We studied the impact on the structure and expression of host chromatin by the human retrovirus HTLV-1. We show that HTLV-1 disrupts host chromatin structure by forming loops between the provirus and the host genome; certain loops depend on the critical chromatin architectural protein CTCF, which we recently discovered binds to the HTLV-1 provirus. We show that the provirus causes two distinct patterns of abnormal transcription of the host genome in cis: bidirectional transcription in the host genome immediately flanking the provirus, and clone-specific transcription in cis at non-contiguous loci up to >300 kb from the integration site. We conclude that HTLV-1 causes insertional mutagenesis up to the megabase range in the host genome in >10 CTCF bir (CTCF)  by regulr (CTCF) . Aberranr (CTCF) .+ T cells known as adult T-cell leukemia/lymphoma (ATL). Unlike HIV-1 infection, there remains no satisfactory treatment for either the inflammatory or malignant diseases caused by HTLV-1.Recently we found  that CTC4 and 105 clones of infected T cells, each clone carrying a single copy of the provirus in a unique genomic location  in the human genome . 4C is a powerful tool to study the 3D chromatin looping between a specified genomic region (the 'viewpoint') with respect to the rest of the genome. The standard 4C protocol has limitations in that it is only semiquantitative . To impr+ T lymphocytes of HTLV-1-infected individuals  in 9 of the 10 infected T-cell clones examined –10. The We wished to identify whether the HTLV-1 provirus makes preferential contacts with the host genome in cis. The provirus is present in a single copy per cell  Cook et. First, Next, we used the heterozygous SNPs to distinguish the chromosome carrying the provirus from its homologous chromosome, using computationally-determined phased haplotypes . In this way, the infected chromosome could be distinguished from the uninfected chromosome for at least 100 kb either side of the integration site in 8 of the 10 clones –5. To va−4, Fisher’s exact test; We wished to test the hypothesis that the observed abnormal long-range chromatin contacts are associated with the presence of CTCF-BS identified in the T cell clones by ChIP-seq. The results showed that that CTCF-BS were enriched at the chromatin contacts in the host genome: ~50% of q4C peaks overlapped with at least one CTCF-BS ; in 10% We then tested whether CTCF binding to the provirus is required for formation of provirus-host contacts. We used a CRISPR-modified cell line (ED) , derivedtax read intensity in the RNA-seq above or below the median intensity of all clones). The results showed that, whereas abnormal upstream antisense expression was present in most clones, an increase in same-sense transcription was specific to those clones with high plus-strand expression  on the HTLV-1-infected T cell clones, and quantified the density of reads mapping to discrete 1 kb windows up to 2 Mb from the respective proviral integration site. The results showed upregulated transcription in the host genome immediately flanking the HTLV-1 provirus, in all clones examined . This uppression .PNPLA  ~44 kb upstream; no transcripts were detected in the intervening host genome  in the negative strand of the genome; the abnormal transcription observed in clone 3.60 was present in the positive strand with alternative splicing, resulting in a putative novel transcript  presented here reveal reproducible contacts between the HTLV-1 provirus and the flanking host genome at least 1.4 Mb from the integration site. Each clone has a unique pattern of chromatin contacts, depending on the site of integration of the provirus in the host genome. The allelic bias observed in the q4C data, with the preferential detection of SNPs on the chromosome carrying the provirus, indicates that the provirus makes preferential contacts in cis with host chromatin at distances at least 4 Mb from the provirus. Preferential contacts in cis may extend beyond this distance, but detection is likely to be limited by the sensitivity of the q4C technique.tax gene abrogated a major contact with a site in the host genome, respectively ~50 and 17 kb downstream, where CTCF was shown to bind. Second, of the 44 host loci identified by the peak-calling algorithm as making contact with the provirus, 22 contained one or more CTCF-BS . Third, consistent with recent observations made by others  transcription, predominantly in the opposite transcriptional sense to the provirus upstream of the 5′ LTR and, to a lesser extent, in the same transcriptional sense downstream of the 3′ LTR. The observed abnormal transcription involved both intergenic regions and identified host genes ; in additax-expressing cells , IFNG, IL6, IL15, GM-CSF, TNFB, and CCL22  was added to the culture every 3 days, and the retroviral integrase inhibitor raltegravir (Selleck) was maintained at 10 μM throughout cell culture, to prevent secondary infection. In addition, the cells were activated every 14 days by the addition of beads coated with antibodies against CD2, CD3 and CD28 (Miltenyi-Biotech). All experiments were carried out on cells harvested on Day 9 of this cycle, after addition of fresh media on Day 8.The HTLV-1-infected T-lymphocyte clones were derived by limiting dilution from peripheral blood mononuclear cells (PBMCs) of donors attending the National Centre for Human Retrovirology (NCHR) at Imperial College Healthcare NHS Trust, St Mary's Hospital, London. All donors gave written informed consent in accordance with the Declaration of Helsinki to donate blood samples to the Communicable Diseases Research Tissue Bank, approved by the UK National Research Ethics Service (15/SC/0089). The derivation of these clones and the genomic insertion site of the single-copy HTLV-1 provirus in each clone were previously reported . The celWe used Cas9 ribonucleoprotein transfection  to selec(5′-AATGATACGGCGACCACCGAGATCTACACCCTCCAAGGATAATAGCCCGTC-3′) and P7 (Illumina). The 8 PCR2 products were combined and purified by QIAquick PCR Purification Kit (Qiagen). The libraries were quantified by qPCR using Illumina primers P5 and P7. Stock 4C libraries were diluted accordingly and clustered on the sequencing flow cell.http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). In order to avoid misalignments due to composite reads (containing NlaIII fragments from multiple contacts), in silico digestion at NlaIII binding sites (CATG) was performed on the reads. ‘Digested’ Fastq files were subsequently aligned (as single reads) against a merged reference of the human genome (hg19 assembly) and the HTLV-1 genome (accession number AB513134). For each read, only the first digested NlaIII fragment was used for subsequent analysis unless the first fragment was the one directly following the viewpoint (incomplete digestion) in which case the second fragment was used. Successfully aligned pairs were filtered to remove barcode errors, and only pairs where both read 1 and read two mapped to the same chromosome in convergent orientation were kept. Unique read1-read2 pairs were considered a ligation event: the shear site serves as a unique molecular identifier  in 5 kb overlapping windows (1 kb steps) across the alignments for every clone from each sample. Next we trained a three-state hidden Markov model on all windows with more than one ligation event, for chromosomes containing integration sites, ordered by genomic position, using the Expectation-Maximization (EM) algorithm from the depmixS4 package  to find To define continuous genomic regions of interactions for a given chromosome, we applied a cubic smoothing spline to each sample’s respective state space, interpolating over n points across the chromosome with n being 0.25 * the number of overlapping windows in that chromosome. The edges for each peak were defined by the change in sign of the difference of the curve between consecutive points. Peaks were called for a single replicate only if included windows with a minimum of state 2 (probable peak) in both replicates. The peaks were intersected between the replicates using the GenomicRanges package  and intehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and aligned to the same combined reference  as described above for q4C analysis, using GSNAP v2017-05-08 (SCR_006646) . RNA sequencing for each clone was performed using the Ilumina platform. Where more than one replicate was sequenced, the one with a larger number of reads was used in subsequent analysis. Data quality was assessed using FastQC (17-05-08 . Read de_006646)  against 7) were cross-linked with 1% formaldehyde at room temperature for 5 min. Nuclear cell lysates were sonicated with a Covaris S2 and immunoprecipitated using anti-CTCF  antibody. The ChIP DNA libraries (ChIP and input DNAs) were prepared using NEBNext Ultra II DNA Library Prep Kit for Illumina and Multiplex Oligos for Illumina  according to the manufacturer’s instructions. Libraries were sequenced (single-end 50 bp reads) on a HiSeq 2500 (Illumina).Cells ) and peaks were called against the DNA input control using MACS  to call the orientation of the highest scoring motif. The orientation of ~79% of CTCF observed binding sites was determined in this way.CTCF ChIP libraries from three T-cell clones (two of which carry an HTLV-1 provirus), and a DNA input control were sequenced on the Ilumina platform. Sequence data were trimmed to remove adapters and low-quality bases using TrimGalore, and aligned against the same combined reference  as above, using GSNAP. Duplicates were removed using Picard v.2.9.0  (SCR_001876) v3.7 (Genomic DNA (gDNA) was extracted from each subject’s PBMCs and the respective T cell clones using DNeasy Blood and Tissue kit (QIAGEN). Whole-genome sequencing was carried out on PBMC DNA from each subject from whom clones were used in this study, with the exception of subject TBX; DNA from this subject’s clone TBX4B was sequenced because PBMC DNA was not available. DNA sequencing was performed on the Ilumina X10 platform, one sample per lane. Alignment against