Chromatin in the interphase nucleus is organised as a hierarchical series of structural domains, including self-interacting domains called topologically associating domains (TADs). This arrangement is thought to bring enhancers into closer physical proximity with their target genes, which often are located hundreds of kilobases away in linear genomic distance. TADs are demarcated by boundary regions bound by architectural proteins, such as CTCF and cohesin, although much remains to be discovered about the structure and function of these domains. Recent studies of TAD boundaries disrupted in engineered mouse models show that boundary mutations can recapitulate human developmental disorders as a result of aberrant promoter-enhancer interactions in the affected TADs. Similar boundary disruptions in certain cancers can result in oncogene overexpression, and CTCF binding sites at boundaries appear to be hyper-mutated across cancers. Further insights into chromatin organisation, in parallel with accumulating whole genome sequence data for disease cohorts, are likely to yield additional valuable insights into the roles of noncoding sequence variation in human disease. These data have provided a “locus level” view of the regulatory protein binding events and modifications to chromatin occurring  at individual genes. Meanwhile, the development of chromatin conformation capture (3C) methods, most prominently high-throughput chromosome conformation capture (Hi-C), has provided a view of the higher-order folding and nuclear organisation of human chromosomes2. These data reveal a landscape of physically interacting regions  along chromosomes and, at a larger scale, the existence of preferentially self-interacting chromatin domains, extending across hundreds of kilobases up to multi-megabase regions. However, there is no shortage of gaps in our present knowledge3. The mechanistic relationships between the hierarchical structural layers of the epigenome, from locus-level features to higher-order structures and three-dimensional nuclear architecture, are still the subject of active research. We also have an incomplete picture of how genome functions  are driven or constrained by these structures. Beyond this, there has been little evidence for any phenotypic consequences of the disruption of the interactions and domains emerging from Hi-C experiments. However, a number of recent studies have provided new insights into the consequences of pathogenic mutations acting to alter domain structures and compromise genome function.The past decade has seen a series of revolutions in the fields of human chromatin structure and regulatory genomics, driven largely by novel, high-throughput sequencing-based assays and large consortium projects. Projects such as Encyclopedia of DNA Elements (ENCODE)4 and its derivatives—4C, 5C, and Hi-C—are used to studyin vivo contact frequencies between pairs of genomic sequences, revealing the physical arrangement of DNA in the nucleus. Hi-C was developed to assess patterns of interaction between all regions of a given size across the entire genome simultaneously5. The 3C methods have continued to evolve2, but Hi-C was the original high-throughput, genome-wide variant of 3C and still accounts for the majority of 3C datasets available. The earliest low-resolution Hi-C maps showed interactions between 1 Mb regions, confirming the existence of nuclear compartments corresponding to multi-megabase regions within the accessible and transcriptionally active “A” compartment and regions within the relatively closed and inactive “B” compartment5. The highest impact of such datasets was undoubtedly the discovery of topologically associating domains (TADs), thought to represent regulatory domains, which contain many preferentially interacting subregions but very few interactions across their boundaries. TAD boundaries are often defined by relatively simple algorithms, assessing the directional (upstream versus downstream) preferences of interactions occurring along a chromosome and defining boundaries where these preferences change6. TADs were reported in multiple species as structural entities up to around 1 Mb in size in mammals  and enriched for interacting promoter-enhancer contacts8. These early studies noted the association of these domains with varying regional patterns of repressive and activating histone modifications across the genome, the enrichment of CTCF binding at boundaries, and correlations with other aspects of higher-order chromatin organisation, such as patterns of association with the nuclear lamina and replication timing domains6. About 50% of TADs appear to be cell type invariant between embryonic stem cells (ESCs) and the cortex6, and domains are often shared across species boundaries9. Later work showed differing features enriched at boundaries in different cell types, and certain enrichments  were seen across all cell types10. Although CTCF has been intensively studied as an insulator protein, it is not enriched or even detectable at all boundaries, and so the architecture(s) of TAD boundary regions and the mechanism(s) underlying their function remain elusive.The original 3C method11. Since around 60% of CTCF sites are constitutively bound12, a substantial fraction of loops may be present across tissues. The same study11 identified many TAD-like “contact domains” ranging from 40 Kb to 3 Mb in size (median length of 185 Kb), suggesting that a small fraction of boundaries were missed by previous studies with lower-resolution data. Closer investigation of Hi-C interaction matrices revealed that TADs appear to interact in clusters to form a hierarchy of nested domains, or “metaTADs”, at higher levels13. This hierarchy can be modelled as a simple tree-like structure, and rearrangements of the tree link changes in nuclear organisation to transcriptional changes at many promoters during neuronal differentiation—from ESCs via neural progenitor cells to neurons. This tree-like model of organisation provides an intuitive bridge between smaller-scale regulatory domains such as TADs and the A and B nuclear compartments13.The highest-resolution Hi-C datasets have revealed finer-scale, recurrent pairwise interactions between regions, representing “chromatin loops”, and the base of each loop is formed by two presumably interacting anchor points. Many of these loop anchor points appear to be bound by CTCF and cohesin subunits, and a majority were found to contain convergently orientated CTCF binding motifs3, and some have questioned whether TAD organisation is a cause or consequence of genome function14. As for other noncoding genomic sequences, the emphasis is shifting from the immediate biochemical roles of these new structural features to their phenotypic relevance and how their disruption impacts the fitness of real organisms15. Here, we review the latest studies of dysregulation of gene expression due to changes in chromatin conformation  can give rise to the same developmental defect19, suggesting a conserved functional role. A computational study assessed the broader importance of copy number variants (CNVs) affecting boundary elements on human developmental disorders21. The phenotypes of 922 deletion cases recorded in the DECIPHER database22 were related to the known, monogenic diseases associated with genes neighbouring the deletions. This information was used to nominate deletions likely to affect TAD boundaries and result in enhancer adoption. The results suggest that up to about 12% of deletions in the DECIPHER database resulted in enhancer adoption, based on the tissue-specific activity of a given enhancer and the tissue that was affected in the disease phenotype of the patient who carried the deletion.The extent of such disruptions in human developmental disease cases is largely unknown, but recent studies have shown that pathogenic human structural variants (SVs) overlap the boundary regions of TADs, including duplications at the SOX9 locus24, medulloblastoma25, and leukaemia27 samples, consistent with the hypothesis that TAD-disrupting SVs may act as oncogenic “driver” mutations under selection in tumour cells28. Proposed models for such phenomena include (a) deletions of boundaries to allow unusual promoter-enhancer contacts and (b) inversions that span boundaries altering the contents of neighbouring TADs; both mechanisms are thought to give rise to aberrant expression of resident genes. It also seems that boundary integrity can be compromised by hyper-methylation. Flavahanet al.29 showed that gain-of-function mutations in theIDH gene cause hyper-methylation of CpG sites as well as the GC-rich CTCF binding motif; this reduces CTCF binding at a subset of CTCF sites in mutant glioma cells. Genes most upregulated inIDH mutants as a result of this hyper-methylation included several known oncogenes, such asPDGFRA. Furthermore, CRISPR disruption of a neighbouring CTCF binding site at a TAD boundary in wild-type tumours leads to the upregulation ofPDGFRA, providing mutant cells with a growth advantage and enhancing proliferation. Intriguingly, thePDGFRA domain boundary affected by aberrant methylation in tumours can be re-established by treating mutant glioma cells with a de-methylation agent, leading to the downregulation ofPDGFRA.There have been several reports of disruptions of particular TAD boundaries in modest numbers of neuroblastomaet al.27 investigated the impact of domain boundary deletions on oncogene activation in T-cell acute lymphoblastic leukaemia (T-ALL). Using ChIA-PET  against cohesin sites, they identified around 9,000 CTCF-CTCF interactions that were shared across cell lines, demarcating active loop anchor points for “constitutive neighbourhoods”. Most genes implicated in T-ALL pathogenesis were located inside those loops. Next, they investigated the overlaps between cancer deletions (<500 Kb) in a set of T-ALL whole genome sequences and these neighbourhood boundaries. Six boundaries delineating neighbourhoods containing T-ALL pathogenesis genes were found to be recurrently deleted across tumours at unexpectedly frequent levels. Hniszet al.27 then showed that the CRISPR-engineered deletion of boundaries near two known oncogenes (TAL1 andLMO2) leads to the activation of these genes in human embryonic kidney cells, which otherwise do not express these genes. They also demonstrated changes in conformation at these boundary regions using 5C, with the intensity of contacts increasing across the boundaries following the deletion.Hnisz30. They found that recurrent SCNAs deleting a boundary of the TAD containing theIRS4 gene were associated with a marked increase inIRS4 expression in 32 samples from three different tumour types. These deletions were carefully distinguished from samples carrying focal amplifications leading toIRS4 overexpression, and the tumorigenic effects of higherIRS4 expression were validatedin vivo using mouse models. Similar to19, the authors also describe duplications mediating the formation of a new domain and driving overexpression ofIGF2 in 20 colorectal cancer (CRC) tumours. Single copy tandem duplications encompassing anIGF2 TAD boundary and an enhancer in a neighbouring TAD were found to alter conformation and activate the enhancer in CRC cell lines, leading to overexpression ofIGF2, a gene previously implicated in CRC progression. Interestingly, all duplications involving this locus were tandem duplications—rather than dispersed or inverted duplications—suggesting that the resulting head-to-tail orientation of the enhancer andIGF2 may be crucial for the gene’s upregulation31. Overall, these data suggest that enhancer adoption by oncogenes following domain boundary lesions is not a rare process, occurring at rates comparable to those of recurrent in-frame gene fusions.A larger