Super-enhancers (SEs) consist of a cluster of many enhancers bound to a great number of transcription factors. They are critical cis-regulatory elements that determine the identity of various human cell types. During tumorigenesis, DNA mutations and indels, chromosomal rearrangements, three-dimensional chromatin structural changes, and viral infections mediate oncogenic SE activation, and activated SEs have been found to regulate the expression of oncogenic genes. Inhibition specifically targeted to oncogenic SE assembly and activation provides a novel powerful therapeutic strategy for various cancers. In this paper, we first introduce the current understanding of oncogenic SE assembly and activation and then summarize the pathogenic factors and mechanism of oncogenic SE activation. Next, we elaborate on the oncogenic functions of SEs in cancers and the application of SEs as therapeutic targets. Finally, we turn our focus to the use of SEs in basic research and clinical trials. Drugs that block the assembly and activation of large DNA segments involved in enhancing gene expression could help in the treatment of cancer. Faqing Tang of Hunan Cancer Hospital in Changsha, China, and colleagues review the ways in which cancer cells hijack clusters of gene-regulating sequences known as super-enhancers, regulatory gene regions that normally help determine a cell’s unique identity, to drive the aberrant gene activity that fuels tumor growth. The researchers describe how numerous factors, ranging from internal DNA alterations, both large and small, to viral infections and other external assaults, can spur the formation of cancer-causing super-enhancers, leading to out-of-control gene expression. Therapies that selectively target these super-enhancers are now in early clinical testing. However, more studies of super-enhancers and their role in cancer development are needed to inform future drug development. Since their discovery, enhancers have been increasingly studied, with a number of enhancers identified and their structure and regulatory mechanism extensively clarified2. Transcription factors (TFs) bind to enhancers to recruit coactivators such as the mediator complex CREB-binding protein (CBP) and p300 to alter the chromatin spatial structure, resulting in the interaction of TFs with enhancers, promoters or RNA polymerase2. Epigenetic modifications to histones and DNA have been proven to be the main mediators of enhancer editing and maintenance. Histone modification is related to the active state of the enhancer; for example, monomethylation of histone H3 protein at lysine 4 (H3K4me1) and acetylation at lysine 27 (H3K27ac) are correlated with functional enhancers3. Generally, enhancers are short noncoding DNA segments. They can be recognized by TFs and activate transcription independent of enhancer position or orientation in the genome1. Enhancers are transcribed into enhancer RNAs (eRNAs), the expression level of which is associated with the expression of genes nearby proximal enhancers, suggesting that enhancers play important roles in gene transcription4.The enhancer was first defined as a short DNA sequence from the Simian virus 40 genome that had a great ability to enhance the transcription of its target genes in mammalian cells6. Similar to typical enhancers, SEs are occupied by TFs, mediator complexes, chromatin regulators and the RNA polymerase II (pol II) complex, but the density of these active molecules on SEs is several-fold that of typical enhancers. As a result, SEs can drive targeted gene transcription more dramatically than typical enhancers5  chromatin structural changes17, and viral infections19 mediate the generation of oncogenic SEs that drive oncogene transcription in the cells that acquire them22. In this paper, we elaborate on SE structure and activation modes, introduce SE regulatory mechanisms and explain their roles in the initiation and development of tumors, and discuss therapeutic strategies targeting oncogenic SEs.In addition to the typical enhancers, there is another type of enhancer called super-enhancers (SE) or stretch enhancers, which frequently span several kilobases (averaging approximately 9 kb in length). SEs bind with abundant tissue-specific TFs in various cells and master TFs, such as OCT4, SOX2, and Nanog, in embryonic stem cellsrs5 Fig. . Moreove5. The components associated with enhancer activity, such as mediator complexes, chromatin factors, H3K27ac and H3K4me1, histone acetyltransferases, p300 and CBP histone acetyltransferases, RNA pol II, and eRNA, are enriched in association with SEs and exhibit increased chromatin accessibility6. SEs are characterized by the differential binding of tissue-specific TFs. The levels of master TFs such as OCT4, SOX2, and Nanog are similar for typical enhancers and SEs, while Klf4 and Esrrb occupy SEs at significantly higher rates than they occupy typical enhancers6. Compared to typical enhancers, SEs are more frequently bound by terminal transcription factors in the Wnt, TGF-β, and leukemia-inhibitory factor (LIF) signaling pathways, and SE-driven genes are much more sensitive than typical enhancer-driven genes to perturbations in associated enhancer-binding transcriptional regulator genes23. Compared with typical enhancers, individual constituent enhancers of SEs are capable of increasing transcriptional activation levels5. Some evidence indicates that constituent enhancers within an SE interact with each other additively or synergistically and have nonredundant functions in gene regulation11, while the deletion of constituent enhancers may compromise the activity of other SE components11, leading to dysfunction of the entire SE12.An SE is significantly different from a typical enhancer Fig. . Current24  of BRD4 and MED1 can form phase-separated droplets at sites of SE-mediated transcription, and MED1-IDR droplets can compartmentalize and concentrate the transcription apparatus to maintain their separation from nuclear extracts. Thus, it is speculated that SE condensates facilitate the compartmentalization and concentration of the transcriptional components at specific genes through the phase-separating properties of the IDRs in the TFs and cofactors25. Initiation of phase-separated condensate formation has also been associated with the activation domains in the master TFs OCT4 and GCN4 and mediator complexes28. The absence of cohesin leads to the extensive fusion of SEs in the nucleus, which has been implicated in constraining SE–SE interactions29. These reports provide a new model for elucidating transcriptional regulation and explaining the different aspects of SE biology24.The formation of an SE is proposed in a schematic model Fig. . SEs hav24 Fig. . A phase 25 Fig. . Through30, DNase I coupled to high-throughput sequencing (DNase-seq)31, and chromosome conformation capture (3C)32. On the basis of these tools, a series of derived methods, such as ChIP-exo33, FAIRE-seq34, GRO-seq35, ChIA-PET36, ATAC-seq37, STARR-seq38, Hi-C (chromosome conformation capture coupled to sequencing)39, and HiChIP40, have also been applied to identify enhancers/SEs. ChIP-seq is a high-resolution, low-noise, and high-coverage research method for the genome-wide analysis of histone modification, nucleosome localization, and the distribution of transcription factor-binding sites30. ChIP-seq uses histone modification marks to identify molecules presumably associated with SEs, such as transcription factors, the transcription cofactor p300, and the H3K27ac and H3K4me1 histone modifications30. DNase-seq is a biotechnology that uses high-throughput sequencing technology to analyze DNase I hypersensitive sites in enzyme-sensitive regions for enhancer prediction31. The analysis of the mediator cohesin was carried out by ChIP-seq, and the interaction between chromatin can be directly analyzed by 3C, 4C (circularized chromosome conformation capture), 5C (chromosome conformation capture carbon copy), or Hi-C technology, with the genes related to enhancers determined at the same time32. The identification of genome-wide enhancers/SEs enables a more systematic and comprehensive study of enhancers/SEs in biological processes.In SE identification, high-throughput sequencing and next-generation sequencing (NGS) technologies provide particularly powerful tools for genome-wide identification and enhancer/SE prediction. These approaches are primarily based on chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq)41. These specific SEs are known as oncogenic SEs. Oncogenic SEs were first identified in multiple myeloma cells and bind at high density to MED1 and BRD421. H3K27ac ChIP-seq data have been used to identify SEs in 18 human cancer cells, including cells from colorectal, prostate, pancreatic, breast, lung, liver, and cervical cancers and multiple myeloma, CML, T cell leukemia, lymphocytic leukemia, and glioblastoma6. In recent years, several oncogenic SEs have been found in various cancers, including neuroblastoma, small-cell lung cancer, medulloblastoma, esophageal cancer, gastric cancers, and melanoma42.During tumorigenesis, tumor cells acquire specific SEs to promote oncogene expression, which mediates the dysregulation of signaling pathways11. Mechanistically, oncogenic SEs may activate the MAPK signaling pathway to inhibit apoptosis and increase cell proliferation43. SEs also mediate the overexpression of the v-ets erythroblastosis virus E26 oncogene homolog (ERG), resulting in target gene expression to promote cancer development44. In addition, oncogenic SEs increase the expression of CYP24A1, GJA5, SLAMF7, and ETV145. Nucleus translocation of SEs increases MYB expression in adenoid cystic carcinoma (ACC), and SEs promote the expression of TERT in pheochromocytomas and paragangliomas46. CRC-associated SEs are enriched at transcription factor 4 (TCF4) binding sites11. ChIP-seq analysis of CRC cells showed that TCF4 is a terminal TF in the Wnt pathway and occupies the