the same combined reference  was done using BWA-MEM v0.5.9 (RRID:_010910) , and dup76) v3.7 . Pre-prohttps://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html). RNASeq data were aligned to the combined reference using GSNAP ; duplicNonparametric tests were used to examine the association between the number of CTCF-BS and the q4C peak height (Wilcoxon) and the number of q4C peaks (Spearman). The difference in the frequency of q4C peaks at a given distance from the provirus was tested using a chi-squared goodness of fit test. The correlation between the number of q4C peaks and the density of CTCF-BS was examined using Pearson’s test. The curves showing the relationship between allelic imbalance and genomic distance  were comhttp://www.ebi.ac.uk/ega/), hosted by the European Bioinformatics Institute (accession number EGAS00001002259). Custom scripts used to analyse this data are available at https://github.com/ImperialCollegeLondon/q4C-2018 (https://github.com/elifesciences-publications/q4C-2018).Sequence data have been deposited at the European Genome-phenome Archive  technique, they identify chromosomal regions that form loops with the CTCF binding region of the provirus. The loops are shown to form in cis and not in trans and to alter transcription of host genes near the integration site and distant sites within a loop.Overall, this is an interesting and well performed study. The manuscript could be improved by considering the following points:1) The results clearly show that the CTCF binding site in the viral genome can participate in looping with cis host genomic elements, but whether this alters host genomic looping as the authors suggest is less clear. Can the authors define the loop patterns that would be present in the region of the integration site when no provirus is present?2) It would be helpful to provide a schematic of the authors' modifications to the 4C method.3) How have the quantitative aspects of this method been validated?4) The altered chromosomal looping and consequent effects on host gene transcription will be different in each clone with a different integration site. Overall, it is not clear how this affects HTLV-1 persistence or oncogenic potential. Some discussion of these points would strengthen the manuscript.Reviewer #2:eLife, promises to provide an answer to this quandary, and to significantly advance our understanding of important aspects of the HTLV-host interaction.It has been known for many years that insertional activation  of gene expression is the principal mechanism by which they cause cancer, a fact which enabled scientists to identify and study in simple animal models, genes which turned out to be of central importance to human cancer. HTLV has always been a somewhat embarrassing exception; in only a few cases has oncogenic disruption of genes near the site of integration. The elegant study by Melamed et al., submitted to Using a panel of 10 clonal HTLV-infected cell lines, from infected patients, each with a single provirus, the authors developed and used a novel DNEA looping assay to show that proviral sequences, which contain a CTCF binding site can be brought into juxtaposition with regions of neighboring chromosomal DNA up to as many as 4 Mbp away, and that this looping, by bringing viral enhancer elements near cellular promoters, can affect the transcription of genes on the same chromosome, but at a considerable distance, providing a likely explanation of why, in most cases, classical oncogene activation patterns of adjacent genes are not seen in HTLV-induced lymphoma, even if the underlying mechanism might be almost the same.eLife. My comments are mostly just points that might benefit from further discussion.All in all, I found this to be a well-written and convincing study, certainly worthy of publication in 1) Although the primary observations seem quite compelling, the authors seem to tiptoe around the role of chromosomal looping in oncogene activation. There must be additional evidence in support of such a model that they can cite, such as the existence of oncogenic cell lines with aberrant expression of known oncogenes within a few Mbp of an HTLV-1 provirus. The focus of this study on non oncogenic lines makes it seem like they are splitting off an important part of the story.2) It seems highly unlikely that the CTCF binding site is in the provirus by accident. Some discussion – even speculation – to this point would be very welcome. Reviewer #1:[…] Overall, this is an interesting and well performed study. The manuscript could be improved by considering the following points:1) The results clearly show that the CTCF binding site in the viral genome can participate in looping with cis host genomic elements, but whether this alters host genomic looping as the authors suggest is less clear. Can the authors define the loop patterns that would be present in the region of the integration site when no provirus is present?This is indeed an interesting point: it is possible that HTLV-1 destroys normal chromatin loops as well as creating abnormal loops. However, the appropriate method to use to systematically map the loop patterns in the normal genome is HiC, which in principle identifies loops  between any two genomic regions , whereas 4C identifies loops made between a defined ‘viewpoint’ and all other regions . It is outside the scope of this study to carry out HiC analysis in addition to the 4C.2) It would be helpful to provide a schematic of the authors' modifications to the 4C method.Thank you for the suggestion. We have made a schematic diagram to compare conventional 4C and our modified version, q4C, and this is included as Supplementary file 2.3) How have the quantitative aspects of this method been validated?Quantification of chromatin looping. 4C (and indeed other ‘C techniques’) gives relative quantification of chromatin looping: in the absence of single-cell 4C – which no-one has yet developed – absolute quantification of loop frequency is not yet possible. Therefore, validation of the (relative) loop frequency depends primarily on the reproducibility of peak identification in independent biological replicate experiments, as we report in the present Results. The improvement in quantification introduced by the use of sonication instead of restriction enzyme digestion was demonstrated in our high-throughput analysis of retroviral integration sites 4) The altered chromosomal looping and consequent effects on host gene transcription will be different in each clone with a different integration site. Overall, it is not clear how this affects HTLV-1 persistence or oncogenic potential. Some discussion of these points would strengthen the manuscript.The impact of the provirus on chromatin looping and host transcription is indeed clone-specific. Because of the great clonal diversity of HTLV-1-infected cells in each host, identifying the impact of altered chromatin looping on HTLV-1 persistence or oncogenic potential will depend largely on bioinformatic analysis: in particular, to compare the association between CTCF binding sites and proviral integration sites between clones infected in vitro – in the absence of in vivo selection – and those that persist in vivo, and between malignant infected (ATL) clones and non-malignant clones. We are now embarking on this analysis. To emphasize the point raised by the reviewer, we have added the following sentence to the Discussion section:“The altered chromosomal looping and consequent effects on host genome transcription depend on the proviral integration site, and so differ in each infected T-cell clone. Further work is needed to investigate the impact of altered looping and transcription on the persistence, infectivity and oncogenic potential of the infected T-cell clones.”Reviewer #2:[…] All in all, I found this to be a well-written and convincing study, certainly worthy of publication in eLife. My comments are mostly just points that might benefit from further discussion.1) Although the primary observations seem quite compelling, the authors seem to tiptoe around the role of chromosomal looping in oncogene activation. There must be additional evidence in support of such a model that they can cite, such as the existence of oncogenic cell lines with aberrant expression of known oncogenes within a few Mbp of an HTLV-1 provirus. The focus of this study on non oncogenic lines makes it seem like they are splitting off an important part of the story.Relationship of proviral integration to oncogene activation. This is of course a very important point. However, the chief aim of the present paper was to show that insertional mutagenesis – both local and at distant sites – is a general feature of HTLV-1 infection, and yet does not usually cause oncogenic transformation. We now plan to apply the techniques reported here to analyse chromatin looping and host transcription in cases of adult T-cell leukemia/lymphoma. In this planned study, we will focus on primary cells from ATL cases, rather than long-term in vitro cell lines, because ATL clones are notoriously difficult to propagate in vitro, so the relationship between such in vitro cell lines and primary ATL clones is at best uncertain. See also the response to reviewer 1, point 4.2) It seems highly unlikely that the CTCF binding site is in the provirus by accident. Some discussion – even speculation – to this point would be very welcome.We agree wholeheartedly with the reviewer’s implication that CTCF binding must confer some benefit on HTLV-1. However, this has proved a very difficult problem. To clarify the point, as the reviewer suggests, we have added the following short paragraph to the Discussion:“We postulate that CTCF confers a survival or replication advantage to HTLV-1, because of the high degree of sequence conservation of the proviral CTCF binding site and the coincidence of the binding site with a border of epigenetic modifications associated with transcriptional activity . However, the nature and mechanism of this putative advantage are not yet known.”