recent study used matched expression and somatic variation data for 7,416 cancer genomes across 26 tumour types from The Cancer Genome Atlas to systematically identify somatic copy number alterations (SCNAs) likely to mediate gene dysregulation32, consistent with oncogene activation following the rearrangement of nuclear architecture. Several recent studies have also observed unexpected excesses of somatic mutations at CTCF binding sites across tumour types, including CRC33, leukaemia27, and a variety of other cancer types34. Characteristic mutational profiles were observed at positions within the CTCF binding motif, and a striking spike in mutation was seen at a central, well-conserved nucleotide, together with elevated mutation rates at sites immediately flanking the motif34. Surprisingly, these unusual patterns can be explained by selectively neutral biases in mutation rates34 and are broadly consistent with an underlying mutational mechanism involving the interference of DNA binding proteins with the replication machinery, causing elevated mutational burden at active regulatory sites35. The highest mutational loads were seen at constitutively active CTCF binding sites with roles in chromatin loops and TAD domain boundaries and were predicted to compromise CTCF binding and nuclear architecture34. These phenomena therefore appear to provide a deterministic mutation-driven process, expected to lead to altered chromatin architecture and gene dysregulation in many cancers, without invoking more complex hypotheses involving similar selective pressures across many sites and many different tumour types.CTCF hemizygous knockout mice are prone to developing cancer in a wide range of tissues34 and are underpowered to discover individual CTCF sites subject to recurrent mutation. Similarly, most WGS data for tumours lack matched RNA-seq data to study the effects of site disruption on the patterns of expression of neighbouring genes.The future will undoubtedly provide many more insights into the disruptions of chromatin and nuclear organisation underlying disease. Continuing advances in high-throughput sequencing and computational analyses are improving the resolution of Hi-C maps and allow greater precision in defining domain boundaries and other structures. Global analyses of structural disruptions in disease have generally been limited to publicly available Hi-C data, which usually are not well matched to the tissue of interest. Similar caveats apply to the available ChIP-seq data for chromatin features such as CTCF binding. With more cell- and tissue-specific Hi-C and ChIP-seq data available, many more disease-relevant disrupted interactions are likely to be found. This will also help to better distinguish “passenger” disruptions of TAD boundaries from causative “driver” mutations responsible for oncogene dysregulation. Current cancer whole genome sequencing (WGS) datasets are also inadequate for these purposes. Previous studies have examined mutational spectra at thousands of CTCF binding sites across the genome36. More complex SVs, such as inversions and translocations, are even less accurately predicted, and compound SVs (involving different overlapping or nested SVs) are beyond our reach entirely. This point is highly pertinent to cancer genomics since catastrophic rearrangements of entire chromosomes are an increasingly common observation in many cancers37, and SVs appear to be the main factors driving tumorigenesis in some cases38. Given the challenges of reconstructing SVs from standard (short read) sequencing data, it is likely that accurate resolution of all SVs present in a genome will be dependent upon novel sequencing technologies generating much longer sequence reads .In addition to improvements in the data available, there are substantial challenges ahead in WGS data analysis. Currently available algorithms for the detection of CNVs  are generally used in combinations to generate consensus predictions since no single algorithm is considered sufficiently accurate when used alone40. We also know very little about the precise physical location and composition of the protein complexes bound to domain boundaries, how the insulating effect of boundaries is achieved, and what constitutes the minimal requirements for boundary function. Higher-resolution maps derived from Hi-C or related methods will be necessary to explore the detailed features of boundary regions. The fact that some boundaries appear to lack CTCF binding sites, that many TAD boundaries change during cellular differentiation13, and that boundary compositions vary between cell types10 suggests that boundaries may vary in structure and function. The better-studied boundary components, CTCF and cohesin, appear to have distinct functions in domain formation, in that CTCF works to separate neighbouring TADs and cohesin promotes intra-TAD interactions41, but even these proteins remain the subject of ongoing research. A comprehensive picture of boundary architecture and function holds the promise of better understanding, and perhaps correcting, regulatory domain disruptions in disease.As discussed above, our knowledge of the hierarchical strata and functional inter-relationships of higher-order chromatin structure is still far from complete. Although TADs have been widely studied, we still lack detailed models of how they affect transcription. Sharing a location in the same TAD is evidently not sufficient to allow enhancer-promoter interactions to take place, and we do not understand how enhancers find their target genes within TADs. Some data suggest that a particular spacing may be needed to create a loop that successfully brings such elements together, and it is known that increasing the distance between an enhancer and a target promoter can lead to downregulation3C, chromatin conformation capture; ChIP-seq, chromatin immunoprecipitation followed by sequencing; ChIA-PET, chromatin interaction analysis by paired-end tag sequencing; CRISPR, the CRISPR-Cas9 molecular editing system allows alterations to DNA at specific locations in the genome; Hi-C, high-throughput chromosome conformation capture; RNA-seq, isolation of RNA followed by sequencing; TAD, topologically associating domain.


HNF4A, a master transcription factor of hepatocyte identity. 5hmC and HNF4A expression from P1 are dependent on ten-eleven translocation (TET) dioxygenases. In turn, the liver pioneer factor FOXA2 is necessary for TET1 binding to the P1 locus. Both FOXA2 and TETs are required for the 5hmC-related switch in HNF4A expression. The epigenetic event identified here may be a key step for the establishment of the hepatocyte program by HNF4A.Understanding the processes that govern liver progenitor cell differentiation has important implications for the design of strategies targeting chronic liver diseases, whereby regeneration of liver tissue is critical. Although DNA methylation (5mC) and hydroxymethylation (5hmC) are highly dynamic during early embryonic development, less is known about their roles at later stages of differentiation. Using an in vitro model of hepatocyte differentiation, we show here that 5hmC precedes the expression of promoter 1 (P1)-dependent isoforms of   •HNF4A P1 promoter previous to terminal hepatocyte differentiation5hmC marks •TET1-dependent 5hmC is required to activate P1-driven HNF4A expression•Pioneer factor FOXA2 is necessary for TET1 binding to P1 locus•HNF4A promoter switch is reversible upon hepatocyte dedifferentiation HNF4A signals a key switch in expression. Because of its reversibility, this switch is susceptible to experimental manipulation.Hepatocyte identity is impaired in various human chronic liver diseases. Methylation and hydroxymethylation dynamically mark the DNA during development. As shown by Hernandez-Vargas and colleagues, discrete changes in these marks also play a role in establishing hepatocyte identity. Specifically, hydroxymethylation of the hepatocyte promoter  Contrary to other human organs, the cellular hierarchy of the liver is still a matter of debate. Hepatic progenitor cells (HPCs) are able to supply two types of liver epithelial cells, hepatocytes and cholangiocytes, during cellular turnover. Mature hepatocytes retain the capacity to regenerate the liver cell pool after tissue loss  . HoweverDNA methylation  is known to play an important role in early development, whereby waves of demethylation and remethylation take place as part of a genome-wide shaping of the chromatin. The involvement of 5mC in stem cell differentiation has been supported by several reports of enhanced differentiation after treatment with demethylating agents . AdditioHNF4A locus, a master TF of hepatocyte identity. This locus is marked by 5hmC in bipotent progenitors poised for differentiation, and enables a switch in HNF4A isoform expression. This redistribution of 5hmC marks within HNF4A is observed in several in vitro systems and during the transition from fetal to adult human liver.To identify key events involved in liver progenitor cell differentiation, we profiled 5mC marks at the genome-wide level and found discrete loci progressively changing methylation during this process. We focused on the most significant of these, demethylation of the HNF4A, albumin, aldolase B, glutathione S-transferase α, and Cyp3A4) by qRT-PCR  was also validated by pyrosequencing , and regardless of the in vitro model system .When visualizing the HNF4A locus in more detail during ESC differentiation, we observed a global demethylation at the first differentiation transition, from stem to definitive endoderm cells , and one intragenic locus between both promoters .Demethylation can be the result of a cell cycle-dependent deficiency in the addition of methyl residues to nascent DNA strands (passive demethylation) or the consequence of progressive oxidation of methyl-cytosines by ten-eleven translocation (TET) dioxygenases (active demethylation) . In favoificance A. We theromoters A. We fouromoters A. Increaromoters C and iPSromoters B, and esOf note, the observed increase in 5hmC was at least one order of magnitude higher than the decrease in 5mC at the HNF4A P1 locus, a finding validated with a base-resolution technique  B. This lHNF4A in human fetal and adult liver  data of the HNF4A P1 locus, we found that the identified DhMR overlaps with a regulatory region characterized by putative binding for several TFs  to study a potential interaction between FOXA2 and TET proteins, first using HNF4A itself as a positive control, as FOXA2 has been shown to interact with HNF4A during hepatocyte differentiation   A, our fiHNF4A P1 DhMR, using ChIP  to transiently silence TETs or FOXA2 during the first days of progenitor differentiation A. As expilencing B. An eveilencing B. As shoilencing .Figure 6Based on the effect of FOXA2 silencing on 5hmC of HNF4A P1, and the known role of this protein as a pioneer TF during development, we hypothesized that FOXA2 was necessary for recruitment of TET1 to the P1 promoter. To test this notion, we performed ChIP assays during the first week of differentiation (when 5hmC takes place) with and without transient silencing of FOXA2 expression. First, we confirmed that TET1 occupancy is increased after 1 week of differentiation at the P1 locus D. In linTherefore, TETs and FOXA2 are both required for the HNF4A expression switch that marks the early step of bipotent liver progenitor differentiation. These effects are limited to the P1 promoter region, in line with the P1 DhMR being an important regulatory region.By profiling the methylome dynamics of liver progenitor differentiation, we have identified an epigenetic mark (5hmC) that signals the switch in isoform expression of a master TF of hepatocyte identity, HNF4A. We show that TET proteins, involved in such a