The human endogenous retrovirus type-H (HERVH) family is expressed in the preimplantation embryo. A subset of these elements are specifically transcribed in pluripotent stem cells where they appear to exert regulatory activities promoting self-renewal and pluripotency. How HERVH elements achieve such transcriptional specificity remains poorly understood. To uncover the sequence features underlying HERVH transcriptional activity, we performed a phyloregulatory analysis of the long terminal repeats (LTR7) of the HERVH family, which harbor its promoter, using a wealth of regulatory genomics data. We found that the family includes at least eight previously unrecognized subfamilies that have been active at different timepoints in primate evolution and display distinct expression patterns during human embryonic development. Notably, nearly all HERVH elements transcribed in ESCs belong to one of the youngest subfamilies we dubbed LTR7up. LTR7 sequence evolution was driven by a mixture of mutational processes, including point mutations, duplications, and multiple recombination events between subfamilies, that led to transcription factor binding motif modules characteristic of each subfamily. Using a reporter assay, we show that one such motif, a predicted SOX2/3 binding site unique to LTR7up, is essential for robust promoter activity in induced pluripotent stem cells. Together these findings illuminate the mechanisms by which HERVH diversified its expression pattern during evolution to colonize distinct cellular niches within the human embryo. Transposable elements (TEs) are genomic parasites that use the host cell machinery for their own propagation. To propagate in the host genome, they must generate new insertions in germ cells or their embryonic precursors, as to be passed on to the next generation . To thisMany endogenous retroviruses (ERVs) are known to exhibit highly stage-specific expression during early embryonic development . ERVs arThere are four subfamilies of HERVH elements currently recognized in the Dfam  and RepblincRNA-RoR and ESRG loci (ESRG) failed to recapitulate its previous knockdown phenotype (HERVH has been the focus of extensive genomic investigation for its high level of RNA expression in human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) . SeveralSRG loci  or the dSRG loci . These rSRG loci , and a rhenotype . These iThe mechanisms regulating the transcription of HERVH also remain poorly understood. RNA-seq analyses have established that HERVH expression in human ESCs, iPSCs, and the pluripotent epiblast can be attributed to a relatively small subset of loci (estimated between 83 and 209) driven by LTR7 (sensu stricto) sequences . The relTo shed light on these questions, we focused this study on the cis-regulatory evolution of LTR7 elements. We use a ‘phyloregulatory’ approach combining phylogenetic analyses and regulatory genomics to investigate the sequence determinants underlying the partitioning of expression of HERVH/LTR7 subfamilies during early embryonic development.We began our investigation by examining the sequence relationships of the four LTR7 subfamilies currently recognized in the human genome: LTR7 sensu stricto , 7b , 7c , and 7y . We built a maximum likelihood phylogenetic tree from a multiple sequence alignment of a total of 781 5’ LTR and 1073 solo LTR sequences of near-complete length (>350 bp) representing all intact LTR subfamilies extracted from the RepeatMasker output of the hg38 human reference assembly. While 7b and 7y sequences cluster, as expected, into clear monophyletic clades with relatively short internode distances and little subclade structure, sequences from the 7c and LTR7 subfamilies were much more heterogeneous and formed many subclades . NotablyWe next sought to classify LTR7 elements more finely by performing a phylogenetic analysis using a multiple sequence alignment of all intact LTR7 sequences (>350 bp) along with the consensus sequences for the other LTR7 subfamilies for reference. We defined high-confidence subfamilies as those forming a clade supported by >95% ultrafast bootstrap (UFbootstrap) and internal branches > 0.015 (1.5 nucleotide substitutions per 100 bp) separating subgroup nodes. Based on these criteria, LTR7 elements could be divided into eight subfamilies .https://www.dfam.org.While long internal branches with high UFbootstrap support separate LTR7 subfamilies, intra-subfamily internal branches with >95% UFbootstrap support were shorter (<0.015), suggesting that each subfamily was the product of a rapid burst of amplification of a common ancestor. To approximate the sequence of these ancestral elements, we generated majority rule consensus sequence for each of the eight newly defined LTR7 subfamilies . The consensus sequences were deposited at To investigate the evolutionary relationships among the newly defined and previously known LTR7 subfamilies, we conducted a median-joining network analysis  of theirliftover analysis to infer the presence/absence of each human LTR7 locus across five other primate genomes. Insertions shared at orthologous genomic position across a set of species are deemed to be ancestral to these species and thus can be inferred to be at least as old as the divergence time of these species and likely fixed within each species  < 1e-15; Wilcoxon rank-sum test). By contrast, we found no statistical difference between the mean branch length of the subfamilies within these three age groups, suggesting that they were concomitantly active. Taken together, our dating analyses distinguish three major waves of HERV propagation: an older wave 25–40 mya involving 7c, 7o, and 7bc elements, an intermediate wave 9–20 mya involving 7b, 7d1/2, and 7u1, and a most recent wave 4–10 mya implicating primarily 7up1/2, 7u2, and 7y elements.As an independent dating method, we used the terminal branch length separating each insertion from its nearest node in Our data thus far indicate that LTR7 is composed of genetically and evolutionarily distinct subfamilies. Because a subset of HERVH elements linked to LTR7 were previously reported to be transcribed in pluripotent stem cells (human ESCs and iPSCs), we wondered whether this activity was restricted to one or several of the LTR7 subfamilies newly defined herein. To investigate this, we performed a ‘phyloregulatory’ analysis, where we layered locus-specific regulatory data obtained from publicly available genome-wide assays in ESCs  for each LTR insertion on top of a phylogenetic tree depicting their evolutionary relationship. We called an individual LTR7 insertion as positive for a given feature if there is overlap between the coordinates of the LTR and that of a peak called for this mark . We predicted that if transcriptional activity was an ancestral property of a given subfamily, evidence of transcription and ‘activation’ marks should be clustered within the cognate clade. Alternatively, if transcription and activation marks were to be distributed throughout the tree, it would indicate that LTR7 transcriptional activity in pluripotent cells was primarily driven by post-insertional sequence divergence or context-specific effects such as local chromatin or cis-regulatory environments. Differences in the proportion of positive insertions for a given mark between LTR7 subfamilies were tested using a chi-square test with Bonferroni correction. Unless otherwise noted, all proportions compared thereafter were significantly different (padj < 0.05).The results  show thaAs previously noted from ChIP-seq data , we founCongruent with having generally more TF binding and transcriptional activity, 7up loci also have a propensity to be decorated by H3K4me3, a mark of active promoters  and the broader activity mark H3K27ac (89% vs. 48%) . By contTaken together, our phyloregulatory analysis suggests that strong promoter activity in ESCs is restricted to 7up elements.The pattern described above could be explained by two non-mutually exclusive hypotheses: (i) 7up elements (most likely their progenitor) have acquired unique sequences  that promote Pol II recruitment and active transcription, and/or (ii) they somehow escape repressive mechanisms that actively target the other subfamilies, preventing their transcription. For instance, 7up elements may lack sequences targeted by transcriptional repressors such as KRAB-zinc finger proteins (KZFPs) that silence the other subfamilies in ESCs. KZFPs are well known for binding TEs in a subfamily-specific manner where they nucleate inheritable epigenetic silencing  and seveWe also examined the binding profile of ZNF534 and ZNF90, two KZFPs previously reported to be enriched for binding LTR7 elements using ChIP-exo data in human embryonic kidney 293 cells , in ordeTo determine which factors are associated and potentially determinant for 7up promoter activity, we compared the set of ‘highly expressed’ 7up loci to 7up loci which are apparently poorly expressed, using 7d1/d2 as outgroups . While kTogether these data suggest that differential repression cannot explain the differential promoter activity of LTR7 subfamilies in ESCs but rather that highly expressed LTR7up loci are preferentially bound by a cocktail of transcriptional activators that are less prevalent on poorly expressed loci.The data presented above suggest that the transcriptional activity of 7up in ESCs emerged from the gain of a unique combination of TFBS. To identify sequences unique to 7up relative to its closely related subfamilies, we aligned the consensus sequences of the newly defined LTR7 subfamilies and those of 7b/c/y consensus sequences. This multiple sequence alignment revealed blocks of sequences that tend to be highly conserved across subfamilies, only diverging by a few SNPs, while other regions showed insertion/deletion (indel) segments specific to one or a few subfamilies .Upon closer scrutiny, we noticed that the indels characterizing some of the subfamilies were at odds with the evolutionary relationship of the subfamilies defined by overall phylogenetic and network analyses. This was particularly obvious in segments we termed block 2b (where 7y and 7u2 share a large insertion with 7b and 7c) and block 3 (where 7y and 7b share a large insertion). This led us to carefully examine the multiple sequence alignment of the LTR7 consensus sequences to identify indels with different patterns of inter-subfamily relationships. Based on this analysis, we defined seven sequence blocks shared by a different subset of subfamilies, pointing at relationships that were at odds with the overall phylogeny of the LTR7 subfamilies . These oTo systematically test if recombination events between elements drove the evolution of LTR7 subfamilies, we generated parsimony trees for each block of consensus sequences and looked for incongruences with the overall consensus phylogeny. We found a minimum of six instances of clades supported in the block parsimony trees that were incongruent with those supported by the overall phylogeny .We also found some blocks evolved via tandem duplication. Notably, block 2b was absent from 7d1/2 and 7bc/o but present in all other subfamilies. However, block 2b from 7b, 7c, 7u2, and 7y aligned poorly with block 2b from 7up and 7u1. Instead, block 2b from 7up/u1 2b was closely related (~81% nucleotide similarity) to block 2a from the same subfamilies , suggestThe results above suggest that the evolution of HERVH was characterized by extensive diversification of LTR sequences through