c-MYC locus. TCF4 is a target of Wnt signaling that shows a strong H3K27Ac signal after cancer cells acquire oncogenic SEs11. ChIP-seq analysis of H3K27Ac in MCF-7 cells indicated that the SE-targeted ESR1 gene encodes only estrogen receptor alpha (ERα); furthermore, this oncogenic transcription factor can distinguish cancer subtypes through distinct signaling pathways. In ER-positive breast cancer cells, SE-targeted genes are enriched in processes involved in ERα binding, whereas in triple-negative breast cancer cells, the SE-enriched sites are different from those enriched by oncogenic TFs47. The general function of SEs may involve channeling oncogenic signaling pathways into gene expression programs that are required for sustaining cancer development11.Oncogenic SEs promote cell malignancy by increasing oncogene transcription49. Germline and somatic cells appear to acquire SEs through various mechanisms, including genomic deletions, duplications, translocations, insertions, inversions, and single-nucleotide polymorphisms (SNPs). These genetic alterations can disrupt TF-binding sites in putative SEs, modify SE copy number, and change the genomic space, which lead to SE activation or inhibition, ultimately resulting in the deregulation of nearby target genes16. In summary, novel oncogenic SEs may originate through a variety of mechanisms, including (1) mutations and genomic alterations52, (2) chromosomal rearrangements55, (3) spatial alterations in SE location by 3D chromatin structural changes17, and (4) viral oncogenes57.A large number of genome-wide studies have revealed that disease-related somatic variations occur mainly in noncoding genomes and are often enriched in regulatory regionsTAL1 oncogene produce de novo binding sites for the transcription factor MYB, resulting in SE formation to drive TAL1 expression12. Binding to these de novo sites, MYB recruits CBP/p300 acetyltransferase and TAL1 transcription factor complexes to promote the formation of oncogenic SEs and drive key gene expression in leukemogenesis  gene carries chronic lymphocytic leukemia (CLL) susceptibility. The SNP in the 15q15.1 risk locus generates SEs to regulate the proapoptotic gene BMF and disrupts the binding of the TF RELA to the SE, leading to an increase in BCL2 antiapoptotic function and the promotion of tumorigenesis51 , small insertions of 2–18 bp in the noncoding intergenic region upstream of the sis Fig. . In addis51 Fig. .Fig. 2Va53. One classic example is the inversion of a 9-kb fragment in AML cells that redirects an SE from its role as a GATA2 tumor suppressor to an EV1 oncogene enhancer, leading to the downregulation of tumor suppressors and oncogene activation15. Another example of enhancer hijacking was observed in ACC, a chromosomal translocation repositioning a distal SE to a location proximal to the MYB gene, leading to high MYB expression55. Further 3C analysis confirmed chromatin interactions between the MYB promoter and the aberrantly translocated SE. Furthermore, the translocated SE element was found to contain MYB-binding sites, which were actively bound by MYB itself to form a positive feedback loop, further enhancing MYB expression. Most of the samples from a subgroup of medulloblastomas, especially those with highly expressed growth factor independent 1 family proto-oncogenes (GFI1 and GFI1B), had recurrent structural variations that resulted in the relocation of GFI1 and GFI1B into close proximity of foci occupied by active SEs, initiating oncogenic activity41. A recent example of enhancer hijacking was that of hybrid SEs generated by C19MC–TTYH1 gene fusions that amplified the C19MC–LIN28A–MYCN oncogenic circuit and drove the expression of embryo-restricted DNMT3B6 to promote a primitive malignant epigenetic state in embryonal tumors with multilayered rosettes58. In addition to genomic rearrangements, copy number variations can also result in oncogenic SE activation. Somatic copy number and tissue-specific epigenetic analyses of 12 cancer cell types showed that focal amplification of SEs near KLF5, USP12, PARD6B, MYC, and other cancer-related genes could drive aberrant expression of oncogenes54. Another study also showed that aberrant amplification of the 350–2000 kb genomic region containing the MYCN oncogene in neuroblastoma increased MYCN levels23.Genomic rearrangements, inversions, translocations, and deletions move SEs from their natural genomic context to oncogene regions, leading to SE activation. This phenomenon is known as “Super-enhancer hijacking” and has been reported in various cancers, including acute myeloid leukemia (AML), neuroblastoma, medulloblastoma, and colorectal cancer59. These TAD structures are invariant across diverse cell types and evolutionarily conserved in related species. Chromatin interactions are more frequent in TADs than they are outside TADs. It is now clear that TADs have the function of constraining long-range enhancer–promoter interactions, thereby insulating promoters from distal enhancers and SEs. Both genetic and epigenetic disruption of TAD boundaries allows new genes and enhancers/SEs to occupy spaces associated with enhancer/SE hijacking, altering regulatory contacts and leading to cancer59  with an average size of approximately 1 Mb, and these TADs are the structural and functional units of chromosomes that function to spatially confine transcriptional regulatory circuitsr59 Fig. .IRS4 gene in sarcoma and squamous cancer cells53. In another example, tandem duplications at the IGF2 locus were found to extend over the intervening TAD boundary and to encompass an SE in colorectal cancer cells. This finding showed that the tandem duplication of IGF2 and SE elements in the adjoining TAD led to de novo TAD formation and IGF2 overexpression53. Furthermore, CTCF- and cohesion-binding sites acquire mutations in multiple cancer cell types. For example, CTCF- and cohesin-haploinsufficient mice are predisposed to cancer60. In T-ALL cells, CTCF-binding site disruption leads to the activation of TAL1 and LMO2 by regulatory elements outside of the insulated loops, resulting in T cell transformation61. In addition to mutations in TAD boundaries, epigenetic deregulation has also been demonstrated to be a mechanism for TAD disruption in gliomas62. It has been reported that increased methylation at the CTCF site and reduced CTCF binding result in the partial disruption of the TAD structure, which leads to the activation of PDGFRA (an oncogenic driver)62.TADs are formed by a chromatin loop architecture and are often involve the looping factors CCCTC-binding factor (CTCF) and cohesin. The presence of CTCF-associated boundary elements prevents ectopic contacts and insulates TADs from neighboring enhancers. Recently, a cis-expression structural alteration mapping algorithm was developed as a framework to systematically predict cancer-related gene overexpression. Through this approach, scientists have identified a TAD boundary deletion event that leads to the spreading of active chromatin to an adjacently fused TAD and generates an SE element, which can increase the expression of the MYC, MIR155, IKZF3, and BCL2, facilitating lymphoblastoid cell line growth19. Further studies showed that EBV SEs (ESEs) were transcribed into eRNAs, which facilitated the transcriptional activation of the MYC oncogene. Silencing MYC ESE eRNA inhibited the growth of cells56. A recent study indicated that the high-risk HPV oncoprotein E6 activates cervical cancer SEs to promote tumorigenesis by targeting the histone demethylase KDM5C57. Human lymphotropic virus type I (HTLV-I) frequently initiates adult T cell leukemia/lymphoma (ATLL). The proliferation of ATLL cells depends on BATF3 and IRF4, which cooperatively drive ATLL-specific gene expression. The viral transcription factor HBZ is expressed in all ATLL cases, and HBZ binds to the BATF3 SE and regulates the expression of the BATF3 and MYC genes, thereby contributing to ATLL cell proliferation63.Virus infection induces SE formation to drive high-level transcription of some key genes involved in cell proliferation and survival. Viruses with oncogenic activity include Epstein–Barr virus (EBV), human papilloma virus (HPV), human T cell leukemia virus (HTLV), and human hepatitis B virus. After it infects human B cells, EBV produces oncoproteins, including EBNA2, 3A, 3C, and EBNALP. These oncoproteins activate NF-κB subunits and bind to SEs to drive the transcription of prosurvival and antiapoptotic genes such as 65, TGF-β66, and LIF67, by regulating target genes. Moreover, oncogenic SEs are enriched in TF-binding sites that are associated with cancer signaling pathways11. These findings support the idea that SEs act as platforms for integrating regulatory signals that trigger target gene expression.Some oncogenic SEs activate several pathways, including Wnt11. In a mouse model of basal cell carcinoma (BCC), a cell identity switch was enabled by a mostly permissive chromatin state accompanied by rapid Wnt pathway activation and reprogramming of the associated SEs. Treatment of BCC with Wnt pathway inhibitors reduced the residual tumor burden and enhanced cell differentiation64. Wnt signaling collaborates with chromatin architecture to post-transcriptionally dysregulate the expression of canonical cancer drivers. Recently obtained evidence has revealed that Wnt signaling and AHCTF1 promote oncogenic MYC expression through SE-mediated genes65. The cancer cell-specific gating of MYC is regulated by AHCTF1 , which connects nucleoporins to oncogenic SEs via β-catenin65.The Wnt pathway plays an important role in oncogenic SE mediation of tumorigenesis. A previous study showed that Wnt pathway-related SEs were enriched in binding sites for TCF4  in colorectal cancer cells driven by the oncogenic Wnt pathway66. LIF was identified as an essential factor under the control of a cancer-specific SE. Osteosarcoma cells treated with a LIF recombinant protein displayed upregulated