In the present study, we further analyzed the role of E6 and the Myc-Max-Mad network in regulating the hTERT promoter. We confirmed that E6 and Myc interact independently and that Max can also form a complex with E6. However, the E6/Max complex is observed only in the presence of Myc, suggesting that E6 associates with Myc/Max dimers. Consistent with the hypothesis that Myc is required for E6 induction of the hTERT promoter, Myc antagonists (Mad or Mnt) significantly blocked E6-mediated transactivation of the hTERT promoter. Analysis of Myc mutants demonstrated that both the transactivation domain and HLH domain of Myc protein were required for binding E6 and for the consequent transactivation of the hTERT promoter, by either Myc or E6. We also showed that E6 increased phosphorylation of Pol II on the hTERT promoter and induced epigenetic histone modifications of the hTERT promoter. More important, knockdown of Myc expression dramatically decreased engagement of acetyl-histones and Pol II at the hTERT promoter in E6-expressing cells. Thus, E6/Myc interaction triggers the transactivation of the hTERT promoter by modulating both histone modifications, Pol II phosphorylation and promoter engagement, suggesting a novel mechanism for telomerase activation and a new target for HPV- associated human cancer.Human Papillomavirus Viruses (HPVs) are associated with the majority of human cervical and anal cancers and 10-30% of head and neck squamous carcinomas. E6 oncoprotein from high risk HPVs interacts with the p53 tumor suppressor protein to facilitate its degradation and increases telomerase activity for extending the life span of host cells. We published previously that the Myc cellular transcription factor associates with the high-risk HPV E6 protein  DMSO was used to desolve 10058-F4, 4 nM was used to treat primary HFKs with transfection for Luciferase assays or E6E7 tranduced HFK for 16 hrs. Then, cells were harvested for luciferase or telomerase measurement.Myc/Max inhibitor, 10058-F4 [ and β-actin was also amplified with primers (5’-GCTTGCTGATCCACA TCTGC -3’ and 5’-TGGACATCCGCAAAGACCTG-3’) as an internal control.HFKs transduced with either pLXSN or HPV E6 were grown to 80-90% confluency in a 100-mm dishes. ChIP was performed as described previously [PMID: 12821782]. Normal rabbit IgG (Santa Cruz), rabbit anti-Myc polyclonal antibody , Max, Mad, Pol II, Pol II CTD, Pol II CTD S2 were applied to appropriate immunoprecipitation. DNA from IPs as well as from input samples was subjected to a real time PCR with hTERT promoter specific primers (5’-GAGCTGGAA GGTGAAGGGGC-3’ and 5’-TTCCCACGTGCGCAGC AGGA-3’). The relative occupy was normalized by input DNA.Cells were grown on 22 × 22 mm glass cover slips in six-well cluster plates, fixed in 4% (w/v) paraformaldehyde at room temperature for 20 minutes. Cells were permeabilized by 0.1% saponin (10 min) and blocked with.10% donkey serum in PBS (Invitrogen) for 20 minutes at room temperature. Myc antibody  and Alexa Fluor-conjugated donkey secondary antibodies (Molecular Probes) were used to stain Myc expression. DNA was stained by treatment for 5 minutes with 10 uM Hoechst dye 33342 (Sigma).Renilla reniformis luciferase gene as described below. shRNA against Myc and pGL3 were gifts from Dr. Ernest Martinez [The siRNA target sequences were as follows: for c-myc siRNA duplexes targeting four different region in myc (smartpool from Dharmacon), the targets are nt 437-455 5’-CAGAGAAGCTGGCCTCCTA-3’, nt 360-378 5’-CGACGAGACCTTCATCAAA-3, nt 1263-1281 5’-GAAACGACGAGAACAGTTG-3’ and nt 908-926 5’-CCACACATCAGCACAACTA-3’. 60-70% confluency of HeLa cells or 70-80% confluency of SiHa cells, pLXSN-16E6E7 transduced HFKs were tranfected with a final concentration of 40 nM of negative control siRNA, myc specific siRNA duplex mixture  or Max specific siRNA with LipofectAmine 2000 (Invitrogen) following the manufacturer's instructions. Cells were harvested for Luciferase assay 24 hrs posttransfection, and for TRAP assay 48 hrs posttransfection, respectively. For Luciferase assay, SiHa, HeLa, pLXSN-16E6E7 transduced HFK cells were co-tranfected with siRNA duplexes plus pGL3B-hTERT and Luciferase assays were performed as described previously , 19.Real time quantitative TRAP for detection of telomerase activity was performed as described previously , 4, 14.


Activating transcription factor 6 (ATF6) is an endoplasmic reticulum (ER)-localised protein and member of the leucine zipper family of transcription factors. Best known for its role in transducing signals linked to stress to the endoplasmic reticulum, the 50 kDa activated form of ATF6 is now emerging as a major regulator of organogenesis and tissue homeostasis. Responsible for the correct folding, secretion and membrane insertion of a third of the proteome in eukaryotic cells, the ER encompasses a dynamic, labyrinthine network of regulators, chaperones, foldases and cofactors. Such structures are crucial to the extensive protein synthesis required to undergo normal development and maintenance of tissue homeostasis. When an additional protein synthesis burden is placed on the ER, ATF6, in tandem with ER stress transducers inositol requiring enzyme 1 (IRE1) and PKR-like endoplasmic reticulum kinase (PERK), slows the pace of protein translation and induces the production of stress-reducing chaperones and foldases.In the context of development and tissue homeostasis, however, distinct cellular impacts have been attributed to ATF6. Drawing on data published from human, rodent, fish, goat and bovine research, this review first focuses on ATF6-mediated regulation of osteo- and chondrogenesis, ocular development as well as neuro- and myelinogenesis. The purported role of ATF6 in development of the muscular and reproductive systems as well as adipo- and lipogenesis is then described. With relevance to cardiac disease, cancer and brain disorders, the importance of ATF6 in maintaining tissue homeostasis is the subject of the final section.In conclusion, the review encourages further elucidation of ATF6 regulatory operations during organogenesis and tissue homeostasis, to spawn the development of ATF6-targeted therapeutic strategies. Throughout their lifespan, cells endure a barrage of environmental stressors, such as hypoxia and pathogens, which may provoke cellular damage or even death. Consequently, cells retaliate by effectuating diverse evolutionarily conserved defence strategies in order to overcome the presented exogenous challenge. In recent years, endogenous reservoirs of cellular stress have attracted an accumulating wealth of interest and scrutiny . A cardiCells respond to ER stress through activation and employment of the evolutionarily conserved, unfolded protein response. Mechanistically, the adaptive UPR signalling pathway attempts to attenuate proteostatic collapse through increasing chaperone expression, halting protein translation and degrading abnormally folded proteins. The UPR is commanded by three master regulators: protein kinase RNA-like endoplasmic reticulum kinase (PERK), inositol-requiring enzyme 1 (IRE1) and activating transcription factor 6 (ATF6). The PERK, IRE1 and ATF6 axes coordinate downstream components which initially promote cellular protective events; however, in the presence of chronic and unresolved stress, apoptotic mechanisms prevail to eliminate the stressed cells . RecentlBiP and X-box binding protein 1 (Xbp1) contributing to proteostasis and augmentation of the regulatory output of the IRE1 arm, respectively  and ATF6β (703 amino acids); the biochemical and physiological characteristics of the former are significantly better documented than the latter. The C-termini of ATF6 isoforms protrude into the ER lumen, whereas the N-termini face the cytosol. The cytoplasmic portion of ATF6 encompasses basic leucine zipper (bZIP) DNA binding and transcriptional activation domains, which are followed by a 20-amino acid transmembrane domain. Interestingly, although ATF6α and ATF6β possess significant sequence homology, these isoforms exhibit divergent transcriptional activation domains. Indeed, ATF6α is a potent transcriptional activator whereas ATF6β, a poor transcriptional activator, may inhibit activation by ATF6α . The ER Additional ER membrane-tethered bZIP family transcription factors, such as BBF2H7, CRE-binding protein H (CREB-H), Luman and OASIS , may also be subjected to S2P-catalysed regulated intra-membrane proteolysis (RIP) in response to ER stress conditions. However, the functions of these alternative ATF6 family members are ill-defined . NeverthDuring vertebrate embryogenesis, skeletal structures initially are formed on a cartilaginous model. Thus, the molecular and cellular events regulating osteogenesis and chondrogenesis are tightly connected, both spatially and temporally . ATF6 exAtf6 mRNA expression and ATF6 activation. Furthermore, the authors provided evidence for a role for ATF6 in the induction of osteocalcin expression and MTA-induced mineralisation. Indeed, forced expression of ATF6, using a viral vector, significantly enhanced osteocalcin mRNA expression upon comparison to mock-transfected cells. Additionally, chromatin immunoprecipitation data revealed an increase in the binding of activated ATF6 to the osteocalcin promoter region following MTA treatment. Knockdown of ATF6 expression in MC3T3-E1 cells attenuated MTA-induced osteocalcin expression and osteoblastic mineralisation [Mineral trioxide aggregate (MTA) is frequently implemented as an endodontic cement. Treatment of MC3T3-E1 cells (pre-osteoblastic cell line) with MTA induces osteocalcin expression and mineralisation . Followilisation . Togethelisation . Mice delisation . Howeverlisation . Thus, aATF6 variant which was predicted to result in a truncated, non-functional ATF6 product [ATF6 mutations were identified in ten families with ACHM, all of which caused an attenuation of ATF6 transcriptional activity. Patients displayed foveal hypoplasia and poorly formed or absent foveal pits, thereby distinguishing ATF6-associated ACHM from other forms of the disease in which foveal development typically is preserved [ATF6 mutations were identified in a two-year-old patient with early onset, photoreceptor degeneration. Specifically, this patient exhibited stop-gained (results in truncated protein) and splicing  variants on the paternal and maternal alleles, respectively, severely compromising ATF6 function. Moreover, this patient was too young to complete a colour vision test, however, observed foveal aberrations highly corresponded with those observed in ACHM patients [ATF6-associated ACHM patients, potentially creating opportunity for gene therapy.The lens of the eye possesses two distinct cell types: epithelial cells in the anterior portion and fibre cells which are established continuously following differentiation of epithelial precursor cells at the lens equator. Indeed, fibre cell development is associated with pronounced membrane protein