Drosophila provides an elegant and genetically tractable system to investigate these conserved changes at a dazzling developmental pace. Here, we review recent studies applying emerging technologies such as ribosome profiling, in situ Hi-C chromatin probing and live embryo RNA imaging to investigate the transcriptional dynamics at play during Drosophila embryogenesis. In light of this new literature, we revisit the main models of zygotic genome activation (ZGA). We also review the contributions played by zygotic transcription in shaping embryogenesis and explore emerging concepts of processes such as transcriptional bursting and transcriptional memory.Early development is punctuated by a series of pervasive and fast paced transitions. These events reshape a differentiated oocyte into a totipotent embryo and allow it to gradually mount a genetic program of its own, thereby framing a new organism. Specifically, developmental transitions that ensure the maternal to embryonic control of developmental events entail a deep remodeling of transcriptional and transcriptomic landscapes.  Early development unfolds through exquisitely coordinated and deeply conserved biological transitions. Fertilization entails the remodeling of a differentiated oocyte into a totipotent embryo, which involves profound genomic, transcriptomic and proteomic re-organization . Early eDrosophila and many arthropods display a facultative parthenogenetic mode of reproduction, meaning that egg activation can take place independently of sperm entry, although this rarely occurs. The egg-to-embryo transition is triggered by changes in pressure and osmotic balance as the mature oocyte exits through the uterus [Drosophila, the first rounds of nuclear divisions arise every 9 min, leading to the formation of 6000 nuclei in only 2 h [e uterus ,11. Egg only 2 h ,13  and more recently by Langley and colleagues (2014). This exciting area of developmental biology has further expanded over the last few years, along with our understanding of the complex cross-talks resulting in the interrelated emergence of ZGA, checkpoint activation and cellularization. Indeed, the deployment of disruptive technologies to track translation, probe chromosome conformation and image single RNA molecules in Drosophila development is driven by maternal proteins and RNA transcripts synthesized by multiploid nurse cells and deposited in the egg during oogenesis [Drosophila mRNAs can be detected during the first syncytial cycles [bicoid, oskar and gurken, defines anteroposterior and dorsoventral axes, which will later determine the body plan of the developing fly [fluorescent in situ hybridization (FISH) surveys have demonstrated that subellular localization is a pervasive feature of the Drosophila transcriptome. Indeed, detailed profiling of RNA expression/localization features in fly oocytes and embryos, as tabulated in the Fly-FISH (http://fly-fish.ccbr.utoronto.ca/) and Dresden Ovary Table/DOT (http://tomancak-srv1.mpi-cbg.de/DOT/main.html) database repositories, revealed that the vast majority of mRNAs and long non-coding RNAs are localized in a striking array of distribution patterns [Early ogenesis ,22. Longogenesis . These fl cycles ,25,26. Aping fly ,28,29. Apatterns ,30. Whilcis-acting motifs, generally found in the 3′UTR of the target transcripts, and RBPs that adopt tightly regulated spatial and temporal distributions. Indeed, mRNAs encoding the RBP Smaug (SMG) form an anterior-to-posterior gradient in the oocyte and are translationally regulated at egg activation [The transcriptomic landscape is highly dynamic during the cleavage cycles, as several RNA degradation pathways operate to selectively remove large sets of deposited transcripts. Indeed, a study using chromosomal deletions to track the post-transcriptional dynamics of maternal and zygotic transcripts has shown that approximately 35% of maternally-deposited mRNAs are cleared out by the MBT . Comparitivation . SMG acttivation ,33,34. Itivation .Xenopus and mouse has long suggested that some of the zygotic actors involved in maternal clearance are microRNAs (miRNAs) [smg mutants. In addition, Argonaute-1, a key component of the miRNA-induced silencing complex, showed decreased levels in smg mutants. Furthermore, the clearance of predicted maternal targets of SMG-dependent miRNAs is hampered in smg mutants, suggesting that these small RNAs contribute to the zygotic component of maternal mRNA degradation [nanos (nos), starts declining linearly after egg activation. By contrast, targets of zygotic degradation effectors such as bicoid (bcd) are untouched during the first cycles and their removal begins after ZGA. Finally, transcripts targeted through both maternal and zygotic pathways include Hsp83 and adopt highly specific degradation kinetics that reflect the contributions and levels of both maternal and zygotic factors.Strong evidence in zebrafish, (miRNAs) . Small-i(miRNAs) ,38. In s(miRNAs) . Among tradation ,40. Becacyclin B (cycB), which encodes a key regulator of the embryonic cell cycle [The execution of maternal clearance is tightly coupled to translational regulation of RBPs that mediate mRNA degradation. Indeed, the levels of SMG depend on the PAN GU (PNG) Ser/Thr kinase complex, a key translational regulator activated at the oocyte-to-embryo transition. The PNG complex is involved in reprogramming the proteome as the egg becomes an embryo by regulating the translation of hundreds of maternal mRNAs . Its notll cycle ,42,43. Tll cycle . In matupng mutant mothers revealed compromised translational efficiencies in nearly 1000 mRNAs [In another study, ribosome profiling performed on mature oocytes and activated eggs derived from 00 mRNAs . Surpris00 mRNAs .cis-acting motifs by maternal RBPs that are tightly regulated in time and space. As zygotic transcription emerges, a second phase of maternal clearance unfolds through the activity of zygotically expressed determinants .Overall, spatiotemporal regulation enacted through mRNA localization and translation control are key features of embryonic transitions. In the absence of large-scale zygotic transcription, maternally deposited mRNAs and their regulated translation drive the developmental program of early embryogenesis. These maternal transcripts are then selectively removed, a process relying on the recognition of Xenopus embryos showed that early transcription is possible prior to ZGA [engrailed (en), has recently been reported in preblastoderm embryos, with key implications in establishing the synchrony of early mitotic cycles [As maternal clearance takes place during the cleavage cycles, the transcriptomic landscape is remodeled and new populations of transcripts arise upon activation of the zygotic genome. The mechanisms behind ZGA onset are poorly understood and remain an outstanding question in developmental biology, although several models have been proposed  Figure . Seminalr to ZGA ,47. Howec cycles . Regardlc cycles . In linec cycles ,49,50,51c cycles .Drosophila. Interestingly, the authors identified a small subset of genes regulated in a N/C-dependent manner, suggesting that both models prevail, albeit at distinct loci and frequencies [One long-standing model posits that ZGA is triggered once a critical value of nucleocytoplasmic (N/C) ratio is breached in the syncytium ,15,52,53quencies . NeverthHence, diverse mechanisms have been proposed to account for ZGA onset, each of which is supported by empirical evidence . How theDrosophila, called dBigH1, and demonstrate its involvement in ZGA regulation [00BigH1 mutants exhibit high embryonic lethality and a range of developmental defects, including altered nuclear distributions and highly asynchronous divisions. Interestingly, ChIP showed that Pol II is recruited to chromatin earlier in 00BigH1 mutants than in wt embryos. In addition, zygotic mRNAs were more abundant in 00BigH1 mutants than in wt embryos 2h after fertilization. Together, these results show that BigH1, a novel Drosophila variant of the linker histone, regulates ZGA onset and is removed from chromatin prior to the MBT.Histone proteins are fundamental components of chromatin and developmentally-regulated changes in their expression could have profound impacts on genome activation. Indeed, most metazoans express tissue-specific variants of the linker histone H1 and the somatic H1 is often replaced by a developmental variant during early embryogenesis ,56. In agulation . UbiquitThe histone code refers to a set of posttranslational modifications that modulate chromatin compaction and the accessibility of DNA elements . ChangesDrosophila, like Nanog, Pouf5f3 and SoxB1 in vertebrates [-zld germline clones. Overall, Li et al. show that histone marks are depleted during the first cleavage cycles and emerge between NC8 and NC14. Importantly, ZLD is a pioneer TF of the MZT: it can bind its genomic sites in condensed chromatin at NC8 and promote the recruitment of other factors that carry out profound chromatin remodeling at the MBT [The factors that act between NC8 and NC14 to deplete enhancer-associated nucleosomes represent major instigators of ZGA. Transcription factors (TFs) that can recognize their binding sites in a closed chromatin context to promote chromatin remodeling are termed “pioneer TFs”. Several studies have identified such pioneer activity for the Zn-finger transcription factor Zelda/Vielfaltig (ZLD/VFL), a master regulator of early zygotic gene expression ,62. Indetebrates ,66. Inde the MBT ,68.Drosophila embryos, enabling investigations into the developmental implications of genome architecture [Hug et al. performed Hi-C at time-points surrounding the ZGA to determine when chromatin architecture is established during development and how its emergence relates to the onset of zygotic transcription [In addition to histone modifications, the three-dimensional folding of chromosomes can bring distant genomic loci in close physical proximity, with profound impacts on gene expression . Topologitecture ,73,74,75cription . NC8 embBsg25/Elba3 locus, which