Super-enhancers (SEs) are cis-regulatory elements of the human genome that have been widely discussed since the discovery and origin of the term. Super-enhancers have been shown to be strongly associated with the expression of genes crucial for cell differentiation, cell stability maintenance, and tumorigenesis. Our goal was to systematize research studies dedicated to the investigation of structure and functions of super-enhancers as well as to define further perspectives of the field in various applications, such as drug development and clinical use. We overviewed the fundamental studies which provided experimental data on various pathologies and their associations with particular super-enhancers. The analysis of mainstream approaches for SE search and prediction allowed us to accumulate existing data and propose directions for further algorithmic improvements of SEs’ reliability levels and efficiency. Thus, here we provide the description of the most robust algorithms such as ROSE, imPROSE, and DEEPSEN and suggest their further use for various research and development tasks. The most promising research direction, which is based on topic and number of published studies, are cancer-associated super-enhancers and prospective SE-targeted therapy strategies, most of which are discussed in this review. The role of transcriptional regulation mediated by super-enhancers attracted the attention of many research groups during the past decade. Since the introduction of the term, the discussion around super-enhancers has heated up, and the concept of such a cis-regulatory mechanism has not been clearly and completely defined ,2.In 2013, the term “super-enhancer” (SE) was suggested and proposed as a novel regulatory class represented by large clusters of enhancer elements with abnormally high transcription factor enrichment and target gene expression. The definition of SE includes a list of characteristic features that were found to correlate with the occurrence of active super-enhancers within various cell lines. The majority of in vitro studies emphasize the local enrichment of master transcription factors (MTFs), boosted epigenetic modifications  along with the grouping of more than two enhancers (maximum distance between enhancer elements was proposed to be 12.5 kb), and high binding rate with Mediator complex subunits  Similar queries were formulated in order to access papers from the following research databases: Scopus, Web of Science, EMBASE, GHL, VHL, Cochrane, Google Scholar, mRCTs, POPLINE, and SIGLE.Found papers were further classified as relevant and used for the qualitative summary if any of the following rules were applicable: (i) article contains specific information associated with super-enhancers; (ii) is an in silico research article with various SE-search algorithms applications ; (iii) is an in vivo/in vitro research article followed by a well-described methods section. In addition, we formulated the exclusion criteria for the set of retrieved papers: (i) duplicated work; (ii) inaccessible article; (iii) paper published earlier than 2013 (this criterion was necessary to bring up as soon as the term itself appeared only by this year); (iv) articles contrary to the inclusion rules.It is remarkable that the DNA molecule serves as efficient storage of genetic information for multiple cells, and that gene expression regulation results in the unique phenotype of each cell. Turning genes “on” and “off” depends on certain proteins called transcription factors (TFs) attaching to some specific short sequences of DNA (500–1500 bp long) known as enhancers. They are found in both prokaryotic and eukaryotic organisms and, though acting in cis, can be located up to 1 million base pairs away from the target sequence. The formation of SE complexes through numerous TFs binding boosts the activity of the genes nearby. A multicenter study initiative from 2011, the Encyclopedia of DNA Elements (ENCODE) , reportehr3, that could induce the transcription of ie1 promoters up to a thousand-fold change, implying the first differentiation criterion for a SE: higher transcriptional effects [The term “super-enhancer” was introduced by Chen and colleagues in 2004 to describe a baculoviral DNA locus,  effects . Three f effects  and dise effects  and act The main concept of super-enhancers’ action mechanism is very similar to one that was previously described for normal enhancers. Transcription factors bind to specific motifs of super-enhancers and facilitate SE interaction (looping model) with subunits of transcriptional complexes  in the promoter regions of regulated genes. Therefore, they activate target gene expression through the recruitment of numerous transcription machinery units . The nuaNevertheless, there are many other factors which potentially affect SE function and change the transcription probability and rate of dependent genes. For example, it is known that massive eRNA transcription from super-enhancers plays an important role during the cis-regulation of target genes . At the MYC gene. Further, the researchers showed that not only cancer cells but also healthy stem cells  biology genes), muscle cells [MYC gene and that its structure catalyzes the high production of MYC protein. They also found that super-enhancers are even more susceptible to disruption than key chromatin-remodeling proteins such as BRD4, which would give us more therapeutic potential, as BRD4 has well-defined binding pockets that can be targeted. This would also grant the possibility to focus on the master enhancers—cell-specific SEs—instead of studying dozens of them and manipulating gene expression in cells with fewer side effects.It was investigated that the epigenetic regulator BRD4 and the multifunctional cMyc proteins play an important role in mice with leukemia. The scientists reported that blocking Brd4 decreases Myc and prevents cancer cell proliferation, and that Brd4 binds to a 40000-bp stretch near the le cells , and othle cells ,13. In tThese very first publications related to SEs caused quite a heated discussion, as did the publications of the ENCODE consortium, which succumbed to criticism due to sweeping statements proclaiming 80% of the human genome to be functional ,15,16,17Enhancers were considered to be the sites bound by all three master regulators, Oct4, Sox2, and Nanog, according to ChIP-seq;Enhancers within 12.5 kb of each other were stitched together to form a single entity;The stitched SE entities and the remaining TEs were then ranked by the total background-normalized level of Med1 signaling within the locus.As  shows, nHowever, this definition has been criticized because it was not “functional” in essence and did not reflect the unique properties of so-called super-enhancers. Moreover, the clustering procedure in step 2 is algorithmic, implying no specific selection or filtering. Additionally, multiple publications that have defined SEs (and sometimes even within a single paper) deviate from Whyte’s group’s definition, including various characterization marks at different algorithm steps. For example, in a study by Hnisz et al. , H3K27acIt should be noted, however, that H3K27ac may be functionally dispensable for transcription, even for genes associated with super-enhancers, as it was addressed in mouse ESCs . The othIt should be mentioned that the related term “stretch enhancer” has been described in parallel ,22. Streα-globin and Wap SEs can be explained with a simple model [The analytical work comparing two previous experimental datasets of Hay and Shin’s groups proved that all the knockout data for le model , suggesthttps://sunlightwang.github.io/Super-Enhancers/ (accessed on 6 March 2023)), each with comparative and exploratory analyses of publicly available ChIP-seq data. The most volumetric database, SEdb, contains up to 331,000 SEs derived from 541 human cell lines/tissues [The controversy of the usage of the term “super-enhancer” has had a negative impact on drug discovery. Some researchers believe that super-enhancers play a particularly important role in stem cell differentiation and oncogenic pathways , two dom/tissues , and 52 Still, there have not been enough studies disclosing the evolutionary stability of super-enhancers as separate biological units . In the current concept, the definition of a super-enhancer could be formulated as follows: super-enhancers in a broad range of human cell types are (putative) extensive clusters of enhancers with aberrantly high levels of transcription factor binding, which are historically associated with higher expression rates of cell type specification genes (including cell identity and pro-oncogenic genes). In order to provide the most comprehensive definition of SEs while comparing them to typical enhancers and with reference to published data, we provide a summary table with the main feature characteristics for both regulatory elements .The algorithms used to identify typical enhancers and super-enhancers differ in their complexity and the number of genomic features they analyze. While both methods rely on ChIP-seq data for histone modifications, the identification of super-enhancers requires additional data and more complex algorithms along with the tuning of the latter. There are various proposed metrics that could be possibly used for SE identification along with machine learning methods with similar precision and validity. One of these involves a super-enhancer’s frequency across various cell lines. There is emerging evidence for a class of SEs that seem to be universally active; they display a strong association with fast recovering chromatin loops after sequential cohesin removal and restoration  and reveTo study the biology of super-enhancers, the mechanisms of their functioning, their role in a particular cell type, and the effect of various agents in particular inhibitors, a set of both computational and experimental methods is needed. In order to study super-enhancers, they are often used in tandem. Bioinformatics methods are used to process experimental data and search for potential objects, and experimental data itself could be used to refine in silico predictions. Here, we review bioinformatic methods for searching for super-enhancers, focusing on machine learning methods and experimental techniques that have been actively used in recent years.To solve the problem of searching for super-enhancers, classical methods and machine learning (ML) approaches, including neural networks (NN), were used. Most of the algorithms use Chip-Seq data, which gives information about key SE marks   and is eAt the moment, the most-used tool and the gold standard for solving this problem is the linear ROSE algorithm ,9. The mAziz Khan and Xuegong Zhang performed ML analysis of the importance of SE features and developed the imPROSE tool (integrated methods for prediction of super-enhancers) based on ML algorithms to predict which enhancers are SEs . An analThe imPROSE source code is available on GitHub and can be used to choose the model and its features, train the model, and make predictions. The input is a CSV file with the computed available features for the enhancers. The output is also a CSV file with the field Class and the class flag (SE or TE).With the development of the NN approaches came the idea to use them for SE prediction. There are some works offering convolutional neural networks (CNN) to perform this task. DEEPSEN, a CNN implemented in 2019, was the first NN model used for this purpose, and it was implemented on TensorFlow with Python . At thatThe next step was implementing DeepSE in 2021 by Keras . It outpNowadays, neural network methods are undergoing a period of active development, and new approaches are emerging for working with sequences, in particular, biological ones. Their potential applications for in silico searches for super-enhancers look promising.To validate the predictions of super-enhancers with computational methods, it is necessary to carry out a number of experiments. Here, we consider experimental approaches to the study.Much of the experimental research on super-enhancers involves the use of various genome-wide, sequencing-based approaches. They make it possible to identify SEs by their characteristic marks. Like typical enhancers, super-enhancers are characterized by increased sensitivity to DNase I, which is common for active chromatin structures. The results are cell type-specific ,37. ThisMany authors study the functioning of super-enhancers by subjecting them to certain manipulations, including DNA editing, the downregulation of certain genetic elements, or the inhibition of proteins closely connected with SE functioning. Deletions of SE or some of their parts help to estimate their role in epigenetic regulation and are performed using, for example, in vitro or in vivo CRISPR/Cas editing ,57,58,59Another interesting and, in recent years, actively developed direction in the study of super-enhancers is their study in the context of the three-dimensional organization of the genome. Chromatin interaction measurement technologies  are used for this purpose. Often, methods of measuring expression are connected to the study of this topic, as the combination of these techniques makes it possible to establish correlations between changes in the structural organization of the genome in different cell types or after a certain type of exposure and levels of gene expression. These studies are aimed at finding answers to questions about how SEs are organized in space and what effect they can have on the expression of distant but spatially close genes.The functionality of eukaryotic genomes is mostly determined by their 3D organization. Understanding the stochastic expression of large gene cluster subsets dependent on chromatin contacts and 3D regulation has made significant progress ,61,62. MWap SE controls Ramp3 (which plays an important role in cancer development), despite three separating CBSs. Deleting all of them resulted in the elevated expression of Ramp3 in mammary tissue, whereas deleting one distal CBS lowered Ramp3 expression in non-mammary tissues. Although these CTCF sites, including the loop anchor, do not prevent the super-enhancer from being activated, their absence from the mouse genome demonstrates their ability to suppress gene activation [Slco5a1 gene beyond its prominent domain boundary. Thus, the loop extrusion model is complemented by the fact that cohesin loading and extrusion trajectories originate at an enhancer contribute to gene activation [Pcdh, β-globin, and Igh SE clusters in HEC-1-B, K562, and Neuro-2A cells suggested that directional CTCF chromatin looping between convergent CBS elements underlies insulator function and accessibility of the tandem CTCF sites’ balanced promoters in 3D genome folding and regulation [TADs are primarily formed by nested interactions between convergently oriented binding sites of the DNA-binding protein CTCF, which are established as chromatin-bound CTCF that arrest the loop-extruding cohesion complex . Allelictivation . The idetivation . Additiogulation ,69. Recegulation ,71, resugulation . More deCTCF genome-wide depletion alters TAD structure (including TADs merging), meaning that higher-order chromatin structures are supported by groups of essential constitutive CBSs .Another TF mediating SE spatial activity, Aire, is the driver of self-reactive thymocyte clonal deletion and the generation of perinatal regulatory T cells. Aire controls immunological tolerance by driving the promiscuous expression of a large swath of the genome in medullary thymic epithelial cells (mTECs). Thus, ex vivo Hi-C experiments on mTECs from murine thymi together with biochemical and in vivo loss-of-function analyses confirmed that Aire regulates chromatin looping by evicting CTCF from domain boundaries and favoring the accumulation of cohesin on super-enhancers .With the knowledge of constitutive CBSs, given that SEs have varying sensitivity to chemical inhibitors, as discussed later in this review, and given that super-enhancers of many genes act with functional redundancy , it is rFurthermore, constitutive SEs are described in another CRISPR-based work devoted to the study of SE landscape dynamics in cell differentiations of several lineages. The authors discovered that there are three distinct SE patterns (which are probably SE subtypes): conserved, temporally hierarchical, and de novo. The temporal order of establishment of elements within SEs may be guided by DNA sequences. It has been shown in differentiated cells that both early- and late-emerging enhancers are indispensable for target gene expression, whereas in undifferentiated cells, the early enhancers are capable of it .A comparison of stretch enhancers with SEs revealed differences in active chromatin mark loading: SEs were mostly associated with H3K27ac and RNA Pol II and produced enhancer RNA (eRNA), whereas stretch enhancers were enriched with H3K27me3, depleted of H3K27ac, and did not produce eRNA . In addiWhen talking about spatiotemporal SE dynamics, it is important to understand the possible variety of modes of interaction between SEs and their constituent parts. For instance, with the help of an integrated approach including ChIP-seq, scRNA-seq, and scATAC-seq data, the murine retinal SE Vsx2 double modality was shown, which reflected distinct developmental stages and cell type activity in vivo . The CapOne of the pending questions about enhancers and SEs in particular is deciphering their evolution and conservation history. It seems quite complicated to show how a single enhancer/SE can be conserved across the animal kingdom, but enhancers and SEs may be as ancient and conserved as the TFs they interact with.Amphimedon sp. microsyntenic areas control patterns of cell type-specific gene expression. These sponge enhancers lie in the regions of microsynteny that are orthologous to those found in other metazoans and contain substantial histone H3K4me1 enhancer signals, even though they do not share considerable sequence identity with vertebrates [However, a study by Wong et al. demonstrated that in zebrafish and mouse embryos, putative enhancers identified in sponge tebrates . Previoutebrates , whereastebrates  or durintebrates ,86.NPAS3) [HACNS1), a positively selected limb enhancer sequence showing evidence of accelerated evolution and acquiring novel TF-binding sites in the human lineage compared to other vertebrates [HACNS1 show increased transcriptional activity compared with the chimpanzee and macaque orthologous enhancers, which is probably due to mutations that prevent repressor binding to the gene in a distinct 81-bp region [Historically, putative enhancer sequences in humans with higher rates of evolution are associated with so-called human accelerated regions (HARs). These HAR sequences are thought to be responsible for human-specific phenotypic adaptations, as shown with the brain developmental transcription factor neuronal PAS domain-containing protein 3 (NPAS3) . Along wNPAS3) . Anothertebrates . Transgep region . Data onp region . Emerginp region  and plurp region ,94. HoweExtensive comparative epigenomic and transcription factor binding analyses in pigs, humans, and mice proved that SEs evolve rapidly in mammals. In addition, 30 shared transcription factors and BRD4 were found to be conserved activators of mammalian pluripotency-associated SEs in parallel with three pluripotency-associated SEs  that are highly conserved in mammals . In SE cNppa and Nppb genes in the heart  and identified an evolutionarily conserved SE required for the spatiotemporal expression pattern and stress induction of Nppa and Nppb [Studying the evolution of SEs should help to correctly adapt animal models of diseases. Thus, in a characterization of cis-regulatory element functions in 12 diverse tissues from four pig breeds, using strategies similar to those used in ENCODE and the Roadmap Epigenomics projects , more than 220,000 cis-regulatory elements were identified in the pig genome. Surprisingly, regulatory elements between human and pig genomes were found to have higher conservation rates than those between humans and mice . Anotherand Nppb . Apart fand Nppb , and of and Nppb  and eRNAand Nppb ,104. Neeand Nppb ,106. TheIn addition, studies show that SEs have different levels of evolutionary conservation when they are unequivocally defined and calculated via different algorithms ,107. AllTaken together, SE evolution is dependent on the organism and cell type; the inability to identify conserved SEs appears to be due to their ability to evolve faster than both the TFs with which they interact and the genes which they regulate. The terms defining SEs are ambiguous and requires a complement of experimentally validated facts. To achieve robust and reproducible results, a better understanding of how the various enhancer