The correct deployment of genetic programs for development and differentiation relies on finely coordinated regulation of specific gene sets. Genomic regulatory elements play an exceptional role in this process. Thereare few types of gene regulatory elements, including promoters, enhancers, insulators and silencers. Alterations ofgene regulatory elements may cause various pathologies, including cancer, congenital disorders and autoimmunediseases. The development of high-throughput genomic assays has made it possible to significantly accelerate theaccumulation of information about the characteristic epigenetic properties of regulatory elements. In combinationwith high-throughput studies focused on the genome-wide distribution of epigenetic marks, regulatory proteinsand the spatial structure of chromatin, this significantly expands the understanding of the principles of epigeneticregulation of genes and allows potential regulatory elements to be searched for in silico. However, common experimental approaches used to study the local characteristics of chromatin have a number of technical limitations thatmay reduce the reliability of computational identification of genomic regulatory sequences. Taking into account thevariability of the functions of epigenetic determinants and complex multicomponent regulation of genomic elements activity, their functional verification is often required. A plethora of methods have been developed to studythe functional role of regulatory elements on the genome scale. Common experimental approaches for in silico identification of regulatory elements and their inherent technical limitations will be described. The present review is focused on original high-throughput methods of enhancer activity reporter analysis that are currently used to validatepredicted regulatory elements and to perform de novo searches. The methods described allow assessing the functional role of the nucleotide sequence of a regulatory element, to determine its exact boundaries and to assess theinfluence of the local state of chromatin on the activity of enhancers and gene expression. These approaches havecontributed substantially to the understanding of the fundamental principles of gene regulation. The progress of programs for the development and maintenance of body functions is based on the expression of genesets specific to cells and tissues. The gene expression iscoordinated by a multilevel regulatory system that includesgenetic and epigenetic mechanisms based on the interactionof genomic sequences, epigenetic modifications, regulatoryproteins, and specific transcription factors. Certain genomicregions associated with the specific epigenetic determinants,as well as serving as a site for attracting regulatory proteins,are capable of modifying gene expression. Such regulatoryelements in the genome play a key role in the implementationof genetic programs for development, differentiation, andmaintenance of cellular and tissue homeostasis .Dysfunction of genomic regulatory elements may lead tothe development of various pathologies, including cancer,developmental defects and autoimmune diseases . Thegenome wide association studies show that more than 90 %of disease-associated single nucleotide polymorphisms arelocated in non-coding genomic regions . The significant part of the genomicvariants are located in regions that show epigenetic characteristics of enhancers, as well as affect enhancers, specific forthe cell lines involved in the disease pathogenesis . Thegenetic variants associated with the development of type 2diabetes (T2D), which were located in regions of putativeenhancers in pancreatic islets, can be a good example . Today, a lot of information is available regarding thespecific properties of the epigenetic regulatory elementsthat alleviate identification of potential regulatory genomicregions in silico . However, the validationand functional characterization of the regulatory elementsoften requires direct experimental verification. The classicmethods are different modifications of reporter assays andfunctional mutagenesis. With the development of massivelyparallel sequencing methods, methodologies that allow studying the activity of the regulatory elements in genomescale have been developed. This review will describe the existing methodologicalsolutions in the high-throughput analysis of enhancers thathave significantly contributed to the understanding of thefundamental principles of their functions.Several types of genomic regulatory elements, includingpromoters, enhancers, insulators, and silencers are distinguished. Promoters are located near the transcription start siteand serve as a DNA site where the transcription complexis assembled. In eukaryotes, such transcription complexesconsist of the main transcription factors, RNA polymerase,and other regulatory proteins, including those which mediate the interaction with enhancers . Enhancers are nucleotide sequences in genomic DNAthat contain binding sites for transcription factors and cofactors. As part of a protein complex, enhancers can physically interact with the promoter to activate gene expression. Enhancers are able to regulate targetpromoters from a long distance, and regardless of mutualspatial orientation . For example,the ZRS enhancer, the dominant mutation of which leadsto familial forms of polydactyly, is located approximately1 Mb from the controlled Sonic hedgehog (Shh) gene inthe mouse genome . On average, theenhancers are mapped at 20–50 Kb from the target gene invertebrate genomes, and at 4–10 Kb in the genome of thefruit fly .The regulatory interactions network of promoters andenhancers can be quite complex. A separate gene can shareenhancers with other genes, might be regulated either byseveral enhancers or specific enhancers in different typesof cells. The Arx gene expression, for example, is controlled by four enhancers in mouse brain tissue . Regulation of a gene by specific enhancers isalso observed during the development of pathologies. Forexample, the Myc proto-oncogene enhancer is located intranscription termination sites in case of pancreatic cancer. In case of rectal cancer, it is detected in the 5′-region of thegene, and in case of T-cell acute lymphoblastic leukemia, itcan be found downstream of the 3′-region of the gene . Studies conducted in Drosophila melanogaster haveshown that up to 30 percent of enhancers can act as remoteregulatory elements without affecting the expression ofgenes located between them and target genes . This means that there mustbe fine-tuned regulatory mechanisms that address interaction between the target gene promoter and specific enhancer.Today, there are several functionally intersecting conceptsdescribing mechanics of the promoter-enhancer interactions,the main of which are contact formation via protein homooligomers and chromatin looping, caused by the action ofmotor proteins, such as RNA polymerase II and cohesin. The regulatory elements – insulators – play an importantrole in regulation of the chromatin spatial structure. Interacting with specific proteins, insulators are able to blockenhancer-promoter interaction and prevent the distributionof repressive chromatin marks acting as barrier elements. With the development of modern methodsof the nuclear architecture analysis, it became apparent thatthe functional impact of insulators is largely determinedby their participation in the regulation of intra- and interchromosomal contacts . The insulatorproteins play a key role in the formation of topologicallyassociated domain (TAD) . Such fragments are characterized by a high frequency of internalDNA contacts and are often flanked by the binding sites ofinsulator proteins and actively transcribed genes . Along with theregulation of the nucleus spatial structure, insulators areinvolved in many regulatory processes, including activationand repression of the gene expression, alternative splicing,and RNA polymerase pausing . The silencers function is to suppress the gene expression,and such repression is mainly implemented by establishingrepressive chromatin state and competition with activatingproteins .The development of modern methods of high-throughputanalysis has significantly accelerated and simplified thesearch for potential regulatory elements. The assumptionsabout the possible regulatory role of a genomic region areusually based on several types of data, including: (1) DNAaccessibility for regulatory proteins, (2) presence of characteristic epigenetic determinants, (3) evaluation of gene expression and (4) analysis of DNA contacts. Active regulatory elements are associated with specificproteins, and, hence, are free from nucleosomes. The treatment of genomic DNA with DNase I (DNase-seq), micrococcal nuclease (MNase-seq) and Tn5 transposase , followedby high throughput sequencing and FAIRE-seq method, isused to identify such nucleosome-free loci . The listed methods are used for identification of putative enhancers, insulators, and silencers;however, to determine functional class of detected regulatory element, data on DNA accessibility should be combinedwith other descriptive data, e.g. chromatin properties .The genomic mapping of the chromatin characteristicfactors and histone modifications is also used to identifyindividual classes of regulatory elements. The basic methodfor assessing the representation of such epigenetic determinants in a particular genomic region is the chromatin immunoprecipitation followed by massive parallel sequencing(ChIP-seq). Promoters are enriched in H3K4me3 histonemark . Monomethylation at the sameposition of the H3 histone (H3K4me1) is associated withenhancers, and the simultaneous presence of the H3K27me3modification indicates that the enhancer might be poised foractivation, while the H3K27ac modification indicates that theenhancer is active . Enrichment in the p300 histone acetyltransferaseis characteristic of the enhancers . Mapping of specific transcription factors is also used to identifyenhancers. For example, DNA regions enriched by the activeenhancer histone marks, the Mediator complex proteins andthe Oct4, Sox2, Nanog, Klf4, Esrrb master regulators arecalled super-enhancers and control the expression of tissuespecific sets of genes in embryonic stem cells . To identify insulators in vertebrates, the genomicdistribution of the CTCF protein and cofactors involved inthe formation of loops, such as Rad21 and YY, are analyzed.Silencers are enriched by the H3K27me3 histone modification associated with the effect of repressive Polycomb groupproteins, as well as the H3K9me2/3 modifications related toheterochromatin .The spatial organization