signal, colocalize with the pioneer factor FOXA2. Finally, TETs and FOXA2 are both required for the 5hmC and the isoform switch that takes place during the first days of differentiation. A redistribution of methylation marks was also found in two additional in vitro models of hepatocyte differentiation  and in human samples when comparing fetal and adult liver tissues.HNF4A locus in poised bipotent progenitors. Next, oxidation of methyl residues by TET1 proteins will lead to demethylation of the distal HNF4A promoter and the resulting expression of P1-driven isoforms. Therefore, modulation of 5hmC through interaction with TET proteins may represent a general mechanism of pioneer TFs. In line with this, and in addition to its well-known role in neural development , while methylation of the downstream promoter impairs the expression of P1-driven isoforms. A 5hmC increase, leading to a P2-to-P1 switch in expression, is necessary for the commitment of a progenitor to the hepatocyte lineage. Once induced, P1 isoforms may be involved in the direct repression of the P2 promoter, as previously described . FOXA2 melopment , 5hmC anelopment , specifielopment , cardiomelopment , and colelopment .The association between a rather small change in methylation and an important transcriptional switch merits more detailed explanation. First, the bead array assay used to assess 5mC cannot distinguish between 5mC and 5hmC, and in some cases such as the P1 promoter, both DNA marks may display opposite patterns and therefore partially cancel out each other. Second, although we used differentiated populations with approximately 80% hepatocytes , there is a mixed cell composition made of cholangiocytes and undifferentiated progenitor cells that introduce background levels of 5mC. The effect was clear enough to motivate the further analysis of 5hmC, which displayed a much more obvious effect . In addition, results were confirmed with a technique  that simultaneously measures 5mC and 5hmC in the same sample, and at base-resolution level . FinallyFurther studies will be required to dissect the interaction between FOXAs and TETs during hepatocyte differentiation. Of note, it was recently shown that another pioneer TF, FOXA1, physically interacts with the TET1 protein through its CXXC domain . In addiWe have shown that the epigenetic switch in promoter choice is a reversible process, an observation that may be relevant in pathological conditions where loss of hepatocyte identity is a well-known finding. For example, while differentiation of liver progenitors seems to be associated with a progressive disappearance of an inflammation-like state , cytokinHNF4A in the differentiation of a bipotent liver progenitor into hepatocytes. It supports a model whereby FOXA2 behaves as a pioneer factor required by TET proteins during this process  were maintained and differentiated as previously described , and as PLA was performed using the Duolink In Situ Kit (Sigma-Aldrich) following the manufacturer's recommendations. Fixed cells were incubated with specific primary antibody against FOXA2 protein and tested interactants : HNF4, TSFRS4 was used as housekeeping gene.Total RNA was isolated using TRIzol reagent (Invitrogen), and reverse transcription reactions were performed using MMLV-RT (Invitrogen) and random hexamers, according to the manufacturer's protocol. Primers and probes were designed using Universal Probe Library Assay Design Center (Roche). qRT-PCR was performed in triplicates of each condition, using SyBR green (Eurogentec) and a CFX96 PCR system (Bio-Rad). FOXA2, TET1, and TET2  were transfected at the concentration of 20 nM using RNAiMAX lipofectamine (Life Technologies) as recommended by the manufacturer. Cells were washed and medium was replaced 12 hr after transfection.siRNA non-targeting and pool siRNAs against To quantify the percentage of methylated cytosine in individual CpG sites, we performed bisulfite pyrosequencing as previously described . For samMethylation profiles were obtained with Humanmethylation450 Infinium bead arrays (Illumina), using recommended protocols for amplification, labeling, hybridization, and scanning. Each methylation analysis was performed in HepaRG cells differentiated in three independent wells at each time point.GSE72074).Raw methylation data were imported and processed using R/Bioconductor packages . To defiGSE66077  were separated by SDS-PAGE and electrotransferred to Immobilon-P membranes (Millipore). Primary antibodies specific to P1- and P2-driven isoforms of HNF4A (R&D Systems) have been previously described  Table S.For ChIP, cells were crosslinked with formaldehyde and chromatin was sheared using a Bioruptor sonicator (Diagenode). Assays were performed in triplicates of each condition with an SX-8G IP-Star automated system (Diagenode), using antibodies specific for FOXA2, TET1, TET2, and POL2A . For hMeR/Bioconductor packages were used for bead array analyses, as described above. For other comparisons, means and differences of the means with 95% confidence intervals were obtained using GraphPad Prism (GraphPad Software). Mann-Whitney tests were used for unpaired analyses comparing average expression between classes. p Values of less than 0.05 were considered statistically significant. On each plot, SD represents the variation between three biological replicates.P.-B.A., S.E., D.G., F.R.T., and D.M.N. performed the progenitor differentiation experiments. P.-B.A. performed sample preparations for bead array assays, and all validations. P.-B.A., M.-P.L., and M.-P.C. performed the PLA. S.E. and P.-B.A. performed the ChIP and siRNA experiments. F.R.T., V.C., and D.M.N. performed additional differentiation and validation experiments. A.C. and Z.H. provided conceptual assistance. P.-B.A. and H.H.-V. performed the statistical and bioinformatics analyses. A.C. and H.H.-V. supervised the experiments. H.H.-V. coordinated the project and wrote the manuscript. All authors discussed the results and manuscript text.


Escherichia coli promoters. Four sequence combinations with diverse dynamic ranges were chosen to build multi-input transcriptional logic gates regulated by two and three ligand-inducible transcription factors . This work enables predictable control over the dynamic range of regulatory components.One challenge for synthetic biologists is the predictable tuning of genetic circuit regulatory components to elicit desired outputs. Gene expression driven by ligand-inducible transcription factor systems must exhibit the correct ON and OFF characteristics: appropriate activation and leakiness in the presence and absence of inducer, respectively. However, the dynamic range of a promoter  is difficult to control. We report a method that tunes the dynamic range of ligand-inducible promoters to achieve desired ON and OFF characteristics. We build combinatorial sets of AraC-and LasR-regulated promoters containing −10 and −35 sites from synthetic and  For synthetic gene circuits to behave as designed, ligand-inducible promoters should display predictable ON/OFF characteristics. Here the authors design multi-input hybrid promoters to build transcriptional logic gates. Promoters, which initiate transcription in response to transcription factors and associated ligands, are typically derived from endogenous components of the host or related species to ensure compatibility with the host transcription machinery. However, transcription rates of native promoters and their responses to inducers vary widely, as they are tuned to respond at rates appropriate to their natural setting8. This potential incongruity can cause problems when constructing gene circuits, as the fold-change induction of a natural promoter may not allow the synthetic circuit to behave as designed.Synthetic gene circuits are constructed by rewiring transcription factors and promoters to create novel regulatory topologies9. In addition, although not technically part of the promoter, the 5′ untranslated region can be manipulated to alter translation rates and hence protein production12.The first step in tuning a promoter is generally to change its overall output, i.e., to increase or decrease the amount of protein produced. This can be achieved in several ways. For instance, the copy number of the gene can be changed by placing it on different plasmids or by integrating it multiple times within the chromosome. Libraries of constitutive promoters have also been assembled and tested to achieve target expression levels18. Tuning these regulatory pathways involves building and testing circuits with precharacterized −35 and −10 sites19 and Shine–Dalgarno sequences20 of varying strengths until the desired properties  are empirically achieved; however, this approach can be extremely labor-intensive and costly.Many attempts have been made to engineer transcriptional systems that are better suited for use in synthetic gene circuitsFurthermore, although these methods can help to control gene expression, they universally alter protein output regardless of inducer concentration and the dynamic range of gene expression may change in unpredictable ways. For example, if one were to increase the copy number of a gene to increase protein production, the rates of production increase for both the OFF state (in the absence of inducer) and the ON state (in the presence of inducer).22; by using two consecutive promoters, each with its own regulatory features and transcription start site18; or by engineering different transcription factors that bind to the same operator24. Others have engineered promoters that have altered overall production of either mRNA27 or protein10. However, the dynamic range of these synthetic promoters can be small—i.e., their outputs often show only a small difference between OFF and ON states in response to signal ligands that bind the transcription factors. Despite these advances in promoter engineering, the dynamic range of synthetic promoters has been difficult to tune. This problem prohibits the facile construction of multi-layer synthetic gene circuits, which require the output dynamic range of an upstream regulator to be compatible with the input dynamic range of a downstream target. Of note, ligand-inducible transcription factor systems can be manipulated at the level of the transcription factor as well as at the level of the promoter. In prior work, the leakiness and inducibility of the ligand-inducible LacI repressor mutants have been quantified and classified28. Such manipulation at the level of the transcription factor is informative and compatible with our promoter engineering approach, and both can be performed in conjunction to enable facile tuning of the dynamic range of synthetic promoters. Additionally, experimental studies have shown that inducibility and dynamic range can be tuned by changing the copy number of the plasmid encoding the transcription factor30, mutating the transcription factors31, changing the operator sequence6, mutating the RNA polymerase25, and reducing promoter crosstalk32. In addition, methods have been developed to calculate the probability of RNA polymerase binding at a promoter as a function of the number of regulatory proteins in the cell34. Recently, the insulation of minimal promoters was demonstrated to enable precise engineering and biophysical modeling of complex synthetic transcription circuits35. These approaches have inspired our study and are compatible with our promoter engineering approach to accomplish facile tuning of the dynamic range of promoters.To date, hybrid promoters that respond to two or more transcription factors have been constructed in several ways, including: by encoding operator sites for multiple transcription factors into a single promoterEscherichia coli promoters that have a spectrum of dynamic