identification strategies in use today relate to the dynamic activity of gene regulatory regions is required, along with improvements to experimental methods and data analysis and the possible implementation of machine learning approaches . As soonSuper-enhancers were described as regulators in the fields of stem cell biology, differentiation, and cell-specific gene expression . In thisSox2 super-enhancers are responsible for more than 90% of Sox2 expression, and Sox2 seems to be its only target. The knockout of Sox2 SE caused dramatic changes in mESCs morphology and proliferation, as Sox2 is among the key TFs associated with pluripotency [Dppa3 promoter region [Nanog SEs do not have the same effect on cell fate. Supposedly, this feature is explained by the existence of redundancy among enhancer elements. For example, one could become fully functional only if both the −5 SE and −45 SE are deactivated [Spt6 depletion leads to the expression downregulation of the pluripotency maintenance genes, as ESC super-enhancers show high enrichment levels of Spt6. In that study, Spt6 controlled the ratio between H3K27 acetylation and methylation [It is known that maintenance of cell pluripotency requires a huge and complex regulatory network that includes a large number of transcription factors ,110. Supipotency ,112. Nanipotency . Enhancer region . The obtctivated . For botctivated . Spt6 isctivated . It was hylation , which chylation .Vsx2 gene and is important for appropriate retinal development. There are Zfhx3, Prox1, Vsx2, Dbp, Hlf, Otx2, Isl1, and Lhx4 binding sites, and four of them  showed reduced expression if the SE was experimentally disrupted. The direct CRISPR/cas9 deletion of some enhancer elements led to SE malfunction and further developmental issues such as eye or retinal size reduction [Cell- and tissue-specific human SEs are also a common research focus. For example, five  and three  SEs target TFs that are associated with adipocyte-selective and osteoblast-selective SEs, respectively, forming core regulatory circuitry . Furthermore, the findings show that osteoblast-selective SEs pre-exist in hMSCs, whereas adipocyte-selective SEs are generated only after adipocyte induction . In a sieduction . RegardiXenopus tropicalis, endoderm activation-specific TFs  bind to super-enhancers located near the endodermal cell fate genes , thereby activating the target gene’s expression [Endodermal germ layer differentiation and functionality also reveal a strong association with a group of SEs. In pression .Foxp3, Il2ra, and Ctla4 Treg cell signature genes [α-globin super-enhancer. Four enhancer elements within this SE (R1-4) showed high Med-1 signals and were evolutionarily conserved. Deletions of these parts showed that only two of them (R1 and R2) had a significant effect on erythropoiesis in vivo. No synergic effect was observed; therefore, it seems that enhancers within SE act independently and mostly in an additive manner. The authors suggested considering such cases as individual enhancers rather than as higher-order structures [Wap SE consists of three enhancer elements . At mid-pregnancy only E1 is fully occupied by the regulators, but between the 14th and 16th days of pregnancy, E2 and E3 are also activated. This leads to a 100-fold increase in Wap expression. The studied mutagenesis of E1, E2, and E3 enhancer elements revealed significantly different in vivo effects; that of E3 was the most influential. E2 and E3 had additive interactions. The simultaneous inactivation of all three enhancers caused a 1000-fold decrease of Wap expression. That study, in spite of the additive model, testified to the existence of an inner hierarchy of enhancer elements within the SE [Wap SE retained mammary specificity, which indicates the weak regulatory potential of chromatin in mammary gland cell specificity [Ncam1, Mapt, Rbfox3, etc.) and neuronal activity  [Rbpj-enhancer. That study compared renin-phenotype cells at different physiological states. Comparing normal unstressed JG cells, chronically recruited renin-null cells, acutely recruited cells from mice subjected to sodium depletion plus captopril, and constitutively active As4.1 cells a notably reproducible pattern of open chromatin along via ATAC-Seq was revealed [In adult organisms, SEs are also involved in tissue-specific cell differentiation. For example, regulatory T cells become a functionally mature subpopulation only under the control of the Treg super-enhancers. These SEs are activated by Satb1 and regulate the re genes . There aructures . A questructures . There in the SE . Furthercificity . Super-e2, etc.) . With threvealed .Interesting patterns may be associated with eRNA. eRNA expression and stability is considered not to be strong enough for regulation in trans. The higher transcriptional activity of TE compared with SE suggests that some of them could produce the non-coding RNA involved in trans-regulation. For example, alncRNA-EC7 (or Bloodlinc), which is transcribed from a SE needed for the expression of BAND3/SLC4A1, an erythroid membrane transporter, is also trans-acting and regulates about 500 target genes. Bloodlinc also interacts with TFs and chromatin-organizing factors .The use of SEs for cell identity profiling and prediction may be limited in terms of sensitivity and specificity, and it needs to be complemented with promoter-centric data when analyzing gene clusters .Xenopus tropicalis, endoderm activation-specific TFs  bind to super-enhancers located near the endodermal cell fate genes , thereby activating the target gene’s expression [Endodermal germ layer differentiation and functionality also show a strong association with group of SEs. In pression .Because super-enhancers take part in the control of such important processes as the maintenance of stem cell pluripotency, differentiation, and cell-specific gene expression, abnormal changes in their functioning can lead to pathological processes. It is known that there are SEs associated with tumorigenesis, developmental disorders, neurodegenerative processes, and some other diseases ,26.SEs are important regulators, and their activity modulation could cause pathological changes. Approximately 3000 SEs were shown to be cell-specific in microglia, neurons, and oligodendrocytes. A number of them harbored GWAS risk) variants for Alzheimer’s disease. Some GWAS variants were found within super-enhancers and affected Alzheimer-associated gene expression . In addiBACH2 loci under SE regulation. In addition, it was shown that cytokine signaling blockers, which are clinically effective in autoimmune diseases, predominantly affect genes associated with SEs [The research on super-enhancers aids in understanding and explaining aspects of complex inflammation mechanisms. SE-mediated transcription is an important part of inflammatory pathways and is connected with inflammatory disorders. NF-κB participates in the activation of endothelial enhancers and further proinflammatory activation and SE formation. These processes lead to global changes in the BRD4 landscape and the expression of endothelial proinflammatory factors such as TNF-α. Cytokine expression causes the rapid loss of non-inflammatory SEs, pushing cells and tissues into a pro-inflammatory metabolic state. In vivo BET bromodomain inhibition causes the suppression of atherogenesis . A commowith SEs . A diffewith SEs . For athwith SEs .Genome-wide association studies were held to study the role of SNPs in SE regions in type 2 diabetes. The set of genes and SNPs within them were strongly associated with type 2 diabetes through glucose homeostasis, the G-protein coupled signaling pathway, the WNT signaling pathway, the negative regulation of inflammatory response, the positive regulation of lipid metabolism, etc. .It has been demonstrated that particular SNPs and insertions/deletions inside super-enhancers not only correlate with different genetic illnesses but also have a high correlation with oncogenic expression and cancer in general . Super-eOCA-B oncogene is upregulated by BRD4-loaded super-enhancers that interact with POU2AF1 loci and cause the development of diffuse large B-cell lymphoma (DLBCL). A total of 285 genes with the most BRD4-loaded super-enhancers showed considerable downregulation as well as a potent anti-DLBCL effect after DLBCL was treated with BRD4 inhibitors [In addition, super-enhancers should be taken into account as a component of a complex pluripotent transcriptional regulatory network, according to some research that uses the Core Regulatory Circuitry (CRC) paradigm . One of hibitors .According to a study conducted by Betancur P. and colleagues, SEs significantly upregulate the CD47 enzyme, which is highly expressed in T-ALL and breast cancer cells. Namely, a collection of particular SEs responsible for CD47 expression were found in breast cancer cell lines (HER2 or ER+ PR+) and breast tumors (ER+ PR+). However, when several cancer cell lines were tested, the consistency of the evaluated SE sets did not continue to be the same. It was demonstrated that each type of malignant cell has a unique core of super-enhancers that upregulate a unique set of oncogenes. Human mammary epithelium and CD3+ T cells both displayed significantly lower levels of CD47 expression and varied SE-profiles from tumor cells when compared. Accordingly, SEs of CD47 discovered in cancer cells were accompanied by newly generated super-enhancers, increasing the likelihood of high CD47 expression. This was proven using H3K37ac analysis and the treatment results of BRD4 inhibitors. Additionally, decreased CD47 expression and increased phagocytosis frequency in MCF7 cells occurred after inhibiting the TNF pathway with infliximab monoclonal antibodies .RAE1 and EPHA2). The acquired results were confirmed with Hi-C tests to support the typical local regulatory role of the investigated super-enhancers and to provide confidence in the functional gene annotation [According to Chen H. and Liang H.’s work, the expression of super-enhancer RNA and CpG methylation both play crucial roles in the development of the TCGA melanoma cancer type and other malignancies. Super-enhancer areas (377 Mb) containing >300,000 eRNA loci were found and allowed to assess the nuances of SEs activation using RNA-seq data. The Cancer eRNA Atlas was constructed in order to organize and facilitate the usage of the collected data. This online resource  offers inotation . An