synthesis; thus, it was hypothesised that UPR mediators may be activated during lens development. The expression and cleavage of ATF6 was observed in nascent murine fibre cells, suggesting that this transcription factor is activated in the differentiation of fibre cells during embryogenesis Table , Fig. 1  product . Furtherreserved . Thus, Apatients . FortuitRecently, Kroeger et al.  demonstrGfap promoter and impaired astrocyte differentiation. Interestingly, the activity of ATF6, as well as other UPR axes, was increased during astrocyte differentiation and such mild ER stress may permit OASIS induction and thus, promote astrocyte generation [Presently, documentations of the role of bZIP family transcription factors in the development of the nervous system are lacking; nevertheless, exciting progress has been made. OASIS-deficient mice display fewer astrocytes in cerebral cortices during embryonic development, upon comparison to wild-type littermates. Furthermore, astrocyte differentiation is delayed in primary cultured neural precursor cells which lack OASIS . The losneration . Upon coneration . Of noteneration . ATF6 isneration . Strikinneration . TherefoIn developing murine muscle tissue, ATF6 induces apoptosis through the activation of caspase-12 . MoreoveIn relation to reproductive physiology, ATF6α may execute crucial apoptotic functions during the late-luteal phase in rat corpus luteum regression through the ATF6α-CHOP and caspase 12 pathways, although it remains possible that CHOP induction is mediated by other transcription factors, such as XBP1 or ATF4 . Additioadipocyte protein 2 (AP-2), in pre-adipocytes. Indeed, ATF6 knockdown also ameliorated hypoxia-induced upregulation of the transcriptional repressor tetradecanoyl phorbol acetate induced sequence 7 (TIS7) [Knockdown of ATF6α impaired the differentiation of an adipogenic cell line into mature adipocytes in vitro, as well as associated lipogenesis, highlighting the potential importance of ATF6 in adipogenic and lipogenic processes Table , Fig. 1 7 (TIS7) . TherefoSeminal studies revealed that ATF6α signalling promotes resistance to acute and chronic stress in mouse embryonic fibroblasts, suggesting that ATF6 fosters tissue homeostasis and stress remediation in vertebrate systems . RecentlIn summary, ATF6α serves as an important homeostatic regulator operating in cell- and tissue-specific contexts, and aberrant ATF6 signalling may promote pathogenesis of diverse disease states, including cancer.Classically, ATF6 has been linked primarily to cellular processes aimed at relieving the build-up of misfolded proteins that occurs during pathological ER stress. However, an increasing body of literature is pointing to a critical role in normal development and tissue homeostasis. Molecules reported to be induced by ATF6 or those that exploit ATF6 are varied, depending on the tissue context. For example, the RunX2-ATF6-osteocalcin axis is implicated in bone and cartilage development. WP1, MCl-1 and DKK3 have been identified in ATF6-mediated muscle development. SREBP2, AP-2 and TIS7 levels are altered following manipulation of ATF6 expression during adipo/lipogenesis, and reproductive or brain developmental processes cause upregulation of canonical markers of ER stress, such as CHOP, caspase-12, and ER chaperones. Despite these exciting results, it is intriguing to note that descriptions of the contribution of ATF6 in the development of certain anatomical structures such as gastrointestinal and lung tissues are entirely lacking.The critical importance of ATF6-regulated processes to human health is exemplified by the occurrence of achromatopsia in individuals harbouring ATF6 mutations. Furthermore, the cytoprotective properties of ATF6 have been demonstrated in the context of human disorders ranging from cardiac and brain ischemia, to glomerular disease. Some caution is recommended, however, as ATF6-mediated effects are controversial in the context of diabetes and may promote the survival of liver and squamous cell carcinomas. In conclusion, it is clear that only some of the pieces of the ATF6 puzzle are in place and that significant gaps in knowledge remain. The translational potential of molecular manipulation of ATF6 expression using, for example, small molecular weight compounds  or genet


Epigenetics is the process by which gene expression is regulated by events other than alterations of the genome. This includes DNA methylation, histone modifications, chromatin remodeling, microRNAs, and long non-coding RNAs. Methylation of DNA, chromatin remodeling, and histone modifications regulate the chromatin and access of transcription factors to DNA and in turn gene transcription. Alteration of chromatin is now recognized to be deregulated in many cancers. Medulloblastoma is an embryonal tumor of the cerebellum and the most common malignant brain tumor in children, that occurs only rarely in adults. Medulloblastoma is characterized by four major molecularly and histopathologically distinct groups, wingless (WNT), sonic hedgehog (SHH), group 3 (G3), and group 4 (G4), that, except for WNT, are each now subdivided in several subgroups. Gene expression array, next-generation sequencing, and methylation profiling of several hundred primary tumors by several consortia and independent groups revealed that medulloblastomas harbor a paucity of mutations most of which occur in epigenetic regulators, genetic alterations in oncogenes and tumor suppressors, in addition to copy number alterations and chromosome gains and losses. Remarkably, some tumors have no reported mutations, suggesting that some genes required for oncogenesis might be regulated by epigenetic mechanisms which are still to be uncovered and validated. This review will highlight several epigenetic regulators focusing mainly on histone modifiers identified in medulloblastoma. MYC amplification or SHH MBs with TP53 mutations and MYCN amplification remains poor . Ex. Ex10]. Cip1 [Histone acetyltransferases (HATs) act to relax histone-DNA interactions and promote gene transcription. Though less studied in the context of MB, HATs play a crucial role in brain development, and downregulation of the H4K16 HAT, hMOF, has been associated with poorer outcomes in MB patients . SomaticCip1 . AnotherKDM6A/UTX being one of the most common recurrent events in G4 MB [Alterations in histone demethylases are found in G3 and G4 medulloblastoma, consistent with aberrant histone methylation at H3K27 and H3K4. Trancriptional effectors of lysine demethylase (KDM) regulation include genes involved in cell cycle control, differentiation, and pluripotency. Mutations in six KDM family members have been identified, with inactivating mutations in in G4 MB , 19.Another class of epigenetic regulators, bromodomain (BRD) and extraterminal (BET)-containing proteins, recognizes and binds acetylated histone sidechains on open chromatin and recruits the transcriptional machinery. BET/BRD proteins control MYC levels , 25, oneThe polycomb repressor complex PRC2 plays an important role on differentiation, maintenance of cell identity, and proliferation , 29. It KDM6A/UTX mutations occurring throughout the entire gene leading to complete protein loss are mutually exclusive with EZH2 overexpression, suggesting a critical role for H3K27me3 in MB tumorigenesis and potential clinical vulnerability .Previous in vitro studies in MB cell lines using shRNA or the EZH2 inhibitor DZNEP suggested that EZH2 had oncogenic activity . This apIn accord with a context-dependent role of EZH2 as a tumor suppressor or an oncogene, targeted disruption of EZH2 by shRNA or EZH2 inhibitory drugs strongly suppressed the proliferation of other models of pediatric brain tumors. Several companies have now developed small molecules, targeting EZH2 and EED, some of which are currently in clinical trials . Based oZMYM3, a histone binding protein that typically promotes DNA integrity and contributes to the regulation of gene transcription at the H3K4me3 mark, occur exclusively in G4 tumors [ZMYM3 mutations are often found in conjunction with KDM6A/UTX mutations and submedian EZH2 expression, suggestive of a cooperative role and further supporting the importance of H3K27/H3K4 modulation in MB [DDX3X, a DEAD-box RNA helicase which plays a role in chromosomal segregation and cell cycle progression, are enriched in WNT subgroup MB and are thought to contribute directly to pathogenic β-CATENIN signaling [ATP-dependent chromatin remodeling complexes represent another mechanism by which histone-DNA dynamics can be altered. This class of complexes promotes the accessibility of DNA and facilitates transcription factor migration and/or histone exchange . Recurre4 tumors . ZMYM3 mon in MB . Mutatioignaling .MLL3/MLL4 (KMT2C/B) and KDM6A/UTX, respectively, structural rearrangements resulting in enhancer hijacking have also been described in G3 and G4 MB [Large-scale chromatin remodelers, including NuRD complexes, also play a role in enhancer dynamics, as contributing co-repressors or co-activators . In addind G4 MB . Changesnd G4 MB .miR-124. Its expression is suppressed in MBs leading to overexpression of CDK6. CDK6, the catalytic partner of cyclin D2/CCND2 and cyclin D3/CCND3 that phosphorylates RB to drive cells through the G1 phase of the cell cycle, confers a poor prognosis in MB [miR-17~92 was found to be overexpressed and to have oncogenic activity in SHH medulloblastoma [miR-17~92) or downregulated (miR-124) in medulloblastoma, as in other cancers, suggesting that they could be used therapeutically. Consistent with its oncogenic activity, suppression of the miRNA-17~92 cluster by anti-miRNAs inhibits SHH medulloblastoma development, whereas its enforced expression instead accelerates SHH tumor formation [miRNA-17~92 is a target of MYCN, a direct target of SHH signaling that is overexpressed by amplification in SHH MBs [The miRNAs’ and LncRNAs’ landscape in medulloblastoma has been described in the last 10 years, and some have been validated in mouse models . miRNAs is in MB . Anotherblastoma , 44. miRormation . Interes SHH MBs . Many otmiR-17~92 cluster, leading to the de-repression of their targets, including CDK6, MYCN, KDM6A/UTX, and SNCAIP, all major drivers of G4 MB. Consistent with the role of the miR-17~92 as an oncogene, the ectopic expression of the Linc-NeD125 suppressed G3 MB proliferation, migration, and invasion [PVT1 since it is amplified together with MYC in MBs [LncRNAs are RNAs that are transcribed in an antisense manner from any given genomic loci. Their function has been attributed to regulate integrity of the nuclear structure, regulation of gene expression, and/or post-transcriptional processing . Whereasinvasion . AnotherC in MBs . AlthougWith the ultimate goal of identifying therapeutic vulnerabilities, the validation of epigenetic regulators in mouse models and patient-derived xenografts of MB should contribute to both the basic and translational understanding of MBs.