is switched off before gastrulation, showed that loss of Pol II occupancy at NC14 coincides with the loss of its boundary-like structure [-zld embryos, which revealed a loss of insulation of TAD-boundaries at strong ZLD sites, especially at boundaries established in early cycles. Collectively, Hug et al. provide strong evidence that the establishment of long-range interactions broadly coincide with ZGA. Although transcription is not required for the emergence of chromatin conformation, loci transcribed early act as nucleation sites and contribute to the establishment of TAD boundaries. Similarly, ZLD binding contribute significantly to TAD boundary insulation, consistent with ZLD roles as global activator of the zygotic genome.ChIP-seq revealed a strong dose-dependent correlation between Pol II occupancy and TAD boundary-like regions, especially at housekeeping genes and across developmental stages. Analysis of the early zygotic tructure . TogetheIn brief, ZGA coincides with a profound reorganization of chromatin. Prior to NC8, chromatin exhibits a simple and disorganized state, with few histone modifications, NFRs or TADs. The germline-specific histone variant dBigH1 is embedded in chromatin, possibly contributing to its transcriptional silencing. Through pioneer TF activity, factors such as ZLD disrupt the nucleosome barrier between NC8 and NC12 to expose zygotic enhancers and promote the transcription of their target genes. Concomitantly, the activating histone marks H4K8ac, H3K18ac and H3K27ac appear and TADs emerge. By the MBT, dBigH1 has been replaced by histone H1, long-range interactions have gained complexity and stability and the histone marks H3K4me1, H3K4me3, H3K27me3 and H3K36me3 are established.After its emergence, zygotic transcription becomes a major driver of embryonic development. Its contribution is twofold: zygotic products directly enact important functions, notably transcription factors that reshape the developmental program and miRNAs that contribute to maternal clearance. In addition, the process of transcription itself seems to mediate changes in the biology of the embryo. Indeed, active transcription can expose ssDNA and may cause replication stalling when facing a replication fork. These processes have recently been linked to the activation of the DNA replication checkpoint before the MBT. Indeed, studies taking advantage of mutants with impaired ZGA have revealed that transcription contributes to maternal clearance and determines the onset of cellularization and replication checkpoint activation.RNPII215 gene, which encodes the large subunit of Pol II [zld double mutants all undergo 13 syncytial NCs, like wt embryos. Since ZLD loss-of-function rescues the premature transcription phenotype of X161 mutants, a normal number of syncytial cycles in X161 zld double mutants suggests that early transcription causes the precocious cellularization phenotype of X161 single mutants. In addition, Sung et al. used the X161 model to test the contribution of the nucleocytoplasmic ratio on cellularization. Haploid X161 embryos, which present a lowered N/C ratio, underwent only 12 NC, the same number as diploid X161 mutants. This result suggests that the N/C ratio acts independently of ZGA and does not directly regulate the onset of cellularization in Drosophila. Together, these experiments provide strong evidence that the onset of zygotic transcription times key events of early embryonic development.Sung et al. characterized a fly model exhibiting a point mutation in the 3′ untranslated region (3′UTR) of the f Pol II . These mstring, twine and smaug in X161 mutants to monitor the impact of ZGA onset on maternal clearance. They found that the degradation of these maternal transcripts, which starts during the 14th interphase in wt embryos, is already well advanced by NC13 in X161 embryos, in agreement with reports of a zygotic contribution to maternal clearance. The authors also found that premature ZGA leads to a precocious requirement for a functional replication checkpoint. Checkpoint activation pauses M phase entry until the completion of DNA replication to safeguard genome integrity. Its emergence is a gradual process completed at the MBT. The Ser/Thr kinase Chk1, encoded by the Drosophila gene grapes (grp), is a key component of the DNA damage response (DDR), signal transduction cascades that sense DNA lesions to halt mitotic entry [grp double mutants display nuclear envelope and chromatin condensation defects by the 13th interphase, one cycle earlier than grp single mutants, supporting a role of transcription in the onset of the DNA replication checkpoint activation.As discussed earlier, maternal clearance is a complex process relying on factors contributed maternally and on the expression of zygotic products. Sung et al. surveyed the levels of three canonical targets of maternal clearance, ic entry ,79. Chk1Interestingly, the nuclear retention of zygotic transcripts has been identified as a new facet of the DDR during early embryogenesis. Indeed, Iampietro et al. showed that syncytial-stage embryos challenged with genotoxic stress undergo extensive nuclear fallout at the MBT, a mechanism of programmed elimination . The autBlythe and Wieschaus provided further evidence of the interplay between zygotic transcription and replication checkpoint activation. These authors found that checkpoint activation correlates with the amount of DNA engaged by Pol II, independently of the N/C ratio [grp mutants, with widespread genomic occupancy at NC12, NC13 and NC14. This result suggests that the transcriptional machinery is in place independently of the functionality of the replication checkpoint. To investigate the links between checkpoint activation and ZGA at the molecular level, the authors took advantage of RPA70, an important effector of the DDR. RPA70 binds stress-induced ssDNA produced when replication is stalled, leading to ATR (mei-41) recruitment and checkpoint activation. Assessment of RPA70 occupancy through fluorescent microscopy and ChIP-seq revealed a strong correlation with Pol II binding sites, consistent with the hypothesis that Pol II engagement activates the checkpoint at the MBT. In -zld mutants, ChIP-seq analyses revealed altered RPA70 occupancy at zld-dependent promoters. This result suggests that transcription contributes to checkpoint activation./C ratio ,81. Throzld mei-41 (ATR) mutants complete cleavage cycles and that many escape the mitotic catastrophe that characterizes mei-41 mutants. In addition to the -zld model, they used a heterozygous deficiency in the transcriptional activator Trithorax-like/GAGA (Trl), associated to defects in the genomic recruitment of poised Pol II, to interfere with ZGA. They found that mei-41; Df(3L)ED4545/+ (Df(trl)/+) embryos complete cleavage cycles without a mitotic catastrophe after a slightly lengthened NC13 and eventually yield hatching larvae. In addition, heterozygosity of the cyclin B gene (Df(cycB)/+), which lengthens NC13 time, effectively suppressed the mitotic catastrophe of mei-41 mutants. Together, these rescue experiments show that reducing the source of replication stalling by interfering with transcription (in zld and Trl mutants) and providing more time to allow DNA replication (in cycB mutants) can bypass the MBT requirement for a functional replication checkpoint. In conjunction with evidence of RPA70 colocalization with the transcriptional machinery, these results strongly suggest that the replication checkpoint is activated in response to ZGA.To test this hypothesis, the authors attempted to rescue the lethality phenotype associated to mutations in the DDR factor ATR by altering ZGA through different approaches. They showed that most embryos from double In brief, recent studies have used loss-of-function analyses to reveal the contributions of ZGA to key facets of embryonic development. Mutants exhibiting a premature ZGA undergo early cellularization, promptly enact maternal clearance and acquire a precocious requirement for effectors of the DDR. These effects are independent of the N/C ratio and can be rescued by modulating zygotic transcription. Moreover, effectors of the DDR are recruited to chromatin at Pol II occupied loci after ZGA and interfering with the scope of zygotic transcription can bypass the requirement for a functional checkpoint. In addition, DNA damage elicits a Chk2-dependent clearance of damaged nuclei in syncytial embryos through the nuclear retention of essential mRNAs, providing an elegant mechanism to safeguard genome integrity prior to the establishment of a robust DNA replication checkpoint.Drosophila development [While zygotic transcription drives key events of embryonic development, the properties and dynamics of the emerging transcriptional process itself have been challenging to study. The advent of approaches enabling RNA labeling in vivo and in real time has provided a clearer picture of zygotic transcription. Originally developed by Singer and colleagues in yeast, the MS2 system takes advantage of the strong affinity of bacteriophage coat proteins  for specific RNA stem-loops . For imaelopment ,87,88,89Fukaya et al. placed well characterized enhancers upstream and downstream of reporter genes flanked by MS2 and PP7 stem loops [MS2-yellow reporter containing different snail (sna) enhancers of varying strength, along with its proximal promoter in different configurations. They observed major differences in bursting frequencies produced by the sna primary enhancer and by its shadow enhancer, a redundant regulatory sequence, which were correlated to the discrepancy in total RNA outputs. This analysis was extended to the rhomboid (rho), Krüppel; (Kr) and Abdominal-B (Abd-B) enhancers. By testing a set of conditions, Fukaya et al. showed that differential core promoter, distal enhancer and anteroposterior gradient positioning all affect bursting frequency, in line with discrepancies in total RNA outputs. Thus, Fukaya et al. identified the regulation of transcriptional bursting frequency as a key determinant of developmental