ranges. To do so, we used a modular approach in which promoters were assembled from libraries of five main components: (1) the region upstream of the −35 site ; (2) the spacer region between the −10 and −35 sites (in which an operator site for a repressor can reside); (3) the −10 site; (4) the −35 site; and (5) the downstream region encoding the gene of interest. The fold-change induction of the promoters was tuned using a variety of −10 and −35 sites. We also developed a thermodynamic model that predicted the contribution of free energy of binding to the overall transcriptional initiation rate, which we measured in a fluorescence-based plate reader experiment.Here we built a library of Our prediction and identification of the dynamic range allowed us to identify the ideal promoters for building multi-input hybrid promoters that can be used to make transcriptional logic gates. To build transcriptional AND gates with high signal-to-noise ratios, we perused our screened promoter library to identify two members that exhibited high fold-change induction and used their −35 and −10 cores to build hybrid promoters that respond to a variety of small-molecule inducers. Each hybrid promoter exhibited robust AND gate behavior—i.e., strong expression in the presence of all inducers and negligible expression in the absence of one or more inducers.Overall, our results provide a method for efficiently altering the dynamics range of ligand-inducible promoters. This ability is key for constructing synthetic gene regulatory circuits that require precise input and output relationships and will allow researchers to tune complex synthetic gene regulatory circuits in a facile fashion. This paper provides a simple, cost-effective means of engineering promoters that provide user-defined dynamic ranges, which will enable the fine-tuning of the metabolic flux within synthetic biological and chemical circuits inside living cells.E. coli cells, most promoters are regulated by σ70, a housekeeping transcription factor that binds the −10 and −35 sites of a promoter and enables RNA polymerase to bind and initiate transcription36. Transcription rates have been shown to be highly dependent on the sequences of the −10 and −35 sites for promoters regulated by σE and σ70, and certain −10 and −35 site combinations are associated with known transcription rates38.In growing 70 binding to the −10 and −35 sites and the dynamic range of a library of promoters. We first formulated a thermodynamic model for transcriptional initiation based on the probability of σ70 binding to DNA41. First, predicted transcription rates were derived from the probability of σ70 binding, with the assumption that, in the presence of inducer, the probability of activator and σ70 both binding DNA is much higher than the probability of σ70 alone binding. Second, to describe the fold change in expression upon induction, the logarithms of the predicted transcription rates were fitted to the experimentally measured transcription rates, with the assumption that recorded fluorescence is proportional to the transcription rate. To account for the different regulatory architectures in this work, this thermodynamic model was modified to consider the additional binding states of the different transcription factors. Specifically, thermodynamic models were made for single  and multiple (activator and repressor) transcription factors with different states of binding  modeled in order to capture the entire range of possible biochemical interactions  a fixed downstream reporter sequence encompassing the +1 site, a ribosome-binding site and the gene encoding yellow fluorescent protein (YFP). AraC and LasR were chosen because they are both ligand-inducible transcriptional activators but come from two different transcription factor families43. In addition, a LacI-binding site (LacO1) was encoded in the spacer region between the −10 and −35 sites to examine the influence of repressors on the behavior of these promoters.To test our hypothesis that the dynamic range of a ligand-inducible promoter could be tuned by varying the ers Fig. , each ofE. coli strain CY015, which is a ΔlacI ΔaraC strain that also constitutively expressed genomically encoded araC and lasR. We expected the promoters to be activated upon addition of their corresponding inducers, i.e., arabinose for AraC, and 3O-C12-HSL for LasR. To measure the relative promoter units (RPU), we used a standardized method that has been adopted by the research community44. We measured the transcription rate of each promoter in the library with and without inducer and observed that rankings of the −10 and the −35 sites were consistent between AraC- and LasR-regulated promoters regardless of the identity and presence of each inducer —an observation that is consistent with that of prior reports44. Fold-change induction heat maps were consistent between AraC- and LasR-regulated promoters  and transformed into cer Fig. . For cerers Fig. , indicatKeq on transcription rates  in the AraC- . The experimental results supported the predicted effects of tes Fig.  and foldtes Fig. . Of noteaC- Fig.  and LasRaC- Fig.  promoterlacI. The promoters were characterized under four conditions: in the presence of (a) neither inducer, (b) inducer of the activator only (arabinose or 3O-C12-HSL), (c) inducer of the repressor only ), and (d) both inducers. The results were similar to those obtained in the absence of lacI—the ordering of −10 and −35 sites based on measured transcription rates was consistent under all four conditions. This data was also consistent with our two-input thermodynamic model in which extra states were included to account for repressor binding  and a repressor (LacI) Fig. . To thissym) or TetR (TetO2) in the spacer region to allow repression by these proteins13. For the −35 site, we chose to characterize version “d” (TTTACA) further because it provided medium affinity for σ70. First, we slightly modified version “d” (TTTACA) to TTTACT because the terminal nucleotide, T, (a) is the second most common base pair at that position45, (b) creates a one base pair overlap with the LacOsym operator site, (c) is known to decrease the leakiness of the promoters in the presence of the repressor13, and (d) the nature of this DNA substitution mutation does not alter the binding affinity of σ70 for this −35 site25. As for the −10 sites, we chose versions “E” (GATACT) and “F” (GATAAT), because both of these sequences possess a 2 bp overlap with the TetO2-binding site  while providing different dynamic ranges. We posited that the difference in dynamic ranges would manifest as differences in the leakiness  and inducibility (induced transcription rates) of the AND gates built with these sequences. Specifically, version E that exhibited lower leakiness and lower inducibility was expected to provide tighter repression and lower induction, while version F that exhibited higher leakiness and higher inducibility was expected to provide leakier repression and higher induction. Two additional LacO1 and TetO2 sites were also added (one downstream and one ~400 bp upstream of the +1 site) to achieve further repression46.To demonstrate the utility of our predictive model and experimental dataset for the construction of synthetic ligand-inducible promoters that behave as desired, we opted to perform a logic operation that required high inducibility and low leakiness  region. Of note, the TetO2 site at the +1 position provided more repression capacity than the upstream TetO2 site. We observed that these hybrid promoters exhibited high gene expression if and only if all three inducers were present , a LacOent Fig. . These hFinally, to demonstrate the utility of our method for engineering synthetic promoters with desired dynamic ranges, we tested the bD and eG promoters, which possess distinct ON and OFF characteristics compared to dE and dF. Specifically, in contrast to the low-leak and high-signal performance of dE and dF  and TetR (which regulates the response to tetracycline). Because the lac and tet systems evolved separately, the promoters that respond to LacI and TetR are tuned to transcribe downstream genes at rates appropriate to their original setting. This tuning incongruity can cause problems for synthetic biologists, as the transcriptional response of a promoter needs to match the synthetic context of the circuit. Unfortunately, traditional strategies of testing −35 and −10 combinations and Shine–Dalgarno sequences of varying strengths to empirically achieve desired properties  can be extremely time-consuming and labor-intensive.To date, synthetic gene circuits have been constructed primarily by cobbling together regulatory parts drawn from disparate pathways. For instance, the original genetic toggle switch70 and characterized how the fitted binding energies affect the uninduced vs induced transcription rates of ligand-inducible promoters. Despite the relatively small number of examined −10 and −35 sites , our library members spanned the full range of possible input and output relationships, i.e., the data points span the entire breadth of the dynamic range curves shown in Figs. In this study, we developed a method for tuning the difference between the ON and OFF states of a regulated promoter. We created a library of −10 and −35 sites that exhibit a wide range of fitted binding energies  for σ41). Therefore, in the absence of inducers that bind the transcriptional repressors, the predicted transcription rates can deviate from the data for higher values of ln(Keq) . Additionally, when used in conjunction with methods for controlling protein production rates, our approach should provide exquisite control over the dynamic range of gene expression in synthetic gene circuits.Since the characteristics of each −10 and −35 site combination were independent of the transcription factor(s) being used to regulate the promoter Figs. , this apE. coli strains derived from wild-type MG1655. To minimize interference of endogenous LacI with our exogenous LacI repressor, we performed lambda Red recombination49 to prepare a LacI- strain—CY011 (E. coli strain MG1655 ∆lacI). In addition, since an E. coli LuxR homolog, sdiA, can partially activate Prhl50, we knocked out sdiA from CY011 to create CY013 (CY011 ∆sdiA). In order to test our −35 and −10 combinations in the context of three different promoters and transcription regulators, we built a plasmid pCH035 encoding wild-type araC, Ptrc*-rhlR (cloned from ATCC #47085), and PIq-lasR (cloned from ATCC #47085) and performed lambda Red recombination to integrate these genes into the genome of CY013 to obtain CY015 . Next, to test repressor function, we integrated PIq-LacI and PN25-TetR into the original LacI site of CY013 and CY015 via lambda Red recombination to obtain CY019 (CY013 PIq-LacI PN25-TetR) and CY021 (CY015 PIq-LacI PN25-TetR), respectively. After each recombination, antibiotic markers were removed by FLP-FRT recombination.We performed our experiments in 51 wherein BsaI was used to create a specific restriction site in the spacer region. The first segment contains the activator-binding site and the −35 site; the second segment contains the −10 site, a ribosome-binding site, and yfp (T203Y mutant of sfGFP) reporter gene9. After PCR amplification of each segment, all segments were ligated to the plasmid backbone (KanR and pMB1 +rop origin) in all possible combinations. We used a similar strategy to construct the two- and three-input hybrid promoters. The genomic copy of XylR was used, but the majority of the activators were either integrated into the genome  or supplied on a separate plasmid (LuxR) (ChlorR and pSC101 origin).To construct the promoter library, we used a modified Golden Gate strategyhttps://www.addgene.org/Matthew_Bennett/) under the publication