metastasis. UTX is a key activator of LIF transcription. GSK-J4, a UTX inhibitor, impaired SEs at the LIF gene locus, leading to LIF signaling pathway inhibition and subsequent defects 67.In addition, TGF-β and LIF signaling also play vital roles in the development of cancers. TGF-β signaling is particularly important for increased tumor aggression and metastasis. In pancreatic cancer cells, the deletion of an SE in TGFBR2 significantly downregulated the expression of TGFBR2, resulting in impairment of the migration and EMT induced by TGF-β68. In addition, RAS signaling can directly modulate SE function at enhancers by regulating the release of paused transcription. In rhabdomyosarcoma, for example, through the RAF–MEK–ERK MAPK pathway, oncogenic RAS inhibits myogenic differentiation by reducing MYOG expression, which is mediated by ERK2-dependent promoter-proximal stalling of RNA pol II at the MYOG locus. MEK inhibition with trametinib results in the loss of ERK2 at the MYOG promoter and the release of transcriptionally stalled MYOG expression, accompanied by the opening of chromatin and the establishment of SEs at myogenic-specific genes69.In addition to the aforementioned signaling pathways, there are still many others that have important relationships with oncogenic SEs. Mutational RAS activity promotes oncogenic SE formation. The inhibition of aberrant RAS signaling results in the loss of active enhancer marks, SE decommissioning, and decreased gene expression20. Therefore, transcription inhibitors must selectively target oncogenic transcription while inducing only minimal toxicity in normal cells. Currently, there are two main kinds of small molecule inhibitors for targeting oncogenic SEs, BET inhibitors (BETis)70 and cyclin-dependent kinase (CDK) inhibitors71, which can selectively kill cancer cells by inhibiting the transcription of oncogenic SEs. The former are competitive inhibitors of bromodomain and extraterminal domain (BET) family proteins , while the latter mainly target CDK7 and CDK9.Increasing evidence has revealed that SEs play vital roles in tumorigenesis, and oncogenic SEs could be promising therapeutic targets for cancer treatment. Inhibiting SE-driven oncogenic transcription is effective for therapy but presents significant challenges because transcription is a fundamental biological process common to all living cells72. CDK7 is thought to primarily control transcriptional initiation by phosphorylating the RNA pol II C-terminal domain (CTD) at serine 5 (S5), serine 7 (S7), and TFIIE. CDK7 inhibition affects capping, pausing, elongation, and termination mostly through the phosphorylation of CTD and CDK9 in the activated T-loop. Ultimately, CDK7 facilitates the recruitment of the histone methyltransferases SETD1A/B and SETD2 through CTD phosphorylation and/or the activation of CDK9/P-TEFb71. CDK9 has been proven to control elongation. As a major CTD serine 2 (S2) kinase, CDK9 phosphorylates the NELF-E subunit of NELF and the SPT5 subunit of DSIF, allowing the release of RNA pol II that is paused at the proximal promoter to induce productive elongation73.Transcription initiation, pausing, and elongation proceed through ordered activation of regulatory and enzymatic cofactors. Active oncogenic SEs enriched with H3K27ac marks can be recognized by BRD4, which interacts with the mediator coactivator complex, leading to the stepwise recruitment of CDK7  and CDK9 in vivo and in vitro have been observed74. Some of them showed promising results in models established in vitro but have had largely disappointing results in clinical trials75. The first-generation CDK inhibitors, referred to as “pan-CDK” inhibitors, exhibited low affinity for CDKs and high cytotoxicity in vivo75. For example, flavopiridol, the most extensively investigated CDK inhibitor, can induce cell cycle arrest in the G1 and G2 phases in certain contexts and induce a cytotoxic response. Although it has broad-spectrum in vitro activity, it was less active in vivo. Tumor lysis syndrome was reported in approximately 40% of CLL patients treated with flavopiridol76. Thus, second-generation CDK inhibitors were developed with the aim of increased selectivity. BET inhibitors show a favorable activity profile, and hematologic (mainly thrombocytopenia) and nonhematologic adverse events  are reversible upon treatment interruption70.Cancer cells hijack SEs to drive oncogene transcription, continuously promoting cell survival and proliferation. This aberrant SE-driven transcriptional event provides a new avenue for anticancer therapy. Thus, several small molecule inhibitors have been developed, and their potential preclinical effects 23. Treatment with the BETi JQ1 led to preferential loss of the BRD4 associated with SEs and consequent transcriptional elongation defects21. In other studies, heightened sensitivity was potentially attributed to at least two complementary mechanisms: (1) the cooperativity of constituent enhancers and (2) the short half-lives of oncogenic TFs77. SEs enriched with master TFs maintain TF expression via feedforward loops, and SE depletion may result in reduced transcription. In MYCN-amplified neuroblastoma, THZ1 treatment led to preferential downregulation of SE-associated genes, including MYCN, thus inhibiting the autoregulated suppression of MYCN-driven global transcription amplification23.Previous studies have shown that SE-driven genes have a higher sensitivity to chromatin/transcriptional regulator inhibition than traditional enhancer-driven genes78. Chromosomal translocation, involving the NUT gene fusing to the BET gene BRD4, creates an in-frame BRD4–NUT oncogene, resulting in NUT midline carcinoma. Silencing of the BRD4–NUT fusion gene with BETis prevents the differentiation and proliferation of NUT carcinoma cells78. In preclinical models of AML and multiple myeloma, BETis (I-BET151 and JQ1) were reported to have a strong inhibitory effect on tumor progression80. Recent reports have also demonstrated that novel BETis have clear preclinical antitumor activity in a variety of solid tumors and hematologic cancers81. BETis target bromodomains and directly affect major transcription factors and key tissue- or cancer-specific genes, such as MYC82, AR and TMPRSS2–ETS fusion genes83, TERT80, BCL280, FOSL184, E2F285, ITK86, IL7R82, CDK685, IRF487, and IKZF187. In the BET family, BRD4 is considered to be an excellent target of BETis because of its important role in transcription. BETi-sensitive genes are associated with adjacent SEs. The TFs YAP and TAZ play crucial roles in the recruitment of BRD4 to SEs, and the enhancers/SEs occupied by YAP/TAZ show a preferential loss of BRD4 and sensitivity to JQ1 treatment88. Several BETi compounds  midline carcinoma models89, with more than one-half of 1000 cancer cell lines showing IC50 values for THZ1 < 200 nM, leading to global transcriptional downregulation at high doses6. It was initially shown that sensitivity to THZ1 was conferred by targeting SE-driven RUNX1 in T-ALL89. Further reports revealed that THZ1 can selectively target SE-driven transcriptional processes in preclinical cell and mouse tumor models, such as those of triple-negative breast cancer (TNBC)47, MYCN-amplified neuroblastoma23, and small-cell lung cancer77. In AML preclinical models, CKIα inhibitors targeting CDK7 and CDK9 augment CKIα-induced p53 activation, suppress SE-driven oncogenes, and induce apoptosis90. LDC067 is one of the first compounds found to have CDK9 selectivity, and it has been proven to have 55–230-fold greater selectivity for CDK9 than for the other CDKs91. Treatment with LDC067 can inhibit transcription and induce apoptosis in HeLa cells and primary AML blasts91. CDK8 has been implicated in the transcription driven by SE-controlled genes. Cortistatin A exhibits high affinity for CDK8 and CDK19 and has antiproliferative activity against multiple leukemia cell lines in vitro and in AML mouse models92. THZ531, a CDK12/13 inhibitor, synergizes with PARP inhibitors in models of Ewing sarcoma in vitro and in vivo93. Although structural and biological validation of these inhibitors has not been completed, their suppression of SE-associated transcriptional regulators provides a novel approach for targeting oncogenes.Historically, only three CDKs, CDK7, CDK9, and CDK8, were thought to be involved in the regulation of the transcription cycle. The discovery of small molecule inhibitors has provided another potential approach for targeting oncogenic SEs. In addition to pan-CDK inhibitors, there are many inhibitors selectively targeting CDK7 or CDK9, such as THZ1 and LDC067 (Table 94 and THZ195 has been reported in many preclinical models. Therefore, future exploration of SEs will focus on clarifying how SE components regulate SE function and how to better utilize SEs in targeted therapy.SEs play vital roles in transcriptional regulation and have pathogenic ability, especially oncogenic ability, in a context-dependent manner. Despite compelling evidence that SEs regulate cell identity genes, there is still no clear understanding of how SEs play regulatory roles. NGS technology recently provided a new means of mapping the genomic landscape in greater detail. ChIP-seq data and bioinformatics algorithms are being utilized to identify genomic proximity and assign SEs to target genes. However, knowledge of the intrinsic structure of SEs and of their interactions with target genes in three-dimensional space is still lacking; therefore, a comprehensive approach involving 5C and functional screening is needed. Other new technologies, including Hi-C, ATAC-seq, Hi-ChIP, CUT&Tag, the CRISPR genome-editing tool, and single-cell sequencing technology, are also being used to reveal the mechanisms of SEs that regulate transcription and oncogenesis. From a therapeutic standpoint, the discovery of SE targeting by JQ1 led to the development of first- and second-generation BET inhibitors. Since BETs were discovered, small molecule inhibitors targeting individual SE components have shown great promise for clinical application. However, resistance to single-agent treatments of BETis