a mixture of point mutations, indels, and recombination events.We hypothesized that the mosaic pattern of LTR sequence evolution, which represents a mixture of point mutations, duplications, and inter-element recombination, gave rise to TFBS combinations unique to each family that drove shifts in HERVH expression during early embryogenesis. To test this, we aimed to reanalyze the expression profiles of newly defined LTR7 subfamilies during early human embryogenesis and correlate these patterns with the acquisition of embryonic TF binding motifs within each of the subfamilies.To perform this analysis, we first reannotated the hg38 reference genome assembly using RepeatMasker with a custom library consisting of the consensus sequences for the eight newly defined LTR7 subfamilies plus newly generated consensus sequences for 7b, 7c, and 7y subfamilies redefined from the phylogenetic analysis presented in Next we used the newly generated RepeatMasker annotations to examine the RNA expression profiles of the different LTR7 subfamilies using scRNA-seq data from human preimplantation embryos and RNA-seq data from human ESCs  . The first consisted of the full-length 7d consensus sequence (predicted to be inactive in iPSCs), the second contained the full-length 7up1 consensus (predicted to be active), and the third used the same 7up1 consensus sequence but lacking the 8 bp motif unique to 7up1/2 and 7u1 elements overlapping the SOX2/3 motif . The resThe HERVH family has been the subject of intense investigation for its transcriptional and regulatory activities in human pluripotent stem cells. These studies often have treated the entire family as one homogenous, monophyletic entity and it has remained generally unclear which loci are transcribed and potentially important for pluripotency. This is in part because HERVH/LTR7 is an abundant and young family which poses technical challenges to interrogate the activity of individual loci and design experiments targeting specific members of the family . Here, wPrevious studies have treated LTR7 sensu stricto insertions as equivalent representatives of their subfamilies . While sIt has been observed for some time that only a small subset of HERVH elements are expressed in ESCs . Some haThe phyloregulatory approach outlined in this study could be applied to illuminate the regulatory activities of LTR elements in other cellular contexts. In addition to embryogenesis, subsets of LTR7 and LTR7y elements are known to be upregulated in oncogenic states . It woulRecombination is a common and important force in the evolution of exogenous RNA viruses  and ERVsOur findings of extensive sequence block exchange between 7y and 7b  are consPrevious work has highlighted the role of TEs, and LTRs in particular, in donating built-in cis-regulatory sequences promoting the evolutionary rewiring of mammalian transcriptional networks . We showOur evolutionary analysis reveals that multiple HERVH subfamilies were transpositionally active in parallel during the past ~25 my of primate evolution  and 7. TNiche diversification may have also enabled HERVH to evade cell-type-specific repression by host-encoded factors such as KZFPs. KZFPs are thought to emerge and adapt during evolution to silence specific TE subfamilies in a cell-type-specific manner . For exaLastly, our findings may provide new opportunities for stem cell research and regenerative medicine. Our data on 7up reinforces previous findings  that plaIn conclusion, our study highlights the modular cis-regulatory evolution of an ERV which has facilitated its transcriptional partitioning in early embryogenesis. We believe that phyloregulatory dissection of endogenous retroviral LTRs has the potential to further our understanding of the evolution, impact, and applications of these elements in a broad range of biomedical areas.All HERVH-int and accompanying LTRs  were extracted from masked  were defined as belonging to a new bona fide LTR7 subfamily .Majority rule (51%) was used to generate each LTR7 subfamily at nodes described in Non-gap SNPs from the muscle alignment were used to construct a median-joining network  with POPThe RVT domain was extracted from HERVH-int consensus via repbrowser :CACCCTTACCCCGCTCAATGCCAATATCCCATCCCACAGCATGCTTTAAAAGGATTAAAGCCTGTTATCACTCGCCTGCTACAGCATGGCCTTTTAAAGCCTATAAACTCTCCTTACAATTCCCCCATTTTACCTGTCCTAAAACCAGACAAGCCTTACAAGTTAGTTCAGGATCTGTGCCTTATCAACCAAATTGTTTTGCCTATCCACCCCATGGTGCCAAACCCATATACTCTCCTATCCTCAATACCTCCCTCCACAACCCATTATTCTGTTCTGGATCTCAAACATGCTTTCTTTACTATTCCTTTGCACCCTTCATCCCAGCCTCTCTTCGCTTTCACTTGGA.This sequence was blated (best hit) against all annotated HERVH-int in the human genome and matches were extracted. Corresponding LTR7 subdivision annotations from ChIP-seq datasets representing TFs, histone modifications, and regulatory complexes in human ESCs and differentiated cells were retrieved from GSE61475 (38 distinct TFs and histone modifications), GSE69647 , GSE117395 , and GSE78099 (an array of KRAB-ZNFs and TRIM28) . ZNFs enliftover (100% of coordinates lifted) and intersected these peak with the loci from LTR7 subfamilies using bedtools with any overlap. For ChIP-seq binding enrichment on a subset of marks following motif analysis  < 1%. ENCODE-defined blacklisted regions were excluded from called peaks. For phyloregulatory analysis , we thenanalysis , 70% ovePeaks from external ChIP-seq datasets were intersected with LTR7 insertions . LTR7 in‘Highly transcribed’ (fpkm >2) and ‘chimeric’ HERVH from H1 cells (GSE54726)  were intTables with the proportion of solo and 5’ LTRs from a given subfamily positive for select marks  were used to generate heatmaps with the R package ggplot (ggheat) . Those wGRO-seq (H1 cells – GSE64758), whole-genome bisulfite sequencing (WGBS-seq – H1 cells), SOX2 GEO ID GSE125553 , and H3KHuman coordinates for 7b, 7c, and 7y and LTR7 used in alignments and tree generation were lifted over  from GRCTerminal branch lengths from the LTR7 phylogenetic tree  were extMajor recombination breakpoints were identified by eye from the consensus sequence MSA, where SNPs and structural rearrangements seemed to have different relationships between blocks. Putative block recombination events were identified by looking for shared shapes in the block consensus MSA . To testLTR7up blocks 2a and 2b  appearedConsensus sequences for LTR7 subfamilies were generated using the tree from Parsing previously annotated LTR7 into eight subfamilies and evidence of recurrent recombination events caused concern that HERVH LTRs may be misannotated in the RepeatMasker annotations. To compensate, we remasked  GRCh38/hhttp://satijalab.org/seurat/) guidelines. The annotations of cell types were taken as it was classified in original studies. We calculated differential expression and tested their significance level using Kruskal-Wallis test by comparing cell types of interest with the rest of the cells. The obtained p-values were further adjusted by the Benjamini-Hochberg method to calculate the FDR. All the statistics and visualization of RNA-seq were performed on R (https://www.r-project.org/).We downloaded the raw single-cell RNA-seq datasets from early human embryos and ESCs (GSE36552) and the EPI, PE, TE cells (GSE66507) in sra format. Following the conversion of raw files into fastq format, the quality was determined by using the FastQC. We removed two nucleotides from the ends as their quality scores were highly variable compared with the rest of the sequences in RNA-seq reads. Prior to aligning the resulting reads, we first curated the reference genome annotations using the LTR7 classification, as shown in the manuscript. We extracted the genes (genecode V19) and LTR7 subfamilies see  genomic –5 were kept. For enrichment in all subfamilies . All the ChIP-seq narrow peaks were called by MACS2 (FDR < 0.01). To generate a set of unique peaks, we merged ChIP-seq peaks within 50 bp of one another using the mergeBed function from bedtools. We then intersected these peak sets with LTR7 subgroups from hg19 repeat-masked coordinates using bedtools intersectBed with 50% overlap. LTR7up1 and LTR7up2 were harboring the highest number of peaks compared with the rest of the subgroups. To illustrate the enrichment over the LTR7 subgroups, we first extended 500 basepairs from upstream and downstream coordinates from the left boundary of each LTR7subgroups. These 1 kb windows were further divided into 10 bps bins. The normalized ChIP-seq signal over the local lambda (piled up bedGraph outputs from MACS2) was counted in each bin. These counts were then normalized by the total number of mappable reads per million in given samples and presented as signal per million per 10 bps. Finally, these values were averaged across the loci for each bin to illustrate the subfamilies’ level of ChIP-seq enrichment. Replicates were merged prior to plotting. Note: Pearson’s correlation coefficient between replicates across the bins was found to be r > 0.90.SOX2 ChIP-seq and whole-cell extract datasets from primed human ESCs were downloaded in fastq format from GEO ID GSE125553 . Fastq rAll 7up1, 7up2, and 7u1 loci used in other analyses were ranked based on the strength/presence of the SOX3 motif in block 2b. GRO-seq and SOX2 (see above data sources) reads were layered onto these loci in windows 500 bp upstream and 8 kb downstream.The inserts (LTR7 variants or EF1a promoter) with restriction enzyme overhangs were ordered from Genewiz and cloned into pGL3-basic plasmid upstream of the firefly reporter gene . Minipreps were prepared with QIAprep Spin Miniprep kit . Plasmids were sequenced to ensure the correct sequence and directionality of the insert. Twenty-four hours before transfection, human iPSC WTC-11  cells were plated on Vitronectin  coated 12-well plates in Essential 8 Flex medium  with E8 supplement, Rock inhibitor  and 2.5% penicillin-streptomycin. Cells were co-transfected with 800 ng of plasmid of interest and 150 ng plasmid containing EF1a upstream of GFP for normalization with Lipofectamine Stem transfection reagent  according to manufacturer’s instructions. Forty-eight hours after transfection, cell pellet was harvested and luciferase activity was measured with Luciferase Reporter Assay kit  on Glomax (Promega) according to instructions. Transfection efficiency and cell count was normalized with GFP.1.7down:GCTAGCTGTCAGGCCTCTGAGCCCAAGCTAAGCCATCATATCCCCTGTGACCTGCACGTACACATCCAGATGGCCGGTTCCTGCCTTAACTGATGACATTCCACCACAAAAGAAGTGAAAATGGCCTGTTCCTGCCTTAACTGATGACATTATCTTGTGAAATTCCTTCTCCTGGCTCATCCTGGCTCAAAAGCTCCCCTACTGAGCACCTTGTGACCCCCACTCCTGCCCGCCAGAGAACAACCCCCCTTTGACTGTAATTTTCCTTTACCTACCCAAATCCTATAAAACGGCCCCACCCCTATCTCCCTTCGCTGACTCTCTTTTCGGACTCAGCCCGCCTGCACCCAGGTGAAATAAACAGCTTTATTGCTCACACAAAGCCTGTTTGGTGGTCTCTTCACACGGACGCGCATGCTCGAG2.LTR7upcons:GCTAGCTGTCAGGCCTCTGAGCCCAAGCCAAGCCATCGCATCCCCTGTGACTTGCACGTATACGCCCAGATGGCCTGAAGTAACTGAAGAATCACAAAAGAAGTGAATATGCCCTGCCCCACCTTAACTGATGACATTCCACCACAAAAGAAGTGTAAATGGCCGGTCCTTGCCTTAAGTGATGACATTACCTTGTGAAAGTCCTTTTCCTGGCTCATCCTGGCTCAAAAAGCACCCCCACTGAGCACCTTGCGACCCCCACTCCTGCCCGCCAGAGAACAAACCCCCTTTGACTGTAATTTTCCTTTACCTACCCAAATCCTATAAAACGGCCCCACCCTTATCTCCCTTCGCTGACTCTCTTTTCGGACTCAGCCCGCCTGCACCCAGGTGAAATAAACAGCCATGTTGCTCACACAAAGCCTGTTTGGTGGTCTCTTCACACGGACGCGCATGCTCGAG5.LTR7upcons_AAAGAAG_deletion:GCTAGCTGTCAGGCCTCTGAGCCCAAGCCAAGCCATCGCATCCCCTGTGACTTGCACGTATACGCCCAGATGGCCTGAAGTAACTGAAGAATCACAAAAGAAGTGAATATGCCCTGCCCCACCTTAACTGATGACATTCCACCATTGTAAATGGCCGGTCCTTGCCTTAAGTGATGACATTACCTTGTGAAAGTCCTTTTCCTGGCTCATCCTGGCTCAAAAAGCACCCCCACTGAGCACCTTGCGACCCCCACTCCTGCCCGCCAGAGAACAAACCCCCTTTGACTGTAATTTTCCTTTACCTACCCAAATCCTATAAAACGGCCCCACCCTTATCTCCCTTCGCTGACTCTCTTTTCGGACTCAGCCCGCCTGCACCCAGGTGAAATAAACAGCCATGTTGCTCACACAAAGCCTGTTTGGTGGTCTCTTCACACGGACGCGCATGCTCGAG5'NheI highlighted in yellow3'XhoI highlighted in cyan Transposons achieve evolutionary success only upon self-replication in the germline, where novel genomic insertions are passed to the next generation. To define the genetic determinants of transposon specialization to the germline and specifically, specialization to pluripotent embryonic stem cells, this article applies evolutionary analysis, cell type-specific transcriptomics, and expression reporter assays to the human endogenous retrovirus type-H, HERV. Previous links between HERV and the maintenance of pluripotency make the discoveries consequential for the mobile element and genome evolution communities along with those engaged in stem cell biology and regenerative medicine. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Review Commons.][Editors' note: this paper was reviewed by  Summary reviews (experiments):#1: “The overall assessment of the manuscript is consistent between reviewers. There are suggestions that would improve the manuscript, but nothing seems to put in question the main claims of the paper.”#2: “All reviewers are in agreement that this is timely, important, and potentially highly significant contribution. All comments and criticism aiming to advise authors how to improve the revised manuscript by providing more detailed descriptions of methodologies, clarifying some uncertainties regarding data analyses and presentations, and addressing minor editorial shortcomings.”#3: “I agree with the general assessment of all reviewers”Summary reviews (significance):#1: “Interest in dissecting the putative (but still unclear) roles of HERVH/LTR7 in pluripotency has been growing. This work will become a key reference and resource for those studying these elements. It provides clarity on the subfamily structure and their evolutionary relationships, and places that within the context of transcriptional regulation during preimplantation development and ESCs.”#2: “This is potentially highly significant contribution with the impact extending beyond the several areas of fundamental biological and biomedical science into the multiple areas of translational biomedicine. With the adequate context-specific editorial highlights and perspectives this paper would be of major impact and interest to the broad scientific readership and general public.”#3: “As the authors well discuss in the manuscript their analysis brings key information to the table, allowing researchers on the broad fields of hESCs biology and regenerative medicine to use that to refine the characterization of the pluripotent cells. I believe the manuscript will cater not only to transposon and genome evolution afficionados but will also be of interest to the fields of human pluripotency and regenerative medicine.”It is clear from the comments above that all reviewers were strongly enthusiastic about the study. The reviewers also offered excellent feedback and constructive suggestions to improve the arguments and clarity of the manuscript as well as the robustness of some of the computational analyses and data/code availability. We have now carefully addressed all their suggestions, point-by-point, as detailed below. In most cases, their comments resulted in slight changes to the text to clarify certain points or add discussion points. Additionally, we present a few new bioinformatic analyses to address their more substantial suggestions. Substantive revisions can be summarized as follow:– SOX2 reads added to heatmap and aggregate signal plot presented in supplemental figure 2 to ensure LTR7 subfamily enrichment was not an artifact of peak calling (in response to Reviewer 1).– An additional aggregate signal plot and heat map was added as supplemental figure 8 to assess whether there was an obvious correlation between SOX2 motif presence/strength and SOX2 binding and transcriptional activity (in response to Reviewer 3).– Additional enrichment analysis looking into the transcription of LTR7 subfamilies in multiple “primed” and “naïve” ESC lines was added as supplemental figure 10 .1. Point-by-point description of the revisionsReviewer #1:1. I may be mistaken, but as far as I can tell all ESC data are from so-called 'primed' culture conditions. Given some of the differences in expression between ESCs and epiblast , it may be worth also evaluating the expression of the different subfamilies in naïve ESCs. It would also help to support the suggestion that 7u2 could be used as a naïve cell marker.All reviewers agreed that we should expand the analysis of ESC to ‘naïve’ ESC (in addition to ‘primed’ ESCs). We concur and, as suggested by reviewer #1, we have now analyzed and compared RNA expression of all LTR7 subfamilies in multiple primed and naïve ESC lines, with a specific attention for the expression of the subfamilies containing the identified SOX2 motif, 7up and 7u2. The results of this new analysis are consistent with the pattern we discerned from our scRNA-seq analysis of human embryos: we find that LTR7b, y, and u2 transcripts are more abundant in naïve lines, while LTR7o, bc, up1, up2, d1, and u2 transcripts are more abundant in primed cell lines. This is in agreement with the current view in the field that naïve cells are closer to epiblast cells and that LTR7b, y and u2 mark specifically these cells. The results of this analysis are now reported as Supplemental Figure 10 and we have added the following text to integrate these findings:“Indeed, while 7up shows expression preference in primed cell lines, 7u2 shows preference for naïve lines .” – page 28-292. From Figure 3D, it appears that SOX2 binds around half of the expressed 7up loci. If this is a critical factor for expression, could it be that the remaining loci are moderately enriched, below the threshold for peak detection? It may be worth doing a parallel analysis based on enrichment levels. Or perhaps, as the authors suggest, SOX3 is the key protein.Reviewers #1 and #3 recommended that the addition of a non-peak approach to assess SOX2 binding enrichment to 7up might be beneficial. We agree and we have now integrated an analysis of SOX2 ChIP-seq reads (in addition to peaks) into the heatmaps presented in supplemental figure 2. We find that ChIP-seq reads are evenly distributed across 7up1/2 loci (and 7u1 to a lesser extent), indicating that SOX2 binding is not driven by a few LTR loci but is a more general feature of 7up. Additionally, we note that read pileups do not seem biased toward more highly transcribed loci. Moreover, beds, bams, and related data files have now been provided in the github for others who want to replicate or expand the analysis for their own purposes.3. Related to this, the suggestion that SOX3 is the key protein is based on its expression pattern. However, the authors also suggest that 7up elements are kept repressed by KZFPs/KAP1 at early preimplantation stages, which could prevent SOX2 from activating 7up at this point. Moreover, unlike what is claimed in the manuscript, Supplemental Figure 6C shows higher expression of SOX2 than SOX3. Whilst the jury is still out, together with the ChIP-seq data and known roles in pluripotency, the involvement of SOX2 remains likely.specific to ESC than SOX2 RNA, but not more abundant. Additional experiments would be needed to test whether the crucial factor is SOX2 or SOX3.There was some confusion as to whether we thought SOX2 or SOX3 was more likely to be necessary for 7up transcription. We intended to posit that both scenarios are equally likely and have adjusted wording in the manuscript accordingly. Related to this, we originally stated that SOX3 RNA was more abundant in ESC than SOX2 RNA. We have reworded this to reflect supplemental figure 6c where SOX3 RNA is more “In addition, we observed that both SOX2 and SOX3 are expressed in human ESCs but SOX3 was more specifically expressed in ESCs ” – page 204. Is the SOX2/3 motif also present in block 2a, given its close relationship with 2b? If so, can the authors speculate why the 2b motif would be so critical? Could it promote SOX2 homodimerization? Or perhaps acts cooperatively with other TFs , as shown for motifs within RLTR9 elements in mice (PMID: 28348391).Reviewer #1 requested further discussion on the occurrence and position of the SOX2/3 motif in block 2a and if we think homodimerization or cooperative TF binding is responsible for boosting transcriptional activity. This is an interesting speculation and we have added text in the discussion to mention it. Briefly, we do observe that block 2a also contains a SOX2/3 motif, so it is possible that homodimerization or cooperative binding explains the transcriptional activation of 7up in ESC. (5) Furthermore, 7u1 elements also have the SOX2/3 motif in block 2b, yet these elements are not transcribed. We think it likely that these subfamilies need additional TF binding sites for ESC transcription.“This exact motif is also present in the untranscribed 7u1 subfamily. More distantly related subfamilies also have SOX2/3 binding sites elsewhere on their sequence (supp.file 4), indicating that the presence of a SOX2/3 site is not sufficient to confer transcriptional activity in ESC. Perhaps SOX2/3 cooperatively bind with other TF to uniquely activate 7up.” – page 245. Could the authors also briefly discuss why 7u1 elements are not expressed despite having the SOX2 motif ? This would be an interesting consensus to add to the luciferase assay.Please see response to point 4 above.Reviewer #2:1. Please consider designing and executing targeted CRISPR/Cas9 genome editing and/or CRISPR-guided epigenetic silencing experiments to unequivocally demonstrate the role of the 8 nucleotide insert in the LTR7up sequence in maintenance of the pluripotency phenotype;In agreement with reviewers 1 and 3 (see quoted text below), we feel such experiments are beyond the scope of the paper. There has been a slew of HERVH KD and KO experiments in recent years. As we noted in our introduction and discussion, none of these experiments have pinned down the exact role of HERVH in pluripotency. With this manuscript, we aimed to gain new insights and develop resources to eventually address this important question. But clearly it will take extensive experiments in the future to fully understand the role of HERVH in pluripotency. As reviewer 3 notes, we believe we were careful to not overstate the importance of the predicted Sox2/3 motif in the maintenance of pluripotency. Our results only show that these 8 basepairs are necessary for 7up transcription in iPSCs.