earlnotation .MIR21 gene, according to several studies [A specific super-enhancer had an unexpected regulatory role in the neck squamous cell carcinoma, according to a recent study by Wan Y. and colleagues (HNSCC). MiR-21-5p is a well-known oncomiR that has been demonstrated to encourage the development of malignant cancer . NFI, SR studies ,167,168. studies , the FOSThus, super-enhancers are crucial regulatory elements because they modulate cell fate during non-tumor pathological processes as well as those in cancerous cells. Several studies have been conducted in order to explain the concept of super-enhancers; other groups experimentally showed the role of SEs during various states.The important role of super-enhancers in determining cell fate and development of various diseases, including tumors, suggests the idea of targeting super-enhancers’ regulatory elements, such as TFs and Co-TFs, during prospective therapy. Their inhibitors can be used both for research purposes, e.g., studying the biology of super-enhancers, and also for practical purposes, for example, the treatment of socially significant diseases. In this section, we review experimental works suggesting therapies for super-enhancers and their proposed inhibitors .Because the goal is to block transcription, it is logical to influence the proteins that affect the function of RNAPII. That is why the most popular targets are BRD4, chromatin reader, and activator of RNAPII transcription at active chromatin marks , CDK7, wHere, we review SE inhibitors with different target proteins and different mechanisms of action. Some of them are SE-specific, whereas others have broad transcriptional effects but mainly affect SE function. These compounds have in common that they act on key elements involved in the formation and functioning of super-enhancers and therefore significantly change the super-enhancer profile. Next, we consider the experimentally studied groups of super-enhancer inhibitors and their specifics.Bromodomains (BRDs) are protein interaction modules that specifically recognize ε-N-lysine acetylation motifs. They play an important role in the reading process of epigenetic marks. BRDs are evolutionarily conserved and present in diverse nuclear proteins, including those in the BET family. BET proteins have two amino-terminal BRDs that bind to hyperacetylated promoter or enhancer regions . Of partA number of researchers have shown that cancer cells can be resistant to BETi therapy. There are various causes of this resistance in different conditions. One reason can be BRD4 recruitment to chromatin in a bromodomain-independent manner . AnotherCDK7 is a cyclin-dependent kinase that plays an important role in the regulation of RNAPII phosphorylation ,172. TarThe most popular CDK7 inhibitors are THZ1  and THZ2SEs are typically enriched in such post-translational modification histone marks as acetylation at H3 lysine 27 (H3K27ac) and mono-methylation at H3 lysine 4 (H3K4me1) . This lec-MYC, VDR, RB1, EZH2, c-JUN, HOXA2, and HOXA10 while increasing H3K27ac globally [PAX3-FOXO1 fusion oncogene positive rhabdomyosarcoma as a model system and a panel of HDAC inhibitors with diverse and well-characterized isoform selectivity, it was shown that Class I HDAC inhibitors were the most effective, followed by Class IIa (HDAC4/5/7/9), Class IIb (HDAC6/10), Class III (SIRT proteins), and Class IV (HDAC 11) [For example, it was shown that the treatment of transformed cells with largazole leads to the remodeling of the enhancer structure by modulating H3K27ac in a dose dependent manner. It is also important that it preferentially suppresses SE-driven transcripts that are associated with oncogenic activities . In rhabglobally . The effHDAC 11) .PAX8 gene locus. Class I HDAC inhibitors such as panobinostat, romidepsin, and entinostat disrupt PAX8 transcription [MYC, hexokinase 2 (HK2), GAPDH, and enolase 1 (ENO1) in glioblastoma models. These chemicals can be used to reprogram glioblastoma’s central carbon metabolism, which is controlled by SEs [Another example is the dependence of PAX8, a prototype lineage-survival oncogene in epithelial ovarian cancer, on HDAC inhibition through the perturbation of the super-enhancer topology associated with the cription . Panobind by SEs . Among td by SEs . The actd by SEs . Anotherd by SEs .In recent years, some key downstream molecules and pathways of SEs involved in various tumors have attracted more and more attention. New strategies for influencing super-enhancers are developing, and they have great potential.Predominant downstream signaling pathways, for example, the MAPK/ERK pathway, play an important role in cell growth and proliferation for normal cell development and cancer progression . The MEKCombined therapy strategies have a complex effect on super-enhancers, and inhibitors in pairs can show synergies. In addition, the combination of inhibitors reduces the likelihood of drug resistance. One of the most popular combinations is that of BET inhibitors paired with CDK7 inhibitors. Such a therapy has shown increased efficiency in some tumor types.c-MYC, the phosphorylation and proteasomal degradation of the histone acetyltransferase p300, and finally, cell cycle arrest and apoptosis [BET bromodomain inhibitors are also combined with the inhibitors of other kinases. BRD4 is an important player in MYC SE transcription regulation, and cyclin-dependent kinases, especially CDK1 and CDK2, stabilize MYC phosphorylation. Suppression via the combined targeting of MYC expression and stabilization using BET bromodomain inhibition (JQ1) and CDK2 inhibition (milciclib), respectively, is a promising strategy to cure medulloblastoma . BET bropoptosis . It is kpoptosis . Anotherpoptosis . HDAC inpoptosis . Moreovepoptosis . HDAC inpoptosis .In the current review, we discussed the current knowledge on experimental validation and prediction of super-enhancers (SEs), a topic of ongoing discussion in the scientific community. First, we dove into the emergence of the concept of “super-enhancers”. Despite little consensus on how to define SE regions, both in terms of their calculation and theoretical conception, super-enhancers are characterized as separate objects of research, comprising broad enhancer clusters with an exceptionally high degree of enrichment of transcriptional factors and of other gene expression regulators or chromatin marks, as determined with ChIP-seq, RNA-seq, ATAC-seq, etc., and they are historically associated with the increased expression of oncogenes and cell differentiation genes. Such a definition requires further clarification, especially when talking about so-called constitutive SEs, the nature of interactions between the SEs’ constituent parts, and alternative clusters such as stretch enhancers.We also discussed the regulatory potential of super-enhancers, both in normal and pathological conditions such as oncological conditions, neurodegenerative (such as Alzheimer’s and Huntington’s) diseases, inflammatory processes, and others. The location of SE regions and the labelling of TADs as well as the annotation of CTCF binding sites suggest that they share a common nature of organization and interaction, which was confirmed in various studies using the DASE, CapStarr-seq, and ExP STARR-seq algorithms. A number of studies, including those that implemented CRISPR-Cas editing, have shown that SEs, due to their interaction with transcriptional factors, significantly increase the expression of target genes, making a huge contribution to cell differentiation, growth, development, and tumorigenesis.There are some future research directions for SEs in diseases. First, it is still unclear which of the socially significant diseases, in addition to those for which they have already been well-studied and mentioned before, can be associated with changes in the functioning of super-enhancers. Second, the development of therapeutic approaches aimed at targeting SE may be quite promising. Given the potential importance of SEs in cell fate determination and disease development, we examined promising therapeutic avenues, including the use of inhibitors that target positive regulators of SE-mediated transcription. These include selective inhibitors of BET bromodomain-containing proteins and CDK7, histone deacetylase and demethylase inhibitors, and other potential targets and strategies for prospective combination therapies.We provided an overview of bioinformatic methods used to search for SEs, with a focus on machine learning and experimental techniques that have been actively used in recent years. A separate task was to describe the methods of in silico SE prediction, with special attention to machine learning approaches, used for the discovery and prediction of these regulatory elements. The majority of these are based on Chip-Seq data and the use of convolutional (CNN) and recurrent (RNN) neural networks and their combinations, for example, the KEGRU network  and DNABNevertheless, the existing techniques are not sufficient to facilitate complex approaches because modern algorithms ignore the chromatin structure, the probable lateral interactions of SEs and uncharacteristic target genes, the possible breakdown of super-enhancer insulation, hijacking mechanisms, and other non-canonic formation and decomposition mechanisms of super-enhancers. That is why in our future work, we aim to develop an approach that would allow us to take into account the maximum number of nuances that affect the formation of super-enhancers in tumors and try to resolve the existing uncertainty based on regulatory effect blurring.The question about SE evolution remains open. Super-enhancers, which are historically associated with the human-enhanced region, are thought to be responsible for human-specific phenotypic adaptations when compared between mammals. At the same time, according to some data, there is reason to believe that super-enhancers can be much more ancient, if compared exempli gratia with stress response proteins in multicellular organisms. It should be noted that advancements in understanding the molecular mechanisms of diseases In conclusion, despite ongoing research, even if the definition of the object itself is currently incomplete, our understanding of SEs is evolving, and further experimental and computational studies are needed to fully comprehend their role in gene regulation and to further develop therapeutic strategies.