C. elegans, D. melanogaster and H. sapiens and investigate the determinants of their transcription directionality. In all three species, we find that divergent transcription is initiated from two separate core promoter sequences and promoter regions display competition between histone modifications on the + 1 and −1 nucleosomes. In contrast, promoter directionality, sequence composition surrounding promoters, and positional enrichment of chromatin states, are different across species. Integrative models of H3K4me3 levels and core promoter sequence are highly predictive of promoter and enhancer directionality and support two directional classes, skewed and balanced. The relative importance of features to these models are clearly distinct for promoters and enhancers. Differences in regulatory architecture within and between metazoans are therefore abundant, arguing against a unified eukaryotic model.Divergent transcription from promoters and enhancers is pervasive in many species, but it remains unclear if it is a general feature of all eukaryotic cis regulatory elements. To address this, here we define cis regulatory elements in  Divergent transcription from promoters and enhancers occurs in many species, but it is unclear if it is a general feature of all eukaryotic cis regulatory elements. Here the authors define cis regulatory elements in worms, flies, and human; and identify several differences in regulatory architecture among metazoans. C. elegans, M. musculus, and H. sapiens5, though is far less common in D. melanogaster6.The application of deep-sequencing assays led to the unanticipated observation that the promoters of many genes are transcribed in both directions, a phenomenon dubbed divergent transcription. In divergent promoters, transcripts made in the direction antisense to the annotated gene are non-protein-coding and highly unstable such that they can typically only be detected in assays enriching for nascent RNA. Divergent transcription is pervasive across many eukaryotes including yeast, 8. A different, Nrd1-complex mediated mechanism was found to destabilize divergent promoter transcripts in yeast10.In mammals, the asymmetric output of divergent promoters was suggested to be the result of a post-transcriptional competition model between the splicing machinery and the cleavage/polyadenylation machinery. Here, enriched splice site sequences lead to transcript extension and stabilization in the forward direction, whereas enriched cleavage sequences lead to transcription termination and RNA degradation by the nuclear exosome complex in the reverse unstable direction11. Divergent promoters initiate transcription from two separate core promoters upstream antisense to each other within a single nucleosome depleted region (NDR), forming two distinct polymerase pre-initiation complexes (PICs)14. Differences in the sequence-encoded strengths of the forward- and reverse-directed core promoters were reported to potentially drive variation in promoter directionality in H. sapiens HeLa cells15, in contrast to recent results obtained using massively parallel reporter assays that measure initiation outside the native genomic context17. Therefore, asymmetric output of mammalian divergent promoters is potentially sequence-encoded at both transcription initiation and post-transcriptional termination/degradation.These observations are unable to fully explain transcription directionality since nascent RNA data shows considerable variation in forward/reverse transcription initiation rates18. H3K4 methylation and other histone post-translational modifications (PTMs) on promoter NDR-flanking nucleosomes are known to influence transcription initiation and elongation rates via direct physical interactions with PICs21, which may contribute to directional variation of transcription initiation within promoter NDRs.The level of divergent transcription is also reflected in a unique promoter chromatin environment exemplified primarily by differences in levels and distribution of methylation on lysine 4 of histone H3 (H3K4me1/2/3) upstream of the promoter NDR23. While enhancers have been long known to feature different chromatin states than those of promoters24, recent studies have suggested that promoters and enhancers are not distinct types of regulatory elements since they both feature divergent transcription, with H3K4 methylation states varying according to differences in transcription initiation rates26. Of note, the striking similarities in architecture between promoters and enhancers does not necessarily translate to functional equivalence27.Divergent transcription is also observed in distal gene regulatory elements such as enhancers, producing and/or long non-coding RNAs with varying stabilities sometimes referred to as enhancer RNAs (eRNAs). Transcriptional activity has been recently identified as a defining feature of active enhancers in mammalsDrosophila melanogaster, Caenorhabditis elegans, and Homo sapiens. In all three species, we observe strict directional correlations between core promoter sequence strengths and initiation quantity, as well as highly direction-specific correlations between active histone modifications upstream and downstream of promoter NDRs. We find forward/reverse histone modification levels and core promoter sequence strengths alone to be highly predictive of both promoter and enhancer initiation directionality. In our models, sequence and PTMs contribute differently to two regulatory types, directionally balanced transcription initiation and directionally skewed transcriptional initiation, and these contributions differ between enhancers and promoters. Sequence content asymmetry upstream vs. downstream of promoter NDRs is distinct across species and suggests species-specific mechanisms for post-transcriptional contributions to transcript directionality. Finally, low-level divergent transcription initiation is detected from active enhancers in all three species, with putative enhancer activity strongly enriching for divergent transcription in D. melanogaster promoters.While divergent transcription in mammals is reflected in both DNA sequence and chromatin, the precise contribution of sequence and chromatin features to transcription initiation directionality . This yielded 18,067 promoter NDRs in the H. sapiens cell line, 6926 in the D. melanogaster cell line, and 10,912 in the L3-stage whole C. elegans  on ans Fig. .H. sapiens GM12878, D. melanogaster S2 cells and L3-stage whole C. elegans30. These nuclear run-on-based assays are sensitive to detecting TSSs of both stable and unstable nascent transcripts from promoters and enhancers. For 14,371 (79%), 6280 (91%), and 10,786 (99%) NDR regions in H. sapiens, D. melanogaster, and C. elegans, respectively, at least one PRO/GRO-cap read mapped to at least one strand. Evaluating the forward (annotated gene) reads against those on the reverse strand enables a minimally biased view of promoter transcription initiation directionality across the three species , further supporting the notion that promoter directionality is not purely determined post-transcriptionally . Bayesian information criterion analysis of cluster numbers suggested 2, 1, and 2 mixture components as optimal for ely Fig. . Therefoene Fig. . Two popH. sapiens and C. elegans, respectively, a substantially smaller number of 441 promoter NDRs met these criteria in D. melanogaster S2 cells. To confirm that this D. melanogaster set is not simply composed of unannotated stable-stable bidirectional promoters, we generated PEAT  data in S2 cells31, an assay which measures TSSs of stable polyadenylated transcripts, and found the reverse signal of the selected divergent promoters to be preferentially depleted in stable transcripts . While these criteria yielded 5231 and 2670 divergently transcribed promoter NDRs in 35. To assess initiation pattern distributions in divergent promoters, we applied an entropy-based metric36 to the forward and reverse TSSs within divergent promoters, with lower values indicating high signal over few bases; i.e., so-called focused promoters. Overall, there is a high degree of agreement between forward and reverse initiation patterns in all three species, suggesting that reverse TSSs in our stringent groups are not randomly located events . These data strongly suggest that all three species initiate forward- and reverse-directed transcription from forward- and reverse-directed core promoter sequences within NDRs.To examine the role of the core promoter sequence in directing reverse initiation from divergent promoter NDRs, we turned to position-specific Markov chain models of core promotersDRs Fig. , which iDRs Fig. . An enriDRs Fig. . In all,D. melanogaster show substantially higher model scores compared to H. sapiens and C. elegans core promoters , but also detected decreased forward model scores for regions harboring core promoter elements in the reverse core promoters . We then characterized combinatorial chromatin states for those four PTMs at 10-bp resolution using a multivariate Gaussian Hidden Markov Model (see Methods). We identified 11 chromatin states, which we refer to according to their spatial trends . Forward and reverse nascent transcription initiation levels in those regions indicate that distal active enhancers in all three species display consistent but not necessarily bidirectional transcription initiation  enriches for divergently transcribed elements relative to distal NDRs that do not intersect STARR-Seq peaks  and H3K4me2 signal  and E2  states well downstream of the promoter region, but only for the STARR-seq enhancer NDRs  in intergenic ers Fig.  promptedDRs Fig. . Gene OnDRs Fig. . AltogetDRs Fig. , these o47. This model serves to predict the directionality ratio, with the mixture components representing the directionality type, skewed or balanced initiation see Fig. . MotivatWe trained two separate models for promoters and enhancers and assigned each promoter and enhancer to the type predicted by the model. Examining the distributions of experimentally measured forward/reverse initiation reveals the presence of two