identifier corresponding to this manuscript.All strains, plasmids, and promoter sequences used in this research are listed in Supplementary Tables BAD, Plas, and Prhl promoters as reference standards against which we standardized the strengths of each member of the promoter library52. To measure the strengths of each −35 and −10 combination for promoters of the two repressors, we used strain CY021, a LacI, and TetR knock in strain. In the two- and thre-input hybrid promoter assay, we transformed plasmids encoding the additional activator (LuxR) and hybrid promoters into CY015.To measure the strengths of each −35 and −10 combination for promoters of the three activators, we first transformed the reporter plasmids encoding our library of promoters into strain CY015. We used the Ptest_promoter = (fluorescence4h−fluorescence2h) per OD60052. Reference standards were included on each 96-well plate. The final promoter strength was calculated in RPU using the equation RPUtest_promoter = Fluotest_promoter×RPUreference_standard)×Fluoreference_standard−1.The strains were cultured overnight in LB media with 50 μg/mL kanamycin (and 25 μg/mL chloramphenicol as needed). The overnight cultures were inoculated at 1% volume per volume into 100 μL M9 media supplemented with 0.4% glycerol and 0.2% casamino acids and transferred into a 96-well plate. After growth with shaking at 37 °C for 2 h, 100 μL M9 media with 2× inducer was added to the cultures. The final concentrations of all inducers were as follows: C4-HSL (10 μM), 3O-C6-HSL (0.1 μM), 3O-C12-HSL (0.1 μM), arabinose (5 mM), xylose (5 mM), IPTG (1 mM), and ATc (100 ng/mL). Cultures were grown at 37 °C and 800 rpm in a plate shaker. The fluorescence of YFP was measured after 2 h using a plate reader  and reported as Fluohttps://github.com/josic/Promoter-Engineering.Commented code and all collected coding data are available in the Github repository at All experimental data are available in Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8


Although enhancers were discovered almost four decades ago, their inner workings remain enigmatic. One important opening into the underlying principle has been provided by observations that enhancers make physical contacts with their target promoters to facilitate the loading of the RNA polymerase complex. However, very little is known about how such chromatin loops are regulated and how they govern transcription in the three-dimensional context of the nuclear architecture. Here, we present current themes of how enhancers may boost gene expression in three dimensions and we identify currently unresolved key questions.Transcriptional enhancers constitute a subclass of regulatory elements that facilitate transcription. Such regions are generally organized by short stretches of DNA enriched in transcription factor-binding sites but also can include very large regions containing clusters of enhancers, termed super-enhancers. These regions increase the probability or the rate (or both) of transcription generally in Since then, it has been demonstrated that enhancers play pivotal roles in the regulation of normal cell physiology and maturation programs. Today, it is estimated, based on high-throughput analyses of chromatin immunopurified DNA (ChIP-seq), that up to a million different enhancers are distributed in the mammalian genome and positioned either proximal or distal to their target promoters4. This information provides two important clues: First, given the estimate of the number of genes, the implication is that most genes are surrounded by more than one enhancer, raising the question of whether such enhancers collaborate or act alone3. Second, the clustering of genes poses a logistic problem for enhancers to boost transcription from specific sets of neighboring genes during development, for example. We now know that the activity of both enhancers and promoters is subject to regulation by chromatin marks, which is discussed in more detail below. The laying down of such chromatin marks by so-called pioneer factors5 needs to be coordinated to enable the correct set of genes and their enhancers to make contact during differentiation. The observation that some enhancers can activate genes more than a million base pairs apart4 also prompted the realization that enhancers operate in three dimensions (3D) to enable their juxtaposition to their distal target genes6. In this way, long-range acting enhancers can paradoxically be in even closer physical proximity to target promoters than more proximal enhancer regions. Rather than providing an extensive overview of different types of enhancer functions, we focus here on recent key observations and questions associated with how the enigmatic encounters between enhancers and promoters promote expression of genes in 3D. This aim is fueled by the realization that we will likely not fully understand enhancer mechanisms without incorporating their mode of action in the 3D context of the nuclear architecture. Therefore, we have organized this overview with a flow of themes converging on novel considerations of how the nuclear architecture and genome organization conspire to increase the specificity and efficiency of transcriptional activation. For more general overviews and images of enhancer functions, please consult references8.Enhancers of transcription were discovered already in 1981, a time when not much was known about basic principles of transcriptional regulation. For an in-depth review on the discovery of enhancers, see reference9. Independently of whether these different perspectives of enhancer function relate to different types of enhancers or reflect enhancer-promoter proximities or both, enhancers have to overcome the by-default poor affinity between RNA polymerase complexes and promoters3. Although promoters have to acquire chromatin marks such as H3K4me3 and H3K27ac to become active, this per se is not sufficient to trigger transcriptional initiation10. Instead, a system has evolved in eukaryotes with enhancer regions mediating the loading of RNA polymerases to their distal target promoters via chromatin looping. This was first documented in an experimental system using a protein bridge to connect the SV40 enhancer to a target β-globin promoter intrans11. For endogenous enhancers, this is a very complicated process that depends on whether the active enhancers have acquired chromatin marks, such as H3K4me1 and H3K27ac, to recruit/render them accessible to clusters of key transcription factors4 and hence long-range interactions12. These in turn interact with the mediator complex, which is made up of multiple factors and, as the name implies, mediates the connection between the primary chromatin fiber and the RNA polymerase complex13.Though not yet fully understood, transcriptional enhancer elements have been conceptually linked either with promoting the probability of transcriptional initiation or by increasing the rate of transcriptional elongation14 to form chromatin loops15. This process is perceived to enable the presentation of the RNA polymerase complex to promoters, which have been marked to become active16. However, the formation of such chromatin loops is not sufficient to activate genes by itself8. The reason is that the large subunit of the RNA polymerase complex recruited to the promoter needs a post-translational modification at serine 2, carried out by protein kinases, such as cyclin-dependent kinases (CDKs) 7–913, to overcome inhibition of transcriptional elongation. Strikingly, a large fraction of transcriptionally inactive genes have polymerases loaded onto their promoters during development17. The advantage of this arrangement presumably reflects that any rate-limiting, enhancer-mediated loading of RNA polymerases to target genes is bypassed to facilitate rapid activation kinetics in response to emerging environmental cues during a biological process to coordinate transcription18. However, recent observations suggest that the distinction between promoters and enhancers can be blurred, as some promoters can act as enhancers for other promoters and vice versa19. Given the close biochemical structure of the H3K4me3-marked promoters and H3K4me1 modifications characterizing enhancers, it is conceivable that relatively simple enzymatic reactions4 can regulate switches between promoter and enhancer functions.Physical interactions between active enhancers and promoters generally involve a collaboration between the mediator and cohesin complexes10. There are at least two issues linked with this deduction: First, we may not have a complete catalogue of chromatin marks, or indeed combinations thereof, involved in enhancer function. Second, current technologies, such as ChIP-seq and Hi-C20, do not have sufficient sensitivity or resolution (or both) to simultaneously measure chromatin states and folding in single cells in relation to a transcriptional process. Both approaches generally use large cell populations that are likely heterogeneous because of differences in cell cycle stages or cell state composition (or both) to obscure the directness of such relationships. Thus, we cannot rule out the existence of dynamic and transient chromatin marks underlying stochastic enhancer-gene interactions that are not picked up by current technologies. Therefore, a more unifying feature underlying the formation of enhancer-promoter complexes posits that known or unknown factors (or both) collaborate in 3D to increase the potential for their mobility and subsequent affinity to each other. This scenario is reinforced by the recent demonstrations that such enhancer-promoter interactions can be influenced by mutually exclusive mechanisms involving either cohesin complexes14 or paired YY1-YY1 interactions21. Time will tell us whether there are many other variations on this theme.As a caveat to this discussion, a screen of the current literature has revealed the existence of enhancer regions apparently devoid of the typical enhancer-specific chromatin marks8. For one, the chromatin fibers likely require release from any topological constraints to facilitate physical encounters between enhancers and promoters. In one early study, this issue was highlighted by the large-scale unfolding of a chromosomal region, targeted by strong viral transcriptional activators, to sample one third of the mammalian nuclear space within an hour22. More recently, it was demonstrated that synchronization of circadian gene transcription by external time cues involves dynamic chromatin movements to and from the repressive environment of the nuclear periphery23. Such events are likely regulated directly or indirectly by factors that influence chromatin compaction and mobility. Interestingly, oscillations in 3D genome organization required circadian complex formation between CTCF, which is associated with many enhancers, and PARP1, which is associated primarily with active promoters24 and is involved in DNA repair24. Knocking down either of these factors thus inhibited the ability of circadian genes to approach the nuclear periphery and counteracted the synchronization of transcriptional oscillations23.There are numerous challenges associated with the loading of an RNA polymerase complex to promoters and their release from promoter proximal pausing, especially when considering that enhancer-promoter interactions can occur over millions of kilobase pairs29. Indeed, binding of the catalytically engaged topoisomerase (TOP) 1 to enhancers and gene bodies and TOP2 to promoters has been described at several genes in response to environmental signals25. Current models of TOP action thus posit that signal-regulated gene expression requires the transient generation of single- and double-strand DNA breaks at regulatory elements, followed by the recruitment of