of the nucleus mediates theinteractions between target genomic loci and distal regulatory elements. The spatial chromatin structure is studied bymethods that allow to fix and analyze DNA-DNA contacts,which originate from the 3C method (chromosome conformation capture) .The most widely used HiC method allows to build DNAgenome-wide contacts map, in contrast to earlier methods.Combinations of chromatin spatial structure analysis andchromatin immunoprecipitation-based methods  make it possible to establish DNAcontacts in genomic regions which are specifically enrichedin specific chromatin proteins or histone modifications. Analysis of the DNA-DNA contacts allows identifying promoter-enhancer interactions, defining borders ofthe topologically associating domain and larger chromatincompartments.The data on the epigenetic characteristics and spatialgenomic organization of model objects are available to awide range of researchers in ENCODE, The EpigenomeRoadmap, FANTOM and other databases .These data are widely used for the prediction and researchof potential regulatory elements. However, it must be noted that the methods of analysisof protein-DNA and DNA-DNA interactions are capable ofdetecting non-functional interaction that can result in a falsepositive result. Local enrichment with characteristic epigenetic determinants detected by ChIP-seq does not necessarily indicate the presence of regulatory elements in a specificgenomic region . This can be due to thefact that implementation of the regulatory element functionmight require the coordinated binding of several transcription factors, and binding of only one of them is simply notenough . Nonfunctional transcription factor binding events can betransient, and caused by the general DNA-binding activity. The chromatin immunoprecipitationmethod detects such transient interactions since it is based onthe fixation of chromatin with formaldehyde with the formation of covalent cross-links between DNA and associatedproteins. Modifications of the ChIP method that eliminatethe need for chromatin fixation and potentially improvethe accuracy of the method have been proposed . A micrococcalnuclease fused with protein A is used in the variation of theCUT&RUN method . Protein Abinds with the specific antibodies to the target protein, andmicrococcal nuclease makes DNA breaks in the region ofits binding. This allows selecting short genomic fragments,which are rich in proteins of interest, and identifying themwith high-throughput sequencing. The Tn5 transposase isused in the CUT&TAG method instead of nuclease, whichmakes it possible to simultaneously introduce DNA adaptersfor massive parallel sequencing, flanking the recognition siteof the protein of interest . However,these methods have been developed recently and have notbeen widely used yet.The false positive results in the ChIP-seq experiments mayalso be due to experimental variations, such as chromatinfragmentation mode, sequencing depth, and the thresholdvalues for the identification of binding sites . It is also importantto note that in the presence of high- and low-affinity proteinbinding sites, the ChIP-seq method predominantly detectshigh-affinity ones . This feature is also alimitation of the method, since it was shown that suboptimalbinding sites for transcription factors in enhancers are needed for fine regulation of gene activity during development. In addition to the technical limitations of experimentalmethods, it is important to note that often functional regulatory elements demonstrate the presence of epigenetic determinants, which is generally uncharacteristic for their class.Functionally tested silencers in the K562 and HepG2B cellcultures, according to the ENCODE database, in additionto being enriched with the H3K9me3 and H3K27me3 repressive histone modifications, also contained H3K36me3and H3K79me2 active chromatin histone marks . Due to the experimental limitations of themethods, the variability of the functions of epigenetic determinants, and the participation of many components inthe implementation of the functions of genomic elements,the determination of their regulatory role often requiresfunctional verification. The functional role of genomic regulatory elements is commonly assessed with different modifications of reporteranalysis. Pioneer work where the functional role of the genomic regulatory elements was demonstrated was devotedto the study of the enhancer of the early gene of the SV40virus . This work showed that a DNAfragment from the 5′-end of the early gene of the SV40virus, consisting of two 72 bp repeats, can cause 200-foldactivation of rabbit β-globin reporter gene expression in theHeLa cells . The standard genetic constructs used for the analysis ofenhancer activity contain a reporter gene under the controlof a minimal promoter, which confers minimal or no expression without additional activation. The genomic sequence ofthe enhancer is cloned into the construct, either upstream ofthe promoter or downstream of the coding sequence of thereporter gene. The obtained construct is used to transformcells and the change in the expression of the reporter genecomparing to a control construct that does not contain apotential enhancer is analyzed. One of the first works aimed at in vivo functional testing of enhancers in genome-wide scale was based on theprinciples of classical reporter analysis .Around 8,000 of the D. melanogaster lines were used,which contained a transgenic construct consisting of potential enhancer, minimal promoter, and Gal4 protein geneintegrated in the same genomic region. The Gal4 expression was assessed at different stages of embryogenesis byin situ hybridization, and 400 embryos at different stagesof development were analyzed for each potential regulatoryelement. As a result, more than three thousand enhancershave been identified. About a quarter were located in thevicinity of regulated genes, and a little more than a quarterwere located at a distance of 20–100 Kb. On average, theywere mapped around 10 Kb from the target genes . About one third of the detected enhancers werelocated in the intergenic regions of regulated genes. Subsequently, it was also functionally confirmed that enhancersare able to regulate not only nearby genes, but also oneslocated through one or two genes . Thedata obtained have significantly expanded the understandingof the fundamental principles of the operation of enhancers;however, the implementation of such projects requires acolossal amount of time and resources. The methods of high-throughput reporter analysis haveevolved from classical approaches and allow simultaneousinterrogation of thousands of regulatory sequences. Thereare two principal approaches in high-throughput reporterassays . Within the first, a reporter gene containsa DNA barcode before the polyadenylation signal, and isplaced under the control of a genomic fragment – a potential enhancer and a minimal promoter . Inthe case of activation of the reporter gene expression, suchDNA barcode will be contained at the 3′-end of its transcript. After the pooling of such constructs, high-throughput sequencing is carried out, and unique DNA barcodescorresponding to each of the studied genomic fragmentsare determined . After transformation using suchconstructs, the presence of DNA barcodes is analyzed bythe transcriptome sequencing (RNA-seq). The expressionlevel of a particular DNA barcode allows to assess activatingability of the corresponding specific regulatory element. Thisapproach is used in the methods of quantitative assessmentof the activity of genomic fragments, called MPRA , different variations of which willbe covered in this review .The second approach allows evaluating the qualitativeability of the genomic fragment to exhibit the enhancers’properties. At first, a pool of genetic constructs that containthe genomic fragment of interest, a minimal promoter, and areporter gene encoding a fluorescent protein or luciferase isprepared. At the next stage, the obtained pool of constructs isused for transformation and cells expressing the fluorescentprotein are sorted using flow cytometry. The activation of thereporter gene expression means that the genomic fragmentof interest demonstrates enhancer activities. The sorted cellsare subjected to DNA isolation, fragments of constructs corresponding to the studied genomic fragments are amplified,and massive parallel sequencing is carried out, thus allowing to identify specific genomic fragments exhibiting theproperties of enhancers. Examples of such methods includeFIREWACh and SIF-seq  . Combinations of the two approaches described above arealso used. In this case, at the first stage cells carrying constructs containing potential enhancers are selected by flowcytometry. Then, the activating ability of specific genomicfragments is quantified by analyzing the representation ofDNA barcodes by the RNA-seq method . MPRA methods are successfully used to study the activating properties of the nucleotide sequence of enhancers, the functional influence of regulatory protein bindingmotifs, and to search for and validate enhancers. Using thismethodology, the effect of single mutations in the composition of three enhancers – ALDOB, ECR11, and LTV1,active in liver cells, was studied .In the course of this research, a DNA library containingmore than 100,000 mutated variants of the enhancers wassynthesized. Such DNA fragments were cloned into constructs containing the minimal promoter, luciferase geneand transcribed DNA barcodes. The resulting DNA libraries were injected into the liver of mice, and a day later thetranscriptome of liver cells was analyzed by RNA-seq .It was found that the majority of single mutations hada weak effect on the activity of the studied enhancers. Inaddition, it was shown that mutations disrupting the enhancer function affect the predicted binding sites of theHNF4 and HNF1 transcription factors, which are active inliver cells . It is important to note that theexperiment also showed significant discrepancies in theoryand practice. Thus, within the ECR11 enhancer, mutationscausing functional disorders were concentrated in the regionthat did not contain predicted binding sites for transcriptionfactors, while mutations in the region containing most ofthese predicted sites did not change the enhancer activity.On the one hand, this clearly demonstrates that MPRA areapplicable to clarify the boundaries of enhancers, and on theother hand, it emphasizes the importance of experimentalverification of predictive data.MPRA are also used for de novo search and validationof predicted enhancers. An elegant approach to search forenhancers