“Reviewer #2 suggests CRISPR experiments, but to me this feels outside of the scope of this paper. Others have explored, and continue to explore, the potential roles of LTR7/HERVH in pluripotency, but this is not the aim of this paper.” – Reviewer #1“I agree with Reviewer #1 on the requirements of the CRISPR experiments. While it would be an extremely interesting approach, dissection of whether LTR7up expression is a marker or a driver of pluripotency can be an entire new manuscript, and the authors were careful not to extrapolate claims of causality related to its contribution to pluripotency.” – Reviewer #32. Please consider to expand sequence alignment and conservation analyses beyond the hg38 human genome reference database to include: (a) recently released "The complete sequence of a human genome" (https://doi.org/10.1101/2021.05.26.445798); (b) assessment of the conservation in human population  of the newly identified LTR7 family, particularly, candidate regulatory sequences that appear to emerge most recently ; (c) assessment of the conservation in several releases of Chimpanzee genomes (may provide some important insights into conservation/variability within non-human primate species) and especially recent high-quality Bonobo genomes ; assessment of the conservation in most recent releases of Neanderthals and Denisovan genomes , in particular, assessment of LTR7 genomic coordinates within human-specific genomic regions lacking ancestral DNA (regions not shared with archaic hominins);Reviewer #2 also suggested expanding our HERVH conservation analysis to (a) the newly released complete human reference genome (b) 1000 genome or other human population genome datasets (c) chimpanzee population genome datasets (d) a newly released high quality bonobo genome (e) the Denisovan and Neanderthal genomes. While we agree that these are interesting ideas, we do not believe that such analyses would reinforce or alter the major findings of the paper. The main reason being that it has been well established that HERVH was active prior to the divergence of African great apes  and our study further confirms that at the level of individual subfamilies. Thus, it is strongly predictable that HERVH insertions have long been fixed in the human population and shared by archaic hominins as well as African hominids. In fact, our lab has previously carried out a search for polymorphic HERVH insertions in the 1000 Genomes and Simons Diversity Genome Project  and did not identify a single polymorphic HERVH insertion in these datasets (unlike HERVK for which we and others have identified a few polymorphic insertions). While we cannot formally rule out rare instances of HERVH insertion polymorphisms among and between hominid species, these are unlikely to change the bigger picture of HERVH evolution depicted in our study. We have now altered the text to clarify these points.“To date, no polymorphic HERVH insertions have been found in the human genome .” – page 1“Insertions shared at orthologous genomic position across a set of species are deemed to be ancestral to these species and thus can be inferred to be at least as old as the divergence time of these species and likely fixed within each species .” – page 8Likewise, expanding our analysis of the newly released “complete” human genome produced by the Telomere-to-Telomere consortium is unlikely to affect our conclusions because we do not expect many HERVH insertions to be lacking from the version of the assembly used for our analysis. This is because HERVH, like other gamma retroviruses, have a propensity to integrate into transcriptionally active genomic regions, not the heterochromatic and centromeric regions that were resolved in the T2T assembly (https://doi.org/10.1101/2021.07.12.451456).3. Please provide detailed descriptions of the "LiftOver" methodology that was utilized in the study, including values of the minimum ratio of bases that must remap to ascertain the successful conversion between the assemblies; Please provide a complete report of the results  for all LT7 sub-families; Specifically, please indicate what sequence conservation levels were chosen to define a successful conversion between different genomes; Please clarify whether only direct "LiftOver" or both direct and reciprocal "LiftOver" analyses were executed during the comparisons of sequence conservation between species; It is impossible to reproduce and validate the reported findings without the adequate description and clarification of these points;Reviewer #2 requested a more detailed description of the liftover methodology. We have expanded the description in the Methods section. Briefly, liftover chain files map syntenic regions between genomes. With closely related species, most of these are 1:1 matches i.e. one region in human matches with only one orthologous chimpanzee region. Sometimes, there are multiple syntenic hits from one species to another , often caused by duplications or deletions in one lineage. Therefore, we only use reciprocal liftover hits in our calculations i.e. one human genomic region that liftovers to one chimpanzee region that liftovers back to the same original human region (default settings of 0.95 overlap both ways). The difference between reciprocal and unidirectional liftover has been clarified in the text. All commands and files used are available in github. The corresponding data files are also now included as Supplementary files.“Those that were successfully lifted over from human to non-human primates (syntenic regions) were then lifted over back to human. Only those that survived both liftovers (1:1 orthologous) were counted as present in non-human primates.” – page 344. For each figure in the main text and the supplement, please provide the corresponding data source tables ; This is a standard requirement for top-ranked journals which is essential for reproducibility of the reported findings;Reviewer #2 correctly noted that some data source tables were missing (point 4) and some Supplementary files had inadequate descriptions (point 6). A description file has been added to accompany more aptly named Supplementary files. All files and commands used are also now available on github.5. One approach to calculation of the estimated evolutionary emergence time of regulatory LTRs is linked to estimates of the divergence time between species based on estimated mutation rates. However, calculations of the mutation rate in humans as well as estimations of the divergence time between humans and chimpanzees have been the controversial subject (please see excellent discussion on these subjects in the Supplement of most recent ScinceAdvaces 2021) paper "An ancestral recombination graph of human, Neanderthal, and Denisovan genomes"; http://advances.sciencemag.org/. If the slower assumed mutation rate (0.5x10-9 per base per year or 1.25x10-8 per base per generation based on comparisons parents to offspring) is accounted for, the human-chimpanzee divergence time was estimated at 13 Mya rather than 6.5 Mya .This approach was successfully implemented in most recent ScienceAdvances (2021) paper "An ancestral recombination graph of human, Neanderthal, and Denisovan genomes"; http://advances.sciencemag.org/. It would be of interest to determine how the proposed evolutionary timeline of the emergence of distinct LTR7 regulatory sequences would be affected when the approach based on slower assumed mutation rate is implemented.Reviewer #2 also expressed interest in how our aging analysis would change with different divergence time calculations. We appreciate that divergence times between these lineages are estimates that are often contested and revised. However, using different divergence times would only affect the dating of subfamily expansion and would not alter the sequence of amplification we infer from our analysis because our dating is based on comparative genomics and an analysis of shared insertions (liftover) rather than a molecular clock.6. Please provide titles and short descriptions for individual Supplementary files with references to figures, tables, or other relevant contents of the main text; This is the standard requirement for peer-reviewed publication which is essential for reproducibility of the reported findingsPlease see above response to point 4.Reviewer #3:1. On the mapping of ChIP-seq reads (and other NGS datasets used), what were the parameters used for dealing with multi-mappers? Some parameters can cause an artificial "inflation" of the inferred signal over abundant repeats. E.g. allowing mismatches + report all would erroneously subscribe an active status to several LTR copies when perhaps in reality all signal is coming from one or few individual copies. This can of course affect the conclusions: if many phylogenetically close LTRs have "active" signals this likely points towards a sequence dependent effect (e.g. a reader TF as in this manuscript), however if most "active" signal derives from a few copies this can point towards a position/context effect (e.g. proximity to other regulatory regions). I like the approach the authors took of extrapolating measured signal of flanking sequences that partially overlap with the LTR annotation, but how does it compare with unique mapping with no mismatches?Reviewer #3 requested a more detailed description of the ChIP-seq mapping methodology, especially regarding multi-mappers. This is an especially important point with relatively young repetitive elements. Our use of a phred score of 33 as a mappability cutoff is already quite stringent. However, we repeated our analysis of SOX2 binding LTR7 subfamilies  using only uniquely mappable reads see . While t“This analysis was repeated with exclusively uniquely mapping reads which did not affect the outcome of analysis (Data not shown).” – page 382. Maybe a metaplot + hierarchical heatmap for the stacked members of each of the 8 LTR subfamilies showing selected regulatory signals depicted on Figure 3? I like figure 3A, but its binary nature doesn't allow us to get a sense of the quantitative distribution of these signals over the many members of each subfamily. The other extreme are the heatmaps on Figure 3 B-C-D which are quantitative but suppress the information about the variability. Perhaps the chosen formats relate to the limitations of the NGS mapping (see above).Please see response to reviewer #1 comment #2 above.3. Relates to the correlation between SOX2/3 binding sites and transcription in ES. If using quantitative approaches to the regulatory data as discussed above, can the authors correlate strength of SOX2/3 motifs with activity? Would this expose another layer of variability within active subfamilies?Reviewer #3 suggested performing an additional analysis to correlate the presence of the SOX2/3 binding site in block 2b and ESC expression. To probe this relationship, we took all 7up 5’ and solo LTRs from the human genome and sorted them based on their HOMER SOX3 pvalue hit for this motif. We then layered on SOX2 ESC ChIP-seq onto these coordinates. We found that there is no correlation between SOX2 binding and the strength of the SOX3 binding motif prediction. The data have been added to the supplement  and the text has been modified to include the results.“Additionally, the strength of the motif match does not correlate with SOX2 binding or transcription within 7up . Perhaps SOX2/3 cooperatively bind with other TF to uniquely activate 7up.” – page 24Minor revisions:Figure 1A and 1B legends were swapped.This has been corrected..Several typos have been correctedFigure 2A was referred to as 3A several times in the text. Figure 4C was also referred to as 4D.This has been remedied.Several phrasing discrepancies were fixed.The text and Supplementary files were unclear on if they were from previously annotated or remasked data sources. This resulted in seeming discrepancies in insertion counts between the text and supplement.The text, Supplementary files, and readmes have been changed to mark more strongly designate their source. In the example listed by reviewer #2. The supp table lists 113 full and 524 solo LTR7b  counts from “4.0.5 repeat Library 20140131”. The labeling of this section has been improved.