Schizosaccharomyces pombe. The fission yeast subtelomeres form three distinct chromatin structures; one is the shelterin complex, which is localized not only at the telomeres but also at the telomere-proximal regions of subtelomeres to form transcriptionally repressive chromatin structures. The others are heterochromatin and knob, which have repressive effects in gene expression, but the subtelomeres are equipped with a mechanism that prevents these condensed chromatin structures from invading adjacent euchromatin regions. On the other hand, recombination reactions within or near subtelomeric sequences allow chromosomes to be circularized, enabling cells to survive in telomere shortening. Furthermore, DNA structures of the subtelomeres are more variable than other chromosomal regions, which may have contributed to biological diversity and evolution while changing gene expression and chromatin structures.Eukaryotes have linear chromosomes with domains called telomeres at both ends. The telomere DNA consists of a simple tandem repeat sequence, and multiple telomere-binding proteins including the shelterin complex maintain chromosome-end structures and regulate various biological reactions, such as protection of chromosome ends and control of telomere DNA length. On the other hand, subtelomeres, which are located adjacent to telomeres, contain a complex mosaic of multiple common segmental sequences and a variety of gene sequences. This review focused on roles of the subtelomeric chromatin and DNA structures in the fission yeast  At the end of eukaryotic linear chromosomes is a region called telomere, where multiple proteins bind to form a complex based on DNA consisting of a tandem repeat sequence  Figure . The seqtebrates , [TTTAGGthaliana , [TTAGGC elegans , [TTAC-rich strand and its complementary C (cytosine)-rich strand. On the other hand, the chromosome ends have a protruding 3′ end, which is called “G-tail” or “G-overhang” . The lenAdjacent to telomeres are regions called subtelomeres, which have characteristic DNA and chromatin structures , thus suppressing gene expression through the condensed structure of heterochromatin [Fission yeast chromosomes form constitutive heterochromatin at pericentromeres, subtelomeres, and the mating-type regation , and hethromatin . Heterochromatin ; howeverIn the heterochromatin of fission yeast, the histone methyltransferase Clr4 is first recruited to a defined region and methylates histone H3K9, and HP1 (heterochromatin protein 1) family proteins bind to the H3K9me (histone H3 methylated at K9) . In turnmat locus contain sequences that are homologous to part of the duplicated sequences (dg and dh repeats) found in the pericentromeres [tlh1-4, respectively [tlh genes and produces siRNA , which recruits Clr4 to the dh region of the subtelomeres (the tlh gene loci) to establish heterochromatin , are mildly inhibited [The SU sequences of the strain 972 contain various genes that are involved in nutrient metabolism and stress responses. Knob has the effect of suppressing gene expression, although not to the same extent as heterochromatin, so that genes in the SU region, such as galactose metabolism genes  strain, in which all the five SH regions were completely deleted from the 972-derived strain , RNAi cannot act on SH regions; however, heterochromatin is established by the shelterin proteins at the chromosome ends and spreads into the SU region, although it does not spread out from the SU region  [In the  region)     . TherefoSD5 shows normal cell proliferation and cell morphology in a nutrient-rich medium [SD5 cells do not have defects in responses to various forms of stress , in meiosis and spore formation after nitrogen starvation, and in telomere length control [+ and Li+ due to repression of the nhe1 gene encoding a plasma membrane Na+ (or Li+)/H+ antiporter by heterochromatin invasion of the SU region. In fact, heterochromatin spread into all of the SU regions in chromosomes 1 and 2 [The strain h medium . Further control . However 1 and 2   and L-asparaginase gene sequences, within or near SU regions, and the chromosomes are circularized by the SSA reaction at these common sequences [Then, are SH sequences essential for fission yeast to survive telomere shortening? Surprisingly, even when telomere shorteining is induced in the equences . Thus, tMore surprisingly, in the absence of SH sequences, when telomere shortening is induced, not only do cells with each of the three chromosomes self-circularize, but also survivors emerge where chromosomes 1 and 2 fused at the LTR and other common sequences to form a single circularized chromosome . How do S. pombe natural wild strains isolated in various countries show significant variations in the DNA structure of subtelomeres. Some of them lack genes present in the SU region of 972 or, conversely, increased their copy number [S. pombe, differences in gene expression around subtelomeres would result in differences in a variety of cellular functions.Subtelomeres are equipped with multiple layers of defense mechanisms to inherit chromosomes to the next generation, i.e., to sustain life. In doing so, subtelomeres may undergo dynamic structural changes, such as the transformation of linear chromosomes into circularized chromosomes. In fact, it was recently shown that subtelomeres are more prone to structural changes than other chromosomal regions even without exposure to critical situations, i.e., subtelomeres are hotspots of genome evolution . Interesy number . FurtherThe reason why subtelomere changes are so striking is probably that subtelomeres often have overlapping genes because they share common sequences, and even if one gene is mutated or deleted, it is not likely to be life-threatening, i.e., not lethal. This may be because subtelomeric changes are easily inherited to the next generation. Conversely, in the case of genes that have only one copy inside the chromosome, the accumulation of mutations may be life-threatening at a faster rate and less likely to remain in future generations.In humans as well as in fission yeast, subtelomeres are hotspots of genomic variation . Since t


Myc-Associated Protein X (MAX), a transcription factor involved in cell cycle regulation, was differentially methylated between GHPA and nonfunctional pituitary adenoma (NFPA). We aimed to validate the differential DNA methylation and related MAX protein expression profiles between NFPA and GHPA.Many patients with growth hormone-secreting pituitary adenoma (GHPA) fail to achieve biochemical remission, warranting investigation into epigenetic and molecular signatures associated with tumorigenesis and hormonal secretion. Prior work exploring the DNA methylome showed DNA methylation levels were measured in 52 surgically resected tumors  at ~100,000 known MAX binding sites derived using ChIP-seq analysis from ENCODE. Findings were correlated with MAX protein expression using a constructed tissue microarray (TMA). Gene ontology analysis was performed to explore downstream genetic and signaling pathways regulated by MAX.GHPA had more hypomethylation events across all known MAX binding sites. Of binding sites defined using ChIP-seq analysis, 1,551 sites had significantly different methylation patterns between the two cohorts; 432 occurred near promoter regions potentially regulated by MAX, including promoters of TNF and MMP9. Gene ontology analysis suggested enrichment in genes involved in oxygen response, immune system regulation, and cell proliferation. Thirteen MAX binding sites were within coding regions of genes. GHPA demonstrated significantly increased expression of MAX protein compared to NFPA.GHPA have significantly different DNA methylation and downstream protein expression levels of MAX compared to NFPA. These differences may influence mechanisms involved with cellular proliferation, tumor invasion and hormonal secretion. Growth hormone-secreting pituitary adenoma (GHPA) cause significant clinical morbidity and mortality , 2. DespKCNAB2 promoter region is found in nonfunctional pituitary adenoma (NFPA) . Ge. Ge21]. We additionally performed an independent immunohistochemistry analysis on a previously constructed tissue microarray of surgically resected patient samples of both NFPA and GHPA. GHPA and NFPA cohort TMAs were stained for MAX protein expression . NotablyAcromegaly caused by GHPA remains a challenging pathology with significant clinical implications, particularly for patients who fail to achieve hormonal remission following aggressive surgical, radiosurgical, and/or pharmacologic therapy , 2, 4. IThe 2017 edition of the World Health Organization (WHO) classification of endocrine tumors emphasized lineage-specific pituitary transcription factors to classify pituitary adenoma, specifically PIT1 (pituitary specific transcription factor 1) and TPIT (pituitary cell restricted factor) , 24. GivMAX and MYC genes in PA. For example, germline MAX mutations are associated with neuroendocrine tumors including prolactinomas and pheochromocytomas [c-MYC was overexpressed in pituitary tumors that were classified as aggressive, larger in size, and diagnosed at a younger age [The oncogenic transcription factor MAX is a key component of the cell cycle. MAX forms homodimers and heterodimers with other transcription factors such as MYC, MXI1, MNT, and MXD1. Multiple reports have highlighted the involvement of ocytomas , 15, 17.nger age , 26. No myc was differentially expressed in somatotroph adenoma compared to gonadotroph adenoma, and that degree of tumor invasion was correlated with degree of myc expression [MAX transcription factor. Nonetheless, these authors urge further validation of possible biomarkers and mechanisms for tumor invasion and subtype specific growth.DNA methylation is an