Streptomyces species, which grow as branched, multigenomic hyphae, are subject to environmental stresses that are associated with changes in chromosomal topology. The topological fluctuations modulate the transcriptional activity of a large number of genes and in Streptomyces are related to the production of antibiotics. To better understand the regulation of topological homeostasis in Streptomyces coelicolor, we investigated the interplay between the activities of the topoisomerase-encoding genes topA and gyrBA. We show that the expression of both genes is supercoiling sensitive. Remarkably, increased chromosomal supercoiling induces the topA promoter but only slightly influences gyrBA transcription, while DNA relaxation affects the topA promoter only marginally but strongly activates the gyrBA operon. Moreover, we showed that exposure to elevated temperatures induces rapid relaxation, which results in changes in the levels of both topoisomerases. We therefore propose a unique mechanism of S. coelicolor chromosomal topology maintenance based on the supercoiling-dependent stimulation, rather than repression, of the transcription of both topoisomerase genes. These findings provide important insight into the maintenance of topological homeostasis in an industrially important antibiotic producer.Maintaining an optimal level of chromosomal supercoiling is critical for the progression of DNA replication and transcription. Moreover, changes in global supercoiling affect the expression of a large number of genes and play a fundamental role in adapting to stress. Topoisomerase I (TopA) and gyrase are key players in the regulation of bacterial chromosomal topology through their respective abilities to relax and compact DNA. Soil bacteria such as IMPORTANCE We describe the unique regulation of genes encoding two topoisomerases, topoisomerase I (TopA) and gyrase, in a model Streptomyces species. Our studies demonstrate the coordination of topoisomerase gene regulation, which is crucial for maintenance of topological homeostasis. Streptomyces species are producers of a plethora of biologically active secondary metabolites, including antibiotics, antitumor agents, and immunosuppressants. The significant regulatory factor controlling the secondary metabolism is the global chromosomal topology. Thus, the investigation of chromosomal topology homeostasis in Streptomyces strains is crucial for their use in industrial applications as producers of secondary metabolites. Despite lacking a nucleus and a mitotic cell cycle, bacteria nevertheless must compact and organize their chromosomal DNA in a small cell volume. The first step of DNA compaction is its negative supercoiling –3, while–6–In all cells, negative DNA supercoiling is maintained by the concerted action of a group of enzymes, called topoisomerases, that catalyze transient breaking and rejoining of phosphodiester bonds . AlthougtopA and gyrAB genes  and the heat shock sigma factor (RpoH)  and other growth conditions   than the optimal length (17 bp) for the typical bacterial promoter  or multhia coli ). The for (RpoH) , 22. Thunvasion) , 23–25. endently , 27, whepromoter . Studiesegulated , 29. Morpromoter , 30. AltStreptomyces strains are appreciated as producers of many useful secondary metabolites , whose synthesis is associated with alterations of chromosomal topology . S. coelicolor TopA differs markedly from its bacterial homologues    in its hal TopA) . This obS. coelicolor. We focused on the expression of topoisomerase genes in response to changes in the TopA level and to the inhibition of gyrase by use of novobiocin. Moreover, we analyzed the changes in topoisomerase level during adaptation to temperature stress. We demonstrate that transcriptional regulation of topoisomerases allows for the efficient maintenance of topological homeostasis in these industrially important bacteria. In addition, due to the topology-dependent gene expression, a better understanding of topoisomerase activity regulation in Streptomyces will contribute to the optimization of secondary metabolite production.In model bacteria, it has been shown that DNA supercoiling affects the transcription of topoisomerase genes , 29, 30.S. coelicolor, followed the general procedures described by Kieser et al. , yielding the MSz-1a (topAΔp2::Aprar) strain. First, the Aprar-oriT cassette was amplified from the pIJ773 vector by using the oligonucleotides prom2_PacI_FWD and prom2_PacI_RV and was subsequently introduced into the H5 cosmid by use of the Redirect system . To this end, we first constructed an H5 cosmid in which the −10 sequence was replaced with the Aprar-oriT cassette (amplified using the oligonucleotides prom1_PacI_FWD and prom1_PacI_RV) flanked by PacI restriction sites. Next, the H5 topAp1::Aprar-oriT cosmid was digested with the PacI restriction enzyme and religated to yield the H5 cosmid topAp1::PacI. To enable conjugative transfer of the H5 topAp1::PacI cosmid, the ampicillin resistance cassette was replaced with an Aprar-oriT cassette  plates without antibiotics and subsequently screened for Kans and Apras colonies, which indicated the exchange of the topAp2::Aprar-oriT plasmid to yield the MSz-4 strain (topAΔp1::PacI). The strain was verified by sequencing of the topA promoter region. The MSz-5 strain was obtained by transformation of the topAΔp1::PacI strain with the pIJ6902topA plasmid  via reporter gene expression, we modified the previously described PS04 strain (containing a deletion at the native topA locus and with topA expressed in trans from the integrating vector pIJ6902) or the merodiploid strain PS07 (containing a second copy of topA delivered in trans in the integrating vector pIJ6902) (topA), with the native copy of the topA gene being replaced by the xylE-hyg-oriT cassette. The xylE-hyg-oriT cassette consisted of two independently amplified DNA fragments: the xylE gene (amplified with the oligonucleotides H5xylE_FW and PacIxylE_RV from the pXE4 vector /10.4).The pWHM3Hyg plasmid (5.9 kbp)  was isolxylE gene ATP. Control S1 nuclease digestions were carried out with an hrdB probe, which was used as a combined semiquantitative standard and a control of RNA quality. After purification, the RNA-probe complex was digested with S1 nuclease.For S1 nuclease mapping, RNA samples were isolated from solid SM cultures grown for 18 to 96 h at 30°C according to a protocol described by Aínsa et al. . S1 nuclCell lysates were separated in 10% SDS-polyacrylamide gels according to standard procedures. After electrophoresis, proteins were transferred to a nitrocellulose membrane and blocked with 2% milk in Tris-buffered saline plus Tween 20 (TBST). The blots were subsequently incubated with polyclonal TopA antiserum  and visualized with alkaline phosphatase-conjugated goat anti-rabbit antibodies  . The banStreptomyces species, as nonmotile soil bacteria, are likely to be exposed to environmental stresses that induce sporulation and antibiotic production, both of which are associated with changes in chromosomal topology. Moreover, in contrast to most bacteria, except for several actinobacterial species, Streptomyces species possess only one type I topoisomerase, TopA. Thus, we expected that the level of TopA should be tightly regulated. Because the first major step in the regulation of gene expression is the control of promoter activity, we set out to investigate the region upstream of the topA gene. The gene encoding TopA (sco3543) is located in a central, evolutionarily conserved region of the S. coelicolor linear chromosome. Interestingly, translation of the topA gene begins at a rare TTG codon, which is used as a start codon for only 3% of genes in S. coelicolor . S1 mapping revealed three bands, suggesting the presence of 3 Ts sites. Two of these Ts sites—Ts1 and Ts1′—are positioned at bp −78 and −83 upstream of the TTG start codon, respectively, whereas an additional site, the Ts2 site, is located at bp −144 , we examined transcript levels in the wild-type strain and in strains harboring mutations in one promoter during vegetative growth (S. coelicolor does not sporulate in liquid cultures). The nested RT-PCRs utilized different forward oligonucleotides and identified only transcripts downstream of the Ts1/Ts1′ (p1 transcript) and Ts2 (p2 transcript) sites (topA transcription in this mutant was driven by the p2 promoter (topA gene (sco3544 gene under the conditions tested.To confirm the transcription of the t) sites . Additiopromoter . On the promoter . NotablyopA gene , confirmtopA transcript (p1 + p2) as well as the p2 promoter-specific transcript (p2). This analysis confirmed that the topA promoters were equally active  during vegetative growth. In the Δp1 strain, the level of the topA transcript was only half the wild-type level .Next, we decided to further analyze the contributions of both promoters to the transcript levels and to dissect their regulation. Using RT-qPCR, we calculated the overall copy number of the pe level , suggestS. coelicolor differentiation, the topA gene is transcribed at a nearly constant level. Transcription of the topA gene is controlled by two equally active promoters: p1 and p2. Interestingly, the elimination of one, but not both, of the promoters is possible without any visible effect on growth or change of the activity of the other, intact promoter.These results show that during E. coli, M. smegmatis, Streptococcus pneumoniae, and Haemophilus influenzae showed that, in bacteria, changes in chromosomal supercoiling density affect the expression of many genes, including topA  and luxCDABE at the phage attachment site (encoding luciferase in the MG04 strain)—under the control of the topA promoter. The obtained strains allowed us to quantify the transcriptional activity of the topA promoter under increased supercoiling conditions and to verify the influence of the chromosomal locus on promoter activity.Studies on ing topA , 46–48. promoter ). The detopA promoter. The activity of the xylE reporter gene product increased up to 1.5-fold compared to its activity in the control culture . The increase of topA promoter activity in the MSz-H1 strain was proportional to the decrease of the cellular level of TopA , the activity of the topA promoter was 3-fold higher than that under standard conditions (TopA depletion (by the absence of the inducer)  highly itopA promoters were sensitive to increased DNA supercoiling. To this end, we used the MG04 strain, in which the topA promoter drives transcription of the luxCDABE operon, to quantify the transcription of the first gene in the operon (luxC). By comparing transcription levels driven by the topA promoter at the low topoisomerase I level  and the wild-type level , we confirmed a >2-fold increase of the overall level of the p1 + p2 transcript  only slightly inhibited gyrB transcription (87% of wild-type activity)  . This prtopA promoter, but the p1 promoter is solely responsible for the induction of topA expression under high supercoiling conditions. Remarkably, gyrase gene transcription is only slightly inhibited by high supercoiling, and thus a lower gyrase level does not provide the mechanism that restores normal supercoiling.In summary, TopA depletion leads to an increase of negative DNA supercoiling and to strong induction of the topA promoter activity. In the Msz-H1 strain, XylE activity with an elevated TopA level  was reduced only to approximately 85% of the level observed in the control culture  (tipAp inducer concentration (5.0 μg/ml) . As observed previously, increasing the TopA level only slightly decreased topA expression, and 82% of promoter activity was retained in the control noninduced strain  .Interestingly, we noticed that in contrast to TopA depletion, its overproduction only slightly inhibited e level) . Surprise level) . Thus, ttopA promoter while TopA overproduction did not result in significant inhibition of the topA promoter suggests that an increase of the TopA level may be compensated by changes in the level of gyrase. We therefore investigated whether the transcription of the gyrBA operon was upregulated by TopA overproduction . Indeed, the increased level of TopA stimulated the gyrBA promoter 2- to 3-fold compared to its activity with the wild-type TopA level  after addition of novobiocin (5 μg/ml), the negative supercoiling density (σ) of the plasmid decreased significantly, suggesting DNA relaxation. Subsequently, the plasmid supercoiling increased, reaching the wild-type level up to 120 min after the exposure to novobiocin (topA transcription. At the highest tested novobiocin concentration (5 μg/ml), we detected decreases in both p1 + p2 and p2 transcripts, to 78% and 83% of their initial levels, respectively (topA promoter-driven reporter gene transcription in the wild-type background (strain MG03) was only slightly or not sensitive to novobiocin , confirming our previous observation that TopA overproduction does not inhibit the topA promoter.Inhibiting gyrase led to a slight inhibition of ectively . The rattopA promoter, the transcription of the gyrBA operon was elevated 9-fold , the transcription of a number of genes changes to facilitate cellular adaptation and survival under new environmental conditions. It has been shown previously that changes in gene transcription profiles are mediated by alterations in chromosomal supercoiling , 13.S. coelicolor chromosomal supercoiling is affected by heat shock, we analyzed the changes in supercoiling of the reporter plasmid after exposure to 42°C. Immediately (10 min) after temperature upshift, the DNA negative supercoiling density (σ) decreased significantly, and the relaxed form was observed to be the most dominant form. The longer incubation at an elevated temperature resulted in a gradual increase of plasmid supercoiling . Subsequently, we determined the protein level by quantitative Western blotting . However, our S1 nuclease mapping clearly showed the presence of both transcriptional start points. A comparison of the topA upstream region and other described S. coelicolor promoters suggested the presence of −35 and −10 sequences in the p1 promoter similar to sequences recognized by the HrdB protein, a homologue of the E. coli sigma70 factor  increased notably when the TopA level was depleted. Thus, the regulation of the eviously , 29, 46.rcoiling . AnalysigyrBA operon in S. coelicolor is highly induced by increased chromosomal relaxation, such as that induced by TopA overproduction or novobiocin treatment. However, the increased relaxation has a weak negative influence on the S. coelicolor topA gene, i.e., the overproduction of TopA slightly inhibits both p1 and p2 activities. Such regulation is remarkably different from the efficient negative-feedback mechanism of topA expression that has been described for other bacterial species  is very efficiently inhibited by chromosomal relaxation induced by novobiocin treatment or TopA overproduction  that blocks topA transcription. Another possibility is based on the observation that the transcription of the hrdB gene decreases under thermal stress conditions (topA promoter may be lowered. In the next step, when gyrBA transcription is elevated, restoring topological homeostasis, the lowered HrdB level keeps the transcription of topA at a decreased level. Thus, the coordinated action of the topA and gyrBA promoters leads to gradual restoration of normal chromosomal supercoiling after its rapid relaxation. It is also possible that the low level of TopA during thermal stress is provided by proteolytic degradation. It has been suggested that in S. coelicolor, the TopA protein is potentially degraded by the Pup-proteasome system (The fraction of relaxed plasmid observed shortly after nditions  (see Fige system . This isE. coli, topA gene expression is precisely regulated by four distinct promoters, including a constitutive promoter as well as a heat shock-responsive element (E. coli strain lacking the topA gene has been shown to be more sensitive to oxidative stress (H. pylori and Salmonella, discrete changes in supercoiling regulate the transcription of genes involved in host persistence (S. coelicolor.It has been shown that small changes of the topological balance are involved in adaptation to stress conditions. In  element , 21. More stress , 63. In sistence , 30. TheStreptomyces coelicolor, the promoters of the TopA- and gyrase-encoding genes are specialized to respond to different supercoiling states (topA gene is stimulated by high supercoiling density and is weakly sensitive to DNA relaxation, whereas gyrase expression is induced only under chromosomal relaxation conditions and is only slightly inhibited by increased supercoiling. Thus, in S. coelicolor, both the topA and gyrB genes are subject to positive regulation by a shift of chromosomal supercoiling. The negative-feedback regulation is relatively weak and may only precisely tune transcription rather than preventing the loss of topological balance (topA coupled to the upregulation of the gyrase genes is involved in the dramatic chromosomal compaction observed after thermal stress. Thus, we suggest that Streptomyces evolved a specific system of topological homeostasis maintenance that involves positive regulation of topoisomerase genes and presumably is connected with the stress response and with changes in chromosomal condensation during development. Insights into the Streptomyces topological homeostasis maintenance mechanism should prove useful for understanding topologically dependent gene expression, particularly in antibiotic pathways.We have demonstrated here that in g states . The top balance . However


Eukaryotic transcription is regulated through two complexes, the general transcription factor IID (TFIID) and the coactivator Spt–Ada–Gcn5 acetyltransferase (SAGA). Recent findings confirm that both TFIID and SAGA contribute to the synthesis of nearly all transcripts and are recruited genome-wide in yeast. However, how this broad recruitment confers selectivity under specific conditions remains an open question. Here we find that the SAGA/TREX-2 subunit Sus1 associates with upstream regulatory regions of many yeast genes and that heat shock drastically changes Sus1 binding. While Sus1 binding to TFIID-dominated genes is not affected by temperature, its recruitment to SAGA-dominated genes and RP genes is significantly disturbed under heat shock, with Sus1 relocated to environmental stress-responsive genes in these conditions. Moreover, in contrast to recent results showing that SAGA deubiquitinating enzyme Ubp8 is dispensable for RNA synthesis, genomic run-on experiments demonstrate that Sus1 contributes to synthesis and stability of a wide range of transcripts.Our study provides support for a model in which SAGA/TREX-2 factor Sus1 acts as a global transcriptional regulator in yeast but has differential activity at yeast genes as a function of their transcription rate or during stress conditions.The online version of this article (10.1186/s13072-018-0184-2) contains supplementary material, which is available to authorized users. Expression of protein-coding genes is a coordinated process that consists of a series of linked steps in mRNA fate: transcription, processing, export to the cytoplasm, translation and degradation. Regulation of gene expression is essential to cellular metabolism and to adaptation to stress and developmental processes. A key step in eukaryotic transcription is the binding of a sophisticated repertoire of regulators that collectively function to control the timing of expression of each gene. Eukaryotic RNA polymerase II (RNAPII) genes have been classically divided into two main groups attending to the presence or absence of the TATA sequence in their promoters . While tSAGA and SLIK are organized in submodules –6, 10 anADH1 and GAL1), where it promotes the interaction with export factors, contributing to the coupling of transcription and mRNA metabolism [Apart from SAGA-dependent events affecting histone modifications during transcription initiation, several studies have identified SAGA roles other than those of upstream regions. In fact, yeast SAGA subunits are not restricted to promoters, as they also localize to coding sequences of specific genes –19. In ttabolism .Saccharomyces cerevisiae [Deciphering how SAGA’s different enzymatic activities control the expression of their target genes is a major challenge in fundamental transcription research. Classical results from genome-wide approaches suggested that SAGA binds to a subset of promoters in yeast –30 that revisiae . In contrevisiae –38.In this study, we show for the first time the genome-wide recruitment of the SAGA/TREX-2 factor, Sus1, to RNAPII-transcribed genes. Sus1 accumulates at upstream activator sequences (UASs) at a variety of gene classes: ribosomal protein (RP), SAGA-dominated and TFIID-dominated genes. The amount of Sus1 binding is correlated with the transcription rate (TR) of target genes. Analyses of Sus1 chromatin recruitment under heat shock revealed that while this stress scarcely affected Sus1 binding to TFIID-dominated genes, the binding pattern at SAGA and RP genes was substantially affected. Consistent with the genome-wide association of Sus1 to UAS, we found that transcription rates and stabilities of most yeast transcripts are changed in the absence of Sus1. Lastly, we demonstrate that both SAGA and SLIK participate in Sus1 recruitment to SAGA-dominated genes. In sum, our results support a model in which Sus1 is intimately tied to the expression of yeast RNAPII genes.To identify the target genes of Sus1 on the genome, we analysed the genome-wide chromatin recruitment of Sus1 by chromatin immunoprecipitation-exonuclease (ChIP-exo) experiments. Heat maps showing shifted 5′-end sequencing reads (tags) for Sus1-WT at 25 °C (blue) and after 15 min at 37 °C (red) were aligned by the midpoint between transcription start site (TSS) and transcription end site (TES)  cells and gene expression levels were analysed using DNA arrays. However, as the final amount of any transcript is determined by the balance between its transcription and degradation rates [sus1Δ cells . In addition, we used previously published relative mRNA abundance (RA) estimates [sus1Δ and WT cells were much less correlated  revealed that translation-related genes tend to have high TR ratios, while energy derivation genes are at the lowest TR ratios , while environmental stress-response upregulated (ESR-up) [sus1Δ versus WT against TR in WT and observed a positive trend, indicating that Sus1 is more effective at destabilizing mRNA of highly expressed genes   transcrines Fig. c. SimilaSince our results showed a positive relationship between HL fold change and TR levels affected by Sus1 deletion Fig. c, we nexOur analyses revealed that upon heat shock Sus1 binding to RP genes was significantly reduced, while its presence at SAGA-dominated genes was augmented Fig. b. These ARG1 in ubp8Δ (SAGA) and in sac3Δ (TREX-2) mutants [ARG1 UAS [spt8Δ and Spt7 C-terminal-truncated mutant (spt7-1180) in which SLIK, but not SAGA, exists [spt7-1180 and spt8Δ strains at 25 °C (blue) and after 15 min at 37 °C (red) were aligned to RP, SAGA-dominated and TFIID-dominated gene classes. Similar to the results observed for Sus1 in a WT background  bound to both 5′ and 3′ gene ends, whereas at RP genes the association corresponded mainly to the 5′-end  and upon heat shock (red line). Data are plotted around the transcription start site (TSS) oriented to the right, together with nucleosome (MNase) traces at 25 °C (grey fill) and after heat shock (yellow trace) from [We previously addressed the contribution of SAGA and TREX-2 to Sus1 chromatin association via investigating Sus1 recruitment to  mutants . The expARG1 UAS . Sus1 isARG1 UAS . The fun, exists , 49, 50,ce) from . Sus1 reIn this study, we demonstrate a general role for Sus1 in transcription and mRNA stability in yeast cells. Sus1 is recruited to the upstream activation sequences of many genes and is necessary for mRNA synthesis of a large number of transcripts independently of the presence of a TATA sequence. The classical model of the role of SAGA/TFIID in regulating transcription states that they possess distinct but overlapping selectivity for promoters, thereby fostering a cascade of events that leads to the correct expression of their targeted genes. In classical studies, TATA-containing genes were relatively more dependent on SAGA, whereas TATA-like genes were relatively more dependent on the general transcription factor TFIID . NumerouSUS1 has a strong negative effect on the transcription rate and ii) mRNAs were generally more stable in the sus1Δ mutant. Similar phenotypes have recently been described for the SAGA/SLIK structural mutants spt7Δ and spt20Δ [sus1Δ presented here complement these observations, suggesting that Sus1 is part of the SAGA-related factors that influence the stability of RNAPII-transcribed mRNAs. However, since Sus1 is a shared component of SAGA/SLIK and TREX-2, we cannot attribute unambiguously the observed Sus1-dependent effect on transcription only to SAGA/SLIK. Further work is required to study the contribution of TREX-2 to this phenotype.Seminal studies that established the regulatory role of SAGA and the functional clustering of its subunits measured steady-state RNA levels, without differentiating between transcription and degradation rates , 31. In d spt20Δ  and for d spt20Δ . The GROSUS1 tend to have higher TR. This could reflect a compensatory crosstalk mechanism in which the general reduction in TR by SUS1 deletion is compensated by increase in HL in order to maintain mRNA levels, or vice versa. Such effect has previously been reported for decay factors [We observe that genes more stabilized by absence of  factors , 53 and  factors , 55. In  factors , 56. The factors , makes i factors , which i factors . Consequ factors .spt8Δ and truncated spt7-1180. We addressed the contribution of SLIK to Sus1 recruitment by demonstrating that SLIK contributes to recruitment of Sus1 genome-wide, along with SAGA. Therefore, these results provide insights into the shared roles of SAGA and SLIK.SAGA and SLIK share all but two of their components: Spt8 and full Spt7. The distinctive roles of the two complexes are still not clear and are difficult to address because of this high degree of overlap. We examine here an “ONLY-SLIK” phenotype by using mutants of We propose here that under standard conditions Sus1 is recruited at a basal level to the UAS regions of expressed RP, SAGA-dominated and TFIID-dominated genes, at levels that reflect their transcriptional activity. Upon heat stress, Sus1 is preferentially associates with promoters of SAGA-dominated genes  in parallel with gene activation. Since Sus1-dependent deubiquitylation is genome-wide , Sus1 coURA3 marker) and MYC (HIS3 marker) as C-terminal tags was performed as previously described [The yeast strains used in this study are listed in Additional file escribed , 61. For® Pan Mouse IgG for 2 