The epigenetic state of chromatin is altered by regulators which influence gene expression in response to environmental stimuli. While several post-translational modifications contribute to chromatin accessibility and transcriptional programs, our understanding of the role that specific phosphorylation sites play is limited. In cancer, kinases and phosphatases are commonly deregulated resulting in increased oncogenic signaling and loss of epigenetic regulation. Aberrant epigenetic states are known to promote cellular plasticity and the development of therapeutic resistance in many cancer types, highlighting the importance of these mechanisms to cancer cell phenotypes. Protein Phosphatase 2A (PP2A) is a heterotrimeric holoenzyme that targets a diverse array of cellular proteins. The composition of the PP2A complex influences its cellular targets and activity. For this reason, PP2A can be tumor suppressive or oncogenic depending on cellular context. Understanding the nuances of PP2A regulation and its effect on epigenetic alterations can lead to new therapeutic avenues that afford more specificity and contribute to the growth of personalized medicine in the oncology field. In this review, we summarize the known PP2A-regulated substrates and potential phosphorylation sites that contribute to cancer cell epigenetics and possible strategies to therapeutically leverage this phosphatase to suppress tumor growth.  Aberrant epigenetic regulation of the cancer genome contributes to a wide range of phenotypes, including therapeutic resistance and cellular plasticity . In normIn addition to specific DNA sequences that influence gene transcription, there exists a higher order structure to the genome that provides an additional layer of regulation to maintain cellular homeostasis. The nucleosome comprises DNA wrapped around a histone octamer , which shields the associated DNA from the transcriptional machinery. Reversible epigenetic marks, such as DNA methylation or posttranslational modifications (PTMs) on histones, allow for dynamic changes in the genome without altering the DNA sequence. In response to repressive epigenetic marks, histones will condense to form heterochromatin, or ‘silent’ chromatin regions. Active epigenetic marks help direct remodeling complexes to specific sites of DNA to displace histones and expose critical coding regions, providing additional security that only genes in euchromatic, ‘active’ chromatin regions are readily expressed. Densely packed regions of chromatin are moved to the nuclear lamina to make room for the transcriptional machinery within areas of open chromatin. This dynamic spatial organization, or 3D architecture, generates topologically associated domains (TADs) within the nucleus. During tumorigenesis, the epigenetic regulation of these various chromatin states is frequently altered, significantly contributing to cancer phenotypes and therapeutic response . HoweverThere is a diverse array of cellular complexes that balance the deposition, reading, and removal of epigenetic marks to rapidly alter gene expression based on changes in the cellular environment. These epigenetic modifiers can be divided into three main categories: ‘readers’, ‘writers’ and ‘erasers’. Epigenetic ‘writers’ and ‘erasers’ deposit or remove epigenetic marks, such as methylation, on DNA and histones ,8. EpigeKinase inhibitors have been a primary focus for anti-tumor therapeutics contributing to the rise in personalized medicine . HoweverThe function of PP2A and its contribution to tumorigenesis is highly dependent on the composition of the PP2A holoenzyme. Here, we review the complex mechanisms by which PP2A regulates the epigenetic state of cancer cells and potential ways to leverage this activity for anti-cancer therapeutics.The loss of normal chromatin regulation leads to irregular nuclear morphologies, heterochromatin redistribution and deregulated transcriptional programs, emphasizing the importance of understanding how chromatin states are dynamically regulated . ChromatHeterochromatic regions are formed in response to repressive histone modifications, such as histone H3 lysine 9 di- and trimethylation (H3K9me2/me3) ,33. Theset al. demonstrated that PRR14 contains a conserved short linear motif (SLiM) consistent with PP2A-B56 substrates. This SLiM motif drives the association of PRR14 and PP2A complexes containing B56α (PP2A-B56α)  ,38. PRR12A-B56α)  37,39).,39.et alAdditionally, direct phosphorylation of Lamin proteins during mitosis promotes depolymerization and breakdown of the nuclear lamina so that cells may properly undergo cell division . Both PPIn addition to anchoring heterochromatic regions, Lamins also contribute to the distribution of nuclear pore complexes (NPCs) across the membrane and the transcriptional regulation of genes associated with these structures. Although the majority of DNA associated with the nuclear lamina is heterochromatic, the regions surrounding the NPC are commonly euchromatic and highly transcribed . NPC-assTogether, these studies support a model where CIP2A-mediated inhibition of PP2A at the nuclear periphery to facilitate the oncogenic activities of MYC. However, the PP2A subunits that are involved in the process and the contribution of Lamin A/C phosphorylation to association with oncogenic phenotypes need to be further explored.Phosphorylation of histone H3 (H3) at the serine 10 residue (pH3S10) provides a docking site for chromatin modifiers and increases the acetylation of neighboring residues . This PTPP2A is reported to alter the activity of chromatin remodeling complexes that regulate or ‘read’ histone epigenetic marks in addition to the direct dephosphorylation of histones. Aberrant regulation of PP2A holoenzyme composition and activity alters the precise signaling that methylation- and acetylation-associated complexes provide, further contributing to oncogenesis by indirectly influencing euchromatic region accessibility.Histone acetylation primarily results in the relaxation of the chromatin and increased transcription of nearby genes. Acetylation marks are added and removed by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively, which function in a coordinated effort to maintain normal transcriptional programs. While increased acetylation is often associated with cancer, elevated levels of HDAC activity can similarly contribute to tumor phenotypes by silencing key tumor suppressor genes, such as p53 ,63. DespPP2A also regulates the activity of lysine acetylation ‘readers’, including the Bromodomain and Extraterminal (BET) protein, Bromodomain-containing 4 (BRD4). BRD4 accumulates at euchromatic regions to promote gene transcription through the recruitment of transcription factors to the transcription start site or as a part of super-enhancer complexes . Phosphoet al. unexpectedly demonstrated that rather than global transcriptional repression, the BET inhibitor, JQ1, only modified the expression of a limited number of genes, indicating that specific genes displayed unique susceptibility to BET inhibition. Of the affected targets, the MYC super-enhancer was acutely reliant on BRD4-dependent transcriptional activation  complexes play an important role in suppressing gene transcription by removing acetylation marks and promoting formation of heterochromatin. Given that deregulation of HDAC activity can contribute to either tumor suppressive or oncogenic signaling, proper HDAC nuclear localization is extremely important for cellular homeostasis . Class IPP2A-mediated regulation of HDAC complexes greatly depends on the composition of the PP2A holoenzyme . In contHDAC4 signaling contributes to several cancer phenotypes, including increased proliferation and suppression of differentiation transcriptional programs . AlthougRegulation of HDAC4 by PP2A has also been shown to impact the differentiation state of fibroblasts . ReprogrHistone methylation marks are extremely diverse in function and can be associated with promotion or inhibition of gene transcription. The role of each methylation mark depends on the genomic site, the presence of other neighboring histone PTMs or epigenetic marks, as well as the number of methyl groups deposited on any given residue  . SimilarWhile methylation of lysine residues (K) is more common, the field of arginine (R) methyltransferases has significantly expanded within the past decade. There are three subclasses of protein arginine methyltransferases (PRMTs): (i) asymmetric dimethylarginine (ADMA), (ii) symmetric dimethylarginine (SDMA) and (iii) monomethyl arginine (MMA) . All PRMPRMT1 is the predominant ADMA methyltransferase, accounting for approximately 85% of total arginine methyltransferase activity . PRMT1 pPRMT5 is the predominant SDMA methyltransferase and is generally associated with gene repression . There iIn addition to histones, cytosine nucleotides can also be directly methylated. Methylated cytosine results in the eventual formation of large stretches of CpG islands, or areas of dense DNA methylation, normally found at promoters of actively repressed genes. Reversal of methylated cytosine is a process that requires multiple oxidative reactions by ten-eleven translocation (TET) proteins and conversion to an abasic site that can be recognized for base excision repair . The TETet al. earlier this year  has been used as a targeted approach to identify not only if two proteins interact (phosphatase-substrate) but the location of the complex within the cell. This additional spatial information is invaluable for dissecting potential subcellular domains of phosphatase function  and dynamic shifts in protein localization or PP2A holoenzyme composition in response to perturbations such as oncogenic mutation. To decipher transcriptional regulatory complexes, chromatin immunoprecipitation with selective isolation of chromatin-associated proteins (ChIP-SICAP) is now being used to isolate both protein complexes and their associated DNA in the As we begin to unravel the context-specific roles of PP2A in the regulation of epigenetic and transcriptional mechanisms, new compounds could be developed to capitalize on the unique functions of individual PP2A B subunits. Already, compounds are emerging that preferentially activate unique PP2A complexes (SMAPs and iHAP1), suggesting that eventually we may be able to tailor therapeutics to specific PP2A-dependent cancer phenotypes . Since t


Arabidopsis. However, a few research groups have reported the presence of TAD-like structures in the plant kingdom.The genome is the most functional part of a cell, and genomic contents are organized in a compact three-dimensional (3D) structure. The genome contains millions of nucleotide bases organized in its proper frame. Rapid development in genome sequencing and advanced microscopy techniques have enabled us to understand the 3D spatial organization of the genome. Chromosome capture methods using a ligation approach and the visualization tool of a 3D genome browser have facilitated detailed exploration of the genome. Topologically associated domains (TADs), lamin-associated domains, CCCTC-binding factor domains, cohesin, and chromatin structures are the prominent identified components that encode the 3D structure of the genome. Although TADs are the major contributors to 3D genome organization, they are absent in  Opuntia) .,60.18,60Chromosome conformation capture-on-chip, commonly called 4C or circular chromosome conformation capture, is useful for interaction study of one region with the remaining part of the genome . The circular chromosome capture method, which is a part of the 3C technique, is used to address the existence of an epigenetically controlled network of chromosomal interactions. The 4C method is based on the principle of proximity ligation , where tMapping of all the contacts within a large genomic region can also be performed by chromosome conformation capture carbon copy (5C) . In 5C   and photAlthough 3C- and Hi-C-based techniques are remarkable for understanding chromosome folding in vivo, they have limited resolution due to noise and the coarse capture radius of restriction enzymes. Therefore, a Hi-C variant, known as Micro-C, has been developed to further resolve the structure . Micro-CAnother approach, known as proximity-assisted ChlP-seq (PLAC-seq), is also effective and useful for mapping long-range chromatin interactions . It usesGAM is designed to analyze the 3D chromatin structure without the requirement