was implemented in the STARR-seq method . The authors used theability of enhancers to activate expression regardless of theposition relative to the gene and promoter, and developedreporter constructs, containing studied genomic fragmentscloned into open reading frame downstream from minimalpromoter. In case a genomic region exhibits an enhancerfunction, this will lead to its transcription in cells. Theexpression level of this fragment in the cell transcriptomemakes it possible to assess its activating function. This approach completely eliminates the need to use DNA barcodes,since the fragments play that role themselves. To seek for enhancers, a plasmid library containingmillions of random fragments of the fruit fly genome wasprepared. After transfection of the S2 cell culture, a highthroughput RNA-seq transcription profile analysis was performed. As a result, thousands of genomic fragments wereidentified, which demonstrated the properties of enhancers.The most active were located close to housekeeping genesand developmental transcription factors. About a third of thefragments demonstrating pronounced activating properties inthe S2 cell genome were located in areas of closed chromatin,lacking H3K27ac mark of active enhancers. Thus, it seemsunlikely that such fragments are capable of performing the role of enhancers in the genome of the studied cells, andthis finding highlights some of the limitations of episomalMPRA, which will be discussed below.An interesting modification of the STARR-seq methodwas used in a subsequent work on the study of enhancersin human embryonic stem cells . Inthe original work, DNA libraries were obtained by ultrasonic fragmentation of the D. melanogaster genomic DNAand subsequent mass cloning of the obtained fragments. However, this approach is poorlyapplicable to larger genomes, since a sufficient representation of regulatory elements in the resulting DNA librariesis an extremely difficult task to achieve. Indeed, the use ofthe original STARR-seq method to study the regulatoryelements of the mouse genome will require the creationof more than 200 million unique constructs . Experimental verification showed that the use of aplasmid library containing 1.3 million unique fragmentsof the human genome made it possible to identify only sixenhancers .To overcome this limitation, the ChIP-STARR-seqmethod was proposed. In the original paper, the chromatinimmunoprecipitation was used to isolate genomic fragmentsenriched with the OCT4, NANOG transcription factors, aswell as the H3K4me1 and H3K27ac histone modifications. Obtained DNA fragments were thencloned into DNA libraries similar to those used in the original method. It was found that only a part of the genomicfragments that demonstrate enrichment by these factors ingenome exhibited enhancer activity. Only about 25 % of thefragments bound by OCT4 showed enhancer properties. Forthe fragments enriched in NANOG and the H3K4me1 andH3K27 histone modifications, the results were 15, 9, and10 %, respectively. It has been shown that neither individualfactors nor their combinations are capable of unambiguouslypredicting enhancers. In addition, a group of enhancers associated with the regulation of general cellular processes,which had not previously been found in ESCs, were found.It turned out that such enhancers demonstrate a ratherweak enrichment in TF OCT4 and NANOG, as well as inthe histone modification H3K4me1, and, most likely, forthis reason they were not previously detected in prospecting studies based on the chromatin immunoprecipitationmethod. Data on chromatin accessibility and the genomic distribution of histone modifications and regulatory proteinsdeposited in open repositories allow to predict regulatoryelements. Using MPRA, the activity of regulatory elementsin the K562 cells and the E1 human embryonic stem cells,identified on the basis of chromatin structure analysis andannotated in ENCODE, was studied . It turned out that only about a quarter of them had aneffect on gene expression, which underlines the importanceof such experimental verification .At the same time, this effect may be due to the experimentallimitations of the described MPRA. Indeed, the methodsdescribed above are episomal, which means that reporterconstructs are not integrated into the genome, hence the activity of enhancers is assessed outside the chromatin context.Significant differences in the activity of enhancers analyzedin episomal manner and upon integration into the genomewere also confirmed experimentally .This experimental discrepancy looks logical, becausethe observation of the effect of chromatin structure ongene regulation was demonstrated in classical genetic experiments long ago . The use of an originalhigh-throughput reporter analysis method, combined withMPRA-approaches, made it possible to characterize the localeffects of chromatin on gene expression in mouse embryonicstem cells  . Within theframework of this study, using the PiggyBac transposasebased genomic integration system, reporter constructs containing unique DNA barcodes at the 3′-end of the reportergene were randomly inserted into the cell genome.In the next step, such insertions were mapped and eachDNA barcode was associated with a specific genomic locus.In total, more than 17 thousand of such inserts were received.Then, the expression level of DNA barcodes was analyzedusing the RNA-seq method, which made it possible to assess the transcriptional activity of each insertion as well asthe effect of the local chromatin structure on it. Reporterconstructs integrated into regions of compacted chromatinand regions of domains associated with the nuclear lamina,as expected, showed a reduced level of expression. Reporterconstructions located within 200 Kb from active genes weremore actively transcribed. It is interesting to mention thatan increased frequency of enhancers was observed withinapproximately the same range. Enhancers had an activatingeffect on the expression of reporter constructs at a distanceof up to 20 Kb. It is important to note that in this case alinear distance is considered, and the spatial structure ofchromatin is not taken into account. It was assumed that theformation of extended, actively transcribed regions is basedon the action of several enhancers. This emphasizes theneed to study regulatory elements in conditions close to native ones.The effect of chromatin on the function of regulatoryelements is to some extent taken into account in MPRA,which are based on the genomic integration of the reporterconstruct . These FIREWACh and SIF-seq methodswere used to identify enhancers in mouse ESCs, but did notallow quantitative assessment of the activity of regulatoryelements .The FIREWACh method is based on genomic integration of reporter constructs using lentiviral transduction. This method of genomic integration ensuresthe insertion of the construct into random regions of thegenome . Thus, an adequate comparisonof the activity of various regulatory elements seems to bedifficult, because it is highly likely that reporter constructs will be integrated into different genomic regions, with anunpredictable effect of the local chromatin environment. The SIF-seq method avoided such a drawback, sincethe integration of reporter constructs is carried out in thesame region of the genome located in the region of theHprt gene  . However, thismight serve as a disadvantage, since the correct operationof an enhancer is determined by a specific set of chromatinfactors, and it is highly likely that it will become non-functional when transferred to a non-identical chromatin environment.The approaches described above did not allow answering one of the fundamental questions of understanding theprinciples of enhancers’ activity, namely, to what extent is itdetermined by the DNA sequence, and to what extent – bythe properties of the surrounding chromatin? A systematicstudy of this issue was carried out in the research on theinfluence of different chromatin environments on the comparative activity of enhancers . Withinthe framework of this study, 15 lines of the K562 cells wereprepared, containing single insertions of reporter constructslocated in different chromatin environments and containing the Cre-recombinase (loxP) recognition sites, allowingtargeted insertion of transgenes. Such insertions containeda DNA barcode and a polyadenylation signal outside of thefragment flanked by loxP-sites, with a single unique DNAbarcode corresponding to each line. The described lines were pooled together, and Cre-mediated integration was used to integrate reporter constructs,that contained a reporter gene ending with a DNA barcodeunder the control of the minimal promoter and the genomicfragment of interest. As such fragments, 300 synthesizedregulatory elements were used, which were previouslystudied by episomal MPRA and ranked according to thelevel of activity . For each genomicfragment, the corresponding unique DNA barcodes had beenpreviously established. In case of successful integration, theoriginal loxP cassette was replaced with a reporter constructcontaining the putative enhancers. Moreover, in the case ofactivation of the reporter gene, two DNA barcodes will betranscribed in its composition. Deciphering barcodes allowsto identify which fragment was analyzed in which cell line.The analysis of the representation of combinations ofDNA barcodes in the transcriptome of cells made it possible to assess the level of activity of the studied regulatoryelements in different chromatin environments. It was foundthat the chromatin environment has pronounced effect onthe activity of cis-elements. However, being placed in thesame chromatin environment, regulatory elements save theirrelative activity. It was also demonstrated that the activity ofthe promoter affects the expression of reporter constructs, butat the same time does not affect the comparative activity ofregulatory elements. The results obtained support the modelaccording to which the nucleotide sequence of the enhancerdetermines its overall activity, which is already modulatedby the structure of the chromatin environment. MPRA methods allow to perform detailed study of theregulatory potential of the genomic fragments, and it is aconvenient tool for studying the effect of variations in thenucleotide sequence on their function. However, it is necessary to note the limitations of the methods, which should betaken into account when interpreting the results obtained.The common drawback of MPRA is the need to use a minimal promoter that is unable to activate the expression ofthe reporter gene in the absence of an enhancer, since thepresence of basal