epigenetic mechanism by which chromatin is made more accessible or less accessible to transcription . In this study, we show that MAX transcription factor binding sites are globally hypomethylated and demonstrate increased accessibility for transcription factor binding in GHPA compared to NFPA. Using an independent dataset, we also corroborated that MAX protein expression levels are elevated in GHPA compared to NFPA, perhaps working as a component of a positive feedback loop. These results also agree with the findings of Salomon et al., who reported differing methylation profiles between PA subtypes . More dipression . Our stuAs transcription factors often act on promoter regions to regulate gene expression, we investigated which hypomethylated MAX binding sites were in promoter regions; in total 432 were identified. Secondary analysis using gene ontology demonstrated that these genes regulated by MAX binding sites are involved in responses to oxygen containing compounds, immune regulation, and cell proliferation. Target genes of MAX in GHPA included known genes involved in pituitary tumorigenesis. For example, tumor necrosis factor (TNF) has been shown to have direct effects on cultured anterior pituitary cells, blunting the release of hormones in response to hypothalamic factors . MMP9, wStandard treatment for GHPA which fail surgical or radiosurgical therapy includes somatostatin receptor therapy (SSRT), which activates a cellular pathway that ultimately inhibits GH secretion. These medications (most commonly octreotide and lanreotide) have limited effectiveness, as up to 10% of all patients have an innate resistance to medical therapy, and long-term response is seen in only 17–41% of cases –35. OtheThere are several limitations to the current study which coach our findings in exploratory analysis which warrant further study. Though we describe clear differences in areas of hypomethylation and MAX protein expression between GHPA and NFPA the specific binding of these transcription factors was not confirmed using CHIP-seq data. To further validate and confirm these findings it is necessary to utilize other confirmatory methods, including CHIP-seq or pyrosequencing, in a new cohort of data. It would be worthwhile to find patients whom preserving normal pituitary gland during adenoma resection is not possible, which by then an internal control of normal gland would be obtained. In addition, we lack tissue culture proliferation assays showing differential aggressiveness in between NFPA and GHPA, though we plan this for future studies. Finally, our gene ontology studies are preliminary and merely demonstrate the potential for downstream, tissue and organ system-based effects to be seen by changes to the cellular or epigenetic milieu. Despite these limitations, we believe our work demonstrates the potential for further biological study into the epigenetic modifications of MAX binding sites as a potential therapeutic target.In this study, we validate molecular and epigenetic difference between NFPA and GHPA with clear implications to downstream molecular pathways known to be involved in tumorigenesis. Further studies are warranted to assess the role of targeted epigenetic and molecular therapeutics for GHPA.S1 Fig(TIFF)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


The interferon-triggered innate immune response has been observed to be under strong diversifying selection to counteract the many pathogens hosts have to defend against. In particular, rewiring of gene transcription regulation allows organisms to rapidly acquire new phenotypes by removing and adding genes into the innate immune gene network. Dissecting the molecular processes by which this rewiring takes place, either by changing the DNA regulatory elements or by changing the activity of the regulators across species, is key to better understand this evolutionary process.To better comprehend the evolutionary dynamics that have occurred in the initial transcriptional response to interferon in primates, we present Precision Run-On (PRO-seq) datasets made after 1 h of interferon-α2 stimulation on human and rhesus macaque lymphoblastoid cell lines. Further, we tested the difference between using either species’ cognate interferon versus using the other orthologous interferon to account for any potential impacts in the interaction of the orthologous interferons with their cellular membrane receptors. This data provides insights into the regulatory mechanisms that drive species-specific responses to environmental perturbations, such as the one driven by the interactions of pathogens and their hosts. Eukaryotic gene transcription is controlled by promoters and enhancers that are proximal and distal, respectively, to the genes they regulate . When thRegulatory elements have been shown to evolve more rapidly than the genes they regulate , which sIFN molecules are released upon pathogen recognition, and once sensed by cell membrane receptors trigger the formation of transcription factor complexes composed by members of the IRF and STAT protein families, which then regulate the transcription of interferon stimulated genes (ISGs) . HoweverHere we address these questions by testing the difference in the transcriptional response when using species-matching compared to non-species matching IFN-α2 treatments on cells derived from human and rhesus macaque, which diverged around 25 mya . These dWe obtained precision run-on sequencing (PRO-seq) datasets from Epstein-Barr Virus-transformed lymphoblastoid cell lines (LCLs) derived from both a male and a female individual from two primates: human  and rhesus macaque    ” , and theWhile we assayed two distinct cell lines per species, one female and one male, each biological sex was only assayed once.


Trypanosoma brucei, which causes sleeping sickness, and Trypanosoma cruzi, which causes Chagas disease. Genomes of these parasites comprise core regions and species-specific disruptive regions that encode multigene families of surface glycoproteins. Few transcriptional regulators have been identified in these parasites, and the role of spatial organization of the genome in gene expression is unclear. Here we mapped genome-wide chromatin interactions in T. cruzi using chromosome conformation capture (Hi-C), and we show that the core and disruptive regions form three-dimensional chromatin compartments named C and D. These chromatin compartments differ in levels of DNA methylation, nucleosome positioning and chromatin interactions, affecting genome expression dynamics. Our data reveal that the trypanosome genome is organized into chromatin-folding domains and transcription is affected by the local chromatin structure. We propose a model in which epigenetic mechanisms affect gene expression in trypanosomes.Trypanosomes are eukaryotic, unicellular parasites, such as  Trypanosoma cruzi genome and how it affects gene expression.Spatial organization of the  It is widely accepted that post-transcriptional regulation is the main mode of gene expression regulation5. However, recent reports have highlighted the impact of histone post-translational modifications, histone variants, nucleosome positioning, base J and chromatin organization on the regulation of gene expression, cell cycle control and differentiation13.Gene organization, regulation of genome expression and RNA metabolism in trypanosomes differ from that in other eukaryotes. Genes are organized into directional gene clusters (DGCs) separated by strand-switch regions (SSRs), where the transcription sense converges or diverges. The DGCs are transcribed as large polycistronic transcription units (PTUs)15, including trypanosomes8. Recently, it was reported that specific spatial genome organization is required for the monogenic expression of the variant surface glycoprotein (VSG) in Trypanosoma brucei9, denoting the importance of the genome architecture on gene expression in trypanosomes. The surface of trypanosomes is covered with glycoproteins16 whose composition varies among different species, probably owing to molecular mechanisms that underpin the regulation of the expression of these genes. T. brucei is extracellular during completion of its life cycle and relies on antigenic variation of VSGs to evade the host immune system18. By contrast, Trypanosoma cruzi, which has intracellular and extracellular life-cycle stages, simultaneously expresses thousands of slightly different surface proteins to evade host immunity21.Epigenetic mechanisms are important for transcriptional regulation in several protozoan parasites23. In T. cruzi, the core compartment (which presents synteny among trypanosomatids) is composed of conserved genes and the disruptive (loss of synteny) compartment is where most of the surface coding genes are located; in particular, the disruptive compartment is enriched in genes coding for mucins, mucin-associated surface protein and trans-sialidases22. In T. brucei, genes encoding for VSGs are also located in a particular compartment in the genome, which is located mostly in the extreme ends (subtelomeres) of the chromosomes23. While regions have been demarcated, the role of chromatin organization in controlling the expression of non-antigenic genes, general gene expression and pathogenesis in T. cruzi is still largely uncharted.The genomes of trypanosomes are partitioned into two large regions, formally known as compartmentsStage-specific expression of surface proteins is essential for trypanosomes to complete their life cycles, so surface-protein-encoding genes need to be tightly regulated. Physical separation in the genome would be an efficient mechanism to regulate these genes. We hypothesized that specialized genome organization might enable gene regulation mechanisms in trypanosomes. To test