the information from TF PPI features, we randomly shuffled the TF–TF connections in the PPI network Figure . TherefoTo evaluate the model's dependence on the cell-type specificity of TF bindings, we swapped the TF ChIP-seq data across GM12878 and K562, and run ProTECT based on the swapped data. As expected, the prediction accuracy decreased in both cell-types , suggestP-value from the permutation test is generated, which is further used to derive a q-value based on the pFDR approach . Using this approach, 32 pairs of directional PPI features in GM12878 are merged into 16 un-directional features, suggesting there is no statistical preference of binding sites (i.e. enhancers versus promoters) between interacting TFs involved in these PPIs. For example, the features ATF2-SMARCA5 and SMARCA5-ATF2 are merged into an un-directional feature by the model, consistent with the observation that the two directional PPI features have similar abundance in enhancer–promoter interactions . A similTo evaluate the downstream impacts of chromatin interactions mediated by different TF PPIs, we focused on the top 5 module-level PPI features Figure  and C. WP-value < 1.04 × 10–4, Figure cis-eQTLs, we compared our predictions in GM12878 with histone-QTLs from the same cell-line  compared to distance-controlled random samples and other algorithms . Using four eQTL datasets generated from matched cell-types or tissues (e.g. whole blood tissues or lymphoblastoid cell-lines) , the preell-line  and alsos Figure . These ocis-eQTL SNPs within the binding sites of the prioritized TFs in enhancers , compared to the binding sites of other adjacent enhancer-binding TFs   . The pri× 10–19) , consistcis-regulatory elements (i.e. enhancers) and trans-regulatory factors (i.e. TFs) are jointly modeled in long-range chromatin interactions. In traditional studies of trans-regulation of gene expression, analyses have been mainly limited to promoter-binding TFs as candidate trans-regulatory factors , supporting the predicted trans-regulatory links between PAX5 and target genes by ProTECT. Figure i.e. NOL6, contains a strong CTCF binding site. Interestingly, NOL6 is linked with weakened Hi-C interactions in PAX5–/– pro-B cells. These strong experimental validations, along with the enrichment of trans-eQTLs, suggest the biological validity of the predicted enhancer-mediated TF–gene pairs, and provide a new regulatory mechanism to discover and interpret trans-regulatory genetic variants.To obtain additional experimental evidence on the predicted enhancer-mediated TF–gene regulation, we leveraged a differential Hi-C interaction dataset in mouse pro-B cells where 7810 weakened Hi-C interactions were identified following PAX5 knock-out . The tophttps://github.com/wangjr03/PPI-based_prediction_enh_gene_links), to predict long-range enhancer–promoter interactions. By incorporating new features of protein–protein interactions among transcription factors, the algorithm achieves superior performance compared to other methods, based on a rigorously designed genomic bin-split cross-validation procedure. Considering the overfitting risk of high-dimensional inter-dependent TF PPI features, a novel network-community based dimension reduction strategy is used to hierarchically organize TF PPIs into module-level features. This approach efficiently improves the generalizability of the predictive model to make robust predictions based on complex TF PPI patterns, while maintaining the detailed ranking of TF-level PPI features for specific mechanistic understandings of long-range enhancer regulation. With the impacts of confounding factors strictly controlled, the relative contributions of different features are systematically evaluated, which shows that TF PPIs contain substantially additional information beyond activity-based features of enhancers and genes.In this study, we have developed a novel supervised algorithm, ProTECT . The genome-wide predictions of enhancer–promoter interactions in human GM12878 and K562 cell-lines, including both hg19 and hg38 versions, are also available in this GitHub repository. Testing datasets, including input feature matrices and expected predictions, are also provided.ProTECT is an open source infrastructure available in the GitHub repository (gkab841_Supplemental_FilesClick here for additional data file.


Yersinia pseudotuberculosis and a number of other bacterial pathogens. However, the IscR regulon has not yet been defined in any organism. To determine the Yersinia IscR regulon and identify IscR-dependent functions important for virulence, we employed chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) of Y. pseudotuberculosis expressing or lacking iscR following iron starvation conditions, such as those encountered during infection. We found that IscR binds to the promoters of genes involved in iron homeostasis, reactive oxygen species metabolism, and cell envelope remodeling and regulates expression of these genes in response to iron depletion. Consistent with our previous work, we also found that IscR binds in vivo to the promoter of the Ysc type III secretion system (T3SS) master regulator LcrF, leading to regulation of T3SS genes. Interestingly, comparative