Pin1 regulates many biological processes and is also involved in the development of human diseases, like cancer and neurological diseases. Many Pin1 substrates are transcription factors and transcription regulators, including RNA polymerase II (RNAPII) and factors associated with transcription initiation, elongation, termination and post-transcription mRNA decay. By changing the stability, subcellular localization, protein-protein or protein-DNA/RNA interactions of these transcription related proteins, Pin1 modulates the transcription of many genes related to cell proliferation, differentiation, apoptosis and immune response. Here, we will discuss how Pin regulates the properties of these transcription relevant factors for effective gene expression and how Pin1-mediated transcription contributes to the diverse pathophysiological functions of Pin1.Pin1 is a peptidyl-prolyl  There are three distinct families of PPIases: cyclophilins (CyPs), FK506-binding proteins (FKBPs), and parvulins (Prolyl isomerases (PPIases) catalyze the arvulins . Pin1 bearvulins . Becausearvulins . Pin1-mearvulins . Aberranarvulins . While Parvulins , highligThe temporal and spatial eukaryotic gene expression is a highly orchestrated molecular event that is regulated at multiple levels and is responsible for the distinct cellular responses and functions. The multi-level regulation includes the signal-dependent activation of tissue-specific transcription factors, the remodeling of chromatin on promoters and enhancers, the pausing and release of RNAPII, the post-transcriptional processing of mRNA, and the translational regulation . Post-trEukaryotic gene expression is regulated by genomic enhancers and promoters that are recognized by various tissues specific DNA binding transcription factors . Pin1 reA key regulatory step in transcription is the nucleocytoplasmic shuttling of transcription factors, which are synthesized in the cytoplasm and need to be transported into the nucleus, where they bind to the promoters or enhancers to activate gene expression in response to different intra- or extracellular stimuli . A numbeThe nucleocytoplasmic shuttling of β-catenin is regulated by its association with APC (the adenomatous polyposis coli), which contains two active nuclear export sequences (NES) for the nuclear export of β-catenin . Pin1 rep27kip1 , which is essential for T cell activation . Upon T Another major mechanism of Pin1-mediated transcription factor regulation is through ubiquitin-mediated protein degradation . Pin1 caThe tumor suppressor p53 is a key transcription factor regulating cellular pathways such as DNA repair, cell cycle, apoptosis and senescence and is a pivotal gatekeeper against cancer onset and progression . A key rBax, Pig3, and p53AIP1  are two essential protein domains that help define a transcription factor. Pin1 is able to modulate both the DNA binding and transcriptional activity of transcription factors. Pin1 binds to the N-terminal Ser118-Pro motif in the intrinsic activation function 1 (AF1) domain of ERα . BindingWhile changing the DNA binding affinity would affect the transcriptional activity of a transcription factor, Pin1 can also regulate the transcriptional activity by directly binding to the TADs of the transcription factors  Table 1Table 1. Pin1 has also been shown to regulate the transcriptional activity of glucocorticoid receptor (GR) by binding to the TAD. Binding of Pin1 to the phosphorylated Ser203 and Ser221 within the TAD of GR enhances the transactivation of GR . InteresPin1 also regulates the transcriptional activity of HIF-1a. Pin1 interacts with p42/p44 MAPK-mediated phosphorylation of HIF-1a at Ser641 and Ser643 of the transactivation region and promotes its conformational changes for the efficient expression of HIF-1a genes, including VEGF, GLUT1 and PGK1 . It has Transcription factors often recruit transcription co-activators for their full transcriptional potential and biological functions . Pin1 reSteroid receptor-mediated transcription requires the ligand-dependent association of receptors with steroid receptor coactivator 3 (SRC-3) . Pin1 inPin1 also regulates CREB co-activator CRTC2  by binding to phosphorylated CRTC2 at Ser136, which locates within its nuclear localization signal . DiffereA recent study also demonstrates that transcriptional co-activator PRDM16 is negatively regulated by Pin1 . PRDM16 In the Notch1 signaling pathway, activation of CSL  -target genes requires the co-activation function of the intracellular domain of the notch protein (NICD), which is released from the membrane-bound Notch1 protein processed by the γ-secretase . NICD haIn addition to the regulation of transcription co-activators, Pin1 can control gene expression by targeting transcription co-repressors. Silencing mediator for retinoic acid and thyroid hormone receptor (SMRT) is a transcriptional corepressor that participates in diverse signaling pathways and human diseases . Pin1 in1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7 from yeast to human. The proline-rich CTD functions as a docking platform for numerous transcription regulatory proteins involved in transcription initiation, elongation, termination and post-transcription processing  of Rpb1, the largest subunit of RNAPII, which consists of 26-52 tandem heptapeptide repeats with the general consensus sequence Tyrocessing . The CTDocessing . During ocessing . Phosphoocessing . Differeocessing .cis-trans configurations, and thus expands the complexity of the CTD code signature by providing a scaffold for the recruitment of a variety of chromatin and RNA processing factors , Pcf11 (3′ end processing factor), Scp1 (CTD phosphatases) and SCAF8 (splicing factor) bind to phosphorylated CTD with the prolines in trans configuration  and NELF (negative elongation factor) at promoter-proximal pausing sites . CDK9, aPin1 binds to the hSpt5 subunit of DSIF via its phosphorylated carboxyl terminal part 2 (CTR2) domain by Cdk9 . The CTRcis-trans isomerization, which leads to enhanced CDK9 binding to Brd4 and enhanced recruitment of CDK9 to a subset of promoters of Brd4-mediated tumor-promoting genes, including c-MET and MMP9  to a cis-acting element in the 3′-UTR region of PTH mRNA  are small, endogenous non-coding RNAs of 18–24 nucleotides in length and play significant roles in the regulation of gene expression and participate in numerous cellular processes, including cell cycle arrest, cell proliferation and death . miRNAs e-miRNAs . As a ree-miRNAs . Down-ree-miRNAs . TherefoPin1 is involved in almost every step of gene expression, from activation of transcription factors to transcription initiation and termination by targeting a host of transcription factors and transcription regulatory proteins . In manyEpigenetics plays crucial roles in the regulation of gene expression by post-translational modifications of histone proteins and methylation of DNA . EpigenePin1-catalyzed isomerization and the subsequent protein conformational change might accounts for all the functional changes of Pin1 substrates. Protein conformational change often leads to the engagement or disassociation of the interacting proteins. The alteration of protein stability after conformational change is largely affected by the changes in the accessibility to E3 ligases. Conformational change can also alter the accessibility of the NLS or NES to the nuclear import or export machinery, affecting the nucleocytoplasmic shuttling of the transcription factors. However, how Pin1 directly regulates the DNA binding activity via protein conformational change is not quite clear. Although it is generally believed that binding of Pin1 leads to the conformational changes of its substrates, many studies failed to include the isomerase inactive mutant of Pin1 , an issuPin1 could have completely opposite effects on its substrates. Pin1 increases or decreases the stability of transcription factors in a similar phosphorylation and isomerization-dependent manner . Pin1 re–/– homozygous cross breeding was much lower that that of heterozygous mice, indicating a critical role of Pin1 in gene expression and cell division (–/– fibroblasts grow normally but with defect in re-entering the cell cycle from G0 arrest (in vitro or in cultured cells with recombinant or overexpressed Pin1. The significance of these biochemical studies in gene regulation would be strengthened if similar regulatory mechanism would be confirmed in Pin1–/– or Pin1 conditional knockout mice in combination with mouse disease models. A great example is demonstrated in a recent study of the identification of Pin1 as a regulator of thermogenesis by targeting PRDM16 for degradation (While Pin1 is able to regulate gene expression at various levels, it is possible that Pin1 is not absolutely required for the transcription of whole genome. The success rate of Pin1division . Consist0 arrest . Pin1-meradation .While a great deal is known about how Pin1 regulates the activation transcription factors for gene expression in response to stimuli, much less is known about how Pin1 regulates the transcription machinery for the spatiotemporal control of gene expression except that Pin1 helps to construct the CTD code. It also remains to be determined whether these regulations on RNAPII and the associated transcription is a general mechanism that can apply to all genes or whether it is only a gene-specific and cell-specific phenomenon. Furthermore, the subcellular localization, the expression levels and the activity of Pin1 are subject to change in response to stimulation and in diseases conditions , adding XH drafted the manuscript. L-FC revised the manuscript. XH and L-FC reviewed and modified the manuscript. All authors agreed on the final version.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.