gene activity at the MBT.Quantitative RNA detection methods suggest that transcriptional bursting is a key property of gene expression in diverse systems ,91. The em loops . They pesog) in NC13, when the time window permissive to transcription is narrowly restricted by a hasty interphase (10–12 min). Fukaya et al. solved this long-lasting paradox by revisiting Pol II elongation rates in early embryogenesis using dual-fluorescence through the MS2 imaging system [sog transcription during the 13th interphase. In addition, they found that replacing the promoter or introducing a reporter containing an intron had little impact on elongation rate measurements, suggesting that elongation is not the rate-limiting step in transcription.Puzzling disparities have long prevailed between reported rates of Pol II elongation (1.1–1.5 kb/min) and robust detection of several long de novo transcripts before the MBT ,93. Indeg system  (Figure Dictyostelium [Drosophila MBT and impacts total RNA output, likely through modifications incurred at the level of nucleosomes, bound TFs or histone modifications following a first round of transcription. Ferraro et al. envision this emerging property of early transcription as a mechanism of developmental homeostasis, which could help ensure that cells retain the properties of their progenitors.The inheritance of transcriptional states from mother to daughter cells, termed transcriptional memory, has been documented in the amoeba ostelium ,96. In aostelium . This wosog with a short NC13 interphase. Quantitative imaging also showed that transcriptional memory prevails in Drosophila embryogenesis, promoting the rapid post-mitotic re-activation of sequences expressed during NC13, which likely contributes to developmental homeostasis.In brief, the recent deployment of in vivo imaging to document the transcriptional process in real time has revealed new insights into the dynamics of zygotic transcription. In vivo imaging has established the notion of transcriptional bursting, and shown that total gene-specific outputs at ZGA reflect the frequency of transcriptional bursts. It has enabled the revision of Pol II elongation rate at the MBT, reconciling the expression of lengthy genes such as Drosophila development. Maternal control is essential in syncytial-stage embryos to sustain fast-paced proliferation in absence of a sizeable transcriptional output. Thus, we discussed the processes of RNA maternal deposition, localization and targeted clearance. Indeed, Drosophila embryos host a wealth of complex posttranscriptional regulatory processes. The spatiotemporal dosage of RBPs such as SMG reflect translational fine-tuning which, in turn, modulates the dynamics of hundreds of maternal mRNAs. RNA localization is highly prevalent in the large syncytial embryo and likely plays key roles in orchestrating the developmental program. Indeed, protein-coding transcripts adopt a large diversity of spatial distributions in early embryos, including subembryonic and exclusionary patterns, asymmetric anteroposterior localization and more resolved patterns such as membrane, microtubule or mitotic apparatus associations [cis- and trans-determinants regulating RNA localization events.In this review, we aimed to provide an overview of recent findings relevant to the transcriptome dynamics of early ciations . In manyXenopus have identified maternal histone as putative transcriptional repressors [big1 mutants show signs of disorganized chromatin and early zygotic transcription. Nevertheless, we know that the repressor titration model is not sufficient to account for the transcriptional silence of the zygotic genome in Drosophila. Indeed, most zygotic transcripts display similar expression kinetics in haploid and diploid embryos, which present very different N/C ratios [Maternal control is gradually met with an increasing contribution of the zygotic genome, as it progressively acquires transcriptional competence. The mechanisms accounting for ZGA onset are multifaceted and their underpinnings remain unclear. As the N/C ratio increases, a maternal factor responsible for transcriptional quiescence could be diluted against the mass of DNA, loosening the efficiency of the repression. Studies in pressors . In lineC ratios . This obDrosophila, the preferential post-mitotic reactivation of loci transcribed at NC13 has been demonstrated through this approach. In addition, embryonic Pol II elongation rates have been revised via the development of a dual fluorescence system. The deployment of real-time, in vivo RNA labeling to study transcription in Drosophila is still very recent. Future applications will likely contribute to clarify how ZGA is triggered. Indeed, important insights could be revealed by monitoring transcription in mutants of specific chromatin components, such as bigH1, or pioneer TFs, such as zld. This technology can notably reveal transcriptional dynamics at the single cell level, a sizeable advantage when investigating heterogeneous and multifaceted responses such as ZGA.Independently of its underlying mechanisms, once it has been triggered, zygotic transcription contributes to shaping a complex genome topology before the MBT, which largely remains in place throughout the life of the fly. After NC10, the growing population of transcripts produced by cortical nuclei play key roles in driving the course of development through the MBT. The transcriptional process itself exposes ssDNA and triggers the activation of the DNA replication checkpoint, possibly through the formation of stalled replication forks. Cellularization onset is linked to ZGA timing and it coincides with the loss of mitotic synchrony and the expression of additional zygotic genes. Real-time imaging has revealed that zygotic transcription proceeds as bursts, with the frequency of bursting events linked to its total RNA output. An example of transcriptional memory in


In the study of genomic regulation, strategies to integrate the data produced by Next Generation Sequencing (NGS)-based technologies in a meaningful ensemble are eagerly awaited and must continuously evolve. Here, we describe an integrative strategy for the analysis of data generated by chromatin immunoprecipitation followed by NGS which combines algorithms for data overlap, normalization and epigenetic state analysis. The performance of our strategy is illustrated by presenting the analysis of data relative to the transcriptional regulator Estrogen Receptor alpha (ERα) in MCF-7 breast cancer cells and of Glucocorticoid Receptor (GR) in A549 lung cancer cells. We went through the definition of reference cistromes for different experimental contexts, the integration of data relative to co-regulators and the overlay of chromatin states as defined by epigenetic marks in MCF-7 cells. With our strategy, we identified novel features of estrogen-independent ERα activity, including FoxM1 interaction, eRNAs transcription and a peculiar ontology of connected genes. They appear to serve in a developmentally-regulated fashion, and only a fraction of them is poised or active in a defined cell type at any specific time. Enhancer activity status is quite precisely defined by histone Post Translational Modifications (PTMs), TF and coregulator binding, and enhancer RNAs (eRNAs) transcription2. The genomic activity of a TF or a coregulatory factor  is studied using Chromatin immunoprecipitation (ChIP) in combination with Next Generation Sequencing (NGS). Binding sites are often taken as a proxy for the regulatory effects of TRs. However, not all binding events are functionally important3. First, the DNA-bound TR may lack a key cofactor or PTMs. Second, it has been shown that only more stable binding events are productive, as opposed to erratic, short-lived events that nonetheless are picked up by ChIP analysis4. Identifying true functional TR Binding Sites (TRBSs) has great relevance not only in regulatory genomics, but also in medical genetics and pathology5. This task can be afforded by leveraging the increasingly wide data available in public repositories concerning, in addition to TR binding, data on chromatin accessibility, histone PTMs, CpG methylation, as well as expression data by microarray and RNA-Seq technologies6. This data can be mined allowing construction of robust cistromes annotated with their activity status, finally obtaining classification of TRBS subsets with coherent functions.DNA regulatory regions represent an important part of the genome, where DNA binding Transcription Factors (TF) and a large number of co-regulators cooperate to convey cellular information and control gene activity. Recent genome-wide analyses, conducted by ENCODE and other projects in a variety of cell lines and tissues, led to the unexpected observation that distant or proximal non-promotorial regulatory regions, defined as enhancers, outnumber gene promoters by a factor of tenDespite simple rationale, data integration is not trivial due to wide heterogeneity of the data available. The first reason is technical, since data derive from several variants of the ChIP assay or chromatin accessibility assays, or other, run on different NGS platforms at different sequencing coverages, often resulting in quite diverging numbers of binding sites. Second, data have different formats, either as raw sequencing reads or processed data including genomic coordinates (ChIP peak sets), genomic coverage , or reads alignment files.12 but, while these tools can be successfully used for comparative analysis of ChIP data, a “start-to-end” strategy to dissect progressively a TR genomic activity by means of genomic and epigenomic data integration still awaits implementation.Thus, when integrating heterogeneous data from different studies, a robust approach is mandatory. Two major issues should be dealt with: first, how binding regions are defined; second, since measurements with ChIP are inherently not quantitative, data normalization is required. Bioinformatics tools to afford these issues exist14. Surprisingly though, there is no systematic