activity can significantly distort the results.At the same time, the selected promoter can significantlyinfluence the activity of a particular enhancer . In this sense, the analysisof the activity of enhancers in combination with variouspromoters seems to be an ideal experiment. However, suchwork seems to be extremely difficult and time-consumingThe synthesis of DNA fragments used as studied regulatory elements imposes restrictions on the total length of sucha fragment. Usually, the length of the studied fragments islimited to about 200 bp, which often complicates the analysisof the influence of the rest of the enhancer regions fallingoutside these limits . MPRA methods based on episomal constructs do not take into accountthe possible influence of the chromatin environment on theregulatory element; therefore, they can be used to study thedirect activating ability of a DNA sequence. MPRA basedon the genomic integration of reporter constructs make itpossible to overcome this limitation to some extent. However, random or site-specific integration still does not allowthe analysis of the activity of a regulatory element in nativegenomic environment. The impossibility of studying theenhancers function in native environment is a serious MPRAlimitation, since the function of the regulatory genomic element depends on the structure of the surrounding chromatinand the spatial organization of the locus.Modern methods of high-throughput CRISPR/Cas9mutagenesis, as well as methods of directed expressionmodulation based on the use of an inactivated form of theCas9 endonuclease (dCas9) fused with activator or repressorproteins, make it possible to study regulatory elements innative genomic environment . While thereare obvious advantages, such methods also have potentialdrawbacks. For example, point mutations produced bythe targeted mutagenesis may not be sufficient to disruptenhancer function. In addition, directed mutagenesis isassociated with errors in the recognition of target genomeregions (off-targets), which can lead to the generation ofexperimental noise. It is important to note that the KRAB repressor protein,which is widely used for the targeted inactivation of enhancers, is capable of initiating the formation of heterochromatin regions of 1–2 Kb in length .This feature can reduce the resolution of the method andcomplicate the identification of specific functional fragments of the enhancer, as well as increase unwanted side effectsin the case of the presence of erroneous dCas9 recognitionsites. In addition to possible technical difficulties, in thecase of a successful disruption of the enhancer function,phenotypic manifestations can be restored rather quicklydue to the presumable existence of duplicate enhancers.Thus, MPRA and high-performance methods based onthe CRISPR/Cas9 system are quite complementary andmake it possible to characterize in detail the regulatoryfunctions of the studied genomic fragments. Coupled withvast amounts of accumulated data on the chromatin structureand spatial organization in various cells and tissues, the useof such methods makes it possible to significantly advancein the understanding of the mechanisms of precise regulation of gene expression during development and in variouspathologies. Altogether, this allows hoping that in the nearfuture modern genomics will be able to move from a detailed functional description of regulatory elements to thecreation of quantitative biological models for the regulationof gene expression.The authors declare no conflict of interest.Akhtar-Zaidi B., Cowper-Sal-lari R., Corradin O., Saiakhova A., Bartels C.F., Balasubramanian D., Myeroff L., Lutterbaugh J., Jarrar A.,Kalady M.F., Willis J., Moore J.H., Tesar P.J., Laframboise T., Markowitz S., Lupien M., Scacheri P.C. Epigenomic enhancer prof ilingdefines a signature of colon cancer. Science. 2012;336:736-739.Akhtar W., de Jong J., Pindyurin A.V., Pagie L., Meuleman W., de Ridder J., Berns A., Wessels L.F., van Lohuizen M., van Steensel B.Chromatin position effects assayed by thousands of reporters integrated in parallel. Cell. 2013;154:914-927.Andersson R., Gebhard C., Miguel-Escalada I., Hoof I., Bornholdt J.,Boyd M., Chen Y., …, Suzuki H., Hayashizaki Y., Muller F., Forrest A.R.R., Carninci P., Rehli M., Sandelin A. An atlas of activeenhancers across human cell types and tissues. Nature. 2014;507:455-461.Andersson R., Sandelin A. Determinants of enhancer and promoter activities of regulatory elements. Nat. Rev. Genet. 2020;21:71-87.Arnold C.D., Gerlach D., Stelzer C., Boryn L.M., Rath M., Stark A.Genome-wide quantitative enhancer activity maps identified bySTARR-seq. Science. 2013;339:1074-1077.Banerji J., Rusconi S., Schaffner W. Expression of a beta-globin geneis enhanced by remote SV40 DNA sequences. Cell. 1981;27:299-308.Barakat T.S., Halbritter F., Zhang M., Rendeiro A.F., Perenthaler E.,Bock C., Chambers I. Functional dissection of the enhancer repertoire in human embryonic stem cells. Cell Stem Cell. 2018;23:276-288 e278.Barski A., Cuddapah S., Cui K., Roh T.Y., Schones D.E., Wang Z.,Wei G., Chepelev I., Zhao K. High-resolution profiling of histonemethylations in the human genome. Cell. 2007;129:823-837.Bernstein B.E., Kamal M., Lindblad-Toh K., Bekiranov S., Bailey D.K.,Huebert D.J., McMahon S., Karlsson E.K., Kulbokas E.J. 3rd, Gingeras T.R., Schreiber S.L., Lander E.S. Genomic maps and comparative analysis of histone modifications in human and mouse. Cell.2005;120:169-181.Bernstein B.E., Stamatoyannopoulos J.A., Costello J.F., Ren B., Milosavljevic A., Meissner A., Kellis M., Marra M.A., Beaudet A.L.,Ecker J.R., Farnham P.J., Hirst M., Lander E.S., Mikkelsen T.S.,Thomson J.A. The NIH roadmap epigenomics mapping consortium.Nat. Biotechnol. 2010;28:1045-1048.Birney E., Stamatoyannopoulos J.A., Dutta A., Guigo R., Gingeras T.R., Margulies E.H., Weng Z., …, Lander E.S., Koriabine M.,Nefedov M., Osoegawa K., Yoshinaga Y., Zhu B., de Jong P.J.Identification and analysis of functional elements in 1 % of thehuman genome by the ENCODE pilot project. Nature. 2007;447:799-816.Bonn S., Zinzen R.P., Girardot C., Gustafson E.H., Perez-Gonzalez A., Delhomme N., Ghavi-Helm Y., Wilczynski B., Riddell A.,Furlong E.E. Tissue-specific analysis of chromatin state identifiestemporal signatures of enhancer activity during embryonic development. Nat. Genet. 2012;44:148-156.Bradner J.E., Hnisz D., Young R.A. Transcriptional addiction in cancer.Cell. 2017;168:629-643.Buenrostro J.D., Giresi P.G., Zaba L.C., Chang H.Y., Greenleaf W.J.Transposition of native chromatin for fast and sensitive epigenomicprofiling of open chromatin, DNA-binding proteins and nucleosomeposition. Nat. Methods. 2013;10:1213-1218.Cai H., Levine M. Modulation of enhancer-promoter interactions byinsulators in the Drosophila embryo. Nature. 1995;376:533-536.Canver M.C., Bauer D.E., Orkin S.H. Functional interrogation of noncoding DNA through CRISPR genome editing. Methods. 2017;(121-122):118-129.Chatterjee S., Ahituv N. Gene regulatory elements, major drivers ofhuman disease. Annu. Rev. Genomics Hum. Genet. 2017;18:45-63.Chavez A., Scheiman J., Vora S., Pruitt B.W., Tuttle M., Eswar P.R.I.,Lin S., Kiani S., Guzman C.D., Wiegand D.J., Ter-Ovanesyan D.,Braff J.L., Davidsohn N., Housden B.E., Perrimon N., Weiss R.,Aach J., Collins J.J., Church G.M. Highly efficient Cas9-mediatedtranscriptional programming. Nat. Methods. 2015;12:326-328.Corradin O., Saiakhova A., Akhtar-Zaidi B., Myeroff L., Willis J., Cowper-Sallari R., Lupien M., Markowitz S., Scacheri P.C. Combinatorial effects of multiple enhancer variants in linkage disequilibriumdictate levels of gene expression to confer susceptibility to commontraits. Genome Res. 2014;24:1-13.Creyghton M.P., Cheng A.W., Welstead G.G., Kooistra T., Carey B.W.,Steine E.J., Hanna J., Lodato M.A., Frampton G.M., Sharp P.A.,Boyer L.A., Young R.A., Jaenisch R. Histone H3K27ac separatesactive from poised enhancers and predicts developmental state.Proc. Natl. Acad. Sci. USA. 2010;107:21931-21936.Crocker J., Abe N., Rinaldi L., McGregor A.P., Frankel N., Wang S.,Alsawadi A., Valenti P., Plaza S., Payre F., Mann R.S., Stern D.L.Low affinity binding site clusters confer hox specificity and regulatory robustness. Cell. 2015;160:191-203.Crocker J., Noon E.P., Stern D.L. The soft touch: low-affinity transcription factor binding sites in development and evolution. Curr. Top.Dev. Biol. 2016;117:455-469.Dekker J., Rippe K., Dekker M., Kleckner N. Capturing chromosomeconformation. Science. 2002;295:1306-1311.Diao Y., Li B., Meng Z., Jung I., Lee A.Y., Dixon J., Maliskova L.,Guan K.L., Shen Y., Ren B. A new class of temporarily phenotypicenhancers identified by CRISPR/Cas9-mediated genetic screening.Genome Res. 2016;26:397-405.Dickel D.E., Ypsilanti A.R., Pla R., Zhu Y., Barozzi I., Mannion B.J.,Khin Y.S., Fukuda-Yuzawa Y., Plajzer-Frick I., Pickle C.S., Lee E.A.,Harrington A.N., Pham Q.T., Garvin T.H., Kato M., Osterwalder M.,Akiyama J.A., Afzal V., Rubenstein J.L.R., Pennacchio L.A., Visel A.Ultraconserved enhancers are required for normal development.Cell. 2018;172:491-499.e415.Dickel D.E., Zhu Y., Nord A.S., Wylie J.N., Akiyama J.A., Afzal V.,Plajzer-Frick I., Kirkpatrick A., Gottgens B., Bruneau B.G., Visel A.,Pennacchio L.A. Function-based identification of mammalian enhancers using site-specific integration. Nat. Meth. 2014;11:566-571.Dixon J.R., Jung I., Selvaraj S., Shen Y., Antosiewicz-Bourget J.E.,Lee A.Y., Ye Z., Kim A., Rajagopal N., Xie W., Diao Y., Liang J.,Zhao H., Lobanenkov V.V., Ecker J.R., Thomson J.A., Ren B. Chromatin architecture reorganization during stem cell differentiation.Nature. 2015;518:331-336.Dixon J.R., Selvaraj S., Yue F., Kim A., Li Y., Shen Y., Hu M., Liu J.S.,Ren B. Topological domains in mammalian genomes identified byanalysis of chromatin interactions. Nature. 2012;485:376-380.Ernst J., Kheradpour P., Mikkelsen T.S., Shoresh N., Ward L.D., Epstein C.B., Zhang X., Wang L., Issner R., Coyne M., Ku M., Durham T., Kellis M., Bernstein B.E. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011;473:43-49.Fang R., Yu M., Li G., Chee S., Liu T., Schmitt A.D., Ren B. Mappingof long-range chromatin interactions by proximity ligation-assistedChIP-seq. Cell Res. 2016;26:1345-1348.Farley E.K., Olson K.M., Zhang W., Brandt A.J., Rokhsar D.S.,Levine M.S. Suboptimization of developmental enhancers. Science.2015;350:325-328.Forrest A.R., Kawaji H., Rehli M., Baillie J.K., de Hoon M.J., Haberle V., Lassmann T., …, Bajic V.B., Taylor M.S., Makeev V.J., Sandelin A., Hume D.A., Carninci P., Hayashizaki Y. A promoter-levelmammalian expression atlas. Nature. 