The relationship between transcription and the 3D chromatin structure is debated. Multiple studies have shown that transcription affects global Cohesin binding and 3D genome structures. However, several other studies have indicated that inhibited transcription does not alter chromatin conformations.We provide the most comprehensive evidence to date to demonstrate that transcription plays a relatively modest role in organizing the local, small-scale chromatin structures in mammalian cells. We show degraded Pol I, Pol II, and Pol III proteins in mESCs cause few or no changes in large-scale 3D chromatin structures, selected RNA polymerases with a high abundance of binding sites or active promoter-associated interactions appear to be relatively more affected after the degradation, transcription inhibition alters local, small loop domains, as indicated by high-resolution chromatin interaction maps, and loops with bound Pol II but without Cohesin or CTCF are identified and found to be largely unchanged after transcription inhibition. Interestingly, Pol II depletion for a longer time significantly affects the chromatin accessibility and Cohesin occupancy, suggesting that RNA polymerases are capable of affecting the 3D genome indirectly. These direct and indirect effects explain the previous inconsistent findings on the influence of transcription inhibition on the 3D genome.We conclude that Pol I, Pol II, and Pol III loss alters local, small-scale chromatin interactions in mammalian cells, suggesting that the 3D chromatin structures are pre-established and relatively stable. Drosophila melanogaster, transcription predicts chromatin organization [The relationship between transcription and 3D chromatin structures is one of the most fundamental questions in the postgenomic era –4. Mountnization –14, suggD. melanogaster genome has a much higher gene density than the mammalian genome, inhibiting D. melanogaster transcription significantly alters chromatin interactions both within and between domains, but has very little effect on the 3D topology of TADs [Recent studies combining knockouts and inhibition showed that transcription could relocate Cohesin over mammalian chromatin , indicat of TADs –14. TherThe inhibition of Pol II transcription during the early development of mouse embryos did not affect TAD structures , 18, butAn unchanged pattern after transcription inhibition in mammalian cells is usually based on the aggregate analyses of all chromatin loops , 21–23. Pol I, Pol II, and Pol III are three distinct DNA-dependent RNA polymerases that function together with thousands of transcriptional and chromatin regulators to synthesize rRNA, mRNA, and tRNA, respectively , 27–33. Here, we specifically degraded the largest essential subunits of Pol I, Pol II, and Pol III in murine embryonic stem cells. Large-scale chromatin interactions remained unchanged after Pol II depletion, while small, specific regions were more affected. Chromatin organization re-forms during mitotic exit in the absence of Pol II, supporting the pre-established model of the 3D genome. Therefore, we identified loops with bound Pol II but not with Cohesin or CTCF, and with both our findings and public data sets from different laboratories, we found that they were largely unchanged after transcription was inhibited. Additionally, acute depletion of Pol I and Pol III also did not result in changes in the large-scale chromatin structures, but a fraction of local, small-scale chromatin interactions seemed to be more affected. These results collectively demonstrate that RNA polymerases play a role in organizing local, small-scale 3D chromatin landscapes in mammalian cells. Interestingly, longer-term depletion of Pol II (≥ 6 h) reduced chromatin accessibility and Cohesin occupancy over chromatin. We propose that immediate transcription inhibition does not affect large-scale 3D chromatin structures but that indirect effects of transcription inhibition do affect them.To investigate the roles of RNA polymerase proteins in 3D chromatin organization, we subjected mESCs to RNA polymerase degradation with degron technology. RNA polymerase-mediated transcription can be inhibited with different inhibitors; however, they either do not have the capacity to distinguish different RNA polymerases or are specific to a single RNA polymerase  and only work at high concentrations, thus requiring a long treatment duration , 36. TheWe confirmed that the RPA1, RPB1, and RPC1 proteins, as mAID-GFP fusion proteins, could be depleted in murine embryonic stem cells. To determine the time point for Pol II loss of function, we first induced OsTIR1 expression for 12 h, added auxin, and then performed western blot analyses at different time points. Indeed, rapid degradation can be achieved for a specific RNA polymerase after the addition of auxin  [Previous studies on the relationships between transcription and 3D genome usually focused on Pol II –41; heren5 Hi-C) , which cPol I, Pol II, and Pol III degron cells were subjected to degradation and then were collected for BAT Hi-C analyses. Two biological Hi-C replicates for both untreated cells  and Pol II-degraded cells  were obtained  signals for the loop domains and those of their upstream and downstream 100-kb windows, in decreasing order. Indeed, the top GRO-Seq signal-associated loop domains were smaller and showed a more significant decrease in the frequency of chromatin interactions than the bottom GRO-Seq signal-associated domains in the three different data sets Fig. c. These Bclaf1 locus  analyses for Pol II would be expected to show that Pol II is preferentially associated with short-range chromatin loops . We reancus Fig. e. These Our RNA polymerase degron system degraded Pol II within 1 h  the specific transcription inhibition by the degradation of Pol I, Pol II, and Pol III results in few or no changes to large-scale 3D chromatin structures, as assayed by Hi-C Fig. a–c, f; a–e; (3)  the specMounting evidence has shown a strong correlation between transcription and 3D genome structures , 44, 69,There are many genome architectural proteins  , 70–74, We showed that transcription may indirectly affect 3D genome structures via Cohesin-chromatin binding, and there are many other possibilities. For example, a previous imaging study showed that Pol II transcription constrains the dynamics of chromatin in the nucleus . After tPrevious studies showed that structural factors such as CTCF- and Cohesin-mediated chromatin interactions create frameworks for loop domains and constrain the high-frequency chromatin interactions within them , 75, 76.Our study provides the first comprehensive analyses of the roles of Pol I, Pol II, and Pol III proteins in 3D chromatin organization in mESCs. We demonstrate that RNA polymerases play a relatively modest role in organizing local, small-scale 3D chromatin structures, and we propose that transcription does not regulate the large-scale 3D genome directly but is able to regulate it indirectly. Our study explains the confusing findings on transcription inhibition on the 3D genome, which were the result of difficulties in separating the direct and indirect effects of transcription inhibition. Our study also implies that transcription and 3D chromatin organization are largely uncoupled in the nucleus. Since transcription is not essential for the 3D genome, further studies of genetics, epigenetic features of the 3D genome, and noncoding RNAs may reveal new insights into the 3D genome organization.For more details, see Additional file l-glutamine, β-mercaptoethanol, nonessential amino acids, and penicillin/streptomycin at 37 °C with 5% CO2. These cells were harvested for downstream experiments after two passages of MEF feeders, as previously described [The V6.5 mouse ES (mES) cell line, derived from the inner cell mass (ICM) of C57BL/6 × 129/sv crossed mice, was a gift from R. Young of the Whitehead Institute. These mES cells were cultured with irradiated mouse embryonic fibroblasts in DMEM-KO containing 15% FBS, leukemia inhibiting factor (LIF), escribed . These cFor transfection, plasmids were prepared using a HiPure Plasmid EF Mini Kit  and not linearized before transfection. For parental degron cell line construction, a Tir1-expressing cassette and Rosa26-targeting sgRNA were transfected into the V6.5 wild-type mESC line in equimolar amounts using FuGENE HD (Promega) following the manufacturer’s protocol. After 2 days, the cells were passaged and grown for 1 week in the presence of 5 μg/ml puromycin until single colonies could be collected. Clonal lines were assessed for their ability to express Tir1 after induction with 1 μg/ml tetracycline. To insert the mAID-EGFP cassette into the C-terminus of the Rpa1/Rpb1/Rpc1 subunits, a Tir1 stable-expressing clonal cell line was transfected with an mAID-EGFP targeting vector and sgRNAs. Neomycin was added to the medium at 100 μg/ml, and homozygous clonal lines were selected after genotyping. These clonal lines were assessed for their ability to undergo auxin-inducible degradation of each RNAP-mAID-eGFP and to show expression levels similar to that of RNA polymerases in wild-type mES cells. The clones degraded with the maximum efficiency were chosen for the following assays.Hi-C was conducted as BAT Hi-C .The bridge linker used for BAT Hi-C was synthesized from the forward strand, 