h. Immunoprecipitates (IP) were washed, and samples were eluted at 65 °C for 20 min with 100 µL of elution buffer. Inputs and immunoprecipitation samples were incubated at 65 °C overnight to reverse the cross-linking. Samples were treated with proteinase K, and DNA was purified with phenol/chloroform/isoamyl alcohol extraction and ethanol precipitation. The list of primers for ChIP analysis and RTqPCR can be found in the Additional file ChIP was performed as described  with minTwo biological replicates of each ChIP-exo of Sus1-MYC-tagged protein and a no-tag strain were performed. Cells were grown in YPD at 25 °C. The experiments were performed as in . The heasus1Δ was done and analysed as described in [sus1Δ cells was determined [Genomic run-on (GRO) for WT and ribed in . This meribed in  for a deribed in . Total mtermined  and usedAdditional file 1: Fig. S1. Sus1 occupancy at TFIID-dependent genes was monitored by ChIP analysis of Sus1-TAP in a wild-type strain (Sus1-TAP). As a control, the signal of an isogenic strain bearing no-tagged Sus1 was monitored (No-tag). The occupancy level was calculated as the signal ratio of IP samples in relation to the input signal and relative to an internal control. The resulting normalized ratios were plotted. Error bars represent the SD from at least three independent experiments. Differences in means were assessed by Student’s independent-samples t test. P values < 0.05, indicated with an asterisk, were considered to be statistically significant.Additional file 2: Fig. S2. Gene set enrichment analysis (GSEA) for the highest ChIP-exo reads. The genes were ranked according to the number of mapped reads and searched for GO terms enriched at the top of the list in comparison with the rest of the list using GSEA. The resulting list of over-represented GO terms was reduced and visualized with the ReviGO web server (http://revigo.irb.hr/). a) Binding at 25 °C. Left: Results at the Biological Process GO; right: Results at the Cellular Component GO. b) Binding at 37 °C, results are given for the Biological Process GO. The Cellular Component GO gave no results. The size of the circle for each GO term is proportional to the number of genes included, and the red colour intensity is proportional to the p value.Additional file 3: Fig. S3. Gene set enrichment analysis (GSEA) of TR ratios (sus1Δ/WT). Gene Ontology (GO) terms  over-represented at the top and at the bottom of the ranked list of TR ratio values.Additional file 4: Fig. S4. Gene set enrichment analysis (GSEA) analysis of HL ratios (sus1Δ/WT). Gene Ontology (GO) terms  over-represented at the top and at the bottom of the ranked list of HL ratio values.Additional file 5: Table S1. Is a table listing strain used in this study.Additional file 6: Table S2. Is a table listing Primers for ChIP analysis and RT-qPCR.Additional file 7. ChIP-exo data analysis.


As a result, when one of these proteins is limiting, competitive effects have a significant impact on the transcriptional response of the regulated genes. Here we present a general framework for the analysis of any generic regulatory architecture that accounts for the competitive effects of the regulatory environment by isolating these effects into an effective concentration parameter. These predictions are formulated using the grand-canonical ensemble of statistical mechanics and the fold-change in gene expression is predicted as a function of the number of transcription factors, the strength of interactions between the transcription factors and their DNA binding sites, and the effective concentration of the transcription factor. The effective concentration is set by the transcription factor interactions with competing binding sites within the cell and is determined self-consistently. Using this approach, we analyze regulatory architectures in the grand-canonical ensemble ranging from simple repression and simple activation to scenarios that include repression mediated by DNA looping of distal regulatory sites. It is demonstrated that all the canonical expressions previously derived in the case of an isolated, non-competing gene, can be generalised by a simple substitution to their grand canonical counterpart, which allows for simple intuitive incorporation of the influence of multiple competing transcription factor binding sites. As an example of the strength of this approach, we build on these results to present an analytical description of transcriptional regulation of the  Transcriptional regulation is essential for shaping cellular response and dynamics. At the heart of these responses is the specific arrangement of regulatory features around the promoter that governs how a gene will respond to the available regulatory molecules . A primaStatistical mechanical models have been used to quantitatively describe transcriptional regulation for a variety of regulatory motifs –17. In tWhile statistical mechanical models of gene expression have thus far proven to be very successful, they have traditionally been derived in the “non-interacting” limit, i.e. the gene of interest is treated as being isolated and the relevant molecules only interact with the gene itself and a competing “non-specific reservoir” accounting for the generic interaction between the molecules and the rest of the genome , 29, 30.In this work we go well beyond these earlier efforts to show that the grand-canonical approach can be generalized to include more complex regulatory architectures, opening the door to considering regulation in the setting of real cellular processes. Specifically, we demonstrate how to derive expressions for the fold-change for regulatory architectures that have not previously been described using this formalism, including how to characterize such architectures in the case of multiple gene copies and competing reservoirs for transcription factors. Interestingly, all grand-canonical expressions that are derived in this work differ from their corresponding canonical expressions merely by a simple substitution.lac operon, a regulatory architecture that includes repression, both through proximal binding and by DNA looping interactions, and activation. In the remainder of this article, we derive expressions for the fold-change for regulatory architectures that include repression, activation and repression by DNA looping, and show how to combine different regulatory elements into more complex architectures. We will provide, as a case study, a worked example for the fold change in expression from the Transcription initiation is a complex process involving multiple steps, each with their own rate. In its most simplified form, it can be described in three steps: the binding of RNAP to the promoter to form a closed complex, the (irreversible) isomerisation of the closed complex to an open complex, followed by the escape of the open complex to form an RNAP complex active in transcription –21. Whenϵns〉 − βσ2/2 with 〈ϵns〉, σ the mean and standard deviation, respectively, of the distribution of binding energies to non-specific sites and β = (kBT)−1 [Statistical mechanics provides the tools to calculate the occupation probability of RNAP and transcription factor binding sites, where the RNAP and transcription factors are shared between many different binding sites. The ensemble of choice for a system where the number of molecules is allowed to fluctuate is the grand canonical ensemble. While strictly most suitable for systems with large numbers of particles, the relative fluctuations decrease quickly as es grows . We theres grows . Conseques grows . We have (kBT)−1 . All eneϵP, and a repressor can bind to the operator site with energy ϵR, while the simultaneous binding of both, RNAP and repressor is prohibited by excluded-volume interactions.We start with the simplest nontrivial regulatory architecture referred to as ‘simple repression,’ as illustrated in the first column in μR is the chemical potential of a repressor molecule. Similarly μP is the chemical potential of an RNAP molecule. The indices p and r reflect the number of RNAP and repressor molecules, respectively, that are bound to the gene in a given occupational state with Z the relevant part of the canonical partition function. The factor Z is given by the product of all the Boltzmann exponents of the individual binding free energies of the DNA-bound transcription factors, and of the interactions that take place between them when they are bound in that arrangement. All other internal degrees of freedom remain constant, and therefore do not contribute to the weight of a configurational state. This modular approach allows the framework to be used in conjunction with automated scripts to calculate the statistical weight of a configurational state. While for simple promoter architectures it is possible to write down the statistical weights for all the individual configurational states, this quickly becomes cumbersome when the complexity of the promoter architecture increases. Similar state-weight scripts have been demonstrated for models based on the canonical ensemble, for example in ref [The grand canonical partition function  of a sine in ref .Z = 1, Z is zero and that term is excluded in N statistically independent gene copies we have Ξs = ΞN as in our previous work (Eq (2) in [For the motif of simple repression q (2) in ). It canq (2) in , 7, 8 isλi the fugacity of the cognate transcription factor i. Since all N gene copies are independent and identical, the occupational fraction θi can (and will in the remainder of this work) also be calculated from the single gene partition function Ξ from the easier, but mathematically equivalent equationThe fraction of binding sites occupied by its cognate transcription factor is calculated by θi=1N∂loλP, λR are independent, that is, the value of λP does not depend on the repressor concentration (or fugacity). As shown in the supporting information which istion see .R ≪ Nns, the canonical and grand-canonical expressions become equivalent. To see that, consider the average number of repressors bound to non-specific sites, which is given byλR ≪ 1 which is valid as long as R ≪ Nns [λR = 〈Rns〉/Nns, which, for a single gene copy per cell, asymptotically approaches R/Nns at large R. While not exact for small R, λR ≈ R/Nns is a good approximation in most physiological situations (again with a single gene per cell) where cells typically contain multiple repressor copies.In the limit that 1 ≪  R ≪ Nns . Thus, wIn the remainder of the paper we show that more complicated regulatory architectures that have been analyzed in the canonical ensemble , 8 can eThough it is one of the most common architectures, the simple repression regulatory motif described above is only one of many common regulatory motifs , 50. In N copies of a gene that contains a main and an auxiliary operator site, denoted by m and a, respectively, and a promoter site P for RNAP. The architecture as well as a table of states and weights is shown in Z is the relevant measure of the canonical partition function when p RNAP molecules and r repressors are adsorbed onto the promoter region. Just as in the case of simple repression above, configurations that include a repressor bound to the main site and an RNAP bound to the promoter simultaneously are given zero weight. Further we define ϵP, FL is the free energy cost associated with forming a loop. In writing down the right side of Z:Consider in vitro [Note that in general repressor molecules could bind to operator sites of two different gene copies at the same time, which has been observed in several experiments in vitro , 53. It λPxP ≪ 1, resulting inThe fraction of promoters occupied by an RNAP molecule can by analogy to λRxPxRa).In the a.