groups of promoters/enhancers: one with balanced initiation directionality and one with skewed initiation directionality Fig. ,  b. Ind48. Additionally, it reveals that enhancers, too, feature variable directionality levels encoded in sequence and histone PTM levels. Finally, even though enhancers and promoters have several sequence and chromatin similarities, they might diverge in how those features mechanistically translate into transcription initiation and directionality levels.We used cross-validation to assess the fit of the full model and to compare it to two models trained on sequence or H3K4me3 levels only. While core promoter sequence scores appeared to be the key important feature for the model’s performance in promoters Fig. , H3K4me3D. melanogaster and many in C. elegans and H. sapiens. (2) There are at least four types of regulatory elements with respect to directionality in H. sapiens, skewed and balanced promoters and enhancers. (3) H. sapiens enhancers utilize histone PTMs and core promoter sequences differently than promoters to determine initiation directionality. (4) The dual core promoter sequence and chromatin state asymmetry of D. melanogaster divergent promoters is clearly different from C. elegans and H. sapiens. (5) D. melanogaster enhancers exhibit two types of chromatin states depending on whether they are intragenic/developmental or intergenic/housekeeping. (6) Asymmetric sequence content surrounding promoters  is clearly distinct across species. Although strong similarities exist, we, therefore, conclude that a single model of transcription initiation within and across eukaryotic species is not evident.We presented a comparative analysis of transcription initiation and histone PTM directionality at promoters and enhancers that highlights several differences between regulatory elements and across species: (1) Promoter initiation directionality is distinct across species with very few divergent promoters in 49. However, it appears not to be consistent with recent reports using massively parallel reporters17, which however lack the native genomic chromatin context. Our analysis also indicates a strict direction-specific positive correlation between different histone PTMs on the + 1 and  − 1 nucleosomes of promoter NDRs in all three species  transcription of a divergent product  may also act as a tuning mechanism for the initiation rates of a functional, oppositely-oriented counterpart; (c) the directional variation of initiation across NDRs is determined by directionally competing sequence and chromatin features; and (d) apparently species-specific mechanisms ensure that any divergent, nonfunctional transcripts are efficiently degraded.We propose a refined picture of transcriptional directionality in which (a) skewed directionality is enforced at genuine endogenous promoters, where one side acquired functionality to transcribe a functional trans-acting (m)RNA at relatively higher levels, consistent with recent studies by Jin et al.D. melanogaster S2 cells enriches for divergent transcription . Embryos were harvested from adults by sodium hypochlorite treatment and grown until third larval instar (L3). Synchronized L3 animals were washed five times in M9 buffer and collected on ice. Nuclei were isolated using a glass Dounce homogenizer with 50 strokes tight-fitting insert in buffer A  as described before54. The debris were removed by spinning at 100 × g for 5 min and nuclei were counted by Methylene blue staining. In all, 100.000 nuclei per sample were pelleted by spinning at 1000 × g for 10 min and proceeded immediately to transposition step of the ATAC-seq protocol28. Briefly, nuclei were incubated in 1x tagmentation buffer  supplemented with 2.5 microlitres of Tn5 transposon  for 60 min at 37 °C. DNA was purified using the DNA Clean and Concentrator-5 kit (Zymo Research), followed by PCR amplification using 1x High-fidelity NebNext PCR master mix (New England Biolabs). Libraries were amplified for a total of 13 or 14 cycles.D. melanogaster S2 cells were obtained from Life Tech (#R69007) and grown at 25 °C in Schneider’s Cell medium  with 10% FBS  and 10% l-Glutamine  without antibiotics. Cells were grown in T75 flasks at 25 °C to a confluency of ~ 70%. For ATAC-seq, cells were grown at 25 °C in ExpressFive SFM medium  with 10% heat-inactivated FBS  and 12% l-Glutamine  and 1% Penicillin-Streptomycin . Cells were grown in dishes to a confluency of ~ 80–95%.For ChIP-seq, g for 5 min at 4 °C and washed twice with ice-cold PBS. The cell pellet was resuspended in 10 ml ice-cold cell lysis buffer (5 mM HEPES (pH 8), 85 mM KCl, 0.5% NP-40) with protease inhibitors  and incubated for 10 min at 4 °C. Nuclei were released by 10 strokes with a Wheaton Dounce Homogenizer (tight pestle). The crude nuclear extract was collected by centrifugation at 500 x g for 5 min at 4 °C, resuspended in 1 ml ice-cold nuclear lysis buffer (50 mM HEPES (pH 8), 10 mM EDTA, 0.5% N-Lauroylsarcosine with protease inhibitors) and incubated for 20 min at 4 °C. After addition of 1 ml nuclear lysis buffer samples were sonicated using a Diagenode Bioruptor for 18 cycles (30” ON / 30” OFF) on high. After sonication samples were centrifuged at 14000 x g for 10 min at 4 °C and the supernatant was aliquoted to DNA-low binding tubes. The chromatin was flash frozen in liquid nitrogen and stored at −80 °C.For H3K4me2 ChIP-seq, formaldehyde was added to media to a final concentration of 1% and incubated for 10 min on a shaker at room temperature. The reaction was quenched by adding glycine to a final concentration of 125 mM followed by 5 min of incubation on a shaker at room temperature. The cells were collected by centrifugation at 500 x g for 2 min and the supernatant was discarded. Beads were resuspended in 100 µl TE and RNase A was added to a final concentration of 50 µg/ml followed by incubation at 37 °C for 30 min. The samples were adjusted to a final concentration of 0.5% SDS and Proteinase K was added to a final concentration of 500 µg/ml. Proteins were digested at 37 °C for 90 min followed by reverse cross-linking overnight at 65 °C. DNA was purified using phenol–chloroform extraction followed by ethanol precipitation. Libraries were prepared using the NEXTflex qRNA-Seq Kit v2 from Bioo Scientific  and sequenced on an Illumina NextSeq 500 sequencer.Protein A Sepharose (PAS) beads (Sigma #P9424) were washed twice with RIPA140 , 1 mM EDTA, 1% Triton X100, 0.1% SDS, 0.1% Na-Deoxycholate) with proteinase inhibitors and 1 mg/ml BSA (Sigma #A7906) and incubated overnight. Chromatin was thawed on ice. Fifty micrograms of Chromatin was used per ChIP experiment. RIPA140 with proteinase inhibitor was added to a total volume of 1 ml and incubated with 2 µg of H3K4me2 antibody  for 16 h at 4 °C on a rotating mixer (40 rpm). One percent of the chromatin was used as input controls. Blocked beads were added to the chromatin–antibody complex solution and incubated for 3 h at 4 °C on a rotating mixer at 40 rpm. Complexes were washed once with 1 ml RIPA140, 4 times with 1 ml RIPA500 , 1 mM EDTA, 1% Triton X100, 0.1% SDS, 0.1% Na-Deoxycholate) for 10 min each. Complexes were subsequently washed once in 1 ml LiCl-Buffer , 1 mM EDTA, 0.5% NP-40, 0.5% Na-Deoxycholate) and TE (10 mM Tris–HCl (pH 8), 1 mM EDTA) for 2 min each. Between each wash, beads were spun down at 500 x For ATAC-seq, 200,000 cells were subjected to tagmentation as described above. Libraries were amplified to a total of 13 or 15 cycles.D. melanogaster S2 cells were grown to a density of 3 million per ml in Schneider’s Medium (Invitrogen) supplemented with 10% FCS and 1x Antibiotics. The PEAT library was constructed as described31.For PEAT data, C. elegans whole l3 stage were downloaded from data.modencode.org43 corresponding to experiment IDs 5048, 5157, 3576, and 5054, respectively. ChIP-seq datasets for H3K4me1, H3K4me3, and H3K27ac from D. melanogaster S2 cells were downloaded from the Gene Expression Omnibus . ENCODE ChIP-seq datasets for H3K4me1, H3K4me2, H3K4me3, and H3K27ac from H. sapiens GM12878 cells were downloaded corresponding to GEO sample IDs GSM733772, GSM733769, GSM945188, and GSM733771, respectively, as well as input control sample IDs GSM733742 and GSM945259.ChIP-seq datasets for H3K4me1, H3K4me2, H3K4me3, and H3K27ac from H. sapiens GM12878 cells  and C. elegans whole L3 stage  were downloaded from GEO.GRO-cap datasets for H. sapiens GM12878 cells were downloaded from GEO .ATAC-seq datasets for GSE9784140;)M1BP ChIP-exo was downloaded from GEO . The sequencing library for S2 H3K4me2 ChIP-Seq dataset was prepared with Unique Molecular Identifiers, therefore the first 9 bases of each read were removed using flexbarv2.456 and reads were aligned to dm6 genome build using bowtie2 in paired-end mode keeping only concordantly aligned mates. All ChIP-seq datasets were collapsed using samtools rmdup58 and ATAC-seq and M1BP ChIP-exo datasets were collapsed using MarkDuplicates.jar from Picard tools (http://broadinstitute.github.io/picard). Duplicates were not removed from PRO/GRO-cap datasets. ATAC-seq read pairs with fragments greater than 50 bp were kept for further processing. Start sites of ATAC-seq reads were extended by 15 basepairs upstream and 22 basepairs downstream in a stranded manner, to account for steric hindrance of the transposition reaction59. All reads that intersected ENCODE blacklisted regions (https://sites.google.com/site/anshulkundaje/projects/blacklists) were removed and all replicate BED files were concatenated together for peak-calling and signal generation. Signal bigwig files for ATAC-Seq were generated using JAMM signal generator pipeline29. Depth-normalized, read extended bigwig file for M1BP ChIP-exo was generated and plotted using deeptools60.PRO/GRO-cap datasets were subjected to adapter removal using cutadapt55 (parameters -a GTTGGACTCGAGCGTACATCGTTAGAAGCT -O 30 -m 20—untrimmed-output). The sequences that were not matched for 5′-end were then matched and trimmed for the 3′-end adapter using cutadapt55 (parameters -a GTCGGATAGGCCGTCTTCAGCCGCCTCAAG -O 30 -m 20—untrimmed-output). The two resulting fastq files