analysis leading to definition of a reference cistrome and to identification of the differential activity of ERα in different experimental contexts and with different ligands or, notably, in absence of estrogen as we reported previously15 and that represents possibly one of the most puzzling activity of this TR.A quite impressive number of studies from several labs comprising ours have reported Estrogen Receptor α  genomic binding, ERα-controlled transcriptomes and biological effects of agonists and antagonists in human breast cancer cells16. Our strategy consists in a sequence of integration steps that make it flexible and usable in heterogeneous contexts for any TR of interest.We describe here a “start-to-end” strategy to define a consensus cistrome and dissect it into functional classes, by merging all genomic and epigenomic data available. This procedure, applied to ERα, led to new functional information and, applied to Glucocorticoid Receptor (GR), correctly identified experimentally validated binding sitesWe designed an integrative strategy to analyze heterogeneous genomic datasets, focused on the characterization of three critical aspects of the genomic activity of the TR of interest (TRI): (1) definition of binding sites that are robustly reproducible through different ChIP studies, i.e. a reference cistrome; (2) the co-factors and co-regulators that co-occupy these genomic regions; (3) the epigenetic status of TRI cistrome.These issues are addressed in separate but converging tasks, as illustrated in Fig. 18 and pre-process them into high-level data structures. Then, we integrate the peak sets into a reference cistrome by progressively overlapping their genomic coordinates. A novel algorithm called RefGen, which applies a majority vote-based approach to identify a reproducible set of TRBSs according to a selected “severity” grade, has been implemented to this purpose .The first task  change drastically in response to hormones in these cells20. The majority of studies concern the genomic response to estrogen or the baseline genomic status in cells exposed chronically to low-dose estrogen. In addition, we described a dataset of ERα activity in MCF-7 cells in absence of hormones15 that is comparable to data in other datasets, when cells are “vehicle”-treated, as control.The human breast carcinoma cell line MCF-7 is the most used model system for estrogen-dependent breast cancer and was included in ENCODE Tier 2E2-constitutive) and recovered 14 ERα ChIP datasets obtained in six independent studies . Then, we compared the properties of reference versus dismissed ERBSs  and with sites of long-range chromatin interaction1 (11.43% vs 2.08%). Furthermore, reference ERBSs were enriched in sites detected in primary tumors from patients receiving adjuvant Aromatase Inhibitors (AI) or Tamoxifen (TAM) (1.08–2.33% vs 0.02–0.07%)23, or metastases (26.86% vs 4.06%)22 ; (ii) 45 to 60 minutes E2 treatment (E2-Early); (iii) three to four hours E2 treatment (E2-Late); in addition to the E2-Constitutive described above. We integrated a total of 33 datasets derived from 17 studies, including our own 22 Fig. . Noteworα cistrome for MCF-7 (ERα-Ref), composed by 13,239 ERBSs. These sites present variable degree of co-occurrence in the different experimental contexts. Consequently, we subdivided ERα-Ref in four subsets (C1–C4), following the ERBS presence in one, two, three or all the contexts , while only 1,119 ERBSs (8.5%) were common to all the experimental contexts (C4). ERBSs that are detected by ChIP in all contexts may represent sites at higher affinity. Therefore, we analysed the differences in the intensity of the normalized ERα ChIP peaks, which revealed a progressive increment in the intensity of ERα binding from C1 to C4  and GATA Binding Protein 3 motifs  in C1–C2 ERBSs , whereas CREB  and Tumor Protein 63  motifs were more enriched in C4 ERBSs   while E2-Independent and E2-Early were C4 and C3 ERBSs, suggesting that they represent the set with the highest-affinity for ERα.As far as the context-specific cistromes are concerned, a high fraction of ERBSs observed in Then, with our integrative analysis we defined a reference cistrome of ERα chromatin binding in MCF-7 with a joint analysis of multiple ChIP datasets and we identified the binding sites characterized by persistent receptor-chromatin interaction in hormone-deprived and treated cells.Thus, our integrative strategy was successful in identify subsets of ERα chromatin binding sites in MCF-7 with different features.ERα-Ref . Interestingly, we found clear differences in cofactor binding in several experimental contexts: E2-Independent ERBSs showed, uniquely, at first ranks the Forkhead box protein M1 (FoxM1), the Double-strand-break repair protein rad21 homolog (Rad21), which is a component of the cohesin complex involved in enhancer-promoter looping25, and the CBP coactivator , Promoters (Tss-) and intragenic regions (Gene-), we observed that the different experimental contexts were characterized by specific combinations of chromatin features  was characterized by five states related to gene transcription/intragenic regions and three enhancer states, while in E2-Independent an additional enhancer state, featured by RNA Polymerase II (RNAPII), H3K27ac, and H3K4me1, was uniquely predicted, which we named Enhancer-Transcribed (EnhT)  Fig. . Then, wes, Fig. . Notewor31. We observed that only a fraction of ERBSs shows robust induction of eRNAs transcription, and C4–C3 ERBSs were the subsets in which most of the sites presented induction of eRNAs in response to estrogen E2-independent were predominantly classified as EnhT and Active Enhancer (EnhA), whereas other contexts were mostly classified as EnhA and TssA , specifically, EnhA (30.5%), EnhT (22.7%), and Enhancer-Weak   Fig. . Unexpecion Fig. . Four inion Fig. . Thus, oThen, our strategy can be applied to narrow down a list of TR binding sites to a subset of interaction that are predicted to be functionally relevant for their cistromic, epigenomic and TR interactions properties.16. Thus, experimental classification of GRBSs will provide ideal challenge for our integrative procedure. Following our strategy, we first integrated four GR ChIP datasets from cells treated with DEX for 1 (DEX-Early) or 3 hours (DEX-Late)  composed of 13,466 GRBSs. 5,491 (40.03%) of these were occupied by GR in both the experimental contexts (C2) . Then, GR-Ref was compared to the validated GRBSs, observing that 95.6% of validated GRBSs overlapped GR-Ref. Importantly, 19.4% of C2 GRBS overlapped validated GRBSs as compared to only 3,2% of C1 GRBSs  cistrome in lung cancer A549 cells. Recently, a GR-ChIP-seq library was experimentally validated in reporter assays in response to Dexamethasone (DEX) treatmentte) Fig. , defininC2) Fig. . Most GRBSs Fig. .Figure 4DEX-Independent) or DEX for 1 hour (DEX-Early). After normalization, we calculated the correlations with GR genomic signals, and we observed a clear increase of the average correlation coefficient from DEX-Independent to DEX-Early context  with GR binding signal in DEX-treated cells, while less correlation with CTCF and RNAPII was observed. Interestingly, the rank of GRBSs based on the average correlation computed for these three TFs revealed that the top quartiles of GRBSs was also associated with the highest superimposition with the validated GRBS set (24.34% of overlapped sites)  shared by both enhancers and promoters , while only C1 were enriched in gene body marks (H3K79me2 and H3K36me3)  Fig. . ConsidenhW Fig. .Thus, classification of GR binding through cistrome integration, cofactor analysis and epigenetic features allows identification of functionally relevant sites.32. The novelty is assembling all available TFBS, epigenomic and transcriptomic data in a coherent strategy to functionally classify chromatin binding events of any transcriptional regulator. Two novel methods were developed and implemented to define consensus cistromes and to normalize ChIP genomic signal profiles. Moreover, a full integration approach is proposed in association with chromatin states prediction algorithms.In this work, we present a strategy to guide the reuse, combination, and post-processing analysis of NGS data describing regulatory protein-chromatin interaction. Analysis of one case-study of ERα in MCF-7 cells, where extraordinarily rich data exist, and GR in A549 cells, where experimentally validated binding sites were published, confirmed the validity of this strategy. In the first case, feasibility with abundant data, i.e. computationally demanding, was verified. In the second case, the paucity of data did not hamper adherence of results to experimental validation. It should be stressed that, in the first case, this analysis provided valuable new information on the less studied experimental context - absence of hormones - which is of great interest since hormone deprivation is the therapeutic strategy of drugs as Aromatase Inhibitors in Breast Cancer33. For cistrome definition we used an algorithm based on the majority voting approach, which allows extrapolating the consensus coordinates of TRI binding. This is a computationally efficient strategy to overlap multiple datasets34 that does not require a threshold based on the minimal number of overlapped nucleotides or peak centre position, allowing the analysis of NGS datasets in heterogeneous formats. Moreover, this procedure prompts easy and quick update whenever new data is available. In the case of ERα, the efficiency was evaluated by measuring consistency with functionally relevant datasets (e.g. tumors data). For GR, overlap with peaks reportedly active in luciferase reporter assay16 measured the performance of our analysis.The variability of ChIP measurements is due to many factors, starting from the antibody used to the assay protocol and NGS platform8, without considering the signal profiles obtained from NGS experiments. Here we propose to combine normalization and correlation analysis of different signal profiles. We adapted the normalization method