2014;507:462-470.Fullwood M.J., Ruan Y. ChIP-based methods for the identificationof long-range chromatin interactions. J. Cell. Biochem. 2009;107:30-39.Furlong E.E.M., Levine M. Developmental enhancers and chromosometopology. Science. 2018;361:1341-1345.Gasperini M., Hill A.J., McFaline-Figueroa J.L., Martin B., Kim S.,Zhang M.D., Jackson D., Leith A., Schreiber J., Noble W.S., Trapnell C., Ahituv N., Shendure J. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell. 2019;176:1516.Gaulton K.J., Nammo T., Pasquali L., Simon J.M., Giresi P.G., Fogarty M.P., Panhuis T.M., Mieczkowski P., Secchi A., Bosco D., Berney T., Montanya E., Mohlke K.L., Lieb J.D., Ferrer J. A map ofopen chromatin in human pancreatic islets. Nat. Genet. 2010;42:255-259.Gavrilov A., Eivazova E., Priozhkova I., Lipinski M., Razin S., Vassetzky Y. Chromosome conformation capture (from 3C to 5C) andits ChIP-based modification. Methods Mol. Biol. 2009;567:171-188.Geyer P.K., Corces V.G. DNA position-specific repression of transcription by a Drosophila zinc finger protein. Genes Dev. 1992;6:1865-1873.Ghavi-Helm Y., Klein F.A., Pakozdi T., Ciglar L., Noordermeer D., Huber W., Furlong E.E. Enhancer loops appear stable during development and are associated with paused polymerase. Nature. 2014; 512:96-100.Gomes A.L., Abeel T., Peterson M., Azizi E., Lyubetskaya A., Carvalho L., Galagan J. Decoding ChIP-seq with a double-binding signalrefines binding peaks to single-nucleotides and predicts cooperativeinteraction. Genome Res. 2014;24:1686-1697.Halfon M.S., Carmena A., Gisselbrecht S., Sackerson C.M., Jimenez F.,Baylies M.K., Michelson A.M. Ras pathway specificity is determined by the integration of multiple signal-activated and tissuerestricted transcription factors. Cell. 2000;103:63-74.Hammar P., Leroy P., Mahmutovic A., Marklund E.G., Berg O.G.,Elf J. The lac repressor displays facilitated diffusion in living cells.Science. 2012;336:1595-1598.Harris M.B., Mostecki J., Rothman P.B. Repression of an interleukin4-responsive promoter requires cooperative BCL-6 function. J. Biol.Chem. 2005;280:13114-13121.Heintzman N.D., Stuart R.K., Hon G., Fu Y., Ching C.W., HawkinsR.D.,Barrera L.O., Van Calcar S., Qu C., Ching K.A., Wang W., Weng Z.,Green R.D., Crawford G.E., Ren B. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in thehuman genome. Nat. Genet. 2007;39:311-318.Huang D., Petrykowska H.M., Miller B.F., Elnitski L., Ovcharenko I.Identification of human silencers by correlating cross-tissue epigenetic profiles and gene expression. Genome Res. 2019;29:657-667.Inoue F., Kircher M., Martin B., Cooper G.M., Witten D.M., McManus M.T., Ahituv N., Shendure J. A systematic comparison revealssubstantial differences in chromosomal versus episomal encoding ofenhancer activity. Genome Res. 2017;27:38-52.Jung Y.L., Luquette L.J., Ho J.W., Ferrari F., Tolstorukov M., Minoda A., Issner R., Epstein C.B., Karpen G.H., Kuroda M.I., Park P.J.Impact of sequencing depth in ChIP-seq experiments. Nucleic AcidsRes. 2014;42:e74.Kaya-Okur H.S., Wu S.J., Codomo C.A., Pledger E.S., Bryson T.D.,Henikoff J.G., Ahmad K., Henikoff S. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun.2019;10:1930.Kel A.E., Gossling E., Reuter I., Cheremushkin E., Kel-Margoulis O.V.,Wingender E. MATCH: A tool for searching transcription factorbinding sites in DNA sequences. Nucleic Acids Res. 2003;31:3576-3579.Kellis M., Wold B., Snyder M.P., Bernstein B.E., Kundaje A., Marinov G.K., Ward L.D., Birney E., Crawford G.E., Dekker J., Dunham I., Elnitski L.L., Farnham P.J., Feingold E.A., Gerstein M.,Giddings M.C., Gilbert D.M., Gingeras T.R., Green E.D., Guigo R.,Hubbard T., Kent J., Lieb J.D., Myers R.M., Pazin M.J., Ren B.,Stamatoyannopoulos J.A., Weng Z., White K.P., Hardison R.C. Defining functional DNA elements in the human genome. Proc. Natl.Acad. Sci. USA. 2014;111:6131-6138.Kellum R., Schedl P. A position-effect assay for boundaries of higherorder chromosomal domains. Cell. 1991;64:941-950.Kellum R., Schedl P. A group of scs elements function as domainboundaries in an enhancer-blocking assay. Mol. Cell. Biol. 1992;12:2424-2431.Kheradpour P., Ernst J., Melnikov A., Rogov P., Wang L., Zhang X.,Alston J., Mikkelsen T.S., Kellis M. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massivelyparallel reporter assay. Genome Res. 2013;23:800-811.Kim M.J., Ahituv N. The hydrodynamic tail vein assay as a tool for thestudy of liver promoters and enhancers. Methods Mol. Biol. 2013;1015:279-289.Kundaje A., Meuleman W., Ernst J., Bilenky M., Yen A., Heravi-Moussavi A., Kheradpour P., …, Hirst M., Meissner A., Milosavljevic A.,Ren B., Stamatoyannopoulos J.A., Wang T., Kellis M. Integrativeanalysis of 111 reference human epigenomes. Nature. 2015;518:317-330.Kvon E.Z., Kazmar T., Stampfel G., Yanez-Cuna J.O., Pagani M.,Schernhuber K., Dickson B.J., Stark A. Genome-scale functionalcharacterization of Drosophila developmental enhancers in vivo.Nature. 2014;512:91-95.Kvon E.Z., Stampfel G., Yanez-Cuna J.O., Dickson B.J., Stark A. HOTregions function as patterned developmental enhancers and have adistinct cis-regulatory signature. Genes Dev. 2012;26:908-913.Kwasnieski J.C., Fiore C., Chaudhari H.G., Cohen B.A. High-throughput functional testing of ENCODE segmentation predictions. Genome Res. 2014;24:1595-1602.Kwasnieski J.C., Mogno I., Myers C.A., Corbo J.C., Cohen B.A. Complex effects of nucleotide variants in a mammalian cis-regulatoryelement. Proc. Natl. Acad. Sci. USA. 2012;109:19498-19503.Lanzuolo C., Roure V., Dekker J., Bantignies F., Orlando V. Polycombresponse elements mediate the formation of chromosome higherorder structures in the bithorax complex. Nat. Cell Biol. 2007;9:1167-1174.Lettice L.A., Williamson I., Devenney P.S., Kilanowski F., Dorin J.,Hill R.E. Development of five digits is controlled by a bipartite longrange cis-regulator. Development. 2014;141:1715-1725.Li K., Liu Y., Cao H., Zhang Y., Gu Z., Liu X., Yu A., Kaphle P., Dickerson K.E., Ni M., Xu J. Interrogation of enhancer function byenhancer-targeting CRISPR epigenetic editing. Nat. Commun. 2020;11:485.Li L., He S., Sun J.M., Davie J.R. Gene regulation by Sp1 and Sp3.Biochem. Cell Biol. 2004;82:460-471.Lieberman-Aiden E., van Berkum N.L., Williams L., Imakaev M., Ragoczy T., Telling A., Amit I., Lajoie B.R., Sabo P.J., Dorschner M.O.,Sandstrom R., Bernstein B., Bender M.A., Groudine M., Gnirke A.,Stamatoyannopoulos J., Mirny L.A., Lander E.S., Dekker J. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289-293.Manolio T.A., Collins F.S., Cox N.J., Goldstein D.B., Hindorff L.A.,Hunter D.J., McCarthy M.I., …, Clark A.G., Eichler E.E., Gibson G.,Haines J.L., Mackay T.F., McCarroll S.A., Visscher P.M. Findingthe missing heritability of complex diseases. Nature. 2009;461:747-753.Maricque B.B., Chaudhari H.G., Cohen B.A. A massively parallel reporter assay dissects the influence of chromatin structure on cis-regulatory activity. Nat. Biotechnol. 2018. DOI 10.1038/nbt.4285.Maricque B.B., Dougherty J.D., Cohen B.A. A genome-integrated massively parallel reporter assay reveals DNA sequence determinantsof cis-regulatory activity in neural cells. Nucleic Acids Res. 2017;45:e16.Maurano M.T., Humbert R., Rynes E., Thurman R.E., Haugen E.,Wang H., Reynolds A.P., …, Ziegler S., Cotsapas C., Sotoodehnia N.,Glass I., Sunyaev S.R., Kaul R., Stamatoyannopoulos J.A. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190-1195.Melnikov A., Murugan A., Zhang X., Tesileanu T., Wang L., Rogov P.,Feizi S., Gnirke A., Callan C.G. Jr., Kinney J.B., Kellis M., Lander E.S., Mikkelsen T.S. Systematic dissection and optimization ofinducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 2012;30:271-277.Miguel-Escalada I., Pasquali L., Ferrer J. Transcriptional enhancers:functional insights and role in human disease. Curr. Opin. Genet.Dev. 2015;33:71-76.Muller H.J. Types of visible variations induced by X-rays in Drosophila. J. Genet. 1930;299-334.Mumbach M.R., Rubin A.J., Flynn R.A., Dai C., Khavari P.A., Greenleaf W.J., Chang H.Y. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods. 2016;13:919-922.Murtha M., Tokcaer-Keskin Z., Tang Z., Strino F., Chen X., Wang Y.,Xi X., Basilico C., Brown S., Bonneau R., Kluger Y., Dailey L.FIREWACh: high-throughput functional detection of transcriptionalregulatory modules in mammalian cells. Nat. Methods. 2014;11:559-565.Nagy P.L., Cleary M.L., Brown P.O., Lieb J.D. Genomewide demarcation of RNA polymerase II transcription units revealed by physicalfractionation of chromatin. Proc. Natl. Acad. Sci. USA. 2003;100:6364-6369.Nettling M., Treutler H., Cerquides J., Grosse I. Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information. BMC Genomics. 2016;17:347.Nora E.P., Goloborodko A., Valton A.L., Gibcus J.H., Uebersohn A.,Abdennur N., Dekker J., Mirny L.A., Bruneau B.G. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell. 2017;169:930-944.e922.Pang B., Snyder M.P. Systematic identification of silencers in humancells. Nat. Genet. 2020;52:254-263.Paredes S.H., Melgar M.F., Sethupathy P. Promoter-proximal CCCTCfactor binding is associated with an increase in the transcriptionalpausing index. Bioinformatics. 2013;29:1485-1487.Pasquali L., Gaulton K.J., Rodriguez-Segui S.A., Mularoni L., MiguelEscalada I., Akerman I., Tena J.J., …, Berney T., Gloyn A.L., Ravassard P., Skarmeta J.L.G., Muller F., McCarthy M.I., Ferrer J.Pancreatic islet enhancer clusters enriched in type 2 diabetes riskassociated variants. Nat. Genet. 2014;46:136-143.Patwardhan R.P., Hiatt J.B., Witten D.M., Kim M.J., Smith R.P.,May D., Lee C., Andrie J.M., Lee S.I., Cooper G.M., Ahituv N., Pennacchio L.A., Shendure J. Massively parallel functional dissectionof mammalian enhancers in vivo. Nat. Biotechnol. 2012;30:265-270.Pennacchio L.A., Bickmore W., Dean A., Nobrega M.A., Bejerano G.Enhancers: five essential questions. Nat. Rev. Genet. 2013;14:288-295.Phillips-Cremins J.E., Corces V.G. Chromatin insulators: linking genome organization to cellular function. Mol. Cell. 2013;50:461-474.Phillips-Cremins J.E., Sauria M.E., Sanyal A., Gerasimova T.I., Lajoie B.R., Bell J.S., Ong C.T., Hookway T.A., Guo C., Sun Y.,Bland M.J., Wagstaff W., Dalton S., McDevitt T.C., Sen R., Dekker J., Taylor J., Corces V.G. Architectural protein subclasses shape3D organization of genomes during lineage commitment. Cell.2013;153:1281-1295.Rada-Iglesias A., Bajpai R., Swigut T., Brugmann S.A., Flynn R.A.,Wysocka J. A unique chromatin signature uncovers early developmental enhancers in humans. Nature. 2011;470:279-283.Rao S.S., Huntley M.H., Durand N.C., Stamenova E.K., Bochkov I.D.,Robinson J.T., Sanborn A.L., Machol I., Omer A.D., Lander E.S.,Aiden E.L. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014;159:1665-1680.Rao S.S.P., Huang S.C., Glenn St Hilaire B., Engreitz J.M., Perez E.M.,Kieffer-Kwon K.R., Sanborn A.L., Johnstone S.E., Bascom G.D.,Bochkov I.D., Huang X., Shamim M.S., Shin J., Turner D., Ye Z.,Omer A.D., Robinson J.T., Schlick T., Bernstein B.E., Casellas R.,Lander E.S., Aiden E.L. Cohesin loss eliminates all loop domains.Cell. 2017;171:305-320 e324.Rye M.B., Saetrom P., Drablos F. A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs. Nucleic Acids Res. 2011;39:e25. DOI 10.1093/nar/gkq1187.Sandmann T., Girardot C., Brehme M., Tongprasit W., Stolc V., Furlong E.E. A core transcriptional network for early mesodermdevelopment in Drosophila melanogaster. Genes Dev. 2007;21:436-449.Sanjana N.E., Wright J., Zheng K., Shalem O., Fontanillas P., Joung J.,Cheng C., Regev A., Zhang F. High-resolution interrogation of functional elements in the noncoding genome. Science. 2016;353:1545-1549.Shlyueva D., Stampfel G., Stark A. Transcriptional enhancers: fromproperties to genome-wide predictions. Nat. Rev. Genet. 2014;15:272-286.Shukla S., Kavak E., Gregory M., Imashimizu M., Shutinoski B.,Kashlev M., Oberdoerffer P., Sandberg R., Oberdoerffer S. CTCFpromoted RNA polymerase II pausing links DNA methylation tosplicing. Nature. 2011;479:74-79.Skene P.J., Henikoff S. An efficient targeted nuclease strategy for highresolution mapping of DNA binding sites. eLife. 2017;6:e21856.DOI 10.7554/eLife.21856.Song L., Crawford G.E. DNase-seq: a high-resolution technique formapping active gene regulatory elements across the genome frommammalian cells. Cold Spring Harb. Protoc. 2010;2010(2):pdbprot5384.Song L., Zhang Z., Grasfeder L.L., Boyle A.P., Giresi P.G., Lee B.K., Crawford G.E., Lieb J.D., Furey T.S. Open chromatin defined byDNaseI and FAIRE identifies regulatory elements that shape celltype identity. Genome Res. 2011;21:1757-1767.Srinivasan L., Atchison M.L. YY1 DNA binding and PcG recruitmentrequires CtBP. Genes Dev. 2004;18:2596-2601.Stitzel M.L., Sethupathy P., Pearson D.S., Chines P.S., Song L., Erdos M.R., Welch R., Parker S.C., Boyle A.P., Scott L.J., Margulies E.H., Boehnke M., Furey T.S., Crawford G.E., Collins F.S.Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab.2010;12:443-455.Sur I., Taipale J. The role of enhancers in cancer. Nat. Rev. Cancer.2016;16:483-493.Tiwari V.K., McGarvey K.M., Licchesi J.D., Ohm J.E., Herman J.G.,Schubeler D., Baylin S.B. PcG proteins, DNA methylation, and generepression by chromatin looping. PLoS Biol. 2008;6:2911-2927.Tolhuis B., Palstra R.J., Splinter E., Grosveld F., de Laat W. Loopingand interaction between hypersensitive sites in the active beta-globinlocus. Mol. Cell. 2002;10:1453-1465.Trynka G., Sandor C., Han B., Xu H., Stranger B.E., Liu X.S., Raychaudhuri S. Chromatin marks identify critical cell types for finemapping complex trait variants. Nat. Genet. 2013;45:124-130.Visel A., Blow M.J., Li Z., Zhang T., Akiyama J.A., Holt A., PlajzerFrick I., Shoukry M., Wright C., Chen F., Afzal V., Ren B., Rubin E.M., Pennacchio L.A. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009;457:854-858.Whyte W.A., Orlando D.A., Hnisz D., Abraham B.J., Lin C.Y.,Kagey M.H., Rahl P.B., Lee T.I., Young R.A. Master transcriptionfactors and mediator establish super-enhancers at key cell identitygenes. Cell. 2013;153:307-319.Yang J., Corces V.G. Chromatin insulators: a role in nuclear organization and gene expression. Adv. Cancer Res. 2011;110:43-76.Yang S.H., Cheng P.H., Sullivan R.T., Thomas J.W., Chan A.W. Lentiviral integration preferences in transgenic mice. Genesis. 2008;46:711-718.Zabidi M.A., Arnold C.D., Schernhuber K., Pagani M., Rath M.,Frank O., Stark A. Enhancer-core-promoter specificity separates developmental and housekeeping gene regulation. Nature. 2015;518:556-559.


CRISPR–Cas activator (CRISPRa) systems that selectively turn on transcription of a target gene are a potentially transformative technology for programming cellular function. While in eukaryotes versatile CRISPRa systems exist, in bacteria these systems suffer from a limited ability to activate different genes due to strict distance-dependent requirements of functional target binding sites, and require greater customization to optimize performance in different genetic and cellular contexts. To address this, we apply a rational protein engineering approach to create a new CRISPRa platform that is highly modular to allow for easy customization and has increased targeting flexibility through harnessing engineered Cas proteins. We first demonstrate that transcription activation domains can be recruited by CRISPR–Cas through noncovalent protein-protein interactions, which allows each component to be encoded on separate and easily interchangeable plasmid elements. We then exploit this modularity to rapidly screen a library of different activation domains, creating new systems with distinct regulatory properties. Furthermore, we demonstrate that by harnessing a library of circularly permuted Cas proteins, we can create CRISPRa systems that have different target binding site requirements, which together, allow for expanded target range. Owing to the ease of designing and synthesizing guide RNAs (gRNAs) that determine target specificity, CRISPR–Cas regulators have found broad utility for performing targeted gene perturbation studies to uncover gene function , which uses a nuclease-dead Cas9 (dCas9) and a single guide RNA (sgRNA) complex to sterically block transcription initiation and elongation ,28. ExteWhile in eukaryotes CRISPRa systems have provided robust tools for activating transcription that have resulted in the widespread use of these technologies, in bacteria they have not had the same impact because of several technical limitations. First, it is increasingly clear that currently available ADs offer distinct regulatory properties and trade-offs, and different ADs are likely to be optimal depending on the exact gene target or application. For example, some bacterial ADs, such as those derived from the transcription factor SoxS ,32 or RNBacterial CRISPRa systems represent a potentially transformative technology; however, work remains to be done to create a versatile tool. Towards this goal, we apply a rational protein engineering approach to create a new CRISPRa platform that is highly modular, allowing for facile exchange of new ADs, and presents expanded target range, through the exchange of different engineered dCas9 variants with different binding site requirements. First, recognizing that different ADs have distinct properties, we characterize a panel of ADs and identify the N-terminal domain of the alpha subunit of RNAP (αNTD) to robustly activate compared to previously reported ADs. Next, we explore modular approaches to recruit ADs to dCas9 through noncovalent protein-protein interaction sequences called SYNZIP domains ,40. Thistet, Pbad or a constitutive J23150 promoter in a p15a chloramphenicol resistant vector. sgRNAs were expressed with a J23119 promoter in a ColE1 ampicillin resistant vector. Constructs containing ADs fused to SYNZIP were expressed with J23106 promoter in a CloDF spectinomycin resistant vector. Reporter plasmids were cloned from pJF076Sa plasmid (addgene #113322) (All plasmids used in this study can be found in #113322)  or Golde#113322) .Escherichia coli strain MG1655, except for data shown in Figure E. coli K-12 BW25113 and E. coli K-12 BW25113 ΔrpoZ  was used (E. coli cells and plated on LB + Agar (Difco) plates containing combinations of 100 μg ml−1 carbenicillin, 34 μg ml−1 chloramphenicol, 25 μg ml−1 kanamycin and 50 μg ml−1 spectinomycin, depending on the plasmids used, and incubated ∼17 h overnight at 37 °C. In some experiments involving transformation of ≥3 plasmids, a subset of plasmids was first transformed, competent cells prepared from resulting colonies and the remaining plasmid transformed. Plates were taken out of the incubator and left at room temperature for ∼7 h. Single colonies were used to inoculate four cultures of 300 μl of LB containing antibiotics at the concentrations described above in a 2 ml 96-well block (Costar). 96-well blocks were incubated overnight in a Vortemp 56 (Labnet) incubator bench top shaker at 37 °C and 1000 RPM. 10 μl of the overnight culture were added to 290 μl of LB, with antibiotics and 1 ng ml−1 of anhydrotetracycline (aTc) inducer or 50 mM of arabinose when required. For N-(β-ketocaproyl)-l-homoserine lactone, Cayman chemical] solution was also added with a final concentration of 10 and 100 μM as indicated. After 8–10 h, 50 μl of culture were transferred to a 96-well plate containing 50 μl of phosphate buffered saline (PBS) solution. An Infinite m1000 Pro plate reader (Tecan) was used to measure optical density (OD) at 600 nm, and to measure RFP fluorescence (FL) (540 nm excitation and 600 nm emission). For time course experiments shown in Fluorescence characterization was performed using was used . Each exE. coli MG1655 cells transformed with a combination of the control plasmids pJEC101, pJEC102, pJEC103 and pJEC598 (blank cells), which only contained the antibiotic resistance cassette and thus not expressing the reporter gene  to display the bacterial species from which αNTD sequences were obtained for data shown in Figure An expanded multifurcated phylogenetic tree, based on the NCBI taxonomy database, was generated using phyloT  subunit of RNAP   , but no ne (AHL) , targetine (AHL) . Howevertet and Pbad promoters and the corresponding inducer was added. We observed that with Ptet, probably due to the leakiness of the promoter, more than 2-fold activation was obtained in the absence of the inducer and no further activation was achieved in the presence of the inducer. While Pbad promoter evidenced no activation in the absence of the inducer and >2-fold activation when the inducer was added. , Ruegeria pomeroyi and Rhodobacter capsulatus, and Gammaproteobacteria: Shewanella oneidensis, Pseudomonas fluorescens, Pseudomonas aeruginosa and Pseudomonas stutzeri . A plasmid encoding an N-terminal SYNZIP fusion was created and combined with a sgRNA plasmid library to target this CRISPRa system to different PAMs located upstream of the RFP reporter gene on the non-template strand  in which the original N- and C-termini were fused with a 20 amino acid linker and the order of the amino acid sequence changed to create new N- and C-termini . Our goae strand . Comparie strand . Followivariants  and charvariants . From thn Figure . From thx Figure  showed rx Figure . This suIn our work, we apply rational protein engineering to successfully generate and characterize a novel bacterial CRISPRa system