matching each end were combined, reverse complemented, and then unpaired mates discarded and paired mates matched based on read IDs using custom scripts. The resulting paired fastq files were then mapped using STAR61  to the dm6 genome assembly.PEAT data was processed as follows. Fastq files from each mate were first matched and trimmed for the 5′-end adapter using cutadaptC. elegans L3 and D. melanogaster S2 datasets, no window enrichment cutoff was used (-e 1) but we used the filtered output which performs filtering on the peak level. Only peaks that are larger than 50 basepairs were kept for all species. To ensure that PRO/GRO-cap start site counts in promoter regions are not underestimated due to narrow-width peak calls, final peaks were extended by 75 basepairs in each direction and overlapping peaks were merged. Extended, merged, ATAC-seq peaks were annotated as promoters if they were within + /− 200 bp from Gencode defined transcript starts for H. sapiens, + /− 400 bp  from Flybase defined transcript starts for D. melanogaster, and + /− 500 bp  from refGene defined gene starts for C. elegans. Peaks that fell within these distances on both strands were regarded as bidirectional promoters and removed from the analysis. Peaks were annotated as intergenic if they were not annotated as a promoter and did not intersect known transcript boundaries from the same databases.ATAC-seq peaks were called using JAMM v1.0.7rev5 setting the bin size to 100, mode (-m) to normal and resolution (-r) to peak for all species. To obtain appropriate peak signal cutoffs each dataset, we used an automated threshold cutoff on window enrichment by setting –e to auto for the gm12878 dataset and used the all list output from JAMM. For the C. elegans (17 read 5′-ends) and D. melanogaster (8 read 5′-ends), and the 0.9999 quantile for H. sapiens (14 read 5′-ends). Thus, we had two sets of ATAC-seq peaks for downstream analysis: Set A contained at least one base with these numbers of reads on the forward strand only and Set B contained at least one base with these numbers of reads on both the forward and reverse strands. Set B peaks were filtered for convergent TSS pairs defined when the bases on both strands containing the most read 5′-ends are both over the determined cutoff and are situated downstream from each other.ATAC-peaks containing confident transcription start sites from nascent RNA datasets were selected based on empirical distributions of read 5′-ends per base from control regions. Control regions were selected as follows: first all ATAC-seq peaks containing at least one nascent RNA read 5′-end on at least one strand were selected, then windows equal in size to a given ATAC-seq peak were taken immediately downstream of that ATAC-seq peak on both strands , and any overlapping regions with other peaks were subtracted out. These windows were taken to represent frequently observed signal background within gene bodies that is likely to come from technical issues in the PRO/GRO-cap protocols  and, therefore, are unlikely to represent true transcription start sites. The empirical distribution of nascent RNA read 5′-ends per base was constructed across all the bases in the control windows and cutoffs were determined using the 0.999 quantile for Non-extended peaks called using JAMM setting –m to narrow to obtain more accurate peak edges were used for promoter peak width and distance between TSS and peak edge analysis  or zero-to-one normalized (PRO/GRO-cap) counts were plotted using ggplot229. For S2 H3K4me2 dataset, peaks were called using JAMM v1.0.7rev5 in paired-end mode, since this was the only ChIP-Seq paired-end dataset used in this study. The filtered output peaks produced by JAMM was used for all histone modification datasets.All histone modification peaks were called using JAMM v1.0.7rev5 setting the bin size to 150 and –r to window60 bamCoverage at 10 bp resolution using the fragment length obtained by JAMM and setting normalization to RPKM. To generate average meta-plots, deepTools computeMatrix was used at single basepair resolution. The bigwig files were also used to define the features for partial correlations and the predictive linear model.Histone modification bigwig signal tracks were generated using deepTools37 was used as described previously11. Set A ATAC-Seq peaks (see above) were used for training species-specific first-order (di-nucleotide) models as follows: a window + /− 50 bp surrounding the position with the most nascent RNA read 5′-end counts within the ATAC-seq peak was used to train the TSS sequence model. The model was then run either on the same corresponding windows from both strands of the selected promoters  scores were calculated based on the equation for Shannon entropy similar to a previously described methodg and c are the number of G and C nucleotides in a 50 bp window slid along a given region with a step size of 1 bp. Positional means were then calculated across all regions and plotted with ggplot262.GC percentage was calculated in a sliding 50 bp window with a step size of 1 bp along a given region and then taking the positional mean across all selected regions. GC-skew was calculated as follows:63 with a zero-order Markov background window of 100 bp. Scores below zero were set to zero, and scores were plotted with ggplot262 either as heatmaps  at all other entries (where n is the number of states); the segmentation was obtained using posterior decoding). Baum-Welch was run-on chromosome 1 for gm12878 and on all chromosomes for S2 and L3. The two H3K4me1-only states were added and plotted as one line in state coverage plots in all figures.Histone modification peaks were processed for the chromatin state HMM as previously describedHistone modification features were defined as the maximum histone modification ChIP-Seq signal in a 1 kb window downstream and upstream of the non-extended ATAC-seq peaks (see above) for forward and reverse directions, respectively. The initiation rate was calculated as the total number of PRO/GRO-cap read 5′-ends on each respective strand of the extended ATAC-Seq peaks divided by ATAC-Seq peak width. As core promoter sequence feature, we added all model scores for positions with at least one PRO/GRO-cap read 5′-end and divided by ATAC-Seq peak width (see above). ATAC-Seq features were defined as the maximum ATAC-Seq signal in the non-extended ATAC-seq peaks.65 and heatmaps were plotted using the R package pheatmap66.Spearman partial-correlation coefficients were then obtained using the R package ppcor47, setting the number of clusters to 2. All features were standardized to have a mean of zero and standard deviation of 1 before training the model. H. sapiens promoter and enhancer regions were selected to have divergent TSSs that meet the stringent count cutoff for forward and reverse initiation sites  and enhancers were further selected to intersect H3K4me1, H3K4me2, and H3K27ac peaks (see above). This results in learning two linear models with distinct regression coefficients and assigning each data point a probability of belonging to each of the two mixture components. Each promoter region is then assigned to the mixture component that has the higher probability. To obtain predicted directionality ratios, the prediction from the mixture component that the promoter region is assigned to is used.Features were defined as for the partial-correlation analysis (see above). A linear mixture model was learned using the R package flexmixFor cross-validation, the data was split into ten equal parts and model learning and clustering were repeated ten times; each time the model predictive ability was tested on the held-out test set, summarized using the correlation coefficient. This was done separately for three different models, one that included both core promoter sequence score ratio and H3K4me3 ratio and two that included sequence score ratio only or H3K4me3 only.45 peaks were obtained from the Stark lab website (http://www.starklab.org/data/arnold_science_2013/) and coordinates were lifted over to dm6 genome assembly. Peaks from both replicates were merged and extended by 200 bp in each direction. For Fig. STARR-Seq70. GO terms were filtered for those that are over-represented with FDR value smaller than 0.01 and whose reference set contains at least 100 genes but no more than 2000 genes.Gene Ontology Biological Process enrichment was determined using the PANTHER database overrepresentation test (release: 20171205) and the Panther GO-Slim Annotation set https://github.com/mahmoudibrahim/hmmForChromatin. All other custom code is available upon request.HMM-related scripts for Baum-Welch training and posterior decoding are available at Supplementary Infopeer review file