implemented in DESeq library35 on a set of NGS experiments, because this method was previously observed to be effective in differential binding analysis on the normalized number of ChIP reads mapping to regulatory regions22. Correlation between the normalized signal profiles of TRI and other TRs gives a measure of co-occupancy. The analysis is optimized to compare unimodal genomic signal profiles in the region of chromatin interaction. We are currently working on the extension of our method to multimodal spread signals characterizing some TR complexes. Finally, we propose the classification of TRBSs into functional classes based on redundant patterns of cistromic and epigenomic ChIP signals. Our strategy is to classify the epigenome of the experimental model system into a discrete number of chromatin states, subsequently superimposed to the TRI cistrome, whereas other integrative tools like Seqminer9 or EaSeq.10 exploit the simple co-occurrence of ChIP-derived patterns, more prone to some bias. In conclusion, our strategy merges new and public algorithms into a coherent process leading to cistrome definition and classification, using extensive integration of genomic and epigenomic data, in the cell/tissue model system considered. Computational tools like EaSeq.10, HiChIP36, Cistrome37, or CisGenome38 assembly validated algorithms to perform restricted single-step analysis.Regulatory regions are sites of binding of multiple DNA-binding or coregulatory proteins. Describing co-occupation profiles is commonly performed by superposition of genomic intervals40, our strategy revealed at least two undescribed features: first, our novel classification in co-occurrence subsets (C1–C4) revealed that ERBSs common to all contexts (C4), i.e. in both presence and absence of hormones, are a peculiar subset, showing the strongest ChIP signal and the most significant co-occupancy by co-factors; they represent undoubtedly lineage-specific, highly accessible chromatin sites for ERα, and in fact they appear the only ones to quickly respond to E2 stimulation by eRNAs transcription. The second unexpected observation was that ERBSs in absence of hormones15 display features of active enhancers (EnhT) considering both histone PTMs and eRNAs transcription. Furthermore, transcription factor FoxM1, Steroid Receptor Coactivator 1 (SRC1) and the cohesin complex component Rad21 were quite specific to this set. While hormone-independent occupancy of ERBSs by FoxA1, AP2γ and Rad21 at ERBSs was previously demonstrated41, our data show FoxM1 as the most correlated protein in hormone-deprived cells. FoxM1 is an important factor for breast cancer cell growth43. Importantly, FoxM1 and ERα regulate the expression of each other45.Considering ERα analysis, although it was hard to imagine adding value to such an extensively studied field15. GREAT analysis46 showed that EnhT ERBSs-proximal genes are neatly linked to “gland development” and “gland morphogenesis”  demonstrated EnhT ERBSs enrichment in several datasets related to breast cancer and estrogen response   and several long noncoding RNA genes, including DSCAM-AS1, an ERα-dependent lncRNA specific to luminal breast tumors47. The analysis of GRBSs was also informative. In all the steps of our procedure we observed enrichment of experimentally validated GRBSs in specific subsets selected in the different steps of our procedure. Again, it would be desirable to obtain direct proofs of gene regulation. Although GRBSs are more frequently proximal to TSS48, association to regulated genes is questionable. Indicatively, one RNA-Seq experiment of DEX-treated A549 pointed to 644 responsive genes, three-fourth of them within 100 kbp from a GRBSs belonging to GR-Ref , 18, Array Express17, Cistrome37, and supplementary material of target publications. To define the GR reference cistrome for A549 cells four sets of GRBSs were retrieved from GEO. All the analysed datasets are reported in Supplementary Table 49. Moreover, ERBSs mapped on chromosome Y were removed.To define the ERα reference cistrome for the MCF-7 cells , then the genomic position characterized by a coverage value greater than or equal to a predefined threshold τ are selected as reference cistrome. The pseudo-code of RefGen is reported in the Supplementary Note section, and its C++ implementation is available at: https://github.com/giuferrero/RefGen.The definition of a TR reference cistrome was performed by taking as input a list of genomic intervals corresponding to the TRBSs obtained in a set of ChIP experiments. Then, the reference cistrome is composed by the genomic positions which are shared by a desired number of experiments (τ). To efficiently define this reference, an ad-hoc algorithm, namely This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.RefGen was first applied to generate a reference cistrome from the biological replicates of 17 independent ChIP experiments (Supplementary Table E2-Independent); (ii) 45 to 60 minutes E2 treatment (E2-Early); (iii) three to four hours E2 treatment (E2-Late); and (iv) continuous cell growth in estrogen-enriched medium (E2-Constitutive).In the ERα case study RefGen to generate a specific experimental context cistrome. A τ value equal to 75% of the number of input cistromes was applied on these runs. The τ threshold used in these analyses are reported in Supplementary Table shuffleBed function of bedtools50 with option -chrmon. The threshold was selected to better balance the rate of false positive/false negative predictions. A main reference cistrome (ERα-Ref) was also derived by the application of RefGen on the four context-specific cistromes. For this analysis, the binding sites identified at least in one experimental context were selected. The same procedure was applied to define the GR cistrome for each of the two analysed experimental contexts .For each of the four experimental contexts, the cistromes of the experiments belonging to the same experimental context were further processed by The overlap between ERBSs and ERα GIS-ChIA-PET data from the ENCODE project was performed using the coordinates of ChIA-PET anchor regions retrieved from GSM97021217. An overlap was confirmed valid if observed for two out of three available ChIA-PET biological replicates. The overlap between ERBSs and a list of 1,248 ERα/H3K27ac co-bound enhancers (Active Enhancers) was performed using the list provided in ref. RefGen by setting the τ threshold equal to the number of biological replicates available for each patient group.ChIP-Seq on primary tumor biopsies from breast cancer patients taken at surgery before treatment with Aromatase Inhibitor (AI) or Tamoxifen (TAM) were downloaded from GSE4086711 and GSE3222246 respectively. Data of patients responsive or not to therapy were considered separately to define an ERα cistrome for each treatment outcome. Three sets of ERBSs defined in metastatic breast cancer samples were also considered. The cistromes were defined using 2. The overlaps between ERBSs and these regions were considered valid if they were observed in all the available biological replicates.Coordinates of amplified and heterozygous deleted regions in the MCF-7 were retrieved from GSE40698The overlap between the GR cistrome and the set of DEX-Responsive GRBSs was performed by considering the set of 1,376 significant bindings sites provided in ref. 46 was exploited to perform the ontological analysis of the genes mapped nearby to ERBSs. Using the default settings of the program, the median distance between ERBSs and associated genes was 93,203 bp. The Gene Ontology Biological Process, Cellular Component and Molecular Function terms significantly enriched for both the binomial and the hyper-geometric by a p-value lower than 0.05 were considered.GREAT algorithm v3.051 in default settings. A genomic region of +/−100 bp focused on each binding sites center was considered for this analysis.Prediction of TF binding motifs was performed using the Centrimo algorithm of the MEME-ChIP pipeline v.4.9.1NormChIP. This algorithm extends the DESeq normalization method35 on ChIP signal profiles.The normalization of ChIP signal profiles was performed with a new algorithm called M so that a cell M stores the count of aligned reads in the j interval/bin of experiment i. For each row M, the NormChIP algorithm computes the geometric mean across the counts of bin j in all the experiments as reported in equation  any later version.NormChIP was applied to define a reference genomic signal profile for the ERα ChIP experiments. One reference was generated for each considered experimental context. The raw NGS data of the experiments selected for the definition of ERα-Ref were realigned using Bowtie v2.1.052 in default settings. The ERα ChIP signal profiles were computed considering a genomic window of ± 5 kbp centered on each ERBS of the ERα-Ref. These regions were fractioned in consecutive non-overlapping 50-bp bins and reads aligned within each bin were counted with Seqminer53 v1.3.3e in default settings. The genomic signal profiles were normalized using NormChIP. Then, a reference for each experimental context was defined by averaging the normalized signal profiles. The same procedure was applied on the GR ChIP-Seq experiments.NormChIP performance was tested using five ERα ChIP signal profiles obtained by different groups in the same experimental condition (E2-Late). The datasets were selected based on the alignment rate (>90%). NormChIP normalization was compared to i) the raw signal profile and ii) the signal normalized on the number of sequenced reads . Results of the performance test are shown in Supplementary Figure 52 in default settings. The available datasets against the same factor and performed in the same experimental context were considered in the further analysis if their percentages of aligned reads were greater than 80%.TFs and co-regulators ChIP datasets were aligned with Bowtie v2.1.0NormChIP. The normalized signal profiles obtained in the same experimental contexts were averaged. The resulting signal profiles were also normalized