5′-/5Phos/CGC GAT ATC/iBiodT/TA TCT GAC T-3′, and reverse strand, 5′-/5Phos/GTCAGATAAGATATCGCGT-3′. For linker preparation, we dissolved two reverse complementary single-strand linkers in 1 × TNE annealing buffer at a concentration of 100 μM, and a dTTP overhang was maintained at the 3′ ends of both strands, and then, an annealing mixture was prepared at an optimized F/R molecular ratio , heated to boiling and cooled slowly to room temperature. Cells were dissociated and resuspended in freshly made 1% formaldehyde (methanol free) at a volume of 10 ml for every 6 million cells and incubated at room temperature for 10 min. Glycine was added to a final concentration of 125 mM to quench the formaldehyde, and then, the mixture incubated at room temperature for 5 min. The cells were pelleted at 2500 rpm for 5 min at 4 °C, and the supernatant was removed. Then, the pelleted cells were stored at − 80 °C or processed for use using the Hi-C protocol.2O, 6 μl of T4 DNA ligase (NEB # M0202), 12 μl of 10 mg/ml BSA, and 2 μl of bridge linker (200 ng/μl)), and rotated at 700 rpm at room temperature for 6 h. The nuclei were lysed gently with 500 μl of ice-cold NP-40 lysis buffer on ice for 5 min. The cell lysate was layered on top of 2.5 volumes of a sucrose cushion consisting of 24% (wt/vol) sucrose in NP-40 lysis buffer. This sample was centrifuged at 12,000 rpm for 10 min at 4 °C to isolate the nuclei pellet. The nuclear pellet was washed once with 1× PBS/1 mM EDTA. The nuclear pellet was resuspended gently with 0.5 ml of glycerol buffer followed by the addition of an equal volume of nuclei lysis buffer on ice for 2 min. The sample was centrifuged at 12,000 rpm for 2 min at 4 °C to isolate the chromatin pellet. The chromatin pellet was washed twice with PBS/1 mM EDTA.The cell pellet was resuspended in 550 μl of lysis buffer , incubated on ice for 20 min, and spun at 5000 rpm for 5 min at 4 °C. Then, the supernatant was discarded, and the nuclei were washed twice with 1× NEB CutSmart buffer. Then, the pellet was gently resuspended in 50 μl of 0.5% SDS and incubated for 10 min at 62 °C. After heating, 140 μl of H2O and 25 μl 10% Triton X-100 were added, mixed gently, and incubated for 15 min at 37 °C. Next, 25 μl of 10× NEB CutSmart buffer (NEB # B7204S) and 100 U of AluI (NEB Cat # R0137 L) were added to digest chromatin in less than 12 h at 37 °C and rotated at 700 rpm. AluI was inactivated by incubating the sample for 20 min at 62 °C. Nuclei were collected and washed twice with 1× NEB buffer 2. The nuclei were resuspended in 400 μl of Klenow (3′-5′ exo-) solution (40 μl f NEB buffer 2 (NEB # B7002S), 8 μl of 10 mM dATP (NEB # N0440S), 40 μl of 10% Triton X-100, 304 μl of H2O, and 8 μl of Klenow (3′-5′ exo-) (NEB Cat # M0212L)), and incubated at 37 °C for 1 h while rotating at 700 rpm. The nuclei were collected and washed twice with 1× T4 DNA ligase buffer and resuspended in 1200 μl of proximity ligation solution (120 μl of T4 DNA ligase buffer (NEB # B0202S), 120 μl of 10% Triton X-100, 940 μl of HThe supernatant was removed, and then, 70 μl of 10× lambda exonuclease buffer, 6 μl of lambda exonuclease (NEB # M0262S), 6 μl of exonuclease I (NEB # M0293S), and 618 μl of H2O were added. The mixture was rotated at 700 rpm at 37 °C for 1 h. The chromatin was pelleted at 12,000 rpm for 2 min at 4 °C. The chromatin pellet was washed twice with PBS/1 mM EDTA. To reverse the DNA cross-linking, the chromatin was resuspended in 1370 μl of digestion buffer ) and incubated for 30 min at 55 °C, and then, 130 μl of 5 M NaCl was added, and the mixture was incubated overnight at 65 °C. Genomic DNA was thoroughly extracted with an equal volume of phenol/chloroform/isoamyl alcohol and centrifuged at 12,000 rpm for 15 min at 4 °C. The aqueous top layer was poured into a new tube, and 1/10 vol of 3 M NaAc and 2 vol of 100% ethanol were added. The DNA was recovered by centrifugation at 12,000 rpm for 20 min at 4 °C. The pellet was rinsed with 75% ethanol, the ethanol was decanted, and the pellet was air-dried. The DNA pellet was dissolved in 200 μl of 10 mM Tris-HCl, pH 8, for 15 min at 37 °C. The DNA was sonicated by a Biorupter with the following settings: high energy, 30-s working time, 60-s intervals, and 2 cycles. The DNA on 2% agarose gel was measured and found to be of the expected length 0.2–2 kb.A total of 30 μl of 10 mg/ml Dynabeads M280 streptavidin (Thermo Fisher Scientific # 11205D) was added to a clean 1.5-ml microfuge tube and washed three times with 600 μl of 1× Tween wash buffer , one time with 2× BB . The beads were separated using magnets, and the supernatant was discarded. The beads were resuspended in 200 μl of 2× BB, and then, the beads were transferred to a sample tube. The beads were incubated with a sheared DNA sample for 15 min at RT with rotation and separated with magnets, and then, the supernatant was discarded. The beads were washed five times with 2× SSC/0.5% SDS and three times with TE buffer, and then, they were transferred to a fresh tube after the last wash.2O adjusted to a total volume of 50 μl. The reaction was incubated at 55 °C for 10 min and then at 10 °C for 10 min in a PCR machine. The samples were placed on a magnet, and the supernatant was removed. The beads were washed twice with 2× SSC/0.5% SDS, twice with TE buffer, and transferred to a fresh tube after the last wash. The beads were resuspended with 30 μl of TB buffer. For PCR amplification with the following master mix: 10 μl of DNA library-coated beads, 10 μl of H20, 15 μl of NPM mix, 5 μl of PPC PCR primer, 5 μl of Index Primer1 (i7), and 5 μl of Index Primer2 (i5) (Illumina # FC-121-1030), and the total volume was adjusted to 50 μl. The following PCR program was used: 72 °C for 3 min; 98 °C for 10 s, 63 °C for 30 s, 72 °C for 50 s for 10 cycles, and 72 °C for 5 min. The PCR product was run on a 2% agarose gel, and 300–500-bp DNA was purified with a Magen gel purification kit (Magen # D2111-03). The purified DNA was subjected to HiseqXten 150 × 150 pair-end sequencing.The beads were washed in 100 μl of 1× TD buffer. The following master mix was prepared: 50 ng of proximity ligated DNA, 25 μl of tagmentation buffer, 5 μl of transposase enzyme (TDE1) (Illumina # FC-121-1030), and 20 μl of HHiChIP was carried out based on a modified previously published protocol . The celThe Ocean-C method was modified based on a previously described protocol . The celThe ChIP procedure was modified based on a previously published protocol . After cAll Hi-C, HiChIP, and Ocean-C libraries were sequenced on an Illumina HiSeq 10X instrument (150-bp paired-end mode). First, the bridge linker sequences were trimmed using trimLinker tool in the ChIA-PET2 software , and paiN = 833)  meta-feature analysis by pilling up individual submatrices into an average matrix. In general, this method is similar to that of the meta-gene analysis that is commonly performed for acquiring ChIP-Seq data. For the meta-TAD analysis, we first created a union set of TAD coordinates identified in both the untreated and treated samples and then extracted 25-kb resolution Hi-C O/E maps for all the TADs and their neighboring regions, chosen to be of the same length as the TAD, after rescaling each TAD to a 90 × 90 submatrix. For display and visual consistency with the contact probability, we set the background levels of all interaction matrices to 0–1 and chose a proper color palette in ggplot2. Differences between the untreated and treated Hi-C data sets were computed by subtracting the normalized O/E signal. To understand whether high-density RNAP-binding hotspots reshaped the genome to establish a local, small-scale structural change, we performed paired-end spatial chromatin analysis (PE-SCAn), an algorithm combining ChIP-Seq data with Hi-C data . At 25-kp value threshold of 10−5 to ensure high confidence [Pooled ChIP libraries were prepared and sequenced at a sequencing depth of ~ 90–100 million reads per sample. Raw fastq reads were trimmed by trim_galore and mapped to the mouse reference genome mm10 using bowtie2 (version 2.3.2)  with thenfidence . The peanfidence . For subAdditional file 1: Fig. S1. Characterization of the RNAP degron cell lines and uncropped images for western blot. Fig. S2. Quality controls for the Pol I, Pol II, and Pol III degradation Hi-C data sets. Fig. S3. Loop strength analyses of high-resolution chromatin interactions after Pol II depletion. Fig. S4. Quality controls for the Pol II degradation time course Hi-C data sets. Fig. S5. Examples of super-enhancer regions mildly enhancing chromatin interactions across loop domains after Pol II depletion. Fig. S6. Pol II depletion has relatively modest effect on promoter-associated chromatin interactions. Fig. S7. long-term depletion of Pol II leads to general disruption of chromatin accessibility.Additional file 2: Table S1. RNA-Seq RPKM values for the Pol II untreated and degron cells. Table S2. Summary statistics of the Hi-C, HiChIP, and Ocean-C data. Table S3. Hi-C identified TADs (contact domains). Table S4. HiCCUPS identified loop domains. Table S5. Pol II PLAC-Seq high confidence interactions identified using the Origami pipeline. Table S6. RNAP ChIP-Seq peaks. Table S7. PCR primer sequences used in this study. Table S8. List of data sets used in this study.Additional file 3. More method details.Additional file 4. Review history.