genomic analysis suggested over 93% of IscR binding sites were conserved between Y. pseudotuberculosis and the related plague agent Yersinia pestis. Surprisingly, we found that the IscR positively regulated sufABCDSE Fe-S cluster biogenesis pathway was required for T3SS activity. These data suggest that IscR regulates the T3SS in Yersinia through maturation of an Fe-S cluster protein critical for type III secretion, in addition to its known role in activating T3SS genes through LcrF. Altogether, our study shows that iron starvation triggers IscR to coregulate multiple, distinct pathways relevant to promoting bacterial survival during infection.The iron-sulfur cluster coordinating transcription factor IscR is important for the virulence of  Yet, in the mammalian host, the level of free iron is only 10−18 M due to the concerted action of mammalian iron storage and carrier proteins  type III secretion system (T3SS), encoded on a 70-kb virulence plasmid pYV/pCD1  and chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-Seq) to identify genes directly regulated by IscR following iron starvation, which Yersinia experiences during disseminated infection. Interestingly, comparative genomics revealed very high predicted conservation of the IscR regulon in Y. pestis. One highly conserved IscR binding site was found in the promoter of the suf Fe-S cluster biogenesis operon. Surprisingly, our data show that IscR direct regulation of the Y. pseudotuberculosissuf operon is critical for T3SS activity under iron-depleted conditions.In Ysc T3SS , 19. Iscph nodes –38. In cYersinia experience iron starvation during extraintestinal infection  (4), which can both be utilized by Yersinia via distinct uptake systems  . Culture systems –14, and systems – to C. Aspression .iscR in addition to genes involved in iron uptake , iron sulfur cluster biogenesis (sufABCDSE), lipopolysaccharide (LPS) biosynthesis and modification , lipoprotein and outer membrane protein targeting , cell wall remodeling and defense, the dusB-fis virulence-associated operon, and the T3SS. Upregulation of iron uptake genes and the suf operon in response to iron starvation was expected given previous studies , antioxidants , and the AI-2 autoinducer pathway (lsrR regulator and lsr operon). Altogether, these results suggest that Y. pseudotuberculosis increases iron metabolism and cell envelope remodeling during iron starvation, a condition known to be experienced by Yersinia during extraintestinal infection  or upregulated (cluster II) in response to iron limitation. , when IscR levels are highest in the WT strain, a total of 324 genes were differentially expressed in the wild-type strain versus the ΔiscR strain  were carried on pYV and were among the genes with the largest fold changes between the wild-type and ΔiscR strains. As our previous data showed that apo-IscR directly regulates the T3SS master regulator LcrF, the majority of these pYV-encoded genes are likely to be indirectly regulated by IscR. In iron-limited conditions, such as those encountered by Yersinia during extraintestinal infection, the dominant form of IscR is predicted to be apo-IscR. Genes induced by apo-IscR would be expected to be more highly expressed during iron starvation compared to after iron supplementation as well as have decreased expression upon deletion of iscR. Genes whose expression patterns were consistent with apo-IscR induction of their promoters included the T3SS genes yscCDEFGHK, the sufABCDSE operon, metal transport genes iucABCDiutA, alcC, cirA, fhuC_1, and oprC, and cell envelope biosynthesis and remodeling genes pagP, amiD, and ydhO  Whole-cell extracts from WT Y. pseudotuberculosis or a strain harboring a chromosomally encoded 3xFLAG-tagged IscR were visualized using anti-FLAG or anti-RpoA antibodies. (B) To measure the relative efficiency of the Ysc T3SS, Yersinia strains were grown under T3SS-inducing conditions, and secreted proteins precipitated by trichloroacetic acid were visualized using Coomassie blue. Relative amounts of the T3SS effector protein YopE were quantified by densitometry compared to a spiked-in BSA protein control. The averages of three biological replicates ± standard deviations are shown. (C) Yersinia strains were grown under T3SS-inducing conditions, and relative iscS mRNA levels were evaluated by qPCR and normalized to 16s rRNA. The average of three biological replicates ± standard deviation is shown. ***, P < 0.001; **, P < 0.01; n.s., not significant (one-way ANOVA with Dunnett’s post hoc test). Download FIG S1, PDF file, 0.2 MB.3xFLAG-tagged IscR rescues an Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00633-21.9DATA SET S2Y. pseudotuberculosis. Download Data Set S2, XLSX file, 0.1 MB.ChIP-Seq data assessing genome wide enrichment of IscR binding sites in Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Yersinia is the type II site found 367 nucleotides upstream of the yscW start codon . No. NoYersiYersinia A and B. 10.1128/mBio.00633-21.2FIG S2E. coli IscR type I binding sites were used to generate an IscR type I binding motif using MEME suite tools. The Y. pseudotuberculosis IP2666 genome was scanned for IscR type I sites using FIMO specifically upstream of the