In this study, Dergai et al. sought to understand the mechanism by which the absence or presence of a TATA box results in specific polymerase recruitment. They examined how SNAPc and general transcription factors required for Pol II or Pol III transcription of SNAPc-dependent genes  assemble to ensure specific polymerase recruitment and provide a model for specific Pol recruitment at SNAPc-dependent promoters. RNA polymerase II (Pol II) small nuclear RNA (snRNA) promoters and type 3 Pol III promoters have highly similar structures; both contain an interchangeable enhancer and “proximal sequence element” (PSE), which recruits the SNAP complex (SNAPc). The main distinguishing feature is the presence, in the type 3 promoters only, of a TATA box, which determines Pol III specificity. To understand the mechanism by which the absence or presence of a TATA box results in specific Pol recruitment, we examined how SNAPc and general transcription factors required for Pol II or Pol III transcription of SNAPc-dependent genes  assemble to ensure specific Pol recruitment. TFIIB and BRF2 could each, in a mutually exclusive fashion, be recruited to SNAPc. In contrast, TBP–TFIIB and TBP–BRF2 complexes were not recruited unless a TATA box was present, which allowed selective and efficient recruitment of the TBP–BRF2 complex. Thus, TBP both prevented BRF2 recruitment to Pol II promoters and enhanced BRF2 recruitment to Pol III promoters. On Pol II promoters, TBP recruitment was separate from TFIIB recruitment and enhanced by TFIIA. Our results provide a model for specific Pol recruitment at SNAPc-dependent promoters. Mammalian nuclear genomes are transcribed by three specialized RNA polymerases. RNA polymerase I (Pol I) generates the precursor of 28S, 18S, and 5.8S ribosomal RNA (rRNA); Pol II transcribes all protein-coding genes, long noncoding RNA genes, and most genes encoding small nuclear RNAs (snRNAs); and Pol III synthesizes short noncoding RNAs such as transfer RNAs (tRNAs), 5S rRNA, U6 snRNA, and other short untranslated RNAs. Each group of genes has particular promoter elements, gene body architecture, and termination signals.Pol II promoters can be divided into mRNA TATA-containing, mRNA TATA-less, and snRNA (TATA-less) promoters. Assembly of a preinitiation complex (PIC) on mRNA TATA-containing promoters, which involves binding of TATA-box-binding protein (TBP) to the TATA box stabilized by TFIIA and TFIIB, has been extensively studied in vitro , and thePol III promoters can be divided into three broad categories. Types 1 and 2 are gene-internal and recruit the assembly factors TFIIIA and TFIIIC in the first case or TFIIIC directly in the second case. In turn, TFIIIC binds the Pol III-recruiting factor BRF1–TFIIIB, which is composed of three subunits: TBP, the TFIIB-related factor BRF1, and BDP1 . Type 3 The Pol II snRNA promoters and the type 3 Pol III promoters thus have the particularity of sharing two of their promoter elements (the DSE and the PSE) as well as a number of transcription factors; i.e., DSE-binding factors, the PSE-binding factor SNAPc, and TBP. Although the PSEs of Pol II and Pol III transcribed genes differ slightly in their consensus sequences, they are interchangeable and do not contribute substantially to polymerase selection . On the Although the predominant role of the TATA box in determining polymerase recruitment specificity at SNAPc-dependent promoters is clearly established, the mechanism by which specific polymerase recruitment is actually achieved is not understood. In particular, why is Pol II recruited to the TATA box of mRNA promoters but not the TATA box of Pol III SNAPc-dependent promoters? How are TBP and TFIIB recruited to SNAPc-dependent promoters in the absence of a TATA box? In general, how are RNA polymerase-specific PICs assembled at SNAPc-dependent promoters? To address these questions, we used minimal sets of transcription factors capable of nucleating specific PICs on Pol II or Pol III SNAPc-dependent promoters and examined the specificity of various individual protein–protein interactions. Our results reveal mutually exclusive protein–protein associations and a key role for TBP in both favoring BRF2 recruitment to TATA-containing promoters and preventing BRF2 recruitment to TATA-less promoters. Our results provide a model explaining how RNA polymerase specificity is achieved at snRNA promoters.RPPH1 gene, which can be occupied by either Pol II or Pol III . We found peaks of TBP at a very similar position close to the TSSs of all promoters analyzed, and the average tag densities were quite similar in Pol II and Pol III genes. Thus, regardless of the composition of assembled PICs, TBP is present, and its position relative to the TSS is conserved.We exploited publicly available ChIP-seq (chromatin immunoprecipitation [ChIP] combined with high-throughput sequencing) data to examine the distribution of Pol II and Pol III transcription factors at SNAPc-dependent promoters as well as at tRNA promoters. We selected active SNAPc-dependent promoters based on TBP occupancy and then separated them into TATA-containing (Pol III) and TATA-less (Pol II). The  Pol III , was anaSupplemental Fig. 1). Thus, Pol II detected at Pol III TATA-containing SNAPc-dependent promoters probably corresponds to transiently misrecruited enzyme molecules rather than transcriptionally engaged ones.The TFIIB tag density profile resembled that of TBP but with a much higher tag density at Pol II genes as compared with Pol III genes. Nevertheless, we observed some TFIIB at TATA-containing Pol III SNAPc-dependent genes, whereas there was no detectable TFIIB at Pol III SNAPc-independent tRNA genes. Consistent with the TFIIB tag profiles, the Pol II tag profile was highest at Pol II snRNA genes, much lower but detectable at Pol III SNAPc-dependent genes, and undetectable at tRNA genes . This fiSupplemental Fig. 1; Supplemental Fig. 1; Supplemental Fig. 1).TFIIA, TAF1, and TAF7 were all present at Pol II SNAPc-dependent promoters but not at Pol III promoters, with the TAF1 and TAF7 peaks located downstream from the TSS, as observed in mRNA promoters  are consistent with the gene being occupied by Pol II and Pol III, with a prevalence of Pol III, as described before  and Pol II (SNORD113) promoters. Thus, both TFIIB and BRF2 enhance loading of SNAPc to a PSE, and, at least for BRF2, the effect is independent of the Pol II or III promoter origin of the PSE.Addition of TFIIB enhanced in a dose-dependent manner the footprinting of SNAPc to a PSE as measured by DNase I protection assay D on PSEsU6 snRNA promoter fragments with combinations of intact or mutated PSE and TATA box, (2) SNAPc as a nucleating transcription factor, and (3) both Flag-tagged TFIIB and BRF2, recruiters of Pol II and Pol III, respectively, and, in some cases, TBP. We then collected the DNA fragments on streptavidin beads and detected bound proteins by immunoblot.The efficient recruitment of TFIIB as well as that of BRF2 to a SNAPc–PSE complex indicates that these factors alone are not sufficient to trigger specific assembly of a Pol II or Pol III transcription initiation complex. On the other hand, the presence of a TATA box downstream from the PSE strongly favors the recruitment of Pol III over Pol II, whereas its absence favors the recruitment of Pol II . This isSNAPc was efficiently recruited by PSE-containing DNA probes, but not by probes lacking the PSE, regardless of the presence of TBP or a TATA box A, lane 3TFIIB was efficiently recruited to DNA probes containing a PSE with or without a TATA box, but, strikingly, this recruitment was strongly decreased in the presence of TBP  and withWe further explored TFIIB versus BRF2 recruitment to a SNAPc–PSE complex. When TFIIB and BRF2 were added simultaneously to a SNAPc–PSE complex, the amount of each bound protein was slightly less than when added separately, suggesting that TFIIB and BRF2 compete for the same SNAPc interface B. In thiWe observed above that TBP prevented efficient binding of TFIIB to a SNAPc–PSE complex A. We furThe idea that a TFIIB–TBP complex cannot bind to SNAPc implies mutually exclusive contacts between TFIIB and TBP and between TFIIB and SNAPc. We used the known structure of the TBP–TFIIB complex bound to a TATA box as found at mRNA promoters (Protein Data Bank [PDB] ID 1C9B)  to desigSupplemental Fig. 2). Upon addition of TBP, the binding of both TFIIB and the TFIIB core domain to a SNAPc–PSE complex was reduced .The CCD region of the TFIIB N-terminal domain see A interac reduced . In lineSupplemental Fig. 3A,B). This indicates that the core domain of TBP can associate directly with SNAPc. We mutated residues to alanines in two separate regions of the TBP core domain—an α-helix surface (region 1) and a conserved loop (region 2) that are directly involved in interactions with TFIIB—and tested the resulting proteins for binding to GST-TFIIB or SNAPc immobilized on beads , and this cooperative binding requires the N-terminal domain of TBP . Howeveron beads A. The muon beads B. In addon beads C. Thus, Supplemental Fig. 3C), and increased TFIIA binding and concomitant decreased BRF2 binding were observed upon addition of increasing amounts of TFIIA, consistent with BRF2 and TFIIA binding to TBP being mutually exclusive . A similar cooperative binding could be observed in an EMSA where, on a probe with a PSE (and lacking a TATA box), addition of either TBP or TFIIA alone to a SNAPc–PSE complex did not cause a visible supershift, whereas addition of both proteins together did  and performed ChIP-qPCR (ChIP combined with quantitative PCR) analysis with an anti-Flag antibody. We observed decreased occupancy of the DE → A mutant relative to wild-type BRF2 at the U6 promoter and slightly increased occupancy at the U2 gene  and its derivatives were obtained by PCR with biotinylated primer.The biotinylated fragment of the Flp-In T-REx 293 cells were cultured in DMEM containing 10% tetracycline-free fetal calf serum (Bioconcept) and penicillin/streptomycin. Cells were transfected with JetPeI (Polyplus) and selected with 150 mg/mL hygromycin.The antibodies used were as follows: α-Flag (Sigma), α-SNAP43 (cs46) , α-SNAP5Escherichia coli for 4 h at 27°C with 0.4 mM IPTG . Cells were grown at 37°C in M9ZB medium until the optical density reached 0.6 (A600). The temperature was then reduced, and IPTG was added. Cells were harvested by centrifugation at 3000g for 20 min at 4°C, and the pellets were stored at −80°C. Cells were lysed in buffer A1  through sonication and clarified by centrifugation at 14,000g for 30 min at 4°C. The extracts were incubated with Ni-NTA Superflow resin for 3 h at 4°C with constant stirring. The beads were washed three times with five bead volumes of buffer W1 , and the bound proteins were eluted with buffer containing 300 mM imidazole. Proteins were dialyzed against buffer B1 .Full-length human TBP, BRF2, TFIIB, and BDP1(1–470) double-tagged with Flag and His tags were cloned in a pSB vector and expressed in the BL21 (DE3) strain of E. coli for 4 h at 27°C, and cells were harvested, resuspended in buffer A2 , and lysed through sonication. Clarified bacterial extract was incubated with glutathione beads (Sigma) for 3 h at 4°C. GST-tagged proteins were either used for GST pull-down assays or subjected to thrombin cleavage and used for in vitro transcription, EMSA, or binding assays.GST-tagged full-length human TBP was cloned in pET11, and GST-TFIIB was cloned in pGEX4T1. Proteins were expressed in BL21(DE3) Supplemental Table 1 