this hypothesis, we analysed how the spatial organization of the genome influences gene expression in the context of previously defined genome compartments and report our results here.T. cruzi genome is partitioned into compartments22, we wondered whether this linear organization correlates with three-dimensional conformation. To characterize chromatin conformation in T. cruzi cells, we mapped genome-wide interactions by analysing chromosome conformation capture (Hi-C) data that were previously used for genome assembly24. The analysis of interaction matrices at various bin sizes revealed that there are several regions in close spatial proximity indicating the presence of chromatin-folding conformations  usually described in other eukaryotes26. TAD predictor algorithms failed to detect domains in the smallest chromosomes (<1.1 Mb) and at common resolutions for TAD identification. This result is reasonable as the chromosomes of T. cruzi are smaller (<3 Mb) and exhibit reduced non-coding regions . In addition, the concept of TADs has been mainly developed for complex eukaryotes, mainly mammals28. For these reasons, we refer to these structures as CFDs. Both compartments contain CFDs, and remarkably, 80% of them belong to only one compartment, indicating that CFDs in each compartment are mutually exclusive   Fig.  and a hint) Fig. . Examinint) Fig. .Fig. 1ChT. cruzi and are not strain specific.To explore the influence of the three-dimensional genome organization on global gene expression, we performed RNA sequencing (RNA-seq) of different stages of the parasite, and we classified the genes into high-, medium- and low-expressed genes according to the number of transcripts detected. We found that the core compartment predominantly consists of moderate- and high-expressed genes in all the stages analysed. By contrast, the disruptive compartment, which in some cases comprises almost entire chromosomes, is enriched in low-expressed genes or genes with undetectable RNA levels Fig. . These dRegarding differentially expressed genes (DEGs), we found that 94% of genes enriched in epimastigotes belong to the core compartment, while in trypomastigotes, 20% of DEGs are in the core and 80% are in the disruptive compartment  datages Fig. . Howeverges Fig. . Notablyges Fig. . To charges Fig. ; there w31, we compared the genomic distribution of 5mC and 6mA DNA methylation marks in the infective and non-infective stages of T. cruzi. We found 6mA at low levels  both in epimastigotes and trypomastigotes and present at the TCG6mATC motif , and the number of genes they contain can vary from 1 up to 173 genes 23. The CFDs determined also coincide with several DGC-internal regions where the transcription is interrupted in T. brucei  experiments. To select which topological domain boundaries to analyse, we used the Hi-C maps from Brazil A4 to design 3C primers  identified using Hi-C maps of procyclic forms (PFs) and BFs. Most of the boundary regions are shared between both stages  has a three-dimensional—C and D—counterpart both in T. cruzi and T. brucei23.The genome organization of trypanosomes has a common property in that genomes are divided into a core, syntenic among trypanosomatids, and a disruptive, or non-syntenic, compartment. The disruptive compartment is located in the subtelomeres in the African trypanosomesT. cruzi is organized into CFDs. These CFDs are smaller in the D chromatin and present a higher frequency of contacts, indicating that chromatin is more compact in this compartment, which is in agreement with recent FAIRE –seq studies8. In this sense, the D compartment is densely packed with well-positioned nucleosomes, suggesting a less permissive chromatin structure. In turn, the D compartment exhibits a high frequency of inter-chromosomal contacts. This higher compaction of D chromatin may have the role of both avoiding spurious transcription and facilitating DNA recombination events necessary for generating the antigenic variability essential for the immune system evasion strategy of T. cruzi. Comparably, the global mechanism in T. brucei implies the recombination of the VSG genes into subtelomeric regions to switch the expression of the protein that covers the parasite’s surface as the infection progresses and the immune system matures its response33. Therefore, both parasite genomes exhibit a D compartment composed of species- and stage-specific surface proteins, which would generate favourable conditions for generating antigenic diversity, by either recombination, allelic exclusion or differential transcription. On the contrary, it is expected that the C compartment, which does not undergo antigenic variation, presents less variability concerning the structure of the chromatin. In agreement with this, we observed well-conserved CFDs in this compartment in different stages and strains in both parasites. Thus, the main differences in genome architecture are those that allow T. brucei to survive extracellularly and T. cruzi to adapt to both intracellular and extracellular forms.The genome of 34. We investigated whether this three-dimensional genome organization implies selective regulation of gene expression, and we found that the C chromatin is mainly composed of genes with high and medium expression, while most of the genes from the D chromatin exhibit low or undetectable expression and a discrete number of genes highly increase their expression in trypomastigotes. The C compartment functions as a constitutively expressed region as its genes are expressed similarly throughout the life cycle, whereas the D compartment constitutes a specialized region for the infective stage. These findings modify the accepted paradigm that these parasites transcribe indiscriminately and then modulate post-transcriptionally their expression. This is observed in the C compartment, but it does not apply to the D compartment as very few of the hundreds of genes in each multigenic family are highly expressed. Both in T. cruzi and T. brucei, the D compartment constitutes regions of high chromatin compaction, which could favour gene-to-gene regulation, preventing spurious transcription and favouring intra- and inter-chromosomal recombination. In T. brucei, multiple VSG genes are transcribed before a single gene is chosen9, and this choice relies on the formation of an extra-nucleolar structure that contains a local reservoir of RNA polymerase I and RNA processing factors35. In T. cruzi, the expression of the highly expressed surface genes is probably determined by the nucleoplasmic location of the loci and the proximity to the transcription and processing machinery, but Hi-C data of the infectious stage are necessary to study this and other aspects of the genome biology at this stage. Finally, the maintenance of three-dimensional organization along the infective and non-infective stages can constitute an advantage in driving life-cycle-specific transcriptional regulation.One mechanism to ensure coordinated regulation of gene expression of large stretches is the formation of higher-order three-dimensional chromatin structuresT. cruzi. In T. brucei, when we eliminated the bias of DGCs as a reference for transcription start and end, we also found a similar correlation between the CFD and transcription in the C compartment, denoting a common feature of trypanosomes. RBP1 is enriched in these DGC-internal regions that are flanked by CFDs, endorsing that these are regions where transcription starts and ends. Transcription initiation and termination internal to DGC units were previously described in trypanosomes37, and future studies are necessary to determine whether these DGC-internal transcription start and stop sites present similar characteristics as those in the SSRs . Taken together, these results strongly support that the flow of genetic information from DNA to RNA in trypanosomes is regulated at transcription initiation through epigenetic mechanisms.The genome regions between two CFDs are characteristically RNA silent, while the regions contained in CFDs are actively transcribed, indicating that the expression is strongly determined by the three-dimensional organization of the chromatin and does not necessarily correlate with the DGCs. Therefore, the paradigm that there is a correspondence between DGCs and PTUs is not always fulfilled. Importantly, this is more than just a feature of T. cruzi does not respond to this widely accepted model: most of their genes exhibit low and undetectable levels, and a discrete number greatly increase their expression in the trypomastigote stage. These results do not go against the statement ‘trypanosomes mainly regulate at the post-transcription level’, but rather, this would be the expression modulation model in the C compartment. We propose that there are two major regulation models and that they are spatially isolated in the nucleoplasm by the C and D compartments in which the chromatin is organized. The compartmentalization of the genome makes it possible to isolate the molecular mechanisms necessary for the regulation of each type of gene. Moving forwards, the incorporation of time-course data and single-cell studies will reveal further insights into the mechanisms of three-dimensional chromatin structure and gene regulation in trypanosomes.The most accepted idea regarding the differential regulation of gene expression in trypanosomes is that transcription is constitutive and that transcript levels are individually modulated by post-transcriptional mechanisms. However, here we provide evidence that the expression of the D compartment in T. cruzi Dm28c38 parasites were cultured axenically in liver infusion tryptose medium supplemented with 10% (v/v) inactivated fetal bovine serum (Gibco) at 28 °C. Trypomastigotes and intracellular amastigotes were