nfuA, iscRSUA, erpA, and DN756_20960_cysE promoters. The predicted sequences were aligned to the E. coli consensus IscR type I binding motif. (B) IscR ChIP-seq plots illustrating read coverage of IscR binding peaks assigned to the promoter of nfuA, iscRSUA, erpA, and DN756_20960_cysE from all three replicates combined. Dashed lines correspond to the zenith of the predicted 29-bp IscR type I motifs. Download FIG S2, PDF file, 0.1 MB.Global IscR ChIP-seq analysis fails to identify predicted IscR type I sites. (A) Known Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in vivo IscR binding results, we carried out in vitro binding studies on the identified IscR binding sites upstream of the katA and sodB antioxidants as well as the known virulence genes fis and ail  showed that apo-IscR binds to these promoters, although the binding to the ail promoter appeared weaker (ail is missing key residues known to be important for IscR binding. A very large band shift was observed when assessing IscR binding to the katA promoter fragment (unpublished observations), indicating the possibility of more than one IscR binding site in this region. Indeed, when the katA promoter fragment was split into two, each with a bioinformatically identifiable IscR type II motif, binding to both fragments was observed. Collectively, these data demonstrate that IscR binds both in vitro and in vivo to the promoters of genes known or predicted to be involved in virulence.In order to validate our  and ail . IscR ha and ail , 42, 43.e stress . The proe stress , 46. Altnfection , 46. Sind weaker . Indeed,Y. pestis and Y. pseudotuberculosis, suggesting conservation of IscR regulation between these two species  had IscR type II motif sequences in their promoters that were 100% conserved between strains IP2666 and CO92, while 17 (∼8%) contained only differences in nucleotides not critical for IscR binding. Interestingly, the IscR binding sites that were among the most conserved between all three Yersinia species included those within the promoters of sodB, katA, the suf operon, pagP, ompW, yscW-lcrF, dusB-fis, and ail.We previously showed that IscR regulated the T3SS in both  species . To asses IP2666 . Out of ica 8081 , which iis study . Of the colitica . In addicolitica . In orde binding  28). Im. ImY. pe10.1128/mBio.00633-21.3FIG S3Y. pseudotuberculosis , Y. pestis (CO92), and Y. enterocolitica (8081). (A) Distance of the identified IscR binding site in Y. pseudotuberculosis (IP2666) from the start codon of each transcription unit member of the IscR regulon is plotted versus the distance of the predicted IscR binding site from the start codon of the identified ortholog in Y. pseudotuberculosis (IP32953), Y. pestis (CO92), or Y. enterocolitica (8081). (B) The log10 FIMO P value of the identified IscR binding site in Y. pseudotuberculosis (IP2666) is plotted versus the log10 FIMO P value of the predicted IscR binding site upstream of identified orthologs in Y. pseudotuberculosis (IP32953), Y. pestis (CO92), or Y. enterocolitica (8081). Download FIG S3, PDF file, 0.3 MB.Conservation of IscR binding sites in Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00633-21.10DATA SET S3Y. pseudotuberculosis IP2666, Y. pseudotuberculosis IP32953, Y. pestis CO92, and Y. enterocolitica 8081. Download Data Set S3, XLSX file, 0.08 MB.Comparative genomics of IscR binding sites in Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Y. pseudotuberculosis, we collated the ChIP-Seq results with our published RNA-Seq data carried out under the same aerobic, non-iron-starved conditions used for the IscR chromatin precipitation  are plotted versus the genomic position. Dashed lines correspond to the predicted 30-bp IscR type II motif. Arrows indicate transcriptional start sites  of WT (black) and ΔiscR strains (gray) grown in M9 minimal medium containing FeSO4 (non-iron-starved [NIS]), iron starved in Chelex-treated M9 minimal medium with no iron source added back (Chelex) or supplemented with 5 mM hemin (+Heme) or FeSO4 (+FeSO4). **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (EdgeR with a corrected FDR post hoc test). (C) Yersinia strains were grown under rich media, T3SS-inducing conditions. The secretome of these cultures was visualized with Coomassie blue. The effector protein, YopE, was quantified by densitometry relative to the WT control to measure the relative efficiency of the Ysc T3SS. The average of five biological replicates ± standard deviation is shown. ****, P < 0.0001; *, P < 0.05 (one-way ANOVA with Dunnett’s post hoc test). Download FIG S4, PDF file, 0.3 MB.Deletion of DN756_21815 and DN756_21820, identified gene targets of IscR, results in a small but significant decrease in type III secretion. (A) Read coverage of IscR-binding peaks proximal to rt sites . (B) ExpCopyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Yersinia might reveal additional genes associated with in vivo IscR binding whose expression is regulated by IscR. Indeed, 86 TUs with an upstream IscR binding site were found to contain genes differentially expressed in the presence and absence of IscR under at least one of the RNA-Seq conditions , and genes differentially expressed between WT and the iscR mutant but have no IscR ChIP-Seq peak (C). Download FIG S5, PDF file, 0.3 MB.COG analysis of the IscR regulon, functional regulon, and indirect regulon. Clusters of Orthologous Groups of proteins (COG) analysis of genes with an IscR ChIP-Seq peak (A), genes with an IscR ChIP-Seq peak and are differentially expressed between WT and the Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the suf promoter exhibited 100% conservation among all human-pathogenic Yersinia  of WT (black) and ΔiscR strains (gray) grown in M9 minimal medium containing 3.6 μM FeSO4 (non-iron-starved [NIS]), iron starved in Chelex-treated M9 minimal medium with no iron source added back (Chelex) or supplemented with 5 mM hemin (+Heme) or 3.6 μM FeSO4 (+FeSO4).**, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (EdgeR with a corrected FDR post hoc test). Download FIG S6, PDF file, 0.1 MB.Expression of the Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the sufABCDSEY. pseudotuberculosis mutant, which exhibited normal growth and motility  or supplemented with 3.6 μM FeSO4 (+FeSO4). Cultures were grown at 26°C for 10 h, and optical density at 600 nm was measured every hour. Non-iron-starved (NIS) samples were never exposed to Chelex-treated M9. (B) The indicated strains were spotted onto motility agar  and were grown at 26°C. The diameters of the colonies were measured 24 h and 48 h later and used to calculate percent motility. Graphs represent three biological replicates. ****, P < 0.0001 (one-way ANOVA with Dunnett’s post hoc test). (C) Expression of lcrF, iscR, and yopE, relative to 16S rRNA, following iron-starved conditions in M9 minimal medium at 37°C, as measured by qPCR. Black bars represent cultures which were iron starved and supplemented with 3.6 μM FeSO4 before induction of the type III secretion system, while gray bars represent cultures that received 0.036 μM FeSO4 before induction of the type III secretion system. Average of three independent experiments ± standard deviation is shown. ***, P < 0.001; **, P < 0.01; *, P < 0.05 (one-way ANOVA with Dunnett’s post hoc test). Download FIG S7, PDF file, 0.2 MB.The Suf pathway does not affect motility or growth. (A) Iron-starved Copyright © 2021 Balderas et al.2021Balderas et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Yersinia and a number of other bacteria   encountered by Yersinia during infection. In this study, we also found that IscR directly repressed the genes encoding catalase, katA, superoxide dismutase, sodB, and the oxidative stress-associated OmpW (Two important categories of genes found to be regulated by iron and IscR were involved in cell envelope modification and the oxidative stress response. 4 (TLR4) –59. Intey system , enablesy system –63. In cmulatory . Therefoa mallei , 43, 54.ted OmpW . It is psufABCDSE promoter both in vitro and in vivo and that suf expression is consistent with apo-IscR induction of the suf promoter. This is in line with a previous study showing that IscR activates the E. colisuf operon upon exposure to the iron chelator dipyridyl (suf operon leads to reduced Ysc T3SS activity. Loss of the suf operon did not affect T3SS gene expression or protein levels but instead affected secretion of T3SS cargo. This phenotype was observed under standard aerobic non-iron-starved conditions (data not shown) and was even more pronounced under iron-limited conditions where the suf operon becomes more important for synthesizing Fe-S clusters. Loss of the suf operon may affect the electron transport chain and lead to a proton motive force defect, which is required for T3SS activity  but not other LcrF-regulated genes were significantly upregulated under our aerobic iron starvation conditions. Indeed, while mRNA levels of the yscW-lcrF operon trended up in the absence of iron, this difference was not statistically significant. In fact, some T3SS effector protein and chaperone genes , the yscM T3SS regulatory gene, and the needle tip protein lcrV were expressed approximately two- to fourfold more after iron supplementation. However, importantly, most of these genes were downregulated ∼10- to 50-fold by deletion of iscR, demonstrating that the presence of an iscR gene had a much stronger effect on T3SS gene expression than changes in iron availability under aerobic conditions. Y. pseudotuberculosis transits from the small intestine to lymph tissue and vital organs during infection. In the intestines, Yersinia experiences an anaerobic iron-replete environment where it does not require its T3SS  but showed differential expression by that TF only under certain conditions . Additional RNA-Seq analysis of WT and ΔiscR Yersinia will help determine whether the “potentially nonfunctional binding” genes identified in this study are differentially expressed under certain conditions, for example anaerobic iron-replete conditions that mimic those encountered by Yersinia in the intestinal lumen where IscR is also necessary for colonization.The remaining 60% of genes associated with an IscR binding site were not found to be differentially expressed by IscR under any condition tested. Choudhary et al. categorized such genes as having “potentially nonfunctional binding” of the TF . The in E. coli (UPEC) regulates genes found in pathogenicity islands and siderophores not present in commensal E. coli K-12 strain MG1655  of 0.2 in fresh M9+3.6 μM FeSO4, and shaking for 3 h at 37°C.All strains used in this study are listed in ndicated . Non-iroY. pseudotuberculosis aerobically in M9 medium lacking iron treated with Chelex 100 resin to remove all traces of iron in acid-washed glassware, as previously described (4) grown at 26°C aerobically were diluted to an OD600 of 0.1 into Chelex-treated M9 medium and grown for 8 h at 26°C aerobically with agitation. Cultures were then subcultured a second time to an OD600 of 0.1 in fresh Chelex-treated M9 and grown for 12 h at 26°C with agitation. Cultures were then subcultured to an OD600 of 0.1 into 20-ml Chelex-treated M9 medium with either no iron, 5 μM hemin, or 3.6 μM FeSO4 and grown for 3 h at 37°C with agitation.Iron starvation was achieved by growing escribed . SpecifiiscR chromosomal locus to facilitate detection of IscR with FLAG monoclonal antibody  using the NEBuilder HiFi DNA Assembly kit . Recombinant plasmids were transformed into E. coli S17-1 λpir competent cells and later introduced into Y. pseudotuberculosis IP2666 via conjugation. The resulting Kanr, irgansan-resistant (Yersinia selective antibiotic) integrants were grown in the absence of antibiotics and plated on sucrose-containing media to select for clones that had lost sacB . Kans, sucrose-resistant, Congo red-positive colonies were screened by PCR and sequenced. Yersinia carrying only the 3xFLAG-IscR allele secreted levels of YopE similar to those secreted by the wild-type strain, suggesting that the 3xFLAG tag does not affect the ability of IscR to regulate T3SS gene expression  using the NEBuilder HiFi DNA Assembly kit . Mutant strains were generated as described above for the 3xFLAG-IscR strain.Generation of the Δxtension . Primer 15-21820  were use4, using the IP2666plB1 genome (GenBank accession no. CP032566.1 and CP032567.1) for analysis. ChIP assays were performed as previously described  and concatenated into a single file. The trimmed FASTQ files were then aligned to the Y. pseudotuberculosis genome using Bowtie2 v2.3.3.1  to remove trace amounts of iron and allowed to grow for 8 h at 26°C  according to the manufacturer’s protocol. Total RNA was isolated using the RNeasy minikit (Qiagen) per the manufacturer’s protocol. Contaminating DNA was removed using the TURBO DNA-free kit (Life Technologies/Thermo Fischer). rRNA was removed using the RiboMinus Transcriptome isolation kit bacteria (Invitrogen). The cDNA library was prepared using the NEB Ultra Directional RNA Library Prep kit for Illumina. The quality of RNA and cDNA libraries was assessed using an Agilent 2000 Bioanalyzer. Libraries were sequenced using the HiSeq2500 Illumina sequencing platform for 50-bp single reads .For RNA isolated from samples subjected to iron starvation,  at 26°C . The culCP032566.1) and virulence plasmid (CP032567.1) through Bowtie2 (2 fold change (FC) was ≥1 or ≤−1 with an FDR value of <0.01.Trimmomatic  version  Bowtie2 . Low-qua Bowtie2 . Genes wE. coli IscR type I and type II binding motifs were compiled from a training set consisting of known IscR binding sites in E. coli  and transferred to a blotting membrane (Immobilon-P) with a wet mini trans-blot cell (Bio-Rad). Blots were blocked for an hour in Tris-buffered saline with Tween 20 and 5% skim milk and probed with rabbit anti-IscR  and boiled for 15 min. At the time of loading, samples were normalized to the same number of cells by ODnti-IscR , rabbit Y. pseudotuberculosis T3SS induction  to a final concentration of 10%. Samples were incubated on ice for 20 min and pelleted at 13,200 rpm for 15 min at 4°C. Resulting pellets were washed twice with ice-cold 100% acetone and subsequently resuspended in FSB+DTT. Samples were boiled for 5 min prior to running on a 12.5% SDS-PAG.Visualization of T3SS cargo secreted in broth culture was performed as previously described . For standuction , bacteriYersinia growth under anaerobic conditions to detect T3SS activity (4) grown at 26°C aerobically were subcultured to an OD600 of 0.1 into Chelex-treated M9 medium plus 0.9% glucose and grown for 8 h at 26°C aerobically with agitation. Cultures were then subcultured a second time to OD600 0.1 in fresh Chelex-treated M9 media plus 0.9% glucose and grown for 12 h at 26°C with agitation. Subsequently, cultures were then subcultured a third time to an OD600 of 0.2 in M9 medium plus 0.9% glucose supplemented with 3.6 μM FeSO4 (iron replete) or with 0.036 μM FeSO4 (iron limitation), grown for 2 h at 26°C with agitation, and then shifted to 37°C for 4 h with agitation to induce type III secretion. For anaerobic cultures, the cultures were instead diluted a second time to an OD600 of 0.1 in M9 medium plus 0.9% glucose supplemented with 3.6 μM FeSO4 (iron replete) or with 0.036 μM FeSO4 (iron limitation), and transferred to a vinyl anaerobic chamber where they were grown at 26°C for 12 h. Cultures were then shifted to 37°C for another 4 h to induce type III secretion. Samples were then processed as described above for standard secretion assay conditions.We previously found that small amounts of iron are needed