DNA repair machinery to safeguard genomic integrity25. Interestingly, MRE11, which is a repair factor capable of removing covalent TOP1-DNA intermediates, is rapidly recruited to androgen-responsive enhancers30. Moreover, while recruitment of MRE11 facilitates the subsequent binding of ATR, a sensor of single-strand breaks, both MRE11 and ATR are required for the expression of the vast majority of androgen-induced enhancer-derived non-coding RNAs (eRNAs) and for the expression of numerous androgen-induced genes30. These findings point to a role of “programmed” DNA breaks and DNA repair factors beyond the safe regulation of torsional stress during transcription25. A clue to their potential function might be provided by observations that link DNA damage to altered chromatin mobility over long distances33. Given that cell type–specific gene expression is regulated by productive encounters between enhancers and promoters that are often located far apart, the burst of transient DNA breaks and the dynamic formation of DNA repair factories25 might also provide a protein environment that favors the affinity between enhancers and promoters to induce productive interactions.Dynamic changes in chromatin structure, mobility, and the unwinding of the double helix during transcription likely all increase torsional stress that has to be released via factors that regulate DNA topology and repair34 or YY121 pairing, for example—will have the potential to form metastable complexes. Although the inner working of this process is not well understood, there are several links to non-coding transcripts, which have been observed to be a common feature of most active enhancers35. The observation that enhancers can produce such transcripts, termed eRNAs35, is in retrospect not surprising. The reasons are twofold: First, the chromatin marks characterizing promoters can also be found within enhancer regions4; second, the sequence complexity characterizing a functional promoter is low to occur on average every few kilobase pairs in the mammalian genome. What is more surprising is that such eRNAs appear, in some instances, to be responsible for establishing the chromatin loops between enhancers and promoters. For example, it was shown that the reduction of eRNA expression within a distally positioned colon-specific super-enhancer reduced its interaction with theMYC gene36. However, beyond this and a few other examples, it remains to be determined whether eRNAs are more generally a consequence of the transcriptionally permissive environment at active enhancer regions rather than being directly involved in the establishment of enhancer-promoter contacts.For interactions between enhancers and promoters to be manifested, increased opportunities for physical proximities will likely not be sufficient without a degree of attraction between the participating chromatin fibers. Thus, many different regions of the genome may collide with each other but will not interact in a physical and functional sense, even transiently. Conversely, other regions that display affinity to each other—by CTCF37, it was realized early on that its functions also included transcriptional activation38. For example, CTCF can interact with the large subunit of the RNA polymerase II complex, and CTCF binding sites can function as promoters of transcription39. Moreover, while a wild-type CTCF binding site at theXIST promoter was associated with activation ofXIST and hence X chromosome inactivation in female human cells, a naturally occurring mutation at this site led to loss of both CTCF binding andXIST transcription, leading to skewed patterns of X inactivation40. These observations are in keeping with the more recent findings that CTCF binding sites are frequently found in enhancer regions41. Two not mutually exclusive scenarios explaining such data can be envisioned: CTCF binding sites might promote transcription of eRNAs or participate in the communication between enhancers and promoters (or do both). This latter point has usually been associated with the ability of CTCF to recruit the cohesin complex to its binding sites14. However, the absence of a complete overlap in the functions of these factors shows the existence of CTCF-independent recruitment of cohesin complexes to enhancers41. It is also known that both proximal and distal CTCF binding sites can directly interact with each other in the context of chromatin insulators to prevent variegated gene expression42. Similarly, enhancers associated with CTCF binding sites have been shown to have a more stable interaction with promoters: The deletion of a key CTCF binding site led to transcriptional variation in Th2 cells, suggesting that a CTCF-mediated metastable chromatin loop underlies homogenous expression43. Superimposed on these processes, CTCF binding sites can interact with each other to establish topologically associated domains (TADs) of chromatin45 by attenuating the ability of enhancers to access genes in flanking TADs46.Although CTCF has traditionally been assigned to function as a master regulator of the genome via its function as a chromatin insulator protein47, the rate of turnover of such features is likely too slow to enable dynamic adjustments to changing environmental cues. A more plausible principle was provided in one early study demonstrating that transcription through a CTCF binding site was sufficient to transiently evict CTCF from chromatin48. More recently, it was demonstrated that a lineage-specific enhancer could be activated by transcription through a CTCF binding site49. Intriguingly, this process resulted in the relocation of the Bcl11b enhancer from the repressive compartment at the nuclear periphery to enable the subsequent targeting of this enhancer to distal promoters, essential for T-cell differentiation49. It is also possible that CTCF-mediated connections between chromatin fibers can be modulated by post-translational modifications of CTCF. For example, it was shown early on that poly(ADP-ribosy)lation50 and SUMOylation51 of CTCF regulated its ability to confer insulator functions. The link to poly(ADP-ribosy)lated CTCF prompted the realization that CTCF binds to and activates PARP1 to influence not only the organization of chromatin networks incis and intrans but also the oscillating transcription of circadian genes23. Such information bears on the observation that PARP1 binds active promoters24 to potentially set up interaction patterns with CTCF-containing enhancers. Given the role of PARP1 in DNA repair, such a scenario might also facilitate repair of any programmed DNA breaks at promoters and enhancers. It remains to be established whether other post-translational modifications, such as phosphorylation and acetylation, play a role in the CTCF/PARP1-mediated organization of the genome and to what extent this principle collaborates with other factors, such as YY1 and cohesin, to effectuate the transcriptional process.Although the affinity between CTCF and its binding sites can generally be controlled by DNA methylation states32, it is conceivable that both enhancer and promoter regions actively roam the nuclear space to increase their chances for contact. Strictly speaking, all regulatory DNA sequences including a distant enhancer (or enhancers) can be considered to be part of a gene, but for the sake of simplicity, a “gene”—typically a protein-coding transcription unit—is treated here as a separate entity. Nevertheless, the observations that such contacts are enriched within TADs45 show that local physical constraints increase the preferences for proximal interactions. This makes sense, as this chromatin organization restricts the employment of enhancer regions positioned either in distant TADs on the same chromosomes or intrans, on other chromosomes52. Thus, perturbations of TAD boundaries might promote expression patterns unsuitable for proper development or for maintaining cell type–specific transcription or for both53. From these perspectives, the question of whether it is the enhancer or the gene that drives their interactions might seem moot. However, this deduction is at odds for at least some developmentally important genes, such as theHOX clusters, which are positioned in inter-TAD boundaries55. As enhancers in flanking TADs do not readily communicate over such borders54, it is conceivable that it is the gene that searches for enhancers in either of the flanking TADs55. This scenario has also been borne out forMYC, which forms a dynamic network with enhancers in both of the flanking TADs56. Importantly, in comparisons of the dynamic importance of the baits,MYC constitutes by far the most important and central node in a local gene-enhancer network derived from colon cancer cells56. Moreover, with the ultra-sensitive Nodewalk technique to analyze very small samples, it was demonstrated thatMYC stochastically interacted with enhancers in either of the flanking TADs in a mutually exclusive manner56.Given that active marks often increase the mobility of chromatin fibers59. Although such structures have not yet been documented in single nuclei, they have been interpreted as evidence that enhancers collaborate in targeting gene promoters59. This issue is particularly relevant in instances with spatially clustered, multiple enhancer elements spanning tens or hundreds of kilobase pairs to form super-enhancers, which have often evolved in the vicinity of cell fate–determining genes60. Although it is certainly feasible to conclude the existence of enhancer–enhancer interactions to form a virtual network in large cell populations, the stochastic and dynamic character of chromatin fiber movements may be at odds with the idea that multiple and simultaneous enhancer networks would survive sufficiently long to target specific promoters in individual nuclei. Accordingly, single-cell analyses using DNA fluorescencein situ hybridization (FISH) to label regulatory regions identified to interact with each other by the 4C-seq technique are consistent with the view that chromatin networks are organized by “dates” rather than “parties”61. The interpretation that regulatory elements within super-enhancers do not necessarily collaborate to active neighboring genes but instead increase the likelihood for transcriptional activation has also been borne out. Thus, with the Nodewalk technique, it has been demonstrated thatMYC interacts with a preferential subset of regulatory elements within its oncogenic super-enhancer in stochastic manners56. However, it cannot be ruled out that a metastable enhancer-promoter loop at a gene poised for transcription but not yet active is able to attract another enhancer to provide the final trigger of transcriptional activation. This possibility is finding support with the discovery of the so-called “fail-safe” or “split” enhancers that represent spatially separated enhancer units that need to be simultaneously active for the induction of highly specific transcriptional regulation62. At another level, one enhancer might seed active marks on distal or proximal regions containing dormant enhancers to set up super-enhancers and indirectly promote transcription. Although they do not specifically involve enhancers, there are precedents for long-range chromatin loops affecting epigenetic states: A regulatory region binding CTCF and flanking theH19 gene not only prevents DNA methylation of a distal regulatory region it interacts with incis in somatic cells63 but also is able to transfer epigenetic marks intrans to interacting regions during a specific developmental window of mouse spermatogonia61.It has frequently been observed in large cell populations that enhancers extensively communicate with each other to form chromatin hubs6. Nonetheless, without a detailed 3D perspective, we run the risk of