of digestion and ligation . It is bSometimes, proximity-based ligation can fail to detect nuclear structures, such as interactions with nuclear bodies, as DNA regions may be far apart, making direct ligation difficult. This may create an incomplete understanding of genome organization. However, the development of SPRITE  has enabChromosome folding is crucial for regulating proper gene expression and function, and it is a dynamic process that varies widely throughout the cell cycle ,142. TADThe folding of DNA into chromosomes has become a focal point in the study of the 3D genome ,146. TheA study on Hi-C in combination with DNA fluorescence in situ hybridization (DNA-FISH) has led to a notion of chromosome territory and chromosome organization of the A/B compartment . The A/Bβ-globin gene leads to its activation in erythroid progenitor cells [The development of several chromosome-capturing methods has been critical for the rapid progress in studying chromatin contacts, facilitating high-throughput mapping of contacts at different genomic distances at varying levels of resolution. Direct contact between multiple enhancers and promoters of the target gene leads to the formation of an active chromatin hub (ACH) by looping out of the intervening sequences that regulate the gene expression . Mouse βor cells . The coror cells . LDB1 biThe ACH hypothesis reveals how active genes in chromosomes are organized in the interphase nucleus. It has been well reported that enhancers are present at variable distances in highly expressed genes . The hisLADs are regions (0.1–0 Mb) of condensed chromatin that interact and bind with the NL at the nuclear periphery, thereby providing functional organization to the genome. The lamins are a mesh or a network of filament proteins called lamins A, B, and C  The lamiGenome organization is associated with the interaction of the active genomic region with the NPC, inner nuclear membrane, and NL through LADs. LADs maintain a higher order of genomic organization. They are rich in histone H3K27me3, and to a lesser extent, H3K9me2, which is part of the polycomb, repressed genes, and  heterochArabidopsis [Oryza sativa and cotton [3D genome folding is highly organized, with a tightly linked process of DNA replication and transcription. The location of genes in chromosomes influences transcription; active genes are commonly located toward the interior, while repressed genes are localized toward the periphery of the nucleus . All thebidopsis . Howeverd cotton ,213. Bacd cotton .CTCF is a transcription factor protein associated with diverse functions, including positive and negative regulation of enhancers and X-chromosome inactivation . It bindAccording to the loop extrusion model, extrusion of chromatin loops through the cohesion ring helps to organize the genome into spatial domains . CohesinTranscription and associated factors bind to DNA, resulting in transcriptional regulation . KnockdoArabidopsis chromosome 4 by Lee et al. (2010) reported no significant differences between these phases [Arabidopsis in the early and mid S-phases is stochastic, and the replication of euchromatin does not necessarily follow a strict temporal pattern. However, the same study group later observed differences between the early and mid S-phases in maize [Arabidopsis genomes [Arabidopsis and maize genomes showed a similarity in replication timing signal, where the chromosome arms replicate early and the centromeric and pericentromeric arms replicate late [Arabidopsis than in maize, suggesting the differential presence of genic and nongenic (noncoding) regions  [The genome of an organism is very complex, and organized in a 3D conformation by placing and regulating all the necessary genomic information in a specified manner. However, this complex genome undergoes duplication in each preceding cell cycle so that it can produce two identical copies of the same genome during the S-phase. Replication and duplication are ordered processes, and chromatins that experience early replication are rich in transcriptionally active genes in the euchromatic region; genes that are replicated late are enriched in the heterochromatin region ,236,237.e phases . This fiin maize  and Arab genomes . Approxi genomes . Some se genomes . The dis genomes . However genomes . Further genomes . Wang et genomes . Because genomes . This mi genomes . A compaate late . Howeverongenic) . This fiongenic) .Mechanical and biochemical signals perceived at the cell membrane activate transcription factors, which are subsequently directed to the target site to modulate cell- or tissue-specific gene expression ,249,250.−1 [The close interaction between the 3D interphase DNA structure and gene expression has made chromatin folding a rapidly developing field of study. Several previous reports have been continuously challenged by the progress of research. For example, in the solenoid model, chromatin is folded into a 30-nm fibre, which is assembled into higher-order structures . However−1 . It has −1 . However−1 . A recen−1 ,269. The−1 . The mos−1 ,272. Alt−1 . Therefo−1 . It prov−1 . The SRR−1 . This mo−1 . The mod−1 . The siz−1 . There i−1 . Additio−1 . The fir−1 . This tr−1 .A computational string and binder (SBS) model was proposed in polymer physics to understand the mechanism of chromosome compartmentalization, pattern formation, and chromatin folding . Accordiij (i = row and j = column) as the number of observed interactions between the DNA loci falling within the ith and jth bins [The polymer model does not require any training or fitting against the experimental data and is sometimes used to test mechanistic theories of genome organization. This model focuses on global aspects of the genome, such as chromosome territories, rather than inferring specific interactions between certain loci . In Hi-Cjth bins ,282. Thejth bins ,282. Thejth bins . The conjth bins . The heajth bins . Even wijth bins . In 3C-bjth bins . Followijth bins .The Hi-C contact matrices are the major foundation for identifying 3D genome interactions, where the contact decay curve is used to interrogate the nuclear structure of the cell, as they can be generated using low-resolution data . AccuratThe role of an enhancer that resides in the proximity of its target genes and the role of TADs are well known. The volume of the chromatin interaction data increases regularly, and efficient visualization and navigation of these data are the major bottlenecks for their interpretation. These factors make it a daunting task for an individual laboratory to store and explore them properly. To overcome these problems, several visualization tools have been developed, with unique features and limitations. The Hi-C data browser is reportedly the first web-based query tool to visualize Hi-C data as heat maps . HoweverUnderstanding the 3D genome architecture can answer several fundamental biological questions regarding the integration of chromatin packaging patterns with various genomic and epigenomic features. We expect the development of enormous 3D epigenome and transcriptome datasets in the future. The identification of TAD boundaries facilitates the understanding of chromatin insulation. The roles of non-coding genetic contents  can be better understood using a 3D approach. The epigenetic 3D genome map enables the understanding and interpretation of the functional consequences of genetic changes associated with various diseases. Variations in structural variants can be detected using this approach.


Drosophila. In previous studies we have shown that Su(Hw) is also required for pre-replication complex (pre-RC) recruitment on Su(Hw)-bound sites (SBSs) in Drosophila S2 cells and pupa. Here, we describe the effect of Su(Hw) on developmentally regulated amplification of 66D and 7F Drosophila amplicons in follicle cells (DAFCs), widely used as models in replication studies. We show Su(Hw) binding co-localizes with all known DAFCs in Drosophila ovaries, whereas disruption of Su(Hw) binding to 66D and 7F DAFCs causes a two-fold decrease in the amplification of these loci. The complete loss of Su(Hw) binding to chromatin impairs pre-RC recruitment to all amplification regulatory regions of 66D and 7F loci at early oogenesis (prior to DAFCs amplification). These changes coincide with a considerable Su(Hw)-dependent condensation of chromatin at 66D and 7F loci. Although we observed the Brm, ISWI, Mi-2, and CHD1 chromatin remodelers at SBSs genome wide, their remodeler activity does not appear to be responsible for chromatin decondensation at the 66D and 7F amplification regulatory regions. We have discovered that, in addition to the CBP/Nejire and Chameau histone acetyltransferases, the Gcn5 acetyltransferase binds to 66D and 7F DAFCs at SBSs and this binding is dependent on Su(Hw). We propose that the main function of Su(Hw) in developmental amplification of 66D and 7F DAFCs is to establish a chromatin structure that is permissive to pre-RC recruitment.Suppressor of Hairy wing [Su(Hw)] is an insulator protein that participates in regulating chromatin architecture and gene repression in  Drosophila gypsy insulator1, which is capable of both enhancer blocking3 and barrier activity4, i.e. protection of the transgene from the effect of outer regulatory elements and the spread of repressive chromatin. The advent of high-throughput genome-wide analysis methods revealed that although some tested Su(Hw)-bound sites (SBSs) exhibited chromatin barrier activity, other SBSs failed to block enhancer-promoter interaction in transgenic assays6. In addition, more than half of SBSs that are depleted of CP190 and Mod(mdg4)67.2 cofactors repress transcription being placed near the promoter of a transgene6. The transcriptional repressor function of Su(Hw) was found to be responsible for the strongest phenotype of su(Hw) mutants—female sterility7. Su(Hw) binds to the promoter regions of some CNS-enriched target genes in Drosophila ovary to repress their transcription8. A decrease in the transcription of one of these genes, rbp9, successfully restored the fertility of su(Hw)–/– mutants7.Suppressor of Hairy wing [Su(Hw)] was initially described as a DNA-binding protein responsible for the function of the Drosophila S2 cells and pupa10. Prior to this the only known role of Su(Hw) in replication regulation was the demarcation of replication domains11. We proposed that Su(Hw) can act as a key factor influencing pre-RC formation at the SBSs by affecting chromatin structure. In accordance with our data, recent whole-genome studies of Su(Hw) binding in the Drosophila ovary revealed that a significant number of Su(Hw) sites are not involved in either repressive or insulator functions of the protein, and the authors suggested that these sites may be ascribed to a Su(Hw) replication function12.We previously demonstrated that Su(Hw) is both necessary and sufficient for pre-replication complex (pre-RC) recruitment to SBSs in Drosophila amplicons in follicle cells (DAFCs) and described the contribution of Su(Hw) in recruiting the ORC3 subunit of the origin recognition complex (ORC) to SBSs in some of these amplicons13. DAFCs present one of the most well-established systems for investigating the regulation of metazoan replication origins in vivo16. Follicle cells are somatic cells that surround the oocyte and nurse cells during Drosophila oogenesis and secrete the chorion proteins that form the embryo shell. DAFCs contain genes encoding chorion proteins and other products necessary for normal egg shell formation18. At a certain stage of ovary development (in the egg chamber stages 10–13) these genome regions undergo repeated rounds of replication initiation to produce a gradient of increased DNA copy number. The same replication initiation proteins involved in genome replication during S-phase also participate in DAFC amplification15. Recent whole-genome analyses have revealed all DAFCs in the Drosophila genome and unveiled hidden aspects of their function19. Although the developmentally regulated gene amplification is mainly hypothesized as an avenue to achieve a high level of necessary gene products, there is no direct correlation between the rate of locus amplification and the transcription level of the genes within20. These data challenge the seemingly well-described model of DAFCs amplification and suggest the existence of some mechanism responsible for the regulation of the replication but not transcription of DAFCs.In a preliminary