(λPxP≪1)The foldR/Nns by λR and R ≫ 1, the two ensembles are not equivalent—the value of λR is not equal to either R/Nns or λR merely plays the role of the canonical concentrations in otherwise identical expressions.The result of z = λRxR (for simple repression) or zL (for the looping architecture), which should cause data from both types of promoter architecture to collapse onto the same scaling law (1 + z)−1.In order to facilitate a consistent comparison between theory and experimental gene activity data over different scenarios , we can write the result of araBAD operon in the absence of arabinose [zEL given asThe exclusive looping architecture is a variant of the generic looping architecture where binding of RNAP to the promoter site is prohibited if and only if DNA looping occurs. For instance, a famous example of this is seen for AraC regulating the rabinose . In thisθR as a function of λR has been plotted in λPxP ≪ 1) and additionally, we set the average binding energy of the repressors to the Nns non-specific binding sites to zero. The number of adsorbed repressors to non-specific sites in the situation that λR ≪ 1 (which is verified later) is given byλR follows by solving the mass balance equation for repressorsa, b and c given byWe calculate the average number of adsorbed repressors onto both the main and auxiliary sites in the looping scenario illustrated in The figures show several features, which can be explained by the degree of competition between the genes for the available number of transcription factors. The fugacity equals the reservoir concentration of transcription factors on the non-regulatory DNA. When the number of transcription factors is smaller than the number of genes, there is strong competition for the transcription factors. Consequently, the majority of the transcription factors are primarily adsorbed on the genes, while the non-regulatory reservoir is nearly empty. However, when the number of transcription factors exceeds the number of genes, the surplus of transcription factors reside in the non-regulatory reservoir, with a corresponding increase in fugacity. The crossover occurs when the number of transcription factors equals the number of genes. This leads to strong repression in both the looping and exclusive looping promoter architectures, since the most likely singly occupied configurational state for both architectures prohibits RNAP binding. When the concentration of transcription factors increases even more, the doubly occupied configurational states become more important which are repressive in the case of the looping architecture, but which allows transcription in the exclusive looping architectures.In z could be interpreted as the relative weight of states where the main operator site is occupied, as modified by its surroundings. There is no deeper physical meaning that we attribute to the scaling parameter z. The exclusive looping architecture provides a borderline example that can still intuitively be mapped onto this functional form. The scaling parameter z here reflects the occupational weight of the looped state. However, there are many promoter architectures where the fold-change is not completely dominated by the occupation of a single main operator site, which limits the usability and interpretation of this scaling form in those cases.The fold-change of the simple repression and looping promoter architectures are dominated in the weak promoter limit by the occupation of the main operator site. Hence, when the expressions for fold-change are cast into the scaling form of In many situations, a transcription factor actively “recruits” RNAP to bind to a promoter. Essentially, there is an adhesive interaction between the bound transcription factor and the RNAP. In the following section we discuss the situation where genes are regulated by such an activator. The simplest of such situations, from now on referred to as simple activation, as well as the corresponding table of states and weights is shown in ϵA and ϵP, respectively. When both are bound to their appropriate sites simultaneously, there is an additional free energy gain of ϵAP which reflects the effective attraction between RNAP and activator. The situation in the canonical ensemble has been analyzed in [μA its chemical potential. Further we take Z as: Z = 1, Z = xP, Z = xA, and Z = xPxAxAP. Here λA = 0) we again regain λP, λA are independent, that is, λP has the same value in ϵAP. As shown in the supporting information  and theλ, which depends upon the nature  of additional binding reservoirs for that transcription factor.R/Nns by λR, A/Nns by λA or The canonical expressions shown here, in contrast, describe only the case of an isolated, non-interacting gene. While the canonical ensemble can generally be used to describe the situation of multiple gene copies and competitor sites , 34, eaclac operon in Escherichia coli. A sketch of the regulatory architecture is given in In this case study we will show how to calculate the fold-change in gene expression for the regulatory motif of the lac operon asZ the relevant part of the canonical partition function with p, r, a molecules of RNAP, LacI and CRP bound to the gene, respectively. Since the promoter sequence partly overlaps with the main operator site O1, their simultaneous occupation is prohibited by excluded volume interactions. Those states automatically have a weight of 0. There is a partial overlap between the activator site and the auxiliary operator site O3. LacI was found to bind to O3 even with CRP bound to the activator site, but its position is then shifted by 6 bp [ϵAP is the energy bonus that is gained by simultaneously binding RNAP and CRP, which when O3 is bound is modified to i and j, and We write the grand partition function for the regulatory architecture of the  by 6 bp . This, c by 6 bp . These sλPxP and those that are independent of λPxP, so that we can write for the grand partition functionP as0θP asθP is given asλPxP ≪ 1, as well as a second assumption that λPxP ≪ Σ0/ΣP, which makes the fold-change independent of the RNAP fugacity. When repression is stronger than activation (which is the case when ΣP/Σ0 < 1), this second assumption is already implicit in the weak promoter limit. In the case of strong activation, however, this second assumption is stricter than the weak promoter limit and care needs to be taken when applying it. The validity of this assumption needs to evaluated a posteriori. As we will show in the supporting information  and the perturbed fugacities is negligible, and consequently it makes sense to decouple the activator and repressor fugacities completely (f = g = h = 1). We show in λA and λR, shown in 2 and 103.The fugacities Most of the adsorption and interaction energies that are relevant to our calculations are known from previous experiments; only the looping free energies of the lesser studied O1O2 and O2O3 loops, the coupling strength between activator and the O1O3 loop and the reduction in the activation when O3 is occupied have yet to be verified by independent experimental studies. In general, the looping free energy depends on several factors, notably the length of the loop and the number of stable conformations that can be formed in conjunction with the tetrameric repressor. These interactions can be modeled explicitly as is done in e.g. refs , 61. Tablac operon to find the missing energies. In a range of experiments by Oehler in the 1990’s [lac operon was determined in the presence of two concentrations of lac repressor. Different constructs were tested, where some adsorption sites were deleted from the genome, or replaced by the sequence of a different operator. While there exists an uncertainty in the actual number of repressors in these experiments, the number of different mutations that were tested make this study a prime candidate to calibrate the model. Note also that in these experiments the activator site was kept intact, but Oehler et al. did not actively control the number of activators, nor report their concentration. We have assumed a number of ∼1000 activators [Nc = 350 competitor sites [We calibrated the model to existing experiments on the e 1990’s , 62, thetivators , at whicor sites , each wiR, We found et al. in x-axis of the graph, and the corresponding theoretical fold-change on the y-axis, with perfect correspondence between experiment and theory when a point falls on the x = y line that is shown as the blue dotted line in the graph. Most points in the classical results of Oehler et al. fall within five-fold of perfect correspondence, over a very wide range of experimental parameters. For some very repressive constructs, Oehler et al. were only able to determine a lower bound to the level of repression . Those constructs have been marked with a cross in x = y line, indicating that the theoretical framework indeed predicts a lower activity than could be seen experimentally.We plot the results of Oehler lac operon based on the canonical ensemble, which also captures the behaviour of the classical experiments by Oehler et al. In their canonical framework they have included explicitly the association equilibrium of LacI dimers to tetramers, and the binding of LacI to external inducer. Their model appears to be similar in accuracy to ours. Their use of the canonical ensemble is justified here since they do not introduce the CRP activators explicitly. Rather, they scale the effect of reduced activation by the occupation of the O3 auxiliary site with an effective fit parameter. Since LacI is not strongly competed for in wildtype cells, there is no similar titration effect such as is the case for CRP. When CRP is modeled explicitly, or when LacI is competed for, for example by competitor genes or competitive inhibitors, the titration effect that arises needs to be dealt with, and those situations can be modeled in the grand canonical framework. Moreover, the association equilibrium of LacI dimers to tetramers, and the binding of LacI by inducers can be introduced into the framework in a straightforward way.Vilar and Saiz  have proR = 0 (fold-changeR)in the absence and presence of A = 0, A = 1000 activators respectively. The blue curve shows that in the absence of activator, repressors cause a decrease in the transcription-rate of approximately three orders of magnitude. However, the presence of activator may cause up to an additional single order of magnitude of decrease in the fold-change. While the net gene expression due to the presence of the activators remains higher, the presence of the activator causes a greater difference between the unrepressed and the repressed system. A = 0 (fold-changeA) in the absence and presence of R = 0, R = 100 repressors respectively, illustrating that in the absence of repressor, the activators may cause up to a 200-fold-change in transcription rate, which drops down to ∼80-fold in the presence of a larger number of repressors.However, an interesting cooperative effect occurs in the presence of CRP. The presence of activators increases the dynamic range of the repressors, whereas the presence of repressors reduces the dynamic range of the activator. The reason for this is that bound activator assists in forming repressing O1O3 DNA loops. et al. [lac operon in Escherichia coli constructs that are unable to synthesize cAMP. Since CRP needs cAMP to activate the lac operon, the activating response to the cAMP-CRP complex could be induced externally. In the presence of cAMP in the growth medium, induction of the bacteria by IPTG (inactivating lac repressor) caused a >1600-fold-change in transcription levels. In the absence of cAMP, this fold-change dropped to only <250. Saiz and Vilar [This effect was experimentally observed by Kuhlman et al. , who meand Vilar  also addlac operon exists in E. coli cells per chromosome and at slow growth rates the copy number of the lac operon is expected to be one or two. However, fast growing cells have multiple replication forks of the chromosome which can result in a higher copy numbers of the lac operon. Using this theory, we can calculate the effect of the existence of multiple gene copies without significant additional effort. lac operon regulated gene with a copy number of 10 in a single cell, as a function of activator and repressor numbers. At higher repressor concentrations there is no qualitative difference between this case and the single copy number case. At lower repressor concentrations, however, we find first a plateau in the fold-change, followed by a steep drop of over three orders of magnitude upon addition of one or two additional repressor molecules. The presence of multiple copies of the gene causes a titration effect in which the gene copies have to compete for the presence of LacI. The model shows clearly that in a competitive environment the interacting gene model presented here predicts a significantly different transcription rate than the isolated gene models.Usually, a single copy of the A of the lac operon as a function of the number of CRP in the cell in the case where the gene is isolated and when CRP is competed for by 350 competitor sites. CRP is strongly competed for in E. coli and consequently, the availability of CRP to bind to the lac operon is significantly lower than in the case where CRP has no other specific binding sites. The effect of competition on the transcription rate may exceed an order of magnitude.To illustrate this, we show in The rate of transcription initiation of a gene is strongly influenced by the competitive effects of the rest of the genome. The availability of transcription factors is dictated by the number and binding strength of competing binding sites, as well as the size of the non-specific reservoir. As was shown in Figs et al. [et al. [Attempts to include competition in the canonical ensemble have led to the use of combinatorics to keep track of the possible arrangements of transcription factors, as for example was successfully demonstrated by Burger et al. , 33, and [et al. . The resThe application of the grand canonical ensemble to the process of transcription initiation allows the native inclusion of competing binding sites. The reservoirs of binding sites are decoupled, so that one does not need to keep track of the individual arrangements of transcription factors in all the reservoirs simultaneously. Instead, the effect of multiple gene copies and other competing binding sites are embedded in the fugacity of the transcription factor.R/Nns by λR, A/Nns by λA and λi (with i the kind of transcription factor), which can be interpreted as the effective concentration of the transcription factor in the presence of competing binding sites for that transcription factor. This correspondence suggests that the approach could be generalised with the help of automated computer scripts, such as has been done for their canonical counterparts .Competition in cells also manifests itself in the activation or inactivation of transcription factors by inducer molecules. In principle the effects of inducers can also be calculated using this theoretical framework by taking into account the different association states of the transcription factor-inducer equilibrium . In a siS1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.S5 Text(PDF)Click here for additional data file.