presenting a high degree of modularity. Our system encodes the dCas9 and AD as plasmid-independent elements that can be easily exchanged, allowing each to be independently optimized and synergistically combined to create new CRISPRa systems with distinct properties. Through exchange of different ADs, we demonstrate that we can alter regulatory properties, such as fold activation, while exchanging dCas9 variants allows for distinct targeting range. As such, we believe the work described here enhances the current versatility of bacterial CRISPRa systems by creating a highly modular system with expanded target range.Our screening of different ADs identified the N-terminal domain of the α subunit of RNAP (αNTD) to be a robust AD, able to successfully activate transcription when connected to dCas9 through either covalent or noncovalent linker domains. The α subunit of RNAP is naturally composed of two independently folded domains: αNTD and αCTD. In the process of RNAP assembly, αNTD dimerizes with a separate αNTD to initiate assembly, while αCTD is not required for this process, but instead interacts with transcription factors to recruit the RNAP to specific promoter sequences. Interestingly, we observed that the αNTD appeared to be optimal when fused through its C-terminus, in effect, mimicking its natural configuration in which the αCTD interacts with transcription factors to recruit RNAP. In this case, αCTD was replaced by dCas9 which performs the role of recruitment to a target promoter sequence. This activation by recruitment is analogous to regulatory mechanisms found in natural bacterial transcription activators, which rely on the recruitment of RNAP by protein-protein interactions. Interestingly, upon screening a library of αNTDs derived from different bacterial species, we observed a surprising level of portability. This is likely due to the high degree of sequence conservation across different species ,59 and c70 promoter, we anticipate that this approach will allow activation of the large number of endogenous promoters containing PAM binding sites that are non-optimal for other CRISPRa systems .All source data for main and SI figures was deposited in Rice University's Rice Digital Scholarship Archive (gkab211_Supplemental_FileClick here for additional data file.


Satellite cells are the myogenic precursor cells in adult skeletal muscle. The objective of this study was to identify enhancers and transcription factors that regulate gene expression during the differentiation of bovine satellite cells into myotubes.Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) was performed to identify genomic regions where lysine 27 of H3 histone is acetylated (H3K27ac), i.e., active enhancers, from bovine satellite cells before and during differentiation into myotubes. A total of 19,027 and 47,669 H3K27ac-marked enhancers were consistently identified from two biological replicates of before- and during-differentiation bovine satellite cells, respectively. Of these enhancers, 5882 were specific to before-differentiation, 35,723 to during-differentiation, and 13,199 common to before- and during-differentiation bovine satellite cells. Whereas most of the before- or during-differentiation-specific H3K27ac-marked enhancers were located distally to the transcription start site, the enhancers common to before- and during-differentiation were located both distally and proximally to the transcription start site. The three sets of H3K27ac-marked enhancers were associated with functionally different genes and enriched with different transcription factor binding sites. Specifically, many of the H3K27ac-marked enhancers specific to during-differentiation bovine satellite cells were associated with genes involved in muscle structure and development, and were enriched with binding sites for the MyoD, AP-1, KLF, TEAD, and MEF2 families of transcription factors. A positive role was validated for Fos and FosB, two AP-1 family transcription factors, in the differentiation of bovine satellite cells into myotubes by siRNA-mediated knockdown.Tens of thousands of H3K27ac-marked active enhancers have been identified from bovine satellite cells before or during differentiation. These enhancers contain binding sites not only for transcription factors whose role in satellite cell differentiation is well known but also for transcription factors whose role in satellite cell differentiation is unknown. These enhancers and transcription factors are valuable resources for understanding the complex mechanism that mediates gene expression during satellite cell differentiation. Because satellite cell differentiation is a key step in skeletal muscle growth, the enhancers, the transcription factors, and their target genes identified in this study are also valuable resources for identifying and interpreting skeletal muscle trait-associated DNA variants in cattle.The online version contains supplementary material available at 10.1186/s12864-021-08224-7. Skeletal muscle is the largest tissue in the body and plays an important role in physiology , 2. SkelA set of four transcription factors called myogenic regulatory factors (MRFs) are known to play important roles in myogenesis, the formation of muscle fibers from myoblasts or satellite cells , 12. TheClearly, differentiation and fusion of myoblasts or satellite cells into myotubes is a key step in the development and growth of skeletal muscle. The objective of this study was to further understand the regulation of gene expression during the differentiation of bovine satellite cells into myotubes. Cattle are agriculturally important animals, and a better understanding of gene regulation during satellite cell differentiation could lead to the development of novel strategies to improve growth efficiency and meat quality in cattle. Enhancers are DNA sequences that enhance the transcription of associated genes when bound by sequence-specific transcription factors. Active enhancers are genomic regions that are bound by active transcription factors and that actively regulate gene transcription. Genomic regions containing active enhancers are found to be uniquely marked with H3K27ac, where lysine 27 of histone 3 protein is acetylated , 17. We Four ChIP-seq libraries and two Input libraries constructed from bovine satellite cells immediately before and 2 days after induction of differentiation passed the quality control, and deep sequencing generated 23 to 40 million sequencing reads from these libraries Table . BetweenAnalyzing the uniquely mapped reads from each ChIP-seq library against those from the corresponding Input library using the MACS peak calling program identified more than 30,000 and 50,000 H3K27ac-marked genomic regions, i.e., active enhancers, from before-differentiation (BD) and during-differentiation (DD) bovine satellite cells, respectively. A phantompeakqualtools analysis indicated that the four ChIP-seq libraries had normalized strand cross-correlation (NSC) values between 1.16 and 1.18 and relative strand cross-correlation (RSC) values between 0.98 and 1.06  enrichment analyses on genes associated with H3K27ac modification in bovine satellite cells. Top biological processes and cellular components enriched in genes associated with H3K27ac modification specifically in DD bovine satellite cells were related to skeletal muscle structure, development, and adaptation, and cell cycle arrest Tables . It is iMotif enrichment analyses of 35,373 enhancer regions marked with H3K27ac specifically in DD bovine satellite cells indicated enrichment of binding sites for many transcription factors . As shown in Fig. Histone modification affects gene transcription by altering the accessibility of chromatin and the recruitment of transcription factors and cofactors to chromatin . Large-sCompared to the consistent identification of 47,669 H3K27ac-marked active enhancers from two biological replicates of during-differentiation bovine satellite cells, only 19,027 H3K27ac-marked active enhancers were repeatedly identified from two samples of before-differentiation bovine satellite cells. This difference suggests much more active transcription factor binding to the genome, much more active recruitment of histone acetylases, and hence much more active H3K27ac modification in bovine satellite cells during differentiation than before differentiation. There is a possibility that this difference was caused by biological variation, as indicated by the large difference in the numbers of H3K27-marked enhancers identified from two samples of before-differentiation satellite cells. In our research on satellite cells, we have noticed satellite cells from different animals differ in differentiation potential in culture, and this difference suggests animal-to-animal variation in gene expression and histone modification in satellite cells.Enhancers can be located upstream or downstream of TSS, in introns or exons, and near or distantly from the promoters –34. SomeThis study showed that genes associated with H3K27ac modification were expressed at greater levels than those without H3K27ac modification in bovine satellite cells, regardless of the differentiation stage of the cells. This result supports H3K27ac as a histone marker for transcriptional activation . This stH3K27ac modification at enhancers results from transcription factor binding and subsequent recruitment of histone acetyltransferases such as p300 and CBP , 44. In This study shows that many other transcription factors regulate gene expression during satellite cell differentiation, and these other transcription factors include the AP-1 family of transcription factors . Enrichment of binding sites for the AP-1 family of transcription factors in active enhancers in during-differentiation bovine satellite cells is intriguing because the member of the AP-1 family of transcription factors JUN is known to antagonize the stimulatory effect of MYOD1 on myoblast differentiation . HoweverIn summary, we have identified tens of thousands of genomic regions associated with H3K27ac modification, i.e., active enhancers, in before- or during-differentiation bovine satellite cells. These enhancer regions contain binding sites for many transcription factors, including MYOG and MYOD1, whose role in myoblast or satellite cell differentiation is widely known, and the AP-1 transcription factors, AP-4, and many others, whose roles in myoblast or satellite cell differentiation are less known or unknown. These enhancers and transcription factors should be valuable for elucidating the mechanisms that mediate gene transcription during myoblast or satellite cell differentiation. Because