for primer sequences) and expressed as three independent subunits. TFIIAα had a N-terminal Flag tag and a C-terminal His tag, whereas TFIIAβ and TFIIAγ carried only a C-terminal His tag. The three TFIIA subunits were expressed in E. coli for 4 h at 27°C, and the cells were induced with 0.4 mM IPTG. Bacterial pellets were dissolved in buffer A3 . The TFIIA subunits were purified over Ni-NTA columns, quantified, and mixed in equimolar amounts. Native TFIIA was reconstituted by stepwise dialysis .TFIIA was cloned , 10% glycerol, and 5 mM β-mercaptoethanol (buffer A0). Cells were lysed through sonication, and the supernatant was clarified by centrifugation at 14,000g for 1 h at 4°C. The supernatant was loaded on a HisTrap HP column . The column was then washed with buffer A1 (buffer A0 supplemented with 50 mM imidazole). The proteins were eluted with buffer A0 supplemented with 300 mM imidazole. The elution was then diluted with 1 vol of a buffer containing 50 mM HEPES (pH 7.9), 10% glycerol, and 1 mM DTT before application on a heparin HP column . The complex was then eluted with a salt gradient from 250 mM to 1.25 M NaCl. Fractions of interest were pooled, and the His and Strep tags were cleaved overnight at 4°C by treatment with TEV protease and dephosphorylated with λ phosphatase. Imidazole (30 mM) was added to the protein before applying to a HisTrap HP column . The flowthrough was concentrated before application on a Superdex 200 16/600 equilibrated with a buffer containing 100 mM NaCl, 50 mM HEPES (pH 7.9), 10% glycerol, and 1 mM TCEP.SNAPc and SNAPcmini (SNAP190 residues 1–516 and lacking SNAP45) were cloned in pACEBAC1 with a N-terminal Strep tag on SNAP190 and a N-terminal His tag on SNAP50 and expressed in Hi5 cells. Briefly, cells were harvested by centrifugation at 250U6 promoter or promoters with mutated PSE, mutated TATA box, or both elements mutated were incubated with various proteins as indicated in the figures in buffer B1  for 30 min at room temperature, the protein–DNA complexes were then captured on Dynabeads M-280 (Invitrogen) for 30 min at 4°C, and the bead-bound complexes were washed extensively. The proteins attached to beads were then analyzed by Western blot.Biotinylated DNA fragments corresponding to the 32P] ATP containing the mouse U6 PSE was used. The binding reactions were incubated for 20 min at room temperature and contained radiolabeled DNA probe, the proteins indicated in 2, 0.1 mM EDTA, 20 µg of calf serum, 0.8 µg each of pUC118 and poly (dI–dC), 2% polyvinyl alcohol, 10% glycerol, and 1.5 mM DTT. The DNase I digestion was carried out as described , 200 mM ammonium acetate, 5 mM MgCl2, 0.025 mM EDTA, 1 mM DTT, and protease inhibitors tablets (Roche).In vitro transcription assays were performed as described  with minSupplemental Table 2. Means ± SD of immunoprecipitation/input enrichment of three independent experiments were plotted. Hypothesis testing was performed with two-tailed t-test.ChIPs were performed as described in http://ccg.vital-it.ch/ssa/findm.php) with a P-value threshold set at 0.001. The genes in each group were further filtered by TBP occupancy at the TSS to select the active ones, a remapping of the TBP Chip-exo (ChIP using λ exonuclease to digest transcription factor-unbound DNA after ChIP) data from Supplemental Table 3. The RPPH1 gene was analyzed separately.Genes encoding small noncoding RNAs  were split into two groups depending on the presence or absence of a TATA box 25–35 bp upstream of the TSS. The search for the TATA-box motif was performed with the FindM tool ; and incubated for 1 h at 4°C. The resulting protein complexes were washed four times, and their composition was analyzed by Western blot.Protein (0.5–3 µg) was immobilized on either glutathion (Sigma), Strep-Tactin (Qiagen), or Flag (Sigma) beads; incubated with protein analyte in buffer B2 (20 mM HEPES at pH 7.9, 150 mM NaCl, 5 mM MgCl


The DNA damage sensor XPC is involved in nucleotide excision repair. Here we show that in the absence of damage, XPC co-localizes with RNA polymerase II (Pol II) and active post-translational histone modifications marks on a subset of class II promoters in human fibroblasts. XPC depletion triggers specific gene down-expression due to a drop in the deposition of histone H3K9 acetylation mark and pre-initiation complex formation. XPC interacts with the histone acetyltransferase KAT2A and specifically triggers the recruitment of the KAT2A-containing ATAC complex to the promoters of down-expressed genes. We show that a strong E2F1 signature characterizes the XPC/KAT2A-bound promoters and that XPC interacts with E2F1 and promotes its binding to its DNA element. Our data reveal that the DNA repair factor XPC is also an RNA polymerase II cofactor recruiting the ATAC coactivator complex to promoters by interacting with the DNA binding transcription factor E2F1. XPC plays an important role in the nuclear exicision repair pathways. Here the authors show that in addition, XPC plays a role in transcription regulation by interacting with KAT2A and E2F1 and recruiting the ATAC coactivator complex to promoters. This was followed by the characterization of the basal transcription TFIIH as a NER factor involved both in TCR and in global genome repair (GGR), eliminating DNA damage from the entire genome3. This interplay is even tighter, since studies also revealed roles for the other NER factors  in gene expression6.Gene expression is constantly compromised by genotoxic stress that challenges genome integrity and requires the function of several DNA repair pathways to remove DNA lesions. This implies that there must be connections between the disparate events of transcription and DNA repair to orchestrate the expression and repair of genes. A link between DNA repair and transcription was first established after the discovery of a nucleotide excision repair (NER) sub-pathway removing DNA lesions located on the actively transcribed strand blocking elongating RNA polymerase II (Pol II) called the transcription coupled repair (TCR)XPC only develop XP (XP-C) and represent the most frequent NER-defective group. XP is clinically characterized by an extreme sensitivity to ultraviolet (UV) rays from sunlight. XP patients develop severe sunburns and are highly susceptible to develop tumors on sunlight-exposed areas of the skin, including melanoma and squamous cell carcinoma7. XP individuals also present increased susceptibility for lung, breast, and colorectal cancers with possible neurological issues8. The XP pathology has been primarily defined as a DNA repair syndrome due to the inability of patients’ cells to eliminate DNA lesions. However, studies during the last decade suggest that some of their phenotypes may also stem from transcriptional deregulations9.Understanding the roles played by NER factors is of prime importance not only to unveil the molecular details of gene expression but also to understand how mutations in their corresponding genes give rise to several human autosomal recessive disorders like Xeroderma pigmentosum (XP), Cockayne syndrome (CS), and trichothiodystrophy (TTD). Patients bearing mutations in 10. Several observations suggest that XPC is additionally involved in the modifications of the chromatin environment surrounding the DNA lesions, including histone post-translational modifications (PTMs)13. We have shown earlier that NER factors are associated with the Pol II transcription machinery and are sequentially recruited at the promoter of transcribed genes6. The presence of these NER factors at promoter is required to achieve optimal chromatin remodeling, including histone PTMs as well as active DNA demethylation, DNA break induction, and gene looping14. Furthermore, a complex containing XPC and Oct4/Sox2 has been identified as a coactivator in embryonic stem (ES) and induced pluripotent stem cells16. Although the involvement of XPC in transcription is established, its mechanistic role remains largely elusive as well as its transcriptional partners in the pre-initiation complex.Upon NER, XPC, with its partner hHR23B, recognizes all along the genome DNA-distorting lesions inflicted by endogenous and exogenous genotoxic attacks like UV irradiation, thereby initiating only the GGR sub-pathwayIn the present study, we investigated the roles of XPC in class II gene expression. We first assessed the genome-wide localization of XPC and revealed that in the absence of a genomic stress, XPC is mainly recruited to the promoters of active genes where it co-localizes with Pol II. Depletion of XPC leads to deregulation of Pol II recruitment and altered histone marks at promoters, including H3K9ac. We further identified an interaction between XPC and the histone acetyltransferase (HAT) lysine acetyltransferase 2A KAT2A (or GCN5). Our data indicated that XPC, through its interaction with KAT2A, could be associated with both ATAC and SAGA complexes but that only ATAC is detected at the promoters of XPC-dependent genes. GREAT analysis unveiled that a strong E2F1 signature characterizes genes regulated by XPC/KAT2A. We showed that E2F1 interacts with XPC and is required for its localization at the promoter of activated genes. We subsequently identified a complex, including XPC, E2F1, and KAT2A that is specifically recruited to XPC-dependent promoters, conditioning the presence of the ATAC complex and the deposition of the H3K9ac mark. Altogether, our results establish XPC as a cofactor of Pol II, interacting with a transcription factor and recruiting a coactivator complex to remodel chromatin and initiate class II gene expression.DEL cell line  stimulus, which induces a well-characterized modification of the cellular transcriptional program14, we sought to identify the XPC genomic distribution and its impact on gene expression in a well-defined system. We first analyzed the RA response in the three cell lines by studying the transactivation of RARß2 upon ATRA treatment. We observed a correlation at 6 h post ATRA treatment between RARß2 mRNA induction and recruitment of the transcriptional machinery , together with the NER factors  at the RARß2 promoter in