with NormChIP, with respect to different factors measured in the same experimental context. This two-steps normalization strategy was selected to allow both inter-context comparisons (for the same factor) and intra-context comparisons (between different factors).For each factor, the genomic signal profile was normalized using A pair-wise correlation between the signal profile of ERα (or GR) and each of TF/co-regulator signal was computed using the Pearson method. The correlation coefficient was computed between signal profiles measured in each ERBS (or GRBS). Only ChIP datasets obtained in the same experimental context were compared.19. Raw reads were aligned using Bowtie v2.1.052 in default settings. The ChIP read alignments of the histone modifications, were binarized with the BinarizedBed function of Spectacle. The hg19 genome was fractioned in 200 bp non-overlapping bins. Prediction of a 15 chromatin states model, the genome segmentation and the features overlap were performed using LearnModel function with options -i = spectral, -lambda = 1 and -comb.The chromatin state prediction is performed using the Spectacle algorithmE2-Independent, E2-Early and E2-Constitutive experimental contexts. For E2-Early context analysis, the data of H3K9me3 and H3K27me3 of three hours E2-treated MCF-7 were considered, since only these datasets were available at the time of the analysis. For the GR case study, the A549 chromatin states, eight histone marks and CTCF and RNAPII ChIP experiments were used to predict 15 chromatin states for the DEX-Early and DEX-Independent experimental context. Details on the criteria chosen to name the chromatin states defined by Spectacle are reported in the Supplementary Note section and in Supplementary Table For the ERα case study, the MCF-7 chromatin states were predicted considering ChIP datasets against six histone marks, histone acetyltransferase p300, RNAPII, Mediator Complex subunit 1 (MED1), and CTCF. The analysis was performed separately to predict the states for ERα-Ref (or GR-Ref) subsets was also performed. The overlap with these genomic features was computed as previously reported54. Then, enrichments were Z-score converted in order to identify the features enriched or depleted in each of the chromatin state.The fraction of epigenomes associated with each chromatin state were overlapped with independent lists of genomic features including: coordinates of Gencode v19 gene body, TSS, Transcription End Sites (TES), CpG islands, Lamin B1 associated domains, and amplified or heterozygous deleted genomic regions. The overlap against the coordinates of different –local option. Three different experiments were considered: GSE4582229, GSE4132431 and GSE2746330. The signal profiles of these experiments were computed within a genomic region of ±5 kbp centered on each ERBS of the ERα-Ref. RNA-Seq data of hormone-deprived MCF-7 cells transfected with control or ERα-specific siRNA from GSE5353215 were analyzed as previously reported47, and by considering Gencode v19 gene annotations and human genome assembly hg19.Raw gene expression data were retrieved from public repositories without further reads quality control. Analysis of GRO-Seq datasets was performed using Bowtie v2.1.0 in default settings and the 35. A transcript was considered differently expressed if associated with an adjusted p-value < 0.05.Processed data of a RNA-Seq experiment of DEX- or Veh-treated A549 cells were retrieved from GSE79432. Differential expression analysis was performed on each gene isoforms using the DESeq. 2 R packageEnhT, EnhA and EnhW E2-Independent ERBSs associated with the differently expressed genes was defined by considering the E2-Independent ERBSs mapped within 100 kbp from the differently expressed genes TSS. The GSEA algorithm55 was used to characterize functionally the genes associated with these classes of E2-Independent ERBSs. The preRanked mode of GSEA was applied using 10,000 random permutations and selecting only the gene-sets associated with a p-value < 0.05. The genes were ranked by decreasing number of associated E2-Independent ERBSs and in case of equal number of sites, the absolute log2FC of expression in siERα-treated cells was considered. The MSigDB v4.0 gene set library was used for the analysis.The list of Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Information


TCF21 as the causal CAD gene at 6q23.2 and characterized its downstream transcriptional network that is enriched for CAD GWAS genes. Here we investigate the hypothesis that TCF21 interacts with a downstream target gene, the aryl hydrocarbon receptor (AHR), a ligand-activated transcription factor that mediates the cellular response to environmental contaminants, including dioxin and polycyclic aromatic hydrocarbons . Perturbation of TCF21 expression in human coronary artery smooth muscle cells (HCASMC) revealed that TCF21 promotes expression of AHR, its heterodimerization partner ARNT, and cooperates with these factors to upregulate a number of inflammatory downstream disease related genes including IL1A, MMP1, and CYP1A1. TCF21 was shown to bind in AHR, ARNT and downstream target gene loci, and co-localization was noted for AHR-ARNT and TCF21 binding sites genome-wide in regions of HCASMC open chromatin. These regions of co-localization were found to be enriched for GWAS signals associated with cardio-metabolic as well as chronic inflammatory disease phenotypes. Finally, we show that similar to TCF21, AHR gene expression is increased in atherosclerotic lesions in mice in vivo using laser capture microdissection, and AHR protein is localized in human carotid atherosclerotic lesions where it is associated with protein kinases with a critical role in innate immune response. These data suggest that TCF21 can cooperate with AHR to activate an inflammatory gene expression program that is exacerbated by environmental stimuli, and may contribute to the overall risk for CAD.Both environmental factors and genetic loci have been associated with coronary artery disease (CAD), however gene-gene and gene-environment interactions that might identify molecular mechanisms of risk are not easily studied by human genetic approaches. We have previously identified the transcription factor  Coronary heart disease is the leading cause of death in the world. Both genes and the environment are important risk factors for the progression of disease, however, how genes may modulate the harmful response to the disease promoting environment is unknown and difficult to study. Here, we show that a common heritable variation in the gene TCF21 may regulate coronary heart disease risk by regulating the response of downstream gene activation by the disease environment. We find that a well-known environmental sensor, aryl-hydrocarbon receptor (AHR), is regulated by TCF21 and also interacts with TCF21, resulting in regulation of pro-inflammatory gene expression in coronary artery smooth muscle cells. We further show that oxidized LDL, a well-known driver of atherosclerosis in the plaque can activate the AHR pathway. This work describes a heritable form of gene-environment interaction identified through genome wide association studies in coronary artery disease, and presents an opportunity to define causal gene-gene and gene-environment interactions. Genome-wide association studies (GWAS) have identified susceptibility loci and candidate genetic variants that predispose to atherosclerotic coronary artery disease (CAD) in humans.–4 DespitTCF21 as the causal gene in this locus. We  We 37] Wnegative regulation of cell fate commitment, but importantly a number of terms were related to inflammatory response . GO terms for phased sites were enriched in cellular and developmental terms  In addition, binomial fold changes for un-phased AHR-TCF sites were ~10 times higher than for phased sites, implicating the indirect interaction of AHR-TCF21 factors as predominant in the human genome.We further assessed the steric relationship of TCF21 and AHR binding by dividing the co-localized TCF and AHR predicted sites into two categories, rotationally phased and un-phased, as rotational phasing has been shown to be crucial for direct protein binding., 39 We cpositive regulation of cytosolic calcium ion concentration, regulation of cytosolic calcium ion concentration), apoptosis , metabolic processes , and cellular signaling (cellular response to stimulus).To further evaluate whether TCF21 and AHR-ARNT complex binding co-localizes in the human genome, we compared ChIP-seq data for these three TFs. We identified TCF21 ChIP-seq peaks in HCASMC that overlapped with AHR and ARNT ChIP-seq sites identified in MCF-7 cells, and obtcoronary heart disease, blood pressure and type 2 diabetes) as well as chronic inflammatory diseases , as well as skeletal phenotypes  and in certain neurological disorders (bipolar disorder and schizophrenia). ARNT binding was localized near GWAS SNPs for chronic inflammatory diseases (lupus erythematosus and ulcerative colitis) and prostate cancer GWAS SNPs. AHR binding co-localized with CAD variants  as well as chronic inflammatory GWAS SNPs . After intersection of AHR with ARNT and TCF21 the only remaining categories were coronary artery disease and coronary artery calcification, narrowing the importance of the interaction of AHR/ARNT and TCF21 factors to pathophysiological processes in cardiovascular disease. Furthermore, we surveyed the overlap of CARDIoGRAM+C4D GWAS SNPs and AHR-ARNT PWM to consider the potential role of AHR in other CAD associated genes. In total 456 ARNT-AHR sites overlapped with CARDIOGRAM+C4D SNPs (lead plus LD r2>0.8), comprising 0.27 permil (low stringency) and 0.38 permil (high stringency) of total ARNT-AHR PWMs. In comparison, there were only 7 and 5 HNF1A and HNF1B sites, comprising 0.12/0.10 permil of total HNF1A/B sites , expanded by addition of CARDIoGRAM+C4D meta-analysis data, using tTCF21 expression levels directly modulate the AHR response to dioxin , and TCF21 overexpression further increased the luciferase expression induced by dioxin in these cells   . The com binding . These dIL1A, and this effect was reversed when cells were treated with the AHR antagonist alpha-napthoflavone (α-NF)  . In the e (α-NF) . We alsoe (α-NF) .AHR in vascular disease through expression studies in mouse and human vascular tissues[ApoE-/- animals to compare gene expression between ruptured and non-ruptured plaques.