myoblast or satellite cell differentiation is a key step of skeletal muscle development and growth, the enhancers, the transcription factors, and the genes targeted by these enhancers and transcription factors should be also valuable for identifying and interpreting skeletal muscle trait-associated DNA sequences and variants in cattle, which are agriculturally important animals.2 atmosphere. All cell culture reagents were purchased from ThermoFisher Scientific  unless otherwise indicated.Skeletal muscle was collected from Angus-crossbred steers slaughtered at the Virginia Tech Meat Center. Satellite cells was isolated through pronase digestion and differential centrifugation as described before , 55. SatSatellite cells from two steers immediately before and 2 days after induction of differentiation were cross-linked in 1% formaldehyde for 10 min and then lysed in lysis buffer from the ChIP-IT kit . Cell nuclei were suspended in ChIP buffer from the ChIP-IT kit and then sheared on ice by 10 pulses of 20-s sonication using a sonic dismembrator Model 100 at setting 3 (ThermoFisher Scientific) to generate chromatin fragments of 200 to 1000 bp. To identify the genomic regions associated with H3K27ac modification, chromatin fragments were incubated with an anti-histone H3K27ac antibody  at 4 °C overnight with gentle rocking. The H3K27ac antibody-chromatin complexes were separated from unbound chromatin fragments using protein G-Dynal beads (ThermoFisher Scientific). Chromatin fragments immunoprecipitated by the H3K27ac antibody and those before immunoprecipitation  were reverse cross-linked by incubating them at 65 °C for 4 h. DNA was extracted and purified using spin columns from the ChIP-IT kit.ChIP-seq libraries were prepared using the NEBNext ChIP-Seq Library Prep Reagent Set for Illumina , according to the supplier’s instructions. Briefly, ChIP DNA was end repaired using T4 DNA polymerase, Klenow DNA polymerase, and T4 polynucleotide kinase. End-repaired DNA was then added with 3′ dA overhangs using exonuclease minus Klenow DNA polymerase and dATP. The dA-tailed DNA fragments were ligated to the sequencing adaptor. DNA fragments of approximately 300 bp were selected from the adaptor-ligated DNA using AMPure XP Beads. The size-selected DNA fragments were amplified in 12 cycles of PCR using an index primer and a universal PCR primer. Two ChIP-seq libraries were prepared from DNA immunoprecipitated from before- or during-differentiation bovine satellite cells originally isolated from two different cattle. Two Input-seq libraries were prepared from input DNA pooled equally from before- and during-differentiation bovine satellite cells. Two biological replicates were used for ChIP-seq according to the ChIP-seq guidelines and practices proposed by the ENCODE and modENCODE consortia . Each liSequences from ChIP-seq libraries were first trimmed to remove the adapters using Trimmomatic . The triBovine satellite cells in 12-well plates were transfected with 30 nM of siRNA targeting bovine FOS or FOSB mRNA using 6 μL of Lipofectamine RNAiMAX Reagent according to the supplier’s instructions (ThermoFisher Scientific). A universal negative control siRNA  was transfected as a negative control. The sense and antisense siRNA sequences targeting bovine FOS and FOSB mRNAs were CAGAAGAGAUGUCUGUGGCUUCUCU and AGAGAAGCCACAGACAUCUCUUCUG, and GACAUGCCAGGAACCAGUUACUCCA and UGGAGUAACUGGUUCCUGGCAUGUC, respectively. These siRNAs were confirmed to have at least 70% knockdown efficiency in pilot experiments. Following transfection, bovine satellite cells were cultured in differentiation medium for 48 h as descried above. The differentiation degree of satellite cells was assessed by quantifying mRNA expression of markers of differentiated myoblasts, including CKM, MYH2, MYH3, and MYOG, as previously described .-ΔΔCt method [Total RNA was extracted from bovine satellite cells using the Direct-zol RNA Miniprep Kit . Reverse transcription of total RNA into cDNA was performed using ImProm-II reverse transcriptase and random primers according to the manufacture’s instruction . Quantitative PCR was performed using the SYBR Green chemistry as described previously . The relt method . The Ct t method .Gene expression data were analyzed by ANOVA. Two means were compared by t-test, and multiple means were compared by the Tukey test. All data are expressed as mean ± standard error.Additional file 1. Assessment of ChIP-seq enrichment by PhantompeakqualtoolsAdditional file 2. H3K27ac peaks specific to before-differentiation bovine satellite cellsAdditional file 3. H3K27ac peaks specific to during-differentiation bovine satellite cellsAdditional file 4. H3K27ac peaks common to both before- and during-differentiation bovine satellite cellsAdditional file 5. Top 10 GO biological processes enriched in genes associated with H3K27ac modification in before-differentiation bovine satellite cellsAdditional file 6. Top 10 GO cellular components enriched in genes associated with H3K27ac modification in before-differentiation bovine satellite cellsAdditional file 7. Top 10 GO molecular functions enriched in genes associated with H3K27ac modification in before-differentiation bovine satellite cellsAdditional file 8. Top 10 GO biological processes enriched in genes associated with H3K27ac modification in both before- and during-differentiation bovine satellite cellsAdditional file 9. Top 10 GO cellular components enriched in genes associated with H3K27ac modification in both before- and during-differentiation bovine satellite cellsAdditional file 10. Top 10 GO molecular functions enriched in genes associated with H3K27ac modification in both before- and during-differentiation bovine satellite cellsAdditional file 11. Motifs enriched in enhancers marked with H3K27ac in during-differentiation bovine satellite cellsAdditional file 12. Motifs enriched in enhancers marked with H3K27ac in before-differentiation bovine satellite cellsAdditional file 13. Motifs enriched in enhancers marked with H3K27ac in both before- and during-differentiation bovine satellite cells


Immunoglobulin class switch recombination (CSR) plays a crucial role in adaptive immune responses through a change of the effector functions of antibodies and is triggered by T-cell-dependent as well as T-cell-independent antigens. Signals generated following encounter with each type of antigen direct CSR to different isotypes. At the genomic level, CSR occurs between highly repetitive switch sequences located upstream of the constant gene exons of the immunoglobulin heavy chain locus. Transcription of switch sequences is mandatory for CSR and is induced in a stimulation-dependent manner. Switch transcription takes place within dynamic chromatin domains and is regulated by long-range regulatory elements which promote alignment of partner switch regions in CSR centers. Here, we review recent work and models that account for the function of long-range transcriptional regulatory elements and the chromatin-based mechanisms involved in the control of CSR. Ig) loci at different stages of their development. In developing B cells, Ig loci undergo V(D)J recombination catalyzed by the RAG1/RAG2 (RAG) complex. V(D)J recombination targets the variable regions of both Ig heavy chain (IgH) and Ig light chain (IgL) loci and lies at the basis of the vast primary antibody repertoire (IgH and IgL genes through somatic hypermutation (SHM) and the constant (HC) genes of the IgH locus through class switch recombination (CSR). The enzyme activation-induced cytidine deaminase (AID) is absolutely required for SHM and CSR and initiates these processes via transcription-dependent cytosine deamination of single-stranded DNA targets , as an outcome of SHM in germinal centers (GCs) in the context of T-cell-dependent responses , 10. In in vivo following immunization or infection and enables antigen-activated, IgM+-expressing B cells to change the constant domains of Igµ heavy chains, hence the expression of novel isotypes  with different effector functions  and to IgG1 and IgE in the presence of LPS+IL4 or anti-CD40+IL4. These culture systems allow the investigators to address B-cell-autonomous mechanisms that are more difficult to tackle in the context of the complex molecular processes and cellular interactions triggered in vivo by antigens -transformed pro-B lines e.g., –29] and  and 29)]HC gene exons, whose transcription is mandatory for CSR, and is driven by specific promoters  in a signal-dependent manner  sequences, located upstream of the t manner , 8, 14   flanked by matrix attachment regions  Figure , 66, con+ population in Peyer’s patches but did not affect the number of B cells engaged in the GC reaction  (~28 kb) is composed of four B-cell-specific enhancers, hs3a, hs1,2, hs3b, and hs4, that act in synergy. hs1,2 is flanked by inverted repeated intervening sequences (IRISs) and lies at the center of a large palindromic region bordered by two inverted copies of hs3, hs3a and hs3b, whereas the distal hs4 enhancer is located outside of the palindrome (The major n (3'RR) , 68  was deleted, Sγ3 and, to a lesser extent, Sγ1 and Sγ2a transcription, and CSR to the corresponding isotypes were impaired  Figure  , sequencing of cDNAs revealed that inter-allelic recombination accounted for up to 7% of recombination events to Cα in Peyer’s patches and up to 13% to Cγ3 in LPS-activated splenic B cells   .cis-regulatory element of SHM and CSR, the 3'RR can also operate in trans to control these processes in a fraction of activated B cells.Thus, in addition to its established role as a major HC region and part of or the whole 3'RR  that bound multiple transcription factors in Rag2-deficient pro-B cells  protein thought to bind uncommonly long and diverse DNA sequences through different combinations of its 11 ZFs . These cbdomains . Broadly in vivo . CTCF wa in vivo , 100. ItCtcf gene  region] . FollowiThus, the 3'CBEs act as an insulator that prevents transcriptional activation of the eS region and its recombination with Sµ region during CSR.Various studies involved signaling through Toll-like receptors in the induction of AID expression and CSR in early B cells  that doAK wrote the manuscript. AD contributed to the writing of the manuscript and checked the references and figures. All authors contributed to the article and approved the submitted version.This work was supported by the Agence Nationale de la Recherche (ANR-16-CE12-0017), the Institut National du Cancer , the Fondation ARC pour la Recherche sur le Cancer (PJA 20191209515), and the Ligue Contre le Cancer .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.