Drosophila dorso-ventral (DV) patterning is one of the best-understood regulatory networks to date, and illustrates the fundamental role of enhancers in controlling patterning, cell fate specification, and morphogenesis during development. Histone acetylation such as H3K27ac is an excellent marker for active enhancers, but it is challenging to obtain precise locations for enhancers as the highest levels of this modification flank the enhancer regions. How to best identify tissue-specific enhancers in a developmental system de novo with a minimal set of data is still unclear.Using DV patterning as a test system, we develop a simple and effective method to identify tissue-specific enhancers de novo. We sample a broad set of candidate enhancer regions using data on CREB-binding protein co-factor binding or ATAC-seq chromatin accessibility, and then identify those regions with significant differences in histone acetylation between tissues. This method identifies hundreds of novel DV enhancers and outperforms ChIP-seq data of relevant transcription factors when benchmarked with mRNA expression data and transgenic reporter assays. These DV enhancers allow the de novo discovery of the relevant transcription factor motifs involved in DV patterning and contain additional motifs that are evolutionarily conserved and for which the corresponding transcription factors are expressed in a DV-biased fashion. Finally, we identify novel target genes of the regulatory network, implicating morphogenesis genes as early targets of DV patterning.Taken together, our approach has expanded our knowledge of the DV patterning network even further and is a general method to identify enhancers in any developmental system, including mammalian development.The online version of this article (doi:10.1186/s13059-016-1057-2) contains supplementary material, which is available to authorized users. An intrinsic difficulty is that enhancer function during development is highly stage- and tissue-specific, which makes it challenging to obtain sufficient material from developing embryos for analysis. Model organisms such as Drosophila, for which development is well-studied and large amounts of cells can be obtained, are therefore an excellent system to test our ability to identify enhancers involved in embryonic development. Dorso-ventral (DV) pattern formation in the early Drosophila embryo is a good example. DV patterning is one of the earliest patterning processes in the metazoan embryo . It. Itzen elov and Dll that had the characteristics of typical DV enhancers  of regulatory regions, and specifically of the transcription factor binding motifs within them, has long been used to identify functional enhancers and motifs , 62. Wheifs Fig.  left. Liifs Fig.  right.Fip < 10−43), showing that ME and DEE 201-bp sequences are evolutionarily conserved above average. We then calculated the average phastCons score for each identified DV motif among all MEs or DEEs  for Tin, Rib, Zld, Sna, Eve, Dll, BEAF-32, HLH106, Abd-B, and Brk are significantly more conserved than their surrounding regions ”MEs + DEEs with motif” Fig. , which aOur list of newly identified MEs and DEEs is highly enriched for bona fide DV target enhancers based on independent assays such as mRNA expression and transgenic reporters. Yet, these putative DV enhancers may contain false positives, and thus we focused on high-confidence DV enhancers and their potential target genes to gain a better understanding of all the genes that are targeted by the DV network.First, we assembled all putative DV enhancers that have high ChIP-seq occupancy of DV transcription factors  and have a nearby gene that is upregulated in the expected tissue based on mRNA-seq data. We then grouped the enhancers based on their pattern of DV transcription factor occupancy and the tissue in which they are active Fig. . Second,cv-2, Dtg, tok in the Dpp signaling pathway). However, we also identified a number of genes that likely play a role in the morphogenesis of the developing tissues, including genes regulating the cytoskeleton or cell adhesion , suggesting that they are directly activated by Dl on the ventral side and thus could play a role in initiating the gastrulation movements. Interestingly, mew is among the putative morphogenesis genes that is repressed by Dl and expressed on the dorsal side. mew and if are both alpha integrins but with different specificities in the extracellular domain [Among these high-confidence DV enhancers, a large fraction of the target genes encode transcription factors and signal transduction molecules, many of which were previously known or have fitting roles in DV patterning . In this case, the relative difference between tissues is smaller but can still be detected if within the sensitivity of the assay.Analyzing relative differences in H3K27ac between tissues provides several benefits over analyzing each tissue separately. First, differential enhancer activity is likely more biologically relevant than the analysis of individual tissues. Traditionally, differential gene expression , rather than absolute gene expression levels, has been regarded as a hallmark of tissue patterning during development. Second, differential H3K27ac analysis specifically identifies enhancers that are different between tissues and disregards enhancers that are equally active in both tissues. In our system, this enabled us to specifically identify enhancers that are part of the Dl patterning network, including downstream transcription factors, signaling pathways, and morphogenesis genes. Third, differential H3K27ac analysis allows, in principle, the identification of enhancers that are active in only a subset of a tissue . However, enhancers are often in close proximity to each other; therefore, we cannot exclude the possibility that some of the identified regions show differential acetylation due to their proximity to bona fide enhancers (spill-over effect).Indeed, we often identified multiple putative enhancers next to each other. These closely spaced putative enhancers could all be functional, since it is common for multiple enhancers to regulate a single gene’s expression pattern together , 44. HowWhile the number of false positives in our DV enhancer list is unknown, there were false negatives, since our list did not include many of the known DV enhancers. We found that several known enhancers missed the cutoff for significance, not due to lack of differences in H3K27ac but due to noise. This suggests that future improvements in technology, such as tighter selection for embryos of the right stage and improved sequence coverage, have the potential to further improve the accuracy of our method. It will also be interesting to test whether adding data on the acetylation of other histone residues, such as H3K9ac or H3K16ac, H3K122ac, or H3K56ac , improveThe advantage of differential acetylation analysis in combination with candidate region detection is that it does not require prior knowledge of the specific transcription factors involved in each tissue. Since histone modifications are well conserved across species and their antibody specificities are well characterized, this method should be broadly applicable to many uncharacterized developmental systems. The numbers of cells required as starting material for ChIP-seq are also lower for histone modifications than for transcription factors (and are even lower for ATAC-seq ). While Differential acetylation analysis provides an excellent starting point for identifying enhancers and their transcription factor binding motifs in an uncharacterized developmental regulatory network. The challenge in the future will be to make the generation of such data technically possible with relatively low amounts of starting material, e.g., when working with mammalian embryos. Undoubtedly, future improvements and innovations in genomics technology will greatly facilitate this goal.Tl10b is from Indiana University . The gd7 and Tlrm9/rm10 stocks were kind gifts from Mike Levine (Princeton University). For Tl10b embryo collections, TOR60/Tl10b, e1 females and Tl10b/TM3, e1, Sb1, Ser1 males were selected from the stock consisting of genotypes Tl10b/TM3, e1, Sb1, Ser1 and TOR60/TM3, e1, Sb1, Ser1. The gd7/gd7 females and gd7/Y males were used for embryo collections and were obtained from the gd7/winscy, hs-hid stock by heat shocking 1-day-old larvae for 1 h at 37 °C, followed by a second heat shock 24 h later. Tlrm9/rm10 females and males were selected for embryo collections from offspring originating from the crossing of virgin females from the stock Tlrm9/TM3, e1, Sb1, Ser1 with males from the stock Tlrm10/TM3, e1, Sb1, Ser1. Oregon R embryos were used for wild-type samples.The fly stock All genotypes were expanded into population cages, and embryos were collected 2–4 h after egg deposition (AED). Apple juice plates were placed into the population cage at 25 °C for 2 h and then outside at 25 °C for another 2 h. For ChIP-seq experiments, embryos were dechorionated for 1 min with 100 % bleach and then cross-linked for 15 min with 1.8 % formaldehyde . For mRNA-seq and ATAC-seq experiments, embryos were dechorionated but not cross-linked.Tl10b embryos were used for ChIP-seq for Dl and Twi, and H3K27ac, wild-type embryos for CBP and Sna, and gd7 embryos for Mad, Zen, Zld, and H3K27ac.ChIP-seq experiments were performed as described , 23 withDifferent combinations of library preparation kits and barcodes were used for library preparations  according to the manufacturer’s instructions. PolyA-mRNA was isolated using Dynabeads oligo (dT) . Libraries were prepared following the instructions of the TruSeq DNA Sample Preparation Kit  and sequenced on the HiSeq 2500 (Illumina).Total mRNA was extracted from 20–100 mg Tlrm9/rm10, Tl10b, and gd7 were aligned with TopHat v2.0.14 [tophat -G fb557_genes.gtf -I 20 -I 5000 --no-coverage-search --segment-length 25The mRNA-seq replicates from  v2.0.14  to the FCuffdiff, from Cufflinks v2.2.1 , was useg for 5 min at 4 °C, and the supernatant was discarded. ATAC-seq was performed as described [5 transposase and PCR reagents from the Nextera DNA Sample Preparation Kit . In addition, the Nextera index kit  was used to create libraries. Libraries were purified using Agencourt AMPure XP beads , and paired-end sequencing was performed on the NextSeq (Illumina).ATAC-seq was performed in biological duplicates using 20 mg 2–4 h AED Oregon R embryos. Nuclei were isolated by douncing the embryos in HBS buffer , 15 mM NaCl, 40 mM KCl, 2 mM EDTA, 0.5 mM EGTA) in a 2-ml dounce tissue grinder followed by filtering the nuclei suspension through Miracloth . Nuclei were spun at 500 escribed  using 2.macs2 callpeak -t orer_combined_atac.bam -g dm -n orer_combined_atac -f BAMPE --call-summitsATAC-seq paired-end reads were trimmed to 25 base pairs and aligned in paired-end mode with Bowtie v1.1.1, keeping only unique alignments with a maximum of two mismatches per read and an insert size of less than or equal to 1000 bp. MACS2 v2.1.0.20150420  was usedp value