both XP-CWT and XP-CMUT cells  and Pol II. Three independent series using XP-CWT cells yielded 2191 and 16, 440 peaks for GFP-tagged XPC and Pol II, respectively. We identified 1797 common binding events representing 82% of the XPC peaks  were localizing with H3K9ac and 1385 (78%) with both H3K9ac and H3K4me3 , XPA (shXPA), or scrambled (shCtrl) overtime treated with ATRA  could be responsible for the histone marks deposition at the TSS of XPC-regulated genes. Given the role of KAT2A in the deposition of H3K9ac in transcription initiationWT and XP-CDEL cells treated 6 h with ATRA, using antibodies directed against KAT2A, and obtained 19, 758 genomic locations for KAT2A compared with input. Using HOMER annotation, we observed almost 50% of these KAT2A-binding events appearing at promoter regions   co-precipitated the complex containing either XPC or KAT2A  and ATAC (Ada Two A Containing)lls Fig. . We notiCCND1 and DAPK1. As expected, neither ZZZ3 (ATAC subunit) nor TRRAP (SAGA subunit) was detected at the promoter of the XPC-negatively regulated CCND1 gene in XP-CDEL, XP-CMUT, or XP-CWT cells in which KAT2A was not recruited  although our preliminary data suggested the presence of XPC on other DNA regulatory regions such as enhancers, a phenomenon previously shown for pluripotency regulating genes in ES cellsThe consequences of XPC depletion measured in fibroblasts derived from XP-C patients allowed us to identify, at the genomic scale, its importance for the enrichment of Pol II at TSSs and its specific involvement in the proper distribution of histone PTMs, including H3K9ac and H3K4me3 marks. Importantly, the analysis of shXPA cells indicated that the enrichment of the histone PTMs upon transcription was specifically related to the presence of XPC.23.Using ex vivo and in vitro approaches, we demonstrated that XPC interacted with KAT2A and mediated its recruitment and the related H3K9ac deposition at promoter of XPC-positively regulated genes. The co-recruitment of XPC and E2F1 to promoter leads to the recruitment of KAT2A that is present in the SAGA and ATAC coactivator complexes. SAGA and ATAC also contain the close paralog of KAT2A, KAT2B, but only the KAT2A version of these complexes contains XPC. Furthermore, we observed that only ATAC was found to co-localize with XPC upon ATRA treatment so that the interaction of XPC with SAGA and its molecular role remains elusive and requires further investigation. We hypothesize that the SAGA-XPC assembly may target promoters regulated by other specific transcription factors and/or by different signaling cascades since ATAC and SAGA are recruited to different stress-induced genes promotersMUT cells that harbor a punctual mutation (XPC p.Pro334His) affecting XPC NER function without impact on RAR-dependent gene activation. In agreement with this observation, we didn’t observe impact of XPC p.Pro334His mutation on XPC/KAT2A interaction.Experiments performed in cells depleted in XPA show that XPC was still recruited to the pre-initiation complex (PIC) while depletion of XPC impaired the recruitment of XPA, indicating that XPC was the first NER factor to be recruited to the PIC. We next sought to identify the signals that triggered the recruitment of XPC to a subset of genes and identified the consensus binding site of E2F1 at 89% of XPC-activated genes. Using ChIP, we demonstrated that E2F1 and XPC mutually synergized their binding to promoters and consequently contribute both to the recruitment of KAT2A through direct interactions. XPC is then functioning as a bridge between E2F1 and ATAC, increasing the recruitment of the HAT to XPC-activated genes. As thus, XPC could be considered as a cofactor of mRNA expression in mammalian cells, linking a transcription factor (E2F1) to a coactivator (ATAC). This model also implies that all the mutations affecting XP-C patients are not affecting its transcription function, which is what we observed in XP-C27. Interestingly, among the genes positively regulated by XPC, SET1 encoding one methyl transferase depositing H3K4me3 mark has been identified. Concomitantly, a reduced expression of SET1 and a reduced recruitment of the protein at genes activated by XPC were observed in XP-CDEL cells, suggesting an indirect effect of XPC on the H3K4me3 mark  that were shown to interact with XPF28. Also, multi-omic analyses aiming to identify factors and pathways implicated in the cellular response to UV-induced DNA damage have recently shown connections with the immune system29. In this study, the analysis of several IFN mRNA expression in XP-CWT and XP-CDEL cells in absence of ATRA treatment indicated higher expressions of IFN beta, gamma, and alpha in cells XPC-depleted that were unrelated to the RAR-dependent pathway  and GM02096 (XP-CMUT), and rescued XP-C (XP-CWT)19 (Coriell Institute) were used and cultured in appropriate medium. Twelve hours before ligand treatment, cells were incubated with phenol red-free medium containing charcoal-treated fetal calf serum and 40 mg/ml gentamycin. Cells were treated with 10 μM ATRA (MP).HeLa Silencix cells (Tebu-Bio), including shCtrl, shXPC, and shXPA cells, MRC-5 fibroblasts (ATCC # CCL-171), XP-C patient-derived fibroblasts GM14867  and reverse transcribed with SuperScript IV reverse transcriptase (Invitrogen). The quantitative PCR (qPCR) was done using the Lightcycler 480 (Roche). The primer sequences for the different genes used in qPCR are indicated in Supplementary Data 6. Samples were immunoprecipitated with antibodies at 4 °C overnight, and protein G-coated Dynabeads (Invitrogen) were added, incubated for 4 h at 4 °C, and sequentially washed. Protein-DNA complexes were eluted and decrosslinked. DNA fragments were purified using QIAquick PCR purification kit (QIAGEN) and analyzed by qPCR using a set of primers indicated in Supplementary Data Cells were crosslinked at room temperature for 10 min with 1% formaldehyde. Chromatin was prepared and sonicated at 4 °C for 30 min using sonicator Q800R (Qsonica) as previously describedWT and XP-CDEL cells were extracted before or 6 h after ATRA treatment (10 μM) using TRI REAGENT (MRC) and purified by phenol-chloroform extraction. Libraries were prepared with TruSeq Stranded mRNA Sample Preparation kit following guide instruction and subsequently proceed on an Illumina Hiseq 4000 as single-end 50 base reads following Illumina’s instructions. Image analysis and base calling were performed using RTA 2.7.3 and bcl2fastq 2.17.1.14. Reads were mapped onto the hg19 assembly of the human genome. Reads count was performed with HOMER v4.8.3(65) and expression was estimated with EdgeR. Genome ontology was performed with The Database for Annotation and Integrated Discovery v6.7 (https://david.ncifcrf.gov).Total RNA from XP-Chttp://liulab.dfci.harvard.edu/MACS/) under settings where the input fraction was used as negative control. Peaks detected were annotated using HOMER (http://biowhat.ucsd.edu/homer/ngs/annotation.html) as well as TSS protein enrichment comparison. Quantitative comparison of Pol II gene body enrichment was performed using seqMINER (http://bips.u-strasbg.fr/seqminer/). As reference coordinates, we used the MACS-determined peaks or the annotated TSS/TTS of human genes as defined by RefSeq database. Sequence enrichment was performed using RSAT (http://rsat.sb-roscoff.fr) with MACS-determined peaks as reference.Purified DNA fragments analyzed by ChIP-seq were prepared by using the ChIP-IT High Sensitivity Kit (Active Motif) and the related antibodies. ChIP-seq was performed on an Illumina Hiseq 2500 as single-end 50 base reads following Illumina’s instructions. Image analysis and base calling were performed using RTA 1.17.20 and CASAVA 1.8.2. Reads were mapped onto the hg19 assembly of the human genome. Peak detection was performed using MACS  and later cloned in bicistronic plasmid VEAP5317 with hHR23B kindly obtained from A. Poterszman. For recombinant Flag-GCN5 expression in Sf9 cells, the corresponding vector pSK227-KAT2A was kindly provided by L. ToraWT cells at a final concentration of 100 nM using X-tremeGENE siRNA transfection reagent (Sigma-Aldrich) following the manufacturer’s protocol.ON-TARGET plus smart pool siRNA control or targeting human KAT2A and E2F1 were purchased from Thermo Scientific and transfected in XP-CWT and XP-CDEL cells were prepared as previously described36. After GFP-trap or KAT2A, ZZZ3, or TRRAP immunoprecipitation using the appropriate antibodies conjugated to protein G-coated Dynabeads (Invitrogen), followed by extensive washes, was carried out and the different co-precipitated proteins were detected using specific antibodies after immunoblotting.For in vivo co-IPs, nuclear extracts from untreated XP-CFor in vitro co-IPs, recombinant purified Flag-KAT2A was then incubated with recombinant purified Strep-XPC/His-hHR23B and/or c-myc E2F1 before Flag immunoprecipitation was carried out. After washes, bound proteins were resolved by SDS–polyacrylamide gel electrophoresis and detected by western blot.Antibodies toward RBP1 (RNA Pol II) (7C2), RAR (9A6), TBP (3G3), XPA (1E11), KAT2A (5GC-2A6), XPG (1B5), ZZZ3 (2616), XPC (2076), and TRRAP (1B3) were produced at the IGBMC. CDK7 (C-19), E2F1 (C-20), and XPC (D-18) antibodies were purchased by Santa-Cruz Biotechnology. H3k4me3 (ab1012), KAT2B (ab12188), and α-tubulin (ab15246) antibodies were purchased by Abcam and WDR5 (07–706) antibody from Upstate. Antibodies directed toward KAT2A (A4013) were obtained from Epigentek while those against Spt7L (A302–803A) and hSET1 (A300–803A) from Bethyl. Antibodies against H3K9ac (61251) and H3 (61475) were from Active Motif. Antibodies toward GFP (TP401) and M2-Flag (F1804) were purchased by Torrey Pines Biolabs and Sigma-Aldrich, respectively. For immunoblot, the concerned antibodies were diluted at 1–1000 while 5–10 μg of antibodies were used for ChIP experiments.https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP148864.All relevant data are available from the authors upon request. The accession numbers for the sequencing data reported in this paper are Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5