to obtain sufficient activity ; therefoactivity . SpecifiTC method. Three independent biological replicates were collected for each tested condition. For each target transcript, significant differential expression between different bacterial strains was defined by P value of <0.05 of two-way analysis of variance (ANOVA) (one-way ANOVA with Tukey posttest).A total of 5 ml of culture from each condition were pelleted by centrifugation for 5 min at 4,000 rpm (4 krpm). The supernatant was removed, and pellets were resuspended in 500 μl of media and treated with 1 ml Bacterial RNA Protect reagent (Qiagen) according to the manufacturer’s protocol. Total RNA was isolated using the RNeasy minikit (Qiagen) per the manufacturer’s protocol. After harvesting total RNA, genomic DNA was removed via the TURBO-DNA-free kit (Life Technologies/Thermo Fisher). cDNA was generated for each sample by using the Moloney murine leukemia virus (M-MLV) reverse transcriptase (Invitrogen) according to the manufacturer’s instructions, as we previously described . Power SY. pseudotuberculosis chromosome (GenBank accession no. CP032566.1) and virulence plasmid (CP032567.1) were downloaded from NCBI. These files were opened using the Artemis Genome Browser  values. These gene expression values were then scaled by gene. R-package pheatmap was used to create clusters dependent on Euclidean distances and a complete method.iscR coding sequence was PCR amplified from the E. coli K-12 MG1655 chromosome using primers that incorporated a NdeI restriction site at the 5′ end of the gene, and a BamHI site and His6 tag (order listed in 5′–3′ direction) at the 3′ end. The NdeI- and BamHI-digested fragment was cloned into pET11a to generate pPK14263, which was subsequently transformed into strain PK7878 (28). IscR-C92A-His6 was purified as previously described for untagged IscR (ail (−386 to +14 bp relative to the +1 transcription start site [TSS]), dusB-fis (−447 to −247 bp relative to the +1 TSS), sodB (−116 to +84 bp relative to the +1 TSS), katA1 (−240 to −93 bp relative to the +1 TSS), katA2 (−92 to +1 bp relative to the +1 TSS), and sufA (−144 to +56 bp relative to the +1 TSS) were amplified from Y. pseudotuberculosis genomic DNA using primers (The n PK7878 . IscR-C9 primers . AmplifiY. pseudotuberculosis IP32953 (NC_006155.1), Y. pestis CO92 (NC_003143.1), and Y. enterocolitica 8081 (NC_008800.1) through a combination of BLAST and Mauve, a multiple genome alignment tool  compared to logMEME-FIMO-pvalue of orthologous binding site in the indicated strain of Yersinia (binding site in question).Orthologs of the 213 IscR targets were determined in Y. pseudotuberculosis overnight culture was spotted onto motility medium containing 1% tryptone/0.25% agar. The plates were incubated at 26°C for 24 h or 48 h before the diameter of the motile colony was measured.A total of 1-μl http://zam.soe.ucsc.edu/hubs/StoneYersinia/hub.txt.Both ChIP-Seq and RNA-Seq tracks were deposited to the UCSC Microbial Genome browser. For RNA-Seq data, final BAM file alignments were converted to bigwig files using bamCoverage-deepTools . Chromos


Development proceeds by the activation of genes by transcription factors and the inactivation of others by chromatin-mediated gene silencing. In certain cases development can be reversed or redirected by mis-expression of master regulator transcription factors. This must involve the activation of previously silenced genes, and such developmental aberrations are thought to underlie a variety of cancers. Here, we express the wing-specific Vestigial master regulator to reprogram the developing eye, and test the role of silencing in reprogramming using an H3.3K27M oncohistone mutation that dominantly inhibits histone H3K27 trimethylation. We find that production of the oncohistone blocks eye-to-wing reprogramming. CUT&Tag chromatin profiling of mutant tissues shows that H3K27me3 of domains is generally reduced upon oncohistone production, suggesting that a previous developmental program must be silenced for effective transformation. Strikingly, Vg and H3.3K27M synergize to stimulate overgrowth of eye tissue, a phenotype that resembles that of mutations in Polycomb silencing components. Transcriptome profiling of elongating RNA Polymerase II implicates the mis-regulation of signaling factors in overgrowth. Our results demonstrate that growth dysregulation can result from the simple combination of crippled silencing and transcription factor mis-expression, an effect that may explain the origins of oncohistone-bearing cancers. The differentiation of cell fates in multicellular organisms requires that certain genes be activated, and genes for alternative cell fates are repressed by chromatin silencing. Specific histone mutations that cripple silencing have been found associated with brain cancers in human patients, and these cancers may originate from instability of cell fates. We tested this idea by expressing a wing specification factor in the Drosophila eye to reprogram cell fates and create winged eyes. To test if defects in chromatin silencing increased cell reprogramming, we simultaneously expressed a crippling mutant histone. Contrary to expectations, we found that wing-to-eye