The ubiquitin‐proteasome system is an essential regulator of ARMC5, which serves as a new tumour suppressor protein for inhibiting meningiomas and hereditary adrenocortical tumorigenesis. However, the precise mechanism for the deubiquitination of ARMC5 is still not fully understood. A Western blot analysis of ARMC5 was performed and showed that the expression of ARMC5 was decreased in the renal cancer cell tissues and lines. By screening a deubiquitinase library, we identified USP7 as a potential ARMC5 associated deubiquitinase. In this paper, we demonstrated that there was an interaction between USP7 and ARMC5 in vivo and in vitro. Employing the overexpression and knockdown assay indicated that USP7 could greatly increase the steady state of ARMC5 through the ubiquitin‐proteasome pathway and regulate ARMC5 ubiquitination. Moreover, USP7 altered cell cycle G1/S phases and regulated renal cancer cell proliferation by targeting ARMC5. Together, these results suggest that USP7 plays an important role in the RCC proliferation through modulating ARMC5 stability. We identified USP7 as an ARMC5 associated deubiquitinase by screening the library. Meantime, there was an interaction between USP7 and ARMC5 in vivo and in vitro directly. We confirmed USP7 as the first deubiquitylase that stabilized protein ARMC5 by inhibiting the degradation of ARMC5. Silencing of USP7 enhanced ARMC5 ubiquitination and accelerated cell cycle G1/S, subsequently promoted RCC cell proliferation. Together, we clarify that USP7 plays an important role in the regulation of RCC proliferation through modulating ARMC5 stability.22.1The primary and secondary antibodies were purchased from Abcam and Santa Cruz Biotechnology. All experiments were performed in accordance with the Guidelines of the First Affiliated Hospital of Henan Polytechnic University , and experiments were approved by the ethics committee at the First Affiliated Hospital of Henan Polytechnic University. Informed consents were obtained from human participants of this study. The renal carcinoma patients’ and adjacent normal tissues frozen section were, respectively, collected from the Pathology Department, the Second People's Hospital of Jiaozuo City.2/95% mixed ambient air.786‐O and 769‐P (Procell) were cultured in RPMI 1640  supplemented with 10% FBS . A498, ACHN and HEK293T cells (Procell) were cultured in DMEM  supplemented with 10% FBS. Cells were incubated at 37°C in 5% CO2.2Briefly, the processed cells were incubated in RIPA buffer (Thermo Fisher Scientific), and then, the centrifuged protein lysates were separated employing the SDS/PAGE. Separated proteins were transferred to a immobilon‐PVDF Transfer Membrane (Millipore), which was blocked with 5% skim milk for 1 hour at room temperature. Then, the membranes were incubated overnight with the respective primary antibodies: anti‐USP7 , anti‐ARMC5 , anti‐Flag , anti‐Myc , anti‐PCNA , anti‐GAPDH  and anti‐Ub , followed by incubation in secondary antibody for 1 hour at room temperature. The bands were detected using the ChemiDoc XRS imaging system (Bio‐Rad) using ECL detection reagents (Thermo).Alternatively, the centrifuged protein lysates were incubated with primary antibody pre‐absorbed protein G sepharose beads at 4°C for overnight. Then, the incubation solution was washed with the RIPA buffer and boiled in 2× loading buffer for 10 minutes. Protein expression was subjected to Western blot.2.3USP7‐siRNA#1:5′‐UGUAUCUAUUGACUGCCCU‐3′.USP7‐siRNA#2:5′‐CGUGGUGUCAAGGUGUACU‐3′scrambled siRNA 5′‐UUCUCCGAACGUGUCACGU‐3′USP7‐shRNA#1:5′‐TGTATCTATTGACTGCCCTTT‐3′USP7‐shRNA#2:5′‐CGTGGTGTCAAGGTGTACT‐3′ARMC5‐shRNA#1:5′‐UCUUUAGAUCGUCAUCUUGGA‐3′ARMC5‐shRNA#2:5′‐UGUUUAAUCUCAAUUCCAGAG‐3′Negative‐control siRNA (si‐NC) and USP7‐specific siRNA (si‐USP7) were synthesized by Sangon Biotech.Transfections were performed using Lipofectamine‐2000 transfection system  as described in the instruction.2.4pGEX‐4T‐1‐USP7 was constructed according to the manufacturer's instructions. pGEX‐4T‐1‐USP7 was transfected into BL21 cells and then was induced by ispropyl b‐D‐1‐thiogalactopyranoside (IPTG). Bacterially expressed GST‐USP7 was retained on glutathione Sepharose beads  and incubated for 2 hours at 25°C with biosynthesis His‐ARMC5. Then, the complexes’ incubation were eluted at 100°C and subjected to Western blotting.2.5The full‐length USP7 open reading frame was amplified from HEK293T by RT‐PCR and cloned into the described vectors to construct the Myc‐USP7 recombinant vector. Cells were transfected with Myc‐USP7, Myc‐TUD and Myc‐TRAF using Lipofectamine 2000 , according to the manufacturer's instructions.2.6Cells were treated with cycloheximide (50 μg/mL) for various periods of time to block protein synthesis. The protein was collected and measured using Western blot.2.7786‐O and 769‐P cells had transfected HA‐Ub were transfected with the indicated lentiviruses or plasmid followed by treatment with 20 μmol/L MG132 for 6 hours. Cell lysates were incubated in RIPA buffer . The lysates were collected and boiled for 10 minutes. Then, cell lysates and dilution buffer  were incubated at 4°C for 30 minutes with rotation. Next, protein fraction was collected by centrifugation, followed by incubation with anti‐ARMC5 antibodies overnight. The mixture and protein A/G agarose beads were incubated for an additional 1 hour at room temperature. Then, the beads were washed three times with wash buffer . The proteins were released from the beads by boiling beads in SDS/PAGE buffer, and they were analysed using immunoblotting with anti‐Ub monoclonal antibodies.2.8Cells were harvested using cell dissociation buffer  and fixed in 70% ethanol at 4°C for 12 hours. Then, DNA was stained with 0.03 mg/mL propidium iodide containing RNase at 37°C for 30 minutes and subjected to analyse by flow cytometry (Beckman Coulter Life Sciences).2.9786‐O cells that had been transfected with NC‐shRNA or USP7‐shRNAs were suspended with RPMI 1640 supplemented with 0.2% agarose. Then, the above mixture was plated on the top of a solidified layer of RPMI 1640 with 0.6% agarose. The colonies were measured after three weeks. All experiments were conducted in triplicate and repeated twice.2.10The cells were added to the medium counting kit‐8 reagents and incubated at 37°C for 2 hours. The absorbance was measured at the indicated time points . The data were analysed from three independent experiments.33.1To assess expression levels of ARMC5 in RCC, we first measured protein levels of ARMC5 in paired non‐tumour tissue and RCC obtained from the Pathology Department. We found that ARMC5 protein levels were decreased in RCC tissues compared with adjacent normal kidney tissues by Western blot Figure . To gain3.2Recent studies suggest that ARMC5 interacts with E3 ubiquitin ligase CUL3, which leads to ARMC5 degradation through the ubiquitin‐proteasome pathway.3.3As USP7 knockdown affected the levels of ARMC5, we determined whether USP7 acts directly on ARMC5. Co‐immunoprecipitation analysis revealed that endogenous USP7 was pulled down by Flag‐ ARMC5 Figure . A simil3.4USP7 knockdown alone abrogated ARMC5 levels and the reduction of ARMC5 caused by USP7 knockdown could be restored by the proteasome inhibitor MG132  in the HEK293T and 786‐O cells resulted in a significant decrease of endogenous ARMC5 levels Figure . To furt2 Figure , suggest2 Figure . To furt2 Figure . These d3.5−/− cells by ARMC5‐specific short hairpin RNA (shRNA). In contrast, USP7 depletion exhibited no effects on the G1/S phase transition in 786‐O ARMC5−/−cells    Figure . Further) Figure . In addi) Figure . Therefo4Renal cell carcinoma (RCC) is one of the leading cause of cancer‐related death in urinary system, and its incidence accounts for 3.7% in all malignant tumour.ARMC5 is ubiquitously expressed in human tissues and encodes a protein which contains two major domains, including a N‐terminal Armadillo repeat domain and a C‐terminal BTB domain,−/− cells. Similarly, 786‐O cells with USP7‐depleted transfected with exogenous ARMC5 restored the cell proliferation, indicating that USP7 exerts its function via ARMC5.It has been reported that ARMC5 is participate in the regulation of cell cycle. Our results showed that USP7 altered G1/S phases in a ARMC5‐dependent manner, as illustrated that USP7 knockdown alone promoted G1/S transition, whereas silencing of USP7 cells transfected with exogenous ARMC5 reversed G1/S transition. We also confirmed that USP7 acts as tumour suppressor in RCC cells, as demonstrated that cells with USP7 knockdown exhibited increased proliferation. This is accordance with previous reports that USP7 functions as a tumour suppressor.The authors declare no conflict of interest.Guobei Yan: Data curation ; Formal analysis ; Methodology ; Project administration ; Writing‐original draft (lead). Jun Tian: Data curation ; Investigation ; Project administration . Na Liu: Formal analysis ; Funding acquisition ; Investigation ; Project administration . Junhua Wang: Data curation ; Funding acquisition (supporting); Project administration ; Writing‐original draft . Kai Li: Data curation ; Funding acquisition ; Resources ; Software ; Writing‐original draft . Yanli Fu: Investigation ; Methodology ; Project administration . Jingjing Zou: Methodology ; Project administration ; Software . Wei Wei: Investigation ; Methodology ; Project administration . Qing Wang: Investigation ; Methodology ; Software . Suping Li: Project administration .