study we identified SBSs in all currently annotated Drosophila amplification loci.Here, we expand our previous investigations of Su(Hw) function in replication. Although there is evidence that Su(Hw) plays a role in replication origin positioning, the direct impact of Su(Hw) on the replication process has so far not been demonstrated. To fill this gap, we describe a role for Su(Hw) in amplification and pre-RCs positioning in the well-characterized 66D and 7F 22. The complete loss of Su(Hw) binding to chromatin (in su(Hw)v/2, su(Hw)v/E8, su(Hw)2/E8, su(Hw)Pb/v, su(Hw)Pb/2 heteroallelic mutant combinations) causes mid-stage arrest of oogenesis and egg chamber degeneration at approximately stage 9, while mutants with partially retained Su(Hw) binding (su(Hw)v/f) are fertile and demonstrate only defects in nurse cell chromosome structure. Thus we used the fertile su(Hw)v/f mutants to evaluate the effect of Su(Hw) on DAFCs amplification.It is well known that female sterility is the main consequence of loss of Su(Hw) function during fly developmentsu(Hw)v/f mutant flies24. Therefore, we first tested whether Su(Hw) binding to its sites in DAFCs is altered in su(Hw)v/f mutants using ChIP-Seq with antibodies against Su(Hw). For this analysis we dissected whole ovaries from 32 to 34 h-old wild-type (oregon) and su(Hw)v/f mutant females. These ovaries already contain the stage 1–13 egg chambers but do not yet carry embryos25. We detected Su(Hw) binding at all known DAFCs in wild type; however, binding was only disrupted at the 66D and 7F loci in the su(Hw)v/f background v/f ovaries. We observed a two-fold decrease in the amplification level of the 66D and 7F DAFCs upon su(Hw)v/f mutation  binding is retained at a portion of SBSs in nd Figs. . Therefosu(Hw)v/f may be due to a change in the ratio of stage 10–13 egg chambers in the ovaries of mutant flies. Indeed, an increase (10.5%) in apoptosis of mid-stage ovarioles was reported for su(Hw)v/f ovaries in comparison to the wild type22. However, the observed Su(Hw)-dependent decrease in the amplification of 66D and 7F loci v/f oogenesis described above. We suggest that the main reason underlying this decrease is the impact of Su(Hw) on the rate of amplification rather than an effect on the proportion of ovarioles containing amplified chorion loci in su(Hw)v/f mutants.For this amplification analysis we used the whole ovaries, not just the stage 10–13 egg chambers. Therefore, the reduction in 66D and 7F amplification level we observed in su(Hw)v/f 32–34 h-old female ovaries and performed RNA-Seq. Surprisingly, we found no change in the transcription levels of the chorion genes located within the 66D and 7F DAFCs nor any abnormalities in eggshell phenotypes of su(Hw)v/f embryos v/f, the transcription of chorion genes relative to gene copy number is increased in su(Hw)v/f ovaries compared to wild type.To analyze the Su(Hw) effect on gene transcription in the 66D and 7F loci we isolated total RNA from wild-type and os Figs. . Howeversu(Hw)v/f ovaries containing 1–13 egg chamber stages. We performed ChIP-Seq experiments with antibodies against the ORC2 subunit of the origin recognition complex (ORC) and the CDC6 replication licensing protein. These proteins together with Double parked (Cdt1) participate in the loading of MCM2-7 replicative helicase and are known to form pre-RCs that are required for further initiation of DNA replication26. We detected a decrease of ORC2 and CDC6 binding in 66D and 7F DAFCs in su(Hw)v/f ovaries compared to the wild type in ChIP-Seq samples prior to input normalization v/f input reads of these loci, which we associate with overall reduced amplification in this background. After input normalization, the ORC2 and CDC6 profiles are similar between the wild-type and su(Hw)v/f ovaries. As the levels of replication protein binding directly correlate with the level of amplification19, we assumed that the inputs and ORC2/CDC6 ChIP-Seq profiles in 66D and 7F DAFCs during amplification are dependent parameters. Therefore, we suggested that the effect of Su(Hw) on replication protein recruitment to DAFCs cannot be estimated correctly during DAFC amplification. Besides we admit the possibility that the wild-type and su(Hw)v/f ovaries of 1–13 egg chamber stages may contain a slightly different ratio of egg chamber stages. It may potentially affect the amount of replication factors detected in ChIP-Seq analysis.We analyzed the role of Su(Hw) in the recruitment of replication factors during DAFCs amplification in the wild-type and ion Fig. . At the su(Hw)v/E8 heteroallelic mutant flies, which have a complete loss of Su(Hw) binding to chromatin22. Female flies carrying su(Hw)v/E8 alleles are sterile and suffer egg chamber degeneration at stage 922. Therefore, for the analysis we dissected ovaries of recently eclosed 15 h-old flies, which contain egg chambers no older than stage 825 .Previously we had shown that the ORC3 subunit of the ORC complex is recruited to SBSs in 66D and 7F loci in a Su(Hw)-dependent manner during the early stages of oogenesis. Therefore we hypothesized that the study of Su(Hw) impact on pre-RCs formation in DAFCs prior to their amplification might shed light on the subsequent effect on amplification. For this we used su(Hw)v/E8 ovaries in ChIP-Seq. As expected Su(Hw) binding was completely disrupted genome wide in the su(Hw)v/E8 ovaries  binding in wild-type and ies Fig. . Interesies Fig. B. SBSs iies Fig. . This ob9 we found that the recruitment of ORC2 and CDC6 to SBSs genome wide is disrupted in the su(Hw)v/E8 ovaries in comparison with wild type  and found no significant decrease in their binding between the wild-type and su(Hw)v/E8 ovaries v/E8 occurred at the positions of all known amplification regulatory regions of the loci27, and not only at SBSs. We attribute this effect to the impact of Su(Hw) on the chromatin structure of the loci as described below.We performed ChIP-Seq experiments with antibodies against the ORC2 and CDC6 replication proteins. In confirmation of previously published dataype Fig. . As a coies Fig. . The obt30. We suggest chromatin condensation may be the primary reason for the disruption of pre-RC formation in su(Hw)v/E8 mutants. We performed FAIRE-Seq to compare chromatin state in wild-type and su(Hw)v/E8 mutant ovaries during stages 1–8 of egg chamber development. This method determines the profile of open chromatin and has proved to be an effective way to test nucleosome density at regulatory regions31.Pre-replication proteins lack apparent sequence specificity and are rather enriched at nucleosome-depleted and H3.3 marked regionssu(Hw)v/E8 FAIRE-Seqs v/E8 mutants causes decreased chromatin accessibility in these regions. We detected broad regions of open chromatin encompassing the amplification regulatory regions from ACE3 to AER-A in 66D and at ACE1 in 7F in the wild-type ovaries  ovaries of the same developmental stage27. The decondensed chromatin in 66D and 7F was found to significantly overlap with ORC2 and CDC6 binding sites from our ChIP-Seq experiments above v/E8 background. Thus, the disruption of pre-RC binding coincides with chromatin condensation of these regions in su(Hw)v/E8 mutants.We calculated the average distribution of FAIRE-Seq signal on all SBSs for the wild-type and eqs Fig. . These dies Fig. B. Our dasu(Hw)v/E8 mutant ovaries may be due to disrupted nucleosome remodeler recruitment. Earlier it was shown that SBSs overlap with published genome-wide binding datasets for a variety of proteins, including Brm and ISWI remodelers12. To define which remodelers could be affected by disrupted Su(Hw) binding, we tested Su(Hw) interaction with several ATP-dependent chromatin remodelers. We performed co-immunoprecipitations (co-IPs) with antibodies against Su(Hw) and reciprocal experiments with antibodies against representatives of the three chromatin remodeling families: Brahma (SWI/SNF family), ISWI (ISWI family), and Mi-2 and CHD1 (CHD family). Su(Hw) successfully co-precipitated all investigated remodelers from Drosophila S2 cell nuclear extracts v/E8 ovaries, with the most significant perturbation in Mi-2 and CHD1 recruitment v/E8 ovaries was detected at the TSSs of Drosophila v/E8 mutant background.To investigate whether Brahma, ISWI, Mi-2, or CHD1 bind SBSs we performed ChIP-Seq. We detected enrichment of all investigated remodelers on SBSs in wild-type ovaries. This binding was disrupted in ent Fig. B, while ila Fig. . Intriguila Fig. C, we did32. Since Su(Hw) binding is retained at a portion of SBSs in su(Hw)v/f mutant flies24, we tested whether the disruption of the tenth zinc finger in Su(Hw)f (which is expressed in su(Hw)v/f) alters the ability of Su(Hw) to recruit remodelers to SBSs. We performed ChIP with antibodies against Brm, ISWI, Mi-2, and Chd1 on wild-type and su(Hw)v/f ovaries containing 1–13 egg chamber stages v/f-retained SBSs. Thus we conclude Su(Hw)f has the same ability to recruit remodelers as the wild-type Su(Hw) protein.The zinc-finger domain of Su(Hw) can participate in interactions with cofactor proteinsges Fig. . While wsu(Hw)v/E8 may occur due to the changes in histone acetylation at the 66D and 7F loci, as these modifications can affect chromatin state35. The CBP/Nejire and Chameau (Chm) histone acetyltransferases were previously demonstrated to be important for DAFCs amplification36, and Su(Hw) was shown to interact with the Gcn5 acetyltransferase to recruit pre-RCs to SBSs9. Thus we decided to analyze Gcn5 recruitment to the 66D and 7F DAFCs in wild-type and su(Hw)v/E8 ovaries.We hypothesized that chromatin condensation of amplification regulatory regions in su(Hw)v/E8 mutant ovaries v/E8 ovaries. Within the central regions of the 66D and 7F loci we detected several Gcn5 peaks, with the highest peaks colocalizing with SBSs v/E8 mutants we observed a total disruption of Gcn5 binding at SBSs within 66D and 7F. Thus, Su(Hw)-dependent recruitment of Gcn5 to 66D and 7F DAFCs suggests that, in addition to CBP/Nejire and Chm, Gcn5 can regulate developmental amplification of DAFCs.We performed ChIP-Seq with antibodies against Gcn5. As expected, Gcn5 was recruited to SBSs in the wild-type ovaries, and this recruitment was disrupted in ies Fig. . As a coBSs Fig. . In su(Hsu(Hw)v/f ovaries containing 1–13 egg chamber stages to determine whether Su(Hw)f (expressed in su(Hw)v/f) can recruit Gcn5 v/f-retained SBSs was not affected, which suggests Su(Hw)f has the same ability to recruit Gcn5 as the wild-type Su(Hw).We then performed Gcn5 ChIP on the wild-type and cn5 Fig. . Gcn5 re12. We used our ChIP-Seq and FAIRE-Seq datasets for the wild-type and su(Hw)v/e8 ovaries to assign SBSs into functional groups by performing k-means clustering v/e8 (“Su(Hw) direct; ORC2/CDC6 enriched”). This cluster is responsible for the majority of Su(Hw)-dependent chromatin remodeler and Gcn5 recruitment to SBSs and for chromatin opening. Cluster 2 includes 2640 SBSs devoid of ORC2/CDC6 binding and located in condensed chromatin regions (“Su(Hw) direct; ORC2/CDC6 depleted”). Cluster 3 includes weak Su(Hw)-bound sites located in accessible chromatin and highly enriched with ORC2/CDC6. Su(Hw) and other factors remain bound at cluster 3 sites in su(Hw)v/e8 (“Su(Hw) indirect; ORC2/ CDC6 enriched; su(Hw)v/e8-independent”). It is known that su(Hw)v/E8 mutants express Su(Hw) protein completely unable to bind chromatin22. We believe this cluster includes Su(Hw) peaks arising from indirect Su(Hw) recruitment mediated by interactions with other DNA-binding proteins. Thus, the recruitment of replication proteins to the sites of cluster 3 is likely determined by other factors.SBSs can be divided into three subclasses based on distinct motif characteristics: insulator (M4-only), replicational (M4 + M10), and transcriptional repressor (M10-only)ing Fig. . SBSs sp12. Cluster 2 demonstrated the enrichment with either M4- and M10-only motifs associated with Su(Hw) insulator and transcriptional repressor functions. We were unable to identify a Su(Hw) motif in cluster 3 sequences but rather found that this cluster is enriched with a GTGT-motif.To link the identified SBS clusters to known Su(Hw) motifs, we carried out motif analysis with the MEME motif search program Fig. . This an38  binding, which changes during the wild-type ovary development, according to the SBS clusters Table . We obse38 Fig. . TherefoDrosophila Su(Hw) insulator protein its functions remain unclear. This is exemplified by the fact