missing the final pieces of the puzzle with respect to what brings these regions together, the dynamics of their interactions, and how they are regulated. This is exemplified by transcription factories, which consist of several genes being simultaneously transcribed in permissive compartments located at the interface between chromosomes and the inter-chromosomal space65. Although such information suggests that enhancers and genes within TAD units need to exit the physical constraints provided by the core structure of chromosomes, it also raises a number of key questions pertaining to the inner workings of transcription factory formations. Are rapidly-turning-over transcription factories organized by dynamic enhancer networks that attract promoters of genes to become activated66, or do they simply result from physical constraints transiently promoting co-transcription of genes that are proximal to each other in the 3D space68? This issue is furthercompounded by real-time analyses of chromosome movements in relation to the nuclear architecture showing that the mobility of chromatin fibers is decreased when approaching the nuclear periphery or nucleoli71. This feature, which is transient and hence not epigenetic in character, is likely an effect of the physical constraints provided by the microenvironment of these structural hallmarks. Thus, opportunities for interactions between the colorectal super-enhancer and theMYC gene might increase in a manner directly proportional to their distance from the nuclear periphery  or cannot reliably assess the influence of the nuclear architecture on chromatin networks (“C” derived techniques)riphery . Therefo75. This prophetic prediction, termed gene gating, has largely been borne out in lower eukaryotes, such as yeast, but is less well understood in mammalian cells76. However, in the yeast genome, in contrast to the animal genome, long-range enhancers cannot be found77. Moreover, the volume of the yeast nucleus is more than 100-fold smaller than the mammalian counterpart78, implying that any mechanism to gate a mammalian gene to the nuclear pore might have to negotiate these differences. Nonetheless, it is now well established that several nucleoporins that participate in the formation of nuclear pore complexes associate with active chromatin79. Indeed, it has been observed that some nucleoporins, such as NUP93 and NUP153, not only bind enhancers and promoters of developmentally important genes80 but also influence their expression79. Although this information would suggest that nucleoporins might facilitate enhancer-nuclear pore associations, this deduction is complicated by the frequent finding that nucleoporins are often found in the nucleoplasm81. This information suggests that some nucleoporins might have one function in the nucleoplasm to promote enhancer-promoter interactions and another at the nuclear membrane to participate in nuclear export/import. Conversely, it is also possible that enhancers and promoters binding nucleoporins are being recruited to the nuclear pores to enhance the export of processed transcripts. In this scenario, factors that might mediate such a function, such as ELYS84, would need to collaborate with processes negotiating torsional stress induced by enhanced chromatin mobility. Therefore, it is of considerable interest that ELYS has been shown to contribute to genomic stability in mouse intestinal epithelial progenitor cells by promoting DNA repair85. As enhancer-MYC promoter proximities increase when they approach the nuclear periphery , we propose that the nucleoporin-enhancer link mediates the recruitment of active genes to the nuclear pore to enhance the export of a subset of nuclear transcripts  that tend to localize at the nuclear periphery83 . The anc90. Similarly, it remains to be established whether the affinity between enhancers and promoters is metastable in itself to require an active process of disruption by post-translational modifications, for example. Or will the dynamic mobility of other chromatin regions within TADs compromise such stabilities by generating long-range torsional stress or by DNA repair-induced local stiffening of the chromatin fiber or both91? Can an enhancer-promoter loop generate secondary but transient marks to establish a higher affinity between the RNA polymerase complex and target promoters? Finally, the existence of transcription factories is generally acknowledged though often ignored because of a lack of detailed knowledge regarding their composition. Do these represent side products resulting from physical constraints, or do they facilitate coordination of expression?During the last decade, a vast amount of information has uncovered that diverse enhancer functions involve multiple features of the epigenome and its folding in 3D. It has been and remains a formidable task to chisel out how these and many other features collaborate within the single nucleus to provide the high-precision transcriptional regulation that is required during normal development. Key unresolved questions that are still only scratching the surface address how enhancers and genes find each other in the nuclear space and how the stability of these interactions is regulated. Moreover, it is still unknown whether the stability of enhancer-promoter interactions is a factor in deciding the efficiency of RNA polymerase loading on to the promoter and, if so, whether the enhancer needs to stably interact with promoters not yet active but poised for transcription. Would the maintained presence of an enhancer-gene loop facilitate re-initiation of transcription? In one instance involving rRNA genes, for example, it was demonstrated that the enhancer/promoter and terminator regions make contact to form a “ribomotor” to enhance levels of rRNA transcription92 and CARGO dCas993 imaging techniques, and aside from clever combinations of ChIP-seq and Hi-C approaches94 to generate a detailed mapping of radial chromatin features, such as chromatin marks and compaction at a high resolution in 3D in single cells using the chromatinin situ proximity (ChrISP) technique96. However, these techniques will likely not be sufficient to understand the mechanism of enhancer action. This two-tier progress of the chromatin field is further complicated by the realization that biological processes impinging on chromatin features are very dynamic97 and are governed by stochastic principles9. Thus, there is likely a need to characterize a large number of single cells for chromatin structures to generate statistically relevant perceptions of their varying 3D conformations. The potential influence of computer simulations that can be generated from such data98 should not be underestimated. When a role of the nuclear architecture in promoting or antagonizing enhancer-gene interactions is considered, there is the additional requirement to combine analysis of 3D epigenomic features within single cells and score for such features in relation to nuclear structure and gene activity. To resolve the huge challenges lying ahead of this field, there is likely a need to develop a new generation of chromatin techniques.Owing to advances in high-throughput sequencing, dogmas are now being formed in the chromatin biology field at a neck-breaking speed. Although the emergence of high-throughput data has revolutionized our understanding of the enhancer-mediated transcriptional process, it should be pointed out that current chromatin technologies display severe limitations in either resolution or sensitivity as well as in their abilities to correctly quantify key features of chromatin biology. It is now possible to readily study movements of individual loci in real time using, for example, the SNP-CLING


MYC is one of the most frequently deregulated in cancer. In B-cell lymphomas, the MYC gene is subject to chromosomal rearrangements that result in MYC overexpression. In many other cancers, the region surrounding MYC is subject to gene amplification. MYC expression is also controlled at the level of protein and mRNA stability. Neoplastic lesions affecting MYC expression are responsible for a drastic change in the number and the type of genes that are transcriptionally controlled by MYC, depending on differential promoter affinities. Transcriptome profiling of tumor samples has shown that several lncRNAs can be found differentially regulated by MYC in different cancer types and many of them can influence cancer cell viability and proliferation. At the same time, lncRNAs have been shown to be able to control the expression of MYC itself, both at transcriptional and post-transcriptional levels. Given that targeting the MYC-dependent transcriptional program has the potential to reach broad anticancer activity, molecular dissection of the complex regulatory mechanisms governing MYC expression will be crucial in the future for the identification of novel therapeutic strategies.Long non-coding RNAs (lncRNAs) are emerging as important regulators of gene expression networks, acting either at the transcriptional level, by influencing histone modifications, or at the post-transcriptional level, by controlling mRNA stability and translation. Among the gene expression networks known to influence the process of oncogenic transformation, the one controlled by the proto-oncogene  The body of complex multicellular organisms is formed by more than 200 different cell types . With thcis or in trans by interacting with subunits of chromatin modifying protein complexes [Evolutionary genomics has shown that the number of protein coding genes has not changed dramatically in the transition from simple to complex organisms, despite the striking increase in genome size. There is therefore no correlation between the number of coding genes and organism complexity. On the contrary, organism complexity positively correlates with the proportion of the genome that is dedicated to transcribed non coding genes, that in humans is estimated to be around 70% . Among aomplexes . lncRNA omplexes ,11. Theyomplexes . ConsideMYC is a subunit of a basic helix-loop-helix and leucine zipper (bHLHZip) heterodimeric transcription factor. Obligate partner for MYC in the assembly of a transcriptional competent complex is another bHLHZip protein called MAX. MAX is able to interact also with other bHLHZip proteins to form complexes that can either activate or repress transcription. The set of genes controlled by the interplay of MYC and MAX with their binding partners is therefore often referred as the MYC transcriptional network [The product of the proto-oncogene  network . The MYC network ,15,16. M network . Finally network . Genome- network . The num network . Accordi network ,21.In line with the crucial role played by MYC in the definition of cellular transcriptional programs and in line with the role of lncRNAs in the modulation of transcriptional networks in complex organisms, several reports have identified lncRNAs as components of MYC’s transcriptional network. Here, I review the literature describing either lncRNAs found to be regulated by MYC or lncRNA able to affect at any level the activity of the MYC transcriptional network.H19, a lncRNA subjected to genomic imprinting [MYC allele. They both found that in P493-6 cells MYC is able to profoundly affect lncRNA transcription, similar to its effect on protein-coding genes. Use of MYC ChIP-seq data obtained from the same cell line, showed a significant enrichment of MYC bound regions in the vicinity of the transcriptions start sites of the lncRNAs regulated by MYC. For some of the identified lncRNAs, the group of Vogt also performed nuclear run-on experiments to show their direct transcriptional regulation by MYC. Overall, these studies show that MYC is indeed able to affect the transcription of many lncRNA