reprogramming no longer occurs and instead the eye overgrows, a phenotype reminiscent of the cancers where the histone mutation was first identified. We suggest that reprogramming requires chromatin silencing of the previous developmental program, and that blocking reprogramming can uncouple growth-promoting effects from developmental one of tissue specification transcription factors. Developmental programs in multicellular organisms are specified by transcription factors that activate and repress batteries of genes, thereby determining cell fate. Ectopic expression of specific transcription factors can drive changes in cell fate, either by inducing pluripotency from a differentiated state , or by tIn eukaryotes transcription factors interact with chromatin, where genomic DNA is wrapped around histone octamers in nucleosomes. Silencing histone modifications on nucleosomes inhibit factor binding and transcription, and so modulate gene expression programs. Silencing has been suggested to impose directionality and reliability to developmental progression . A key cIn Drosophila, mutation of the histone H3-K27 residue recapitulates Polycomb transformations ,9. TheseHistones and histone modifying enzymes are conserved across eukaryotes, and expression of the H3K27M oncohistone in Drosophila cells recapitulates chromatin and silencing defects seen in gliomas . Here, weyeless-GAL4 (eyGAL) driver to induce transgenes during development of the eye . We did not expect antennal effects since the eyGAL4 driver is limited to the eye portion of the disc in late larval stages, but this driver does transiently express in the antennal portion in earlier stages [E(z) or Polycomb knockdown . Finally, immunostaining of reprogrammed eye discs detected additional induction of wing-specification genes  which weyGAL4-induced Vg expression reprograms the eye, the eyGAL4 driver shuts off. Similarly, successful reprogramming by Polycomb knockdown inactivates the eyGAL4 driver , and therefore Polycomb expression will be restored in reprogrammed cells. This is distinct from cells co-expressing H3.3K27M and Vg, or from genetic mutants of PRC1 or PRC2 [Polycomb results in reprogramming, while permanent loss results in neoplastic growth. Partial reduction of E(z) activity allows cells to live, and thus reveals its requirement for reprogramming by a single transcription factor.While there are similarities between our results and published effects of PRC1 and PRC2 mutations, there are key differences in the regulatory consequences when reprogramming occurs. When  or PRC2 ,40. In tThe idea that cell fate programs are reinforced by chromatin silencing of alternative pathways implies that reducing epigenetic barriers that restrict cell fates will stimulate cell fate transformations. We find the opposite–using ectopic expression of the Vg master regulator factor, we find that compromised silencing does not enhance transformation of the eye; instead cells of the eye disc hyperproliferate. It is startlingly simple to create overgrowth tumors in the Drosophila developing eye: expression of only two proteins–the H3.3K27M oncohistone and a transcription factor–are sufficient. Commitment to a developmental fate requires both the expression of genes for determinative transcription factors and the silencing of genes for alternative fates. Coordinated activation and silencing may shape and stabilize developmental trajectories. Our results highlight that chromatin silencing is essential for transcription factor-induced developmental reprogramming. Cell identity in the Drosophila eye is determined by a network of self-reinforcing transcription factors. To reprogram this tissue expression of Vg must both induce wing specification genes and silence eye-specifying factors. Surprisingly, inhibition of H3K27 methylation does not enhance reprogramming, implying that the activation of silenced wing specification genes is not limiting. Instead, our results argue that H3K27me3-mediated silencing of eye-specific factors is needed for successful reprogramming. This requirement may result from the inducing Vg in a setting where the eye determination program has already been established. Further, the differentiation of retinal cells even when Vg is expressed indicates that eye determination factors must be dominant to wing specification factors, similar to what has been observed after tissue damage in Drosophila . IntriguTP53, thereby enhancing malignancy. In pediatric midline gliomas, additional activating mutations are often in specific signaling receptors such as PDGFRA or ACVR1 [Why does inhibiting reprogramming with the H3K27M oncohistone result in neoplastic growth? Genetic studies in mammalian systems have demonstrated that H3K27M oncohistones are not sufficient to induce tumorigenesis on their own . Insteador ACVR1 ,34,47–49or ACVR1 , thus suor ACVR1 . More geIt is striking that mutations in PRC1 components stimulate uncontrolled proliferation, while mutation of PRC2 components do not ,40. Theshttp://www.flybase.org) and sources are listed in the  The eyGAL4-3-8 driver was used for all experiments shown here, although identical results were obtained with other eyGAL4 constructs and insertions.All