collected from infected Vero cells as described39. Vero cells were cultivated in Dulbecco’s modified Eagle’s medium (DMEM (1×) + GlutaMAX-l, Gibco by Life Technologies) supplemented with 10% (v/v) fetal bovine serum, (Gibco), penicillin 100 U ml−1 and 100 µg ml−1 streptomycin (Thermo Fisher Scientific) at 37 °C in a humidified 5% CO2 atmosphere.40. Purified genomic DNA was fragmented to 10 kb using g-TUBEs (Covaris) to maximize the yield of nanopore sequencing. Genomic libraries were prepared using the Ligation Sequencing Kit (SQK-LSK109) and Native Barcoding Expansion Kit  following the protocol for native barcoding of genomic DNA. Pooled samples were quantified using the Qubit dsDNA HS Assay Kit, loaded onto a R9.4.1 Flow Cell (FLO-MIN106D) and sequenced on an Oxford Nanopore Technologies (ONT) MinION platform for 72 h. The coverage per sample ranged from 29× to 34×. Raw data (multi_read_fast5 files) were converted to single_read_fast5 files using ont_fast5_api v3.1.6 (ONT) and base called using Guppy v3.6.0 (ONT) with dna_r9.4.1_450bps_modbases_dam-dcm-cpg_hac.cfg configuration. The strand-sensitive and single-nucleotide-based detection of DNA base modifications was performed with DeepMod41 v0.1.3, which detects m5C and m6A in DNA using the recurrent neural networks model. Only genomic positions with >5 read coverage and >90% of methylation percentage were considered for the analysis. Dm28c reference genome (TcruziDm28c2018 from TriTrypDB version 51) was used as input, and methylation positions were assigned to different genomic regions using BEDTools42 v2.27.1. Core and disruptive methylation quantifications were made in the largest 30 scaffolds of the Dm28c 2018 genome assembly, representing 38% of the genome size.High-molecular-weight genomic DNA was purified using phenol–chloroform–isoamyl alcohol extraction followed by EtOH precipitation7 was obtained from National Center for Biotechnology Information  using fasterq-dump v3.0.0. Quality control was performed using FastQC v0.11.9, and reads with less than 70 bp after trimming low-quality nucleotides (–nextseq-trim = 20) and clipping the adapter sequence were removed using Cutadapt43 v2.7. For nucleosome calling, paired-end reads were aligned to both reference genomes Dm28c (TcruziDm28c2018 from TriTrypDB version 51) and Brazil A4 (TcruziBrazilA4 from TriTrypDB version 53) using Bowtie44 v1.3.1, allowing up to two mismatches and a maximum insert size of 500 bp. The resulting BAM files were imported and processed in R v4.1.3 to merge biological replicates by chromosome. After duplicate removal with Picards MarkDuplicates (Picard Toolkit 2019), Fourier transform filtering to remove noise and peak calling to accurately define and classify the location of nucleosomes across the genome were performed with nucleR45. Nucleosomes with sharp signals of the shape of the associated peaks were labelled as well-positioned (W) nucleosomes , while flat peaks were labelled as fuzzy nucleosomes 45. In this analysis, well-positioned nucleosomes were considered when the nucleR peak width score and height score were higher than 0.6 and 0.4, respectively. Finally, Nucleosome Dynamics46 v1.0 was used to study differences between stages: occupancy differences (insertions and evictions) and displacement of nucleosomes (shifts).MNase–seq data8 trypomastigotes and 5 × 109 epimastigotes) were collected through centrifugation at 2,200 × g for 10 min at room temperature and washed twice with PBS 1×. Cells were fixed with 1% formaldehyde for 15 min at room temperature on a shaker. The cross-linking was quenched by adding 2.5 ml of 1 M glycine (125 mM) and incubating for 5 min at room temperature, then on ice for 15 min. Fixed cells were then centrifuged, washed twice with ice-cold PBS 1× and resuspended in 1.4 ml of cold lysis buffer . After homogenization with a Dounce homogenizer pestle A, the nuclei were washed twice with 1.25× rCutSmart buffer (New England BioLabs) and resuspended in 0.5 ml of rCutSmart buffer (New England BioLabs) containing 0.3% SDS. Samples were incubated for 40 min at 65 °C and then for 20 min at 37 °C. Then, 2% Triton X-100 was added, and the nuclei were further incubated for 1 h at 37 °C to sequester the SDS. Cross-linked DNA was digested using 400 U of EcoRI-HF (R3101 New England BioLabs) overnight at 37 °C, and the enzyme was inactivated with 1% SDS at 65 °C for 20 min. Digestion products were diluted by the addition of 4 ml 1.1× T4 DNA Ligase Reaction Buffer (New England BioLabs) with 1% Triton X-100 and incubated for 1 h at 37 °C. DNA was ligated for 4 h in a water bath at 16 °C . Then, 300 μg of proteinase K was used to reverse the cross-linking overnight at 65 °C. Finally, samples were incubated with 300 μg RNase A, and the DNA was purified by phenol extraction followed by ethanol precipitation. The control library with all possible ligation products was prepared in parallel using non-cross-linked genomic DNA. Ligation products were detected and quantified in control and cross-linked libraries with PCR using locus-specific primers. We designed a set of six oligos excluding sequences from repetitive regions  with random primers. A total of 80 bp paired-end reads were generated using an Illumina NextSeq 500 MID platform. Raw reads were processed with Cutadapt43 v2.7 to trim low-quality nucleotides (–nextseq-trim = 20) and clip the adapter sequence. The resulting reads with less than 70 bp were discarded (–minimum-length = 70). Trimmed reads were checked for per-base quality using FastQC v0.11.9. Cleaned reads were then aligned to the Dm28c reference genome (TcruziDm28c2018 from TriTrypDB version 51) using Hisat2 (ref. 48) v2.1.0 , and mapped reads with a mapping quality score <10 were discarded with SAMtools49 v1.16.1 as it was previously described50. The coverage files were smoothed, and counts per million (CPM) normalized in a bin size of 10 bp using the bamCoverage function from deepTools51. Salmon52 v1.5.1 was used to estimate transcript levels, and statistical analysis of differential expression of mRNAs was tested with DESeq2 (ref. 53) v1.38.3. The expression of each transcript was quantified in transcript per million (TPM) units. Consistency between replicates was assessed with Pearson correlation and principal component analysis. Genes were considered as differentially expressed when they were statistically significant (Padj < 0.001) and had a fold change in transcript abundance of at least two (|log2FC| > 1). To study the dynamic of RNA levels in genome compartments along the stages, we classified the transcripts into three groups based on their relative abundance. High-, medium- and low-expression genes were determined using quantiles on normalized counts. Also, to investigate expression patterns, we classified the scaffolds into ‘core’, ‘disruptive’ and ‘mixed’ according to the percentage of compartments that make them up. The bimodality coefficient was calculated using the mousetrap54 v3.2.1 R package. The transcriptomic data corresponding to the different subcellular compartments of T. cruzi, Brazil A4 and T. brucei were analysed identically.Total RNA from cell-derived trypomastigotes, epimastigotes and intracellular amastigotes was treated with RiboZero Gold magnetic beads from a eukaryote kit (250 ng as input) as described previously55 v1.1.0. SLA sites identified at the same positions in all transcriptomes were used for the analysis to avoid bias of differential processing between stages. Alignments of reads spanning SLA sites were quantified, normalized and classified as processed and unprocessed according to the presence or absence of spliced leader sequence in the read, respectively.To study immature and nascent transcripts, 5′ untranslated regions (UTR) were first annotated and assigned to protein-coding genes using UTRme42 v2.27.1 with the -bga option. Then, all regions of the genome with zero coverage were extracted, and intervals <500 bp in length were filtered out.To identify RNA zero coverage regions, genome coverage for all positions in BEDGRAPH format was calculated from mapping-quality-filtered BAM files using the genomecov function from BEDTools43 v2.7 to remove low-quality bases from both ends (-q 30), remove flanking N bases from each read (–trim-n) and discard reads containing more than 5 N bases (–max-n 5). The resulting reads with less than 35 bp were discarded (–minimum-length = 35). Raw and trimmed reads were checked for per-base quality using FastQC v0.11.9. Cleaned reads were then aligned to T. brucei reference genome (TbruceiLister427_2018 from TriTrypDB version 53) using Bowtie44 v1.3.1, allowing two mismatches (-v 2). Unique alignments reported (-m 1) were compressed and sorted using SAMtools49 v1.16.1, and duplicate reads were removed using Picard tools v2.25.5 (MarkDuplicates). Normalization and peaking calling were conducted using MACS2 (ref. 56) v2.2.7.1 with a false discovery rate of 5%, and peaks with low fold enrichment (-q 0.05 and –fe-cutoff 1.5) were filtered out. The –g parameter was set at 50081021.The 37 × 2 bp paired-end raw reads were processed with Cutadapt57 v3.1.0. Quality was assessed using MultiQC. TAD calling was performed with TADtools58 and the hicFindTADs function of HiCExplorer59 v3.7.2. The insulation score was calculated at 30, 50, 70, 100 and 150 kb sliding windows with FAN-C tool60 v0.9.26b2. The data integration, comparison and visualization of the different datasets were performed using pyGenomeTracks61 v3.7.Hi-C datasets were processed from raw reads to normalized contact maps using HiC-ProAll figures were designed using GIMP v2.10.32.Further information on research design is available in the Supplementary InformationSupplementary Figs. 1–3 and Tables 1–5.Reporting SummarySource DataStatistical source data and agarose gel.