genes and that changes in lncRNA expression following MYC activation generally mirrors the changes observed in the transcription of protein coding genes. With the exceptions that MYC induced lncRNAs tends to be more cell-type specific relative to coding genes [As a transcription factor able to influence the expression of a high percentage of all cellular protein coding transcripts, MYC is supposed to influence also the expression of lncRNA transcripts, either directly or indirectly. Already before the advent of next generation sequencing (NGS), the use of tiling arrays coupled with Chromatin Immunoprecipitation (ChIP) showed that MYC was extensively binding outside the regulatory regions of the known protein-coding genes, suggesting it was able to influence the transcription of non-coding genes . Indeed,printing . Later oprinting . In the printing  and the printing  both useng genes  and thatng genes . MYC gene [In P493-6 cells MYC induces a transcriptional program resembling that of Burkitt lymphoma (BL), a B-cell lymphoma characterized by the activating translocation of the MYC gene . AccordiMYC gene . SimilarMYC gene  and founMYC gene . TherefoSeveral groups have asked if there are specific lncRNAs regulated by MYC that could play a role in cellular and cancer physiology. Among the validated lncRNAs found by Hart et al., three of them, DANCR, SNHG15 and SNHG16, are genes hosting small nucleolar RNAs (snoRNA) in their introns . InteresMYC-induced long non coding RNA) [MYC-translocation resulting in MYC overexpression. Furthermore the MINCR gene has a MYC binding site at the transcription start site in several cell lines, including BL cell lines and P493-6 cells, and its transcription level goes down after MYC knock-down in BL cell lines [MINCR is an intergenic lncRNA lying between two genes coding for the zinc finger proteins GLI4 and ZNF696. Promoter and gene body of MINCR are characterized by open chromatin typical of an active promoter in all cell lines analyzed in the context of the Encyclopedia of DNA Elements (ENCODE). Accordingly MINCR is relatively highly expressed in all ENCODE cell lines. An analysis of public ChIA Pet data shows that the promoter region of MINCR is engaged in physical contacts with the promoters of GLI4 and ZNF696, suggesting that the three genes may be co-regulated. Although the precise function of MINCR has not been fully elucidated yet, we found clear evidence that MINCR is able to control normal cell cycle progression and it is interfering with the ability of MYC to transactivate a set of genes involved in cell cycle progression. Accordingly the group of Quan found that MINCR knock-down was able to strongly reduce the ability of gallbladder cancer cells to form tumors in nude mice [To identify MYC-regulated lncRNAs that are also regulated in MYC-positive lymphomas and may therefore be functionally relevant for lymphoma development, our group performed an integrated analysis of RNA-seq data from a large cohort of B-cell derived lymphoma samples as well as normal B-cells and compared them with expression data from hT-RPE-MycER cells, an epithelial cell line carrying an activatable MYC protein, as well as to the expression data produced by Vogt group in P493-6 cells . This aning RNA) . MINCR ill lines . MINCR iude mice .MYC is known to be overexpressed also in colorectal cancer (CRC), where it plays an important role in tumor progression downstream of the WNT signaling pathway [Besides B-cell lymphoma, the oncogene  pathway . To iden pathway . Among t pathway  and we f pathway . At the  pathway . InteresCDK6 gene [Another lncRNA identified in CRC cell lines as a target of the WNT/MYC axis is VPS9D1-AS1 (MYU) . VPS9D1-DK6 gene .ODC1 gene, a known MYC target gene, raising the possibility that the two genes share the same regulatory regions. lncRNA-MIF was shown to influence cellular glycolitic activity by controlling MYC degradation working as a micro RNA (miRNA) sponge.Searching for lncRNAs that are both transcriptionally regulated by MYC and able to control MYC expression, the group of Wu identified the transcript ENST00000553181.5 (RP11-320M2.1) that they named lncRNA-MIF, for MYC-inhibitory factor. lncRNA-MIF was identified in P493-6 cells as upregulated upon MYC induction. It has three predicted MYC-binding sites in its promoter that were shown to be bound by MYC and responsive to MYC in reporter assays . InteresOne more lncRNA reported to be regulated by MYC is the Brain Cytoplasmic RNA 1 (BCYRN1). Although initially identified as brain specific transcript , BCYRN1 MYC transcription regulation and are used in normal cells by a multitude of mitogenic stimuli differing form cell type and developmental stage [MYC transcriptional control [Contrary to MAX, whose expression is constitutively high in most tissues, the expression of MYC is tightly regulated by mitogenic stimulation. The amount of active MYC/MAX transcriptional complex is therefore highly dependent on changes in the expression levels of MYC. At least two proximal promoters, P1 and P2, are responsible for al stage . Far ups control . MYC exp control ,47,48. M control .MYC gene is known to lie in a so-called “gene desert”, a chromosomal region characterized by extremely few genes. This observation holds true if we focus on protein coding genes, but the same cannot be asserted if in the concept of gene we include also non-protein coding genes [MYC gene both in human and in mouse genomes. Functional studies are revealing that many of these non-coding genes have an important role in the regulation of MYC expression.The ng genes . Indeed,MYC locus have been extremely useful for the understanding of the role of the regions adjacent to MYC in the control of MYC transcriptional regulation. In Burkitt Lymphomas, the MYC gene is reciprocally rearranged with the heavy or the light chains of the immunoglobulin (Ig) gene loci. The most common translocations with the heavy Ig chain fall either in the first exon or in the first intron of the MYC gene. In some more rare variants the Ig light chain translocates to a 4.5 kb cluster located at least 40–45 kb downstream of MYC, the so-called plasmacytoma variant translocation (PVT-1) region [PVT-1 region are only recently starting to be elucidated. The PVT-1 region encodes the lncRNA PVT1, a multi exonic transcript with numerous isoforms that can be also transcriptionally activated by MYC [PVT1 is found co-amplified with MYC in a high percentage of cancers carrying an amplification of the chromosomal region 8q24.21. Amplification of the PVT1 gene contributes to the pathophysiology of several cancers, including ovarian and breast [MYC alone or the region co amplified with MYC, including the PVT1 gene, Tseng and colleagues demonstrated that in these experimental settings only the combination of both MYC and PVT1 is able to drive neoplasia [PVT1 locus is deleted using genome editing [PVT1 only [PVT-1 locus, the PVT1 transcript was found fused to the constant region of the Ig light chain [Studies on lymphomas and leukemias carrying rearrangements in the ) region . While td by MYC . Interesd breast , prostatd breast , mesothed breast  and acutd breast . Fundameeoplasia . The reaeoplasia . Accordi editing . MYC abiVT1 only , PVT1 maVT1 only . Furtherht chain . It is rMYC gene. Using H3K27ac ChIP-seq data, the group of Young found a large CRC-specific super-enhancer in this region. The super-enhancer was also cancer specific because absent in the healthy colon tissue [MYC gene [MYC promoter and the super enhancer through the interaction with the chromatin loop forming factor CTCF [CCAT1 locus can have very different activities according to the cell of origin: in the lymphoblastoid cell line GM12878 the locus has marks of a strong enhancer without associated transcript; in the cervical cancer derived HeLa S3 cell line and in the hepatocellular carcinoma derived HepG2 cell line the locus has the marks of an actively transcribed gene with an active promoter and a transcript expressed at high levels; in the CRC derived cell line HCT116 the locus has the marks of a super-enhancer  with thMYC: CCAT2 [MYC locus with the lncRNAs affecting MYC expression as well as of all other lncRNA described in this review is shown in Two more non-coding transcripts have been identified in the region upstream of C: CCAT2  and PCATC: CCAT2 . Both ofC: CCAT2 . A graphcis is not limited to the MYC gene but was found associated also to the MYC homolog gene MYCN. Amplification of the genomic DNA region containing the MYCN oncogene is a recurrent event in almost one fourth of all human neuroblastomas [MYCN, the group of Liu identified a non-coding transcript, MYCNUT (lncUSMycN), located 5kb upstream of MYCN [MYCN is MYCNOS. Contrary to MYCNUT, MYCNOS is transcribed antisense of MYCN. MYCNOS was shown to function as a modulator of MYCN, able to regulate MYCN promoter usage and to recruit various proteins to the upstream MYCN promoter [MYC locus, also the MYCN locus is producing lncRNAs that are able to control MYCN expression both at the transcriptional and post-transcriptional level.Interestingly the mechanism of MYC regulation mediated by lncRNAs working in lastomas . By aski of MYCN . MYCNUT promoter . TherefoSeveral other lncRNAs were found to control MYC expression by controlling either the stability of MYC mRNA or the stability/translation of MYC protein. The lncRNA GHET1 was found up-regulated in gastric carcinoma compared to matched non-tumor gastric tissues . FollowiLINC-ROR was originally identified as a lncRNA able to regulate pluripotency in human stem cells . The goulncRNAs were found not only regulating MYC mRNA stability, but also MYC protein degradation and mRNA translation. The lncRNA ENST00000553181.5 (lncRNA-MIF) was found to be able to interact with miR-586, a miRNA also able to target the 3′UTR of FBXW7. Accordingly, the authors found that increased expression of lncRNA-MIF increases FBXW7 levels, presumably working as a sponge for miR-586. Given that FBXW7 is a known E3 ligase for MYC, increased FBXW7 is accompanied by an increase in MYC protein degradation . The lncLong RNA molecules have the peculiar advantage over proteins to potentially act as modular multifunctional platforms. Part of the RNA could fold in a tridimensional structure that could serve as protein-binding domain. At the same time, a second part of the same molecule could use its ability to form complementary RNA/RNA or RNA/DNA hybrid to engage sequence-specific interactions . Based oOn the same line, we have found that the MYC-induced lncRNA MINCR is able to affect MYC transcriptional activity. Upon MINCR knock-down, we found not only that the genes affected are enriched for MYC target genes, but we also observed a decrease in MYC binding to the promoter of a set of these genes . AlthougIt is interesting to speculate that lncRNAs could indeed be the missing “linc” explaining the wide range of binding affinity shown by MYC on the promoters of different genes . Using tIn conclusion, we are starting to understand that lncRNA may have a crucial role in the correct execution of the gene expression program controlled by the MYC transcriptional network. Due to the pleiotropic effect of MYC expression in oncogenic transformation, MYC is an ideal target for pharmacological intervention, and targeting MYC could have broad-range anti-tumor activity. Unfortunately, MYC has no catalytic activity that can be easily inhibited with small compounds. One alternative approach is to target parallel pathways that converge on MYC regulation or that are required for a proper MYC activity. Therefore, the discovery of novel lncRNAs-mediated pathway that control MYC activity could, in turn, suggest novel therapeutic windows for the fight against all cancer that rely on MYC expression.