crosses were performed at 25°C. All mutations and chromosomal rearrangements used here are described in Flybase . This line contains two landing sites [Inducible H3.3 constructs were constructed by cloning the n vector  , and then incubated twice in 0.3% sodium deoxycholate/PBST for 20’ each. Samples were incubated with primary antiserum diluted in PBST supplemented with 10% goat serum at 4° overnight, and finally with fluorescently labeled secondary antibodies . All tissues were stained with 0.5 μg/mL DAPI/PBS, mounted in 80% glycerol on slides, and imaged by epifluorescence on an EVOS FL Auto 2 inverted microscope (Thermo Fisher Scientific) with a 10X objective. At least 20 discs were imaged for each genotype. Pseudo-colored images were adjusted and composited in Adobe Photoshop and Adobe Illustrator. All antibodies used are listed in the Adults were euthanized in a freezer and then imaged using a Sony digital camera mounted on a Nikon SMZ1500 stereomicroscope. Images were color-corrected with Adobe Photoshop and composited in Adobe Illustrator.2 for 1 hour at 37° to tagment chromatin. Reactions were stopped by addition of SDS to 0.16% and protease K to 0.3 mg/mL, incubated at 58° for 1 hour, and DNA was purified by phenol:chloroform extraction and ethanol precipitation.We dissected imaginal discs from 3rd instar larvae in Wash+ buffer . We used 4 wing imaginal discs and 6 eye-antennal imaginal discs for each chromatin profiling experiment. Experiments were performed in duplicate and in parallel to minimize technical variation. We used immuno-tethered CUT&Tag chromatin profiling  with antD. melanogaster genome obtained from FlyBase using Bowtie2, and to the E coli genome for spike-in normalization. A step-by-step protocol is posted: https://www.protocols.io/view/cut-tag-with-drosophila-tissues-bnx5mfq6Libraries were prepared as described , with 14http://genome.ucsc.edu) [S1 Data. We used EPDnew for promoter locations in dm6 genome assembly [S1 Data. H3K27me3 signals in domains signals were extracted as fragment counts per kilobase per million reads (CPKM) across annotated H3K27me3 domains using ‘bedtools MultiCovBed’ in bedTools. Values for each dataset are provided in S1 Data. Differentially-expressed genes were defined counting fragments forCUT&Tag of elongating RNAPII-S2p across gene lengths using ‘bedtools MultiCovBed’ in bedTools. Count tables were imported into degust v. 4.1.1 (degust.erc.monash.edu), normalized with limma-voom and displayed as a volcano plot. Genes, fold-changes, FDRs are listed in S1 Data. For promoter heatmaps, profiling coverage was binned by 10 bp -1 kb to +2 kb around annotated TSSs of EPDnew promoters, ordered by mean signal and plotted using deepTools. The display range was adjusted to the maximum and minimum signals for each heatmap. For average H3K27me3 coverage around PREs, we used 2000 called Polycomb-bound sites in eye imaginal discs [S1 Data. We extracted and displayed mean H3K27me3 signal +20 kb around these sites using the ‘computematrix’ and ‘plotHeatmap’ functions of deepTools with 10 bp binning. Plots were individually scaled to the maximum mean signal in each dataset. For rank-ordering of domains, CPKM for H3K27me3 across domains was sorted for each sample and heatmapped using three-color conditional formatting in MS Excel with Green-Yellow-Red for low-to-high values. The full list is provided in S1 Data.Track screenshots were produced using the UCSC Genome browser (csc.edu) . We manucsc.edu) , and areassembly , and FB assembly . Analysial discs  and selehttps://www.protocols.io/view/cut-tag-with-drosophila-tissues-bnx5mfq6.S1 Text(DOCX)Click here for additional data file.S1 FigA) Spearman correlations between samples for H3K27me3, H3K4me2, and RNAPII-S2p coverage. Correlations between the datasets were generated and displayed by the ‘plotcorrelation’ function in deepTools. (B) Heatmaps of H3K4me2, RNAPII-S2p, and K27M coverage centered on gene starts in >H3.3K27M and >H3.3K27M >Vg eye discs. Bedgraph files for K27M profiling were used for heatmapping by the ‘computematrix’ with 1 bp binning and ‘plotHeatmap’ functions ordered by decreasing signal in deepTools.((EPS)Click here for additional data file.S1 Data>Vg imaginal discs. Sheet 8: Differentially-expressed genes between eye and >H3.3K27M imaginal discs. Sheet 9: Differentially-expressed genes between eye and >H3.3K27M >Vg imaginal discs. Sheet 10: Differentially-expressed genes between eye and >Polycomb_RNAi imaginal discs. Sheet 11: Differentially-expressed genes between eye and wing imaginal discs. Sheet 12: Differentially-expressed genes between eye and >Vg imaginal discs. Sheet 13: Differentially-expressed genes between eye and >H3.3K27M imaginal discs. Sheet 14: Differentially-expressed genes between eye and >H3.3K27M >Vg imaginal discs. Sheet 15: Differentially-expressed genes between eye and >Polycomb_RNAi imaginal discs.Sheet 1: Profiling sample IDs. Sheet 2: H3K27me3 domains coordinates and CPKM table. Sheet 3: Differentially-marked H3K27me3 domains between eye and wing, eye and >Vg, eye and >H3.3K27M, and eye and >H3.3K27M >Vg imaginal discs. Sheet 4: Called Polycomb sites curated from . Sheet 5(XLSX)Click here for additional data file.