[Ahr expression to be higher in the ruptured plaques compared to non-ruptured plaques    . Further 0.0008) . Next, wTCF21 as the causal gene at 6q23.2, characterized its mechanism of association, and shown that binding of this transcription factor is enriched in other CAD associated loci.[AHR gene, because it encodes a transcription factor, allowing direct study of its downstream signaling pathway, and because of the well-characterized link between this factor and environmental exposures that are relevant for cardiovascular disease. This work thus addresses two aspects of CAD that have not been directly approachable with human association studies, investigating both gene-by-gene and gene-by-environment contributions to disease genetic risk.We have identified ted loci., 12 To iAlthough variants in the AHR locus (rs608646) have shown only nominal association with CAD risk in GWAS meta-analyses, this may be due to the technical limitation of the GWAS methodology in the AHR locus or inadequate statistical power. It remains possible if not likely that AHR functions as a hub or master regulator in CAD without harboring regulatory disease variants. We did identify a variant within the AHR locus (rs608646) that has a moderate association (p = 0.0047) in the CARDIOGRAM+C4D GWAS data , and thiAHR and ARNT loci, and increases expression levels of these genes in HCASMC, confirming previously published genomic studies and RNA-seq studies reported here. Second, these studies provide evidence for overlap of the TCF21 and AHR transcriptional networks. Both TCF21 and dioxin were shown to increase expression of disease-related factors such as IL1A, MMP1 and interestingly knockdown of TCF21 was able to almost completely abolish the effect of dioxin, suggesting that the inflammatory activation by AHR is dependent on the presence of TCF21. AHR is well known to promote inflammation in a number of situations, and to work with NFkB in this regard.[In our studies, we have pursued numerous approaches to investigate links between these two genes and their related transcriptional networks, and to investigate mechanisms by which they may work together to modulate CAD risk. First, we have shown with targeted studies that TCF21 binds both the s regard. Also, wes regard., 47Further analyses investigated additional mechanisms of interaction between these two pathways. Using PWMs for both TCF21 and AHR, we found highly significant enrichment for co-localization in regions of open chromatin in HCASMC, and characterized similar organization of these binding sites around transcription factor start sites, suggesting functional interaction between TCF21 and AHR and the basal transcriptional apparatus, as proposed previously for other TFs., 49 The IL1A response to dioxin in TCF21 knockdown, and our studies investigating the phasing of binding site placement also suggests some form of direct or indirect molecular interaction. The striking difference between functional annotations for the two categories of steric relationship are consistent with different functional interactions between AHR–ARNT and TCF21 in the context of DNA binding. GO terms for un-phased sites showed much stronger enrichment and significance compared to those of the phased sites, supporting indirect interaction as the likely functional mechanism. We investigated this possibility using the CYP1A1 gene as model locus where both transcription factors bind. The reporter gene transfection studies with constructs containing both TCF21 and AHR binding sites showed an additive effect suggesting that the transcriptional effects are due to each TF acting independently. However, both TCF21 and AHR are known to bind AR[Co-localization of TF binding often suggests direct functional interaction, and since TCF21 and AHR may regulate transcription in the same direction, an obvious hypothesis is that they bind cooperatively either through direct protein-protein interaction or through joint recruitment of ancillary adaptor proteins. In addito bind AR, 52, andTCF21 and AHR are downstream of retinoic acid signaling pathways that are critical in coronary artery development.[TCF21 and AHR in this setting has not been established.The functional role of TCF21 in vascular disease appears closely related to its role in embryonic coronary artery vascular development where it is expressed in SMC precursor cells, supporting proliferation and migration of these cells. AHR is also expressed in the developing coronary circulation, and the application of the AHR-ligand dioxin in zebrafish inhibited epicardial and proepicardial development., 54 Bothelopment.–58 The pTCF21, one of the most highly replicated GWAS candidate genes for coronary artery disease, and AHR, a gene classically involved with response to environmental toxins raises an interesting hypothesis that this interaction may reflect gene-environment interactions that are contributing to CAD and presents an opportunity to define causal gene-gene and gene-environment interactions relevant to the atherosclerotic lesion. Furthermore, our findings that TCF21 and AHR are expressed in the atherosclerotic plaque and that they interact to modulate inflammatory genes and matrix modifying genes suggest that the interaction may directly promote plaque instability leading to myocardial infarction. This work serves to promote mechanistic studies as an approach to understanding gene-by-gene and gene-by-environment contributions to disease genetic risk. Also, once the exact nature of the interaction of the two proteins is fully elucidated, therapeutic targeting of the pro-inflammatory interaction of TCF21 and AHR might be possible to reduce the effects of the pro-atherogenic stimuli in the vasculature and consequently reduce disease risk.In summary, we describe a novel functional interaction between two bHLH class transcription factors and postulate association of their interaction to the development of atherosclerosis and coronary artery disease . The disPrimary human coronary artery smooth muscle cells (HCASMC) were purchased from three different manufacturers, Lonza, PromoCell and Cell Applications and were cultured in complete smooth muscle basal media  according to the manufacturer's instructions. All experiments were performed with HCASMC between passages 5–8. HEK293 cells were maintained in DMEM containing high glucose, sodium pyruvate and L-glutamine supplemented with 10% FBS.nd generation lentivirus with TCF21 cDNA cloned into pWPI (Addgene #12254). Briefly, for lentiviral transduction, the cells were treated at 60% confluence with MOI of 5 for 24 hours. The virus was removed and replaced with low-serum media for 48 hours prior to collection for downstream applications. For the siRNA transfection, cells were grown to 60% confluence, then treated with 10nM siRNA or scramble control with RNAiMax  for 12 hours. The cells were collected 48 hours after transduction and processed using RNeasy kit  for RNA isolation. TCF21 knockdown was performed with siRNA oligos  using Lipofectamine RNAiMAX  following manufacturer’s protocol.For the TCF21 overexpression study, HCASMC were transduced with 2RNA was isolated using RNeasy mini kit (Qiagen) and total cDNA was prepared using iScript cDNA synthesis kit . Gene expression levels were measured using SYBR Green assays with custom designed probes  and quan5 cells using the Qiagen miRNeasy kit. RNA libraries were prepared using the Illumina TruSeq library kit as described by the manufacturer. RNA molecules were sequenced using Illumina HiSeq 2500. Reads contained in raw fastq files were mapped to hg19 using the RNA-seq aligner STAR (v2.4.0i), that processes data with short run times and yields high numbers of uniquely mapped reads . Second pass mapping with STAR was then performed using a new index that is created with splice junction information contained in the file SJ.out.tab from the first pass STAR mapping. Read that have been mapped with STAR second pass mapping algorithm were subsequently counted using the htseq-count script distributed with the HTSeq Python package (https://pypi.python.org/pypi/HTSeq). Differential expression of exons, genes, and transcripts were assayed using the DESeq2 R package from Bioconductor (http://bioconductor.org/packages/release/bioc/html/DESeq2.html), which uses negative binomial distribution to estimate dispersion and model differential expression such as to permit biological variability to be different among tested genes (transcripts). GO terms enrichment and PCA analysis was performed using GOSeq and Gene Set Enrichment and Projection Displays–GSEPD Bioconductor package.HCASMC were cultured as described above and total RNA was purified from 5.0x10Fifty-two human coronary artery smooth muscle cell lines are genotyped using 30X whole-genome sequencing. Genotype calling follows the GATK best practices recommendations. Briefly, after removing adapter with cutadapt, trimmed FASTQ files were aligned with BWA mem, duplicates were marked with Picard tools. After indel realignment and variant base quality recalibration, single-nucleotide variants and short insertion and deletion variants are jointly called on all samples using the GATK Haplotype caller. Called variants are recalibrated and filtered using GATK's variant quality score recalibration module. We used BEAGLE 4.1 to impute and phase recalibrated variants using 1000 Genome phase 3 version 5a as a reference panel. After imputation and phasing, we filtered variants based on MAF > 0.05, Hardy-Weinberg equilibrium p-value > 1e-6, indel length < 51 bps, dosage r2 > 0.8. Gene expression was quantified using mRNA sequencing to an average depth of 50M 75-bp paired-ended reads. Sequences were aligned using STAR two-pass mapping. To avoid allele-specific mapping bias, we removed potentially mismapped reads using WASP. Read counts and FPKM values were generated using RNAseQC. Expression eQTL were mapped with RASQUAL. To remove potential confounders, we included gender, first 3 principal components inferred on the genotypes and first 8 PEER factors inferred on 10,000 highest expressed genes. Transcription factor binding and epigenetic annotations of variants were assayed by Haploreg v4.1.AHR, ARNT and CYP1A1, and compared against ChIP performed with IgG antibody.  