that many Su(Hw) binding sites in the genome do not correspond to its well-known repressive and enhancer-blocking activities12. Previously we described the ability of Su(Hw) to organize regions with low nucleosome density, which could serve as platforms for pre-RC binding in Drosophila S2 cells and pupa10. Here, we demonstrate a functional impact of Su(Hw) on the developmentally regulated amplification of the 66D and 7F Drosophila follicle cell amplicons  peaks with all DAFCs Figs. A and S1 su(Hw)v/f mutant does not lead to a decrease in the transcription level of genes within these loci v/f ovaries compared to the wild type. This may be attributed to the know repressor role of Su(Hw), or might be the result of a Su(Hw)-independent compensation of chorion gene transcription during 66D and 7F loci underreplication. Further studies will be required to distinguish between these possibilities. The direct correlation between amplification of DAFCs and their transcription had already previously been called into question20, as well the potential role of local transcription in DAFCs amplification39. In line with this, our work suggests that the regulation of developmental gene amplification and transcription can be implemented through various mechanisms.Interestingly, the decrease in 66D and 7F DAFCs amplification rate in the oci Fig. . Instead26. Previously it was thought that binding of replication proteins to DAFCs occurs at stage 10 of egg chamber development when the DAFCs amplification begins40; however, these conclusions were based on immunofluorescence analysis, which is not sensitive enough to detect low-level binding. By employing ChIP-Seq, we showed here that the recruitment of ORC2 and CDC6 to the amplification regulatory regions of 66D and 7F DAFCs begins already during early oogenesis at stages 1–8 of egg chamber development v/E8 background impairs ORC2 and CDC6 recruitment to 66D and 7F. Intriguingly, the disruption of replication proteins recruitment occurs not only at SBSs within these loci but at all amplification regulatory regions. It is well-established that replication proteins lack apparent sequence specificity but are rather enriched at nucleosome-depleted regions28. This led us to attribute the loss of ORC2 and CDC6 binding within the 66D and 7F loci in su(Hw)v/E8 mutants to a potential Su(Hw)-dependent regulation of chromatin structure at these loci.The complete loss of Su(Hw) binding in the 10. We therefore reasoned that changes in chromatin accessibility may underlie the Su(Hw)-dependent disruption of pre-RC formation in 66D and 7F loci. We detected open chromatin regions in the 66D and 7F loci in wild-type ovaries by FAIRE-Seq, which coincided with amplification regulatory elements of these loci v/E8 mutants led to a strong decrease in the chromatin accessibility of 66D and 7F at regions of ORC2- and CDC6-binding.In our previous work, we showed Su(Hw) depletion increases chromatin compactionoci Fig. B. A lossTo determine whether these effects on chromatin accessibility are caused by disrupted recruitment of chromatin-modifying complexes, we first determined that Su(Hw) interacts with the Brm, ISWI, Mi-2, and CHD1 remodelers by co-IP Fig. A. Surpri35. Histone H3 and H4 acetylation is required for chromatin decompaction during DNA replication42 and can regulate the time of origin firing43. Furthermore, the level of histone acetylation correlates with the amplification rate of DAFCs45. The histone acetyltransferases CBP/Nejire and Chm had previously been shown to bind to the 66D and 7F DAFCs and were found to be necessary for their normal amplification36. In the current work we showed that Gcn5 is yet another histone acetyltransferase present at these loci. Moreover, we identified Su(Hw) as the main recruiter of Gcn5 to these loci -mutant background we suggest that they are involved in the formation of open chromatin regions on SBSs in the Drosophila ovary.Our study significantly expands the existing model of Su(Hw) function in positioning pre-RCs on SBSs in the genome Fig. B. Our Ch53. We hypothesize that this impact on genome stability may indeed be due to disrupted pre-RC formation at SBSs in the ovary. Further investigations, such as ChIP-Seq data for γH2Av, a marker of double-stranded DNA breaks54, and H4K20me1, which correlates with replication stress55, in the ovaries of wild-type and su(Hw)-mutant flies will help to directly link the described role of Su(Hw) in genome integrity and our data on Su(Hw)-dependent pre-RC formation.We also confirmed that loss of Su(Hw) causes genome-wide disruption of ORC2 and CDC6 recruitment to SBSs Fig. . In a re12 -bound sites (cluster 3) enriched with a GTGT-motif. This motif was previously linked with Combgap binding, which is involved in recruitment of PcG group proteins57. We propose this cluster reflects Su(Hw) indirect recruitment. Indirect binding was previously shown for BEAF32 architectural protein in ChIP-Seq analysis58. Basing on the GTGT-motif enrichment of cluster 3 sites we suggest that Su(Hw) recruitment to these sites may be mediated by interaction with Combgap protein. There is some evidence of a functional connection between Su(Hw) and Combgap. The ability of SBS to enhance the activity of Combgap-bound bxd Polycomb response element (PRE) in transgenic assay has recently been reported59. Analyzing our data we found bxd PRE among the cluster 3 Su(Hw) bound sites. Upon close examination of Su(Hw) ChIP-Seq profiles at the BX-C locus we did observe Su(Hw) binding at the bxd PRE which is independent of su(Hw)v/E8 background  function in Drosophila development.In summary, our data suggest a new role for Su(Hw) in oregon) , su(Hw)v/E8 and su(Hw)v/f stocks (a kind gift of A. Golovnin laboratory) were used. All experiments on Drosophila reported in the manuscript follow the recommendations in the ARRIVE guidelines. All flies were raised at 25 °C on standard agar medium. Ovaries were dissected from two developmental groups of females: 15 h after eclosion , fixed with 1% of formaldehyde for 5 min and then incubated for 5 min with 125 mM Glycine. Then ovaries were washed with PBS buffer for three times. The remaining ChIP protocol was performed as described previously60. ChIP-Seq libraries were obtained using the NEBNext Ultra™ II DNA library preparation kit (New England Biolabs). Only the library fragments of 250–500 bp were subjected to NGS sequencing. New generation sequencing was performed by Evrogen (evrogen.ru) with the Illumina NovaSeq6000 sequencer. For each of the ChIP‐Seq libraries approximately 5–12 millions of unique paired-end reads were obtained. The paired‐end reads in FastQ format were mapped to the Drosophila genome assembly dm6 using Bowtie261 and filtered .The chromatin immunoprecipitation (ChIP) was performed and analyzed exactly as previously described62. The final bigwig files (representing the protein binding profiles) were obtained using bigwigcompare tool as ratio of ChIP signal to Input v/f ovaries of egg chamber stages 1–13 were preliminary smoothed over a 1 kb window). Pile-up profiles were calculated as a median level of protein binding for the distributions of Su(Hw), ORC2, CDC6, Brm, ISWI, Mi-2, CHD1 and Gcn5 on Su(Hw) binding sites (SBSs) and for ORC2, CDC6, Brm, ISWI, Mi-2, CHD1 and Gcn5 on TSSs in the wild-type (oregon), su(Hw)v/E8 and su(Hw)v/f ovaries of Drosophila. The SBSs in the wild type (oregon) ovaries (stages 1–8 of egg chamber development) were defined by MACS2 with the following parameters: -gsize '120,000,000' -keep-dup '1'-qvalue '0.01' -mfold '5' '30' --bw '375′ 2 > &1 > macs2_stderr63. Corresponding input DNA was used as a control for peak calling.BigWig files were generated using bamCoverage 3.0.2 with scores representing number of reads normalized by the size of the library 62. K-means clustering algorithm was selected and the number of clusters to compute was set to 3. The ChIP-Seq data were clustered using the values at the summits of the SBSs.Clustering analysis was performed using plotHeatmap tool of deepTools package64.The definition of motifs for SBS cluster 1–3 was performed with MEME suite 5.3.365. All obtained ChIP-Seq data were deposited into the Gene Expression Omnibus—GSE168894.The Galaxy-P platform was used for analysis of ChIP-Seq data27 and deposited as NCBI-SRA BioProject SRP057811. We did not use any figures or text from the previously published manuscripts—only data deposited in free access databases.MNase-Seq data from follicle cells (stages 1–8 of egg chamber development) of Oregon-R-modENCODE fly stock were described previouslyoregon) and su(Hw)v/f females were collected in PBS buffer (50 pairs per sample), in two biological repeats. Total DNA was extracted using ExtractDNA Blood kit (Evrogen). The DNA from 2 to 4 h old Drosophila embryos was extracted and used as non-amplified control. All obtained DNA was sheared to 250–500 bp by sonication. DNA-Seq libraries were obtained using the NEBNext Ultra™ II DNA library preparation kit (New England Biolabs). New generation sequencing was performed by Evrogen (evrogen.ru) with the Illumina NovaSeq6000 sequencer. For each DNA‐Seq library approximately 40–55 millions of unique paired-end reads were obtained (10 million paired-end reads were obtained for 2–4 h old embryo DNA). The paired‐end reads in FastQ format were mapped to the Drosophila genome assembly dm6 using Bowtie261 and filtered . The final bigwig files (representing the ovarian DNA enrichment over embryonic DNA) were obtained using bigwigcompare tool as ratio of corresponding DNA from the ovaries to DNA from the 2–4 h old embryos .For DNA extraction the ovaries of 32–34 h old wild-type (oregon) and su(Hw)v/f females were collected in PBS buffer (50 pairs per sample), in two biological repeats. Total RNA was extracted with the TRI reagent (Ambion). PolyA comprising RNA fraction was isolated and prepared for sequencing with the NEBNext Ultra™ II Directional RNA Library Prep Kit. New generation sequencing was performed by Evrogen (evrogen.ru) with the Illumina NovaSeq6000 sequencer. For each RNA‐Seq library approximately 25–30 millions of unique reads were obtained. Obtained reads were mapped using TopHat266. Differential analysis was employed by CuffDiff67. Obtained RNA-Seq data were deposited into the Gene Expression Omnibus—GSE168894.For extraction of RNA the ovaries of 32–34 h old wild-type (oregon) and su(Hw)v/E8 females were collected in PBS buffer (50 pairs per sample). The FAIRE protocol was performed as previously described31. FAIRE-Seq libraries were obtained using the NEBNext DNA library preparation kit (New England Biolabs). Only the library fragments of 250–500 bp were subjected to NGS sequencing. New generation sequencing was performed by Evrogen (evrogen.ru) with the Illumina NovaSeq6000 sequencer. For each FAIRE‐Seq library, approximately 4–5 millions of unique paired-end mappable reads were obtained. The paired‐end reads in FastQ format were mapped to the Drosophila genome assembly dm6 using Bowtie261 and filtered . Obtained FAIRE-Seq data were deposited into the Gene Expression Omnibus—GSE168894.For FAIRE experiment the ovaries of 15 h old wild-type (Drosophila Schneider cell line 2 (S2) cells were used for nuclear protein extract preparation. S2 cells were maintained at 25 °C in Schneider’s insect medium (Sigma) containing 10% FBS (HyClone). The nuclear protein extract was obtained as described previously68. Co-immunoprecipitations were performed as previously described68. The RNA-interference experiments to test antibodies specificity were performed as previously described69.69. Antibodies against Su(Hw) , ORC2 (epitope corresponding to 28–194 amino acid residues) and CDC6 (epitope corresponding to 9–179 amino acid residues) were generated in this study. The epitopes for antibody production were expressed as 6 × His-tagged fusion proteins in Escherichia coli, affinity-purified on Ni Sepharose 6 Fast Flow , according to the manufacturer’s protocol, and injected into rabbits, following the standard immunization procedure. Antibodies were affinity-purified using the same epitopes as were used for immunization. The specificity of antibodies against Su(Hw), ORC2 and CDC6 was characterized in the RNA interference experiments by the specific depletion of corresponding proteins (Fig. For the co-immunoprecipitations and ChIP-Seq analysis of Brm, ISWI, Mi-2, CHD1 and GCN5 we used previously described antibodiesins Fig. . AntibodSupplementary Information 1.