cutoff of 0.01. For this, the reads overlapping a 1-kb window centered on the summit of either 201-bp CBP or ATAC reads were counted in two replicates in gd7 and three replicates in Tl10b. Regions were classified into TSS (less than 1000 bp from a FlyBase 5.57 annotated TSS), intragenic (inside an annotated gene but at least 1000 bp from a TSS), and intergenic (outside an annotated gene but at least 1000 bp from a TSS) enhancers. The average DESeq2 normalized ChIP signal for all replicates is shown in Fig. The Bioconductor package DESeq2 v1.10.1  was usedTl10b H3K27ac, gd7 H3K27ac, and wild-type CBP ChIP-seq profiles over their respective input controls was calculated for putative MEs and DEEs after normalizing for differences in read count and fragment size. Enrichment values at each base relative to the CBP summit were smoothed with a 31-bp sliding window for display. ATAC-seq metapeak profiles display ATAC-seq average reads per million without any smoothing.The average enrichment of Transcription factor enrichments within our 201-bp putative enhancers were calculated by finding the summit within each enhancer for each transcription factor ChIP-seq sample and calculating the read count and fragment length-normalized enrichment over input in a 201-bp window around the summit.gd7or Tl10b were considered. Regions overlapping an expressed gene were assigned to that gene; otherwise, the gene with the closest TSS was assigned. For transcription factors, we selected the top 400 peaks by peakzilla score from the replicate with the most number of detected peaks.To assign regions  to the nearest gene, only genes with an mRNA-seq fragments per kilobase of exon per million fragments mapped (FPKM) of at least 5 in either Drosophila melanogaster motifs in the Bioconductor MotifDb package v1.10 [gd7 or Tl10b (mRNA-seq FPKM greater than 3). Motifs derived from daughterless (da) heterodimers in the FlyFactorSurvey database [fimo --text --bgfile dm3_background.markov motif.meme dm3.fastage v1.10  were firdatabase  were alsdatabase , part ofdatabase , with thThe background frequency file was obtained using the MEME tool fasta-get-markov against the modENCODE cold/warm/hot transcription factor binding regions .p values are corrected for multiple testing using the Benjamini-Hochberg method. As shown in Fig. Using a test and control group of regions, each region is scored for the presence or absence of every motif. The counts of regions containing the motif in the test group are compared to the counts of regions containing the motif in the control group using a one-sided proportion test. Resulting Additional file p value in the enrichment test was displayed as representative of the group.To combine similar motifs, the percentage of overlap among all occurrences was calculated between all pairs of significantly enriched motifs within either the putative enhancers Fig.  or all nDrosophila melanogaster dm3 genome were downloaded from the UCSC Genome Browser (http://hgdownload.soe.ucsc.edu/goldenPath/dm3/database/phastCons15way) [Per-base phastCons scores for the ns15way) . For eacAs shown in Additional file As shown in Fig. A similar analysis was performed see Fig.  using thTlrm9/rm10 compared to both Tl10b and gd7 were also removed in order to exclude neuroectodermal genes. Finally, putative DV enhancers where the tissue of differential H3K27ac did not match the tissue of differential expression of the nearest gene were removed. For the remaining putative MEs, those with Dl ChIP-seq enrichment of at least threefold over input were assigned to the “Dl binding” group. Those with Twi ChIP-seq enrichment of at least fivefold but Dl ChIP-seq enrichment less than threefold were assigned to the “Twi” group. For the remaining putative DEEs, those with a Mad ChIP-seq enrichment of at least threefold were assigned to the “Mad binding” group, and those with a Zen ChIP-seq enrichment of at least threefold but a Mad ChIP-seq enrichment less than threefold were assigned to the “Zen binding” group. All other putative enhancers not assigned to one of the four transcription factor groups are not displayed. Enrichment values displayed in the heatmap were independently normalized for each factor to be between 0 (no enrichment over input or less) and 1 (98th percentile enrichment or higher).As shown in Fig. Additional file 1:Supplementary material: Includes references for known DV enhancers, ChIP-seq and ATAC-seq replicate correlations, and an overview of how some known DV enhancers were assigned to potential target genes. (DOCX 3548 kb)Additional file 2: Table S1.Spreadsheet showing known DV enhancers assembled from the literature that were used in this study. (XLSX 14 kb)Additional file 3:Fig. S1: Gene names for the data shown in Fig. Fig. S2: Transcription factor motifs enriched in top ChIP-seq and ATAC-seq regions, Fig. S3: Differential H3K27ac analysis of ATAC-seq regions is an effective method to identify tissue-specific enhancers, Fig. S4: Genes near putative ATAC-seq derived enhancers are differentially regulated across tissues, Fig. S5: The identified putative DV enhancer regions derived from ATAC-seq are enriched for known DV transcription factor motifs, Fig. S6: Number of genes with one or multiple assigned enhancers, Fig. S7: Transcription factor ChIP-seq signal is preferentially found at the expected corresponding binding motifs present within putative MEs and DEEs. (PDF 2673 kb)Supplementary figures: Additional file 4: Table S2.Spreadsheet of all distal identified DV enhancers, the assigned gene and its expression in the DV mutants, H3K27ac and transcription factor ChIP enrichment, overlap with known DV enhancers and Vienna Tiles, enrichment for transcription factor motifs, and classification as high confidence enhancers Additional file 6: Table S4.Spreadsheet detailing the samples used in this study and the library preparation kits the libraries were created with, number of total reads, aligned reads, and MACS peaks for each sample. (XLSX 11 kb)


Prostate cancer (PCa) is the second most common solid tumor for cancer related deaths in American men. Genome wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with the increased risk of PCa. Because most of the susceptibility SNPs are located in noncoding regions, little is known about their functional mechanisms. We hypothesize that functional SNPs reside in cell type-specific regulatory elements that mediate the binding of critical transcription factors (TFs), which in turn result in changes in target gene expression. Using PCa-specific functional genomics data, here we identify 38 regulatory candidate SNPs and their target genes in PCa. Through risk analysis by incorporating gene expression and clinical data, we identify 6 target genes  as significant predictors of PCa biochemical recurrence. In addition, 5 SNPs  are selected for experimental validation using Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay in LNCaP cells, showing allele-specific enhancer activity. Furthermore, we delete the rs2742624-containing region using CRISPR/Cas9 genome editing and observe the drastic downregulation of its target gene UPK3A. Taken together, our results illustrate that this new methodology can be applied to identify regulatory SNPs and their target genes that likely impact PCa risk. We suggest that similar studies can be performed to characterize regulatory variants in other diseases. Prostate cancer (PCa) is the second most common cause of cancer deaths in American men . It is eWith the advent of high-throughput genomic technologies, genome wide mapping of functional elements became easily feasible. For example, DNase-seq  and FAIRIn addition, various approaches have been developed to identify sequence variants that likely play important biological roles. Gene expression quantitative trait loci (eQTLs) have been widely used to identify the genetic variants impacting gene expression levels . The RegUsing epigenetic marks and open chromatin regions obtained from PCa cells, we identify SNPs that are located in prostate-specific regulatory elements. Through eQTL and motif affinity analysis, we capture a subset of regulatory SNPs that map within a canonical TF binding motif and potentially affect TF genomic occupancy. Using chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays, we tested several candidate SNPs and confirmed allele-specific TF binding and enhancer activity. Furthermore we deleted one polymorphic enhancer by CRISPR/Cas9 genome editing technology, resulting in altered expression of its target gene. Our studies suggest that this methodology could be used to systematically identify regulatory SNPs in a tissue/disease specific manner and precise deletion of individual enhancers could help to determine their functional significance.n = 206,480) with one or more TF binding. Overlapping these TF binding sites with the open chromatin or active enhancer regions, we generated a non-redundant collection of PCa-specific regulatory regions . Through overlapping with genomic positions of SNPs in Affymetrix Genome-Wide SNP Array 6.0 platform used in TCGA PCa project, we found that 7,197 SNPs are located in these PCa-specific regulatory regions.The flow diagram presented in Figure Next, we analyzed these SNPs to identify potential eQTLs and their target genes using RNA-seq and SNP array data available in the TCGA PCa project. Our previous study and others found that genes located within 25 kb of an AR binding site were the most significantly enriched for androgen-regulated genes in PCa; larger genomic windows could include a greater proportion of false positives , 33. Morwww.gtexportal.org), which were considered as genetic changes associated with common human diseases  vector (Promega) was digested with EcoRV and then blunt-end plasmid DNA was purified and added 3′- T overhang using Taq DNA polymerase to generate pGL4.26-T, which allows direct and high-efficiency TA cloning of PCR products. The selected enhancer regions were amplified by polymerase chain reaction (PCR) using primers listed in ChIP from LNCaP cells was carried out as described previously . Brieflyhttp://biotools.umassmed.edu/bioapps/primer3_www.cgi), and synthesized by Integrated DNA Technologies ((−ΔCt), where ΔCt=Ctsample-Ctinput.All primers were designed using Primer 3 , which encodes both the guide RNA, mammalian Cas9 enzyme with 2A-EGFP. 2 μg total px458 vector (encoding 1 μg each of guides#1 and guide#2) was introduced into LNCaP cells by Neon electroporation system (Life Technologies). Cells were resuspended in 500 uL of RPMI/10% FBS and incubated at 37°C with 5% CO2 for 48 hours. BIO-RAD S3™ Cell Sorter was used to capture the cells having high green fluorescent protein signals and then colonies were grown from single cells. Complete deletion of all alleles for a target locus was confirmed by PCR using primers flanking the enhancer. RT-qPCR analysis was performed in triplicate, comparing the deleted cells to parental LNCaP cells.CRISPR/Cas9 target sites were identified using the E-CRISPR design tool available at