signal, used as a endogenous control, were expressed as fold change compared to IgG ChIP sample. Comparisons were performed using student t-test.A detailed protocol was included in a previous publication . HCASMC ntibody. . qPCR vaTCF21 ChIP-Seq raw data from Sazonova et al., were reanalyzed using Model-based Analysis for ChIP-Seq (MACS v1.4.2) pipeline. Parameters were set to default. Summit locations of the peaks were defined for genome wide correlations with PWM using ChIPCor module—part of ChIP-Seq Analysis Server of the Swiss Institute of Bioinformatics . TCF21 ChIP-Seq sites were converted to bigwig files and visualized on UCSC Genome Browser.TCF21 overexpression was achieved using cDNA expression construct driven by a CMV promoter transduced by lentivirus. TCF21 knockdown was performed with siRNA oligos (Origene) following manufacturer’s protocol. Briefly, for lentiviral transduction, the cells were treated at 60% confluence with MOI of 5 for 24 hours. The virus was removed and replaced with low-serum media for 48 hours prior to collection for downstream applications. For the siRNA transfection, cells were grown to 60% confluence, then treated with 10nM siRNA or scramble control with RNAiMax (Invitrogen) for 12 hours. The transduced cells were then treated with TCDD (Sigma Aldrich Cat#48599) at a concentration of 10nM for 24 hours. For the dual luciferase assay, double stranded DNA sequences containing the TCF21 and AHR binding motifs were subcloned into the multiple cloning site (MCS) of the pLuc-MCS vector , located upstream of the translation stop codon and firefly luciferase reporter gene luc2, driven by the PGK minimal promoter and also carrying the renilla luciferase reporter gene hRluc, as an internal control. Culture media was changed after 6 hrs, and dual luciferase activity was measured after 24 hrs using either SpectraMax L luminometer . Relative luciferase activity (firefly/Renilla luciferase ratio) is represented as the fold change of respective control condition as indicated.Oxidized-LDL was purchased from Alfa Aesar . Cells were treated at concentration of 10uM for 6 hours with and without α-NF at 10nM . The changes in downstream genes were confirmed using RT-qPCR.ApoE-/- mice on C56BL/6J background were subjected to 4 weeks of partial ligation followed by 4 days of cuff placement as described previously.[12 week old eviously., 59 For ApoE (-/-) mouse on high fat diet for 12 weeks. Using the published SMART-Seq2 protocol [Using a Leica LMD6000, we performed LCM of atherosclerotic plaques of mouse aortic sinuses. Briefly, following sacrifice, the cardiac chamber was perfused with PBS, then the aortic sinus was dissected and embedded in Optimal Cutting Temperature (OCT) medium (Tissue-Tek). 7um cryosections were placed on to Leica membrane slides, then visualized under the microscope for LCM. Total RNA was extracted using RNeasy Plus Micro kit (Qiagen), and the quality of RNA checked with Agilient Bioanalyzer RNA 6000 Pico kit. The levels of gene expressions were compared using total RNA generated from -80°C immediately after surgery, pulverized to a powder before resuspending in Qiazol lysis reagent (Qiagen) and homogenization with a tissue homogenizer. Total RNA was extracted as described above using the miRNeasy Mini Kit (Qiagen) and RNA quality assessed using a Bioanalyzer 2100 (Agilent). Global gene expression profiles were analyzed by Affymetrix HG-U133 plus 2.0 Genechip microarrays from 127 patient derived plaque samples and 10 donor control samples. Robust multi-array average (RMA) normalization was performed and processed gene expression data presented in Log2 scale.Human atherosclerotic carotid artery lesions were obtained from patients undergoing endarterectomy surgery for carotid stenosis, as part of the Biobank of Karolinska Endarterectomies (BiKE). Details Atherosclerotic plaques from 18 BiKE patients  were analysed using LC-MS/MS as previously described., 61 BrieAHR, ARNT, TCF21, HNF1A, and HNF1B co-expression modules were obtained using GeneFriends using 4164 human microarray data sets or 4133 human RNA-Seq data sets. TCF21 and AHR co-expression modules were defined using 4164 human microarray datasets through GeneFriends and visualized with Cytoscape. TCF21, AHR and ARNT co-expression modules were defined using 4133 human RNA-seq datasets through GeneFriends and visualized with Cytoscape. GeneFriends associations are defined with the threshold of 5%, meaning the gene is associated to a specific gene if it is in the top 5% co-expressed genes for a that gene. Cytoscape network file was imported for visualization from GeneFriends, containing all gene-gene associations marked as "good friends" (top 10 friends with a connection strength of 1), or "lesser friends" (genes ranking between 10 and 20 with a rank of 0.5). If a gene is an indirect connection, i.e. friend of a friend, score of 0.25 is deduced from the connection strength. Core network of direct interactions is marked on a graph with different colors to distinguish direct from indirect interactions. Connecting co-expression modules and visualization was performed using Cytoscape. Clustering was performed using Edge-weighted Spring Embedded layout. Coronary artery disease (CAD) GWAS genes were defined using Cardiogram plus C4D meta-analyses GWAS loci. In total, 77 CAD GWAS genes were used for transcription module analysis in GeneFriends:Microarray to obtain gene expression modules which were subsequently clustered in Cytoskape. Genes TCF21, AHR, COL4A1, SMAD3 and PDFGD from the main cluster were colored and indicated, node size was increased and edges to their first neighbors were colored in red. Underlying edge connections were colored purple with increased transparency. Grouping of transcription modules into three main clusters shows that CAD GWAS genes act through three main regulatory networks with TCF21 and AHR gene modules appearing in the single cluster.http://jaspar.genereg.net) [http://ccg.vital-it.ch/pwmtools/pwmscan.php). Position weight matrix sites were counted in windows of various lengths surrounding centered features using the Feature correlation tool from the ChIPCor module from ChIP-Seq Analysis Server .TCF12 and AHR-ARNT matrices (TCF12—MA0521.1 and AHR-ARNT—MA0006.1) were obtained from the JASPAR database (reg.net) . TCF12 Pbedtools package. Overlap of extended locations of AHR, ARNT and TCF21 ChIP-Seq binding sites and GWAS Catalog SNPs was performed with bed2GwasCatalogBinomialMod1Ggplot script from gwasanalytics package. This script is a modification of the bed2GwasCatalogBinomialGgplot and calculates binomial p-value for genomics overlaps using the following criteria. The P-values were computed using binomial cumulative distribution function b in R (dbinom function). We set the parameter n equal to the total number of GWAS SNPs in a particular GWAS phenotype. Parameter x was set to the number of GWAS SNPs for a given GWAS phenotype that overlap input regions and parameter p was set to the fraction of the uniquely mappable human hg19 genome  that is localized in the input regions and contains assessed GWAS phenotype SNPs. Calculated binomial p-value equals the probability of having x or more of the n test genomic regions in the open chromatin domain given that the probability of that occurring for a single GWAS genomic location is p. Plots were made using ggplot2 package and the wes anderson color palette in R (https://github.com/karthik/wesanderson).AHR, ARNT and TCF21 ChIP-Seq binding site were extended to windows +/-1000bp, +/-2000bp, and +/-5000bp using All experiments were performed by the investigators blinded to the treatments/conditions during the data collection and analysis, using at least two independent preparations and treatments/conditions in triplicate. R/Bioconductor or GraphPad Prism 6.0 was used for statistical analysis. For enrichment analyses, we used both Fisher’s exact test and the cumulative binomial distribution test, as indicated. For comparisons between two groups of equal sample size , an unpaired two-tailed Student’s t-test was performed or in cases of unequal sample sizes or variance a Welch’s unequal variances t-test was performed, as indicated. P values <0.05 were considered statistically significant. For multiple comparison testing, one-way analysis of variance (ANOVA) accompanied by Tukey’s post hoc test were used as appropriate. All error bars represent standard error of the mean (SE).The BiKE study is approved by the Ethical Committee of Northern Stockholm with following ethical permits: EPN DNr 95–276/277; DNr 02–146; DNr 02–147, DNr 2005/83-31; DNR 2009/512-31/2; DNR 2009/295-31/2; 2011/950-32; 2012/619-32 and 213/2137-32. The project is performed under the Swedish biobank regulations and prospective sampling is approved with informed consent procedure (DNr 2009/512-31/2). BiKE is registered at Socialstyrelsen  and Biobank of Karolinska and approved by the Swedish Data Inspection Agency . All samples are collected with oral and written informed consent from patients or organ donor guardians.All animal procedures described in this study were approved by the Institutional Animal Care and Use Committees of Stanford University and conformed to NIH guidelines for care and use of laboratory animals. Specifically, the animal studies were approved by APLAC protocol #10022, last approved on 3-16-17 and will remain in effect until 12-12-19.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S13 Fig(PDF)Click here for additional data file.