PPARGC1A encodes a transcriptional co-activator also termed peroxisome proliferator-activated receptor (PPAR) gamma coactivator 1-alpha (PGC-1α) which orchestrates multiple transcriptional programs. We have recently identified CNS-specific transcripts that are initiated far upstream of the reference gene (RG) promoter. The regulation of these isoforms may be relevant, as experimental and genetic studies implicated the PPARGC1A locus in neurodegenerative diseases. We therefore studied cis- and trans-regulatory elements activating the CNS promoter in comparison to the RG promoter in human neuronal cell lines. A naturally occurring variable guanidine thymidine (GT) repeat polymorphism within a microsatellite region in the proximal CNS promoter increases promoter activity in neuronal cell lines. Both the RG and the CNS promoters are activated by ESRRA, and the PGC-1α isoforms co-activate ESRRA on their own promoters suggesting an autoregulatory feedback loop. The proximal CNS, but not the RG, promoter is induced by FOXA2 and co-activated by PGC-1α resulting in robust activation. Furthermore, the CNS, but not the RG, promoter is targeted by the canonical hypoxia response involving HIF1A. Importantly, the transactivation by HIF1A is modulated by the size of the GT polymorphism. Increased expression of CNS-specific transcripts in response to hypoxia was observed in an established rat model, while RG transcripts encoding the full-length reference protein were not increased. These results suggest a role of the CNS region of the PPARGC1A locus in ischemia and warrant further studies in humans as the activity of the CNS promoter as well as its induction by hypoxia is subject to inter-individual variability due to the GT polymorphism.The online version of this article (10.1007/s12035-019-01731-5) contains supplementary material, which is available to authorized users. PPARGC1A, is a versatile transcriptional co-activator involved in multiple transcriptional programs  (Promega). Primers used are shown in Supplementary Material, Table Expression plasmids pPGC-1α , B4-PGC-ectively ). Long aectively  and CNS-PPARGC1A  have bee3, respectively. The murine hippocampal cell line HT22 was a kind gift from D. Schubert, Salk Institute, La Jolla, CA, USA and cultured in DME (D0547)/F12 1:1 supplemented with 0.21% NaHCO3. Media for all cells were supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin . All cells were cultured in a humidified 5% CO2 atmosphere at 37 °C. Hypoxia culture experiments were performed in an O2-regulatable incubator  or in a H135 HEPA hypoxystation  in a humidified 5% CO2 and 1% O2 atmosphere. Ciclopirox olamine and deferoxamine were obtained from Sigma-Aldrich. Cells were plated in 24-well plates 1 day before transient transfection with plasmid constructs for 24–48 h using Lipofectamine 3000 (Invitrogen) or DNAfectinTM Plus . The pRL-TK plasmid (Promega) was used as a transfection control. Luciferase activities were measured using the Dual Luciferase Reporter Assay System (Promega) as described [+ SD.SH-SY5Y and NTERA-2cl.D1 (NT2/D1) cells were obtained from ATCC and cultured in DME (D6046)/F12 1:1  and DME (D0547)/F12 (Sigma-Aldrich) supplemented with 0.21% NaHCOescribed . Resultsg for 20 min at 4 °C) of suspensions that were intermittently incubated on ice and heated at 95 °C and sonicated . Nuclear and cytoplasmic extracts were prepared using the NE-PER Nuclear and Cytoplasmic Extraction Reagents Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. Protein concentrations were determined by the RCDC Protein Quantification Assay .Total RNA from SH-SY5Y, NT2/D1 cells, and rat brain regions was extracted using the QIAzol Lysis Reagent  and the RNeasy Lipid Tissue Mini kit (Qiagen). Concentrations of RNA were measured using the Nanovue Plus spectrophotometer . In addition, the QIAxcel Advanced Instrument (Qiagen) was used to ascertain RNA quality. RNA integrity scores were between 8 and 9 for all samples used. For total protein preparation of whole cultured cells, cell pellets were solubilized in RIPA buffer containing protease inhibitors . Clear supernatants were obtained after centrifugation . Constitutively expressed RPLP0  mRNA was used for normalization of mRNA abundance.DNase I-treated total RNA from cell cultures (1 μg/reaction) was reverse transcribed with the QuantiTect Reverse Transcription kit (Qiagen), using a mix of random hexamers and oligo-dT primers. cDNAs were amplified in duplicate by real-time PCR using Maxima SYBR Green (Thermo Scientific) or GoTaq qPCR Master Mix (Promega) and primers targeting exons B1 and B4 or B5 and exon 2 to quantify the two main CNS-specific 6 NT2/D1 cells, enzymatic shearing and preparation of input DNA were performed as described [Ntrk2 (TrkB) promoter known to be activated by HIF1A. As a negative control, primers spanning Exon B5 of CNS-PPARGC1A that did not contain a predicted HRE were used. IgG instead of the HIF1A antibody was used to confirm the absence of non-specific bands.The chromatin immunoprecipitation (ChIP)-IT Express Enzymatic kit  was used. Cross-linking of 10escribed . After aescribed  spanningBiomedical Research Center of the Slovak Academy of Sciences, maintained on a 12-h light/dark cycle and given food and water ad libitum. Food was withdrawn 1 day before surgery.The experiments were carried out in accordance with the protocol for animal care approved by European Communities Council Directive (2010/63/EU) with permission of The State Veterinary and Food Administration of the Slovak Republic (4451/14-221 and 4247/15-221) under the supervision of Ethical Council of the Institute of Neurobiology, Bratislava Slovak Academy of Sciences. Every effort was made to minimize animal suffering and reduce the number of animals used. Adult male albino Wistar rats weighing 300–500 g obtained from Velaz (Czech Republic) were housed in the certified vivarium of the Institute of Neurobiology, n = 3 each) sacrificed at the time points of the experimental ischemic animals.Four groups of animals were used: two ischemic groups of 3 rats each with different post-ischemic reperfusion intervals (ischemia 5 min followed by 1 h or 3 h of reperfusion) and two sham control animal groups , hippocampus, thalamus, and cerebellum were separated. To compare the selectively vulnerable CA1 versus the relatively resistant dentate gyrus and CA3 regions, the hippocampus was divided into the CA1 region and the rest of the hippocampus (DG) under a dissecting microscope. Collected tissue was weighed, divided into halves, and frozen at − 20 °C in 5 volumes of RNAlater (Sigma-Aldrich) until RNA analysis.p values are 2-tailed.In transient transfection studies, ANOVA was used for comparisons of means and the Tukey honest significance test for post-hoc comparisons. Two-way ANOVA was used to ascertain effects of transcriptional activators, co-activators, and their interactions. ANOVA was used to compare effects of reduced oxygen tension and CPX. Three-way ANOVA was used to analyze effects on transcript levels in the in vivo model of transient ischemia. Treatment (sham/transient ischemia), post-ischemic reperfusion time (1 h/3 h) and brain regions sampled (1–7) were used as independent variables. Reported 4GC(GT)2GC(GT)4G(GT)3(GC)3 microsatellite showed no variation in DNA from 50 subjects, but the more distal (GT)GC(GT)GC(GT)NT(GT)3(GC)5 microsatellite revealed considerable variation in the number of GT repeats (N). In DNA from 2000 subjects, the GT insertion polymorphism (N) ranged from 9 to 28 repeats with 11 GTs as the most common allele [We previously identified two microsatellite regions located at − 164 and − 24 bp relative to the transcription start site in the CNS-specific promoter region. The more proximal (GT)n allele  known ton allele –48. To d2 by PGC-1α further increases promoter activity resulting in sustained PGC-1α expression [Robust expression of PGC-1α isoforms may be maintained by positive feedback loops. As demonstrated in mouse skeletal muscle, Mef2c binds and activates the reference promoter and co-activation of Mefcpression . To deteWe next compared the role of ESRRA in the transcriptional regulation of the reference and CNS-specific promoters. SH-SY5Y cells were transiently co-transfected with reporter constructs harboring the reference promoter (2.6 kbp) and the CNS-specific promoter (2 kbp) along with plasmids expressing ESRRA and FL reference PGC-1α or FL-B4-PGC-1α. Both promoters were significantly induced by ESRRA, the reference FL-PGC-1α, or FL-B4-PGC-1α. A strong synergistic effect of ESRRA and PGC-1α at both promoters was observed Fig. .p < 0.001). As we identified putative FOXA2 binding sites in the distal (− 1175 bp) and proximal (− 218 bp) CNS promoter using the PROMO 3.0 transcription factor binding site tool/ALGGEN Research Software , we2, respectively. After 6 h in culture, levels of the CNS-specific transcripts B1B4 and B5E2 as well as VEGF transcripts were increased in cultures exposed to 1% oxygen in comparison to cells cultured in 20% O2. A similar pattern was noted at 24 h of culture, but average levels of E1E2 and E5E6A transcripts tended also to be higher under oxygen deprived culture conditions  and deferoxamine (DFO). In NT2/D1 cells, cellular levels of VEGF transcripts and CNS-specific transcripts B1B4 and B5E2 were increased by CPX, but levels of RG transcripts E1E2 and E5E6A transcripts, encoding NT-PGC-1α, were similar to levels in control cells . The truncated promoter constructs were activated by HIF1A to a similar extent as the 2.0 kbp promoter (matched for GT size). Based on the consensus sequence ((A/G)CGTG) [NTRK2 (TRKB) promoter used as a positive control was also amplified in CPX treated cells, while no amplification products were noted with the antibody from the exon B5 region serving as negative control. Hence, these results are consistent with the transactivation assays and support promoter activation by HIF1A via HREs located between − 218 and − 84 of the CNS promoter.Since neuronal cells are particularly sensitive to ischemia, we studied potential effects of reduced oxygen tension on the expression levels of CNS and RG transcripts in NT2/D1 cells cultured in 20% and 1% Oons Fig. . To detells Fig. . CPX trells Fig. . In SH-S/G)CGTG) , the tru/G)CGTG)  of the hB1b4 containing Ppargc1a transcripts in mice and rats [B5e2 transcripts were not detected and such transcripts would have a very short open reading frame of 11 amino acids. To determine whether hypoxia and/or nutrient deprivation influences the expression of CNS-specific PPARGC1A transcripts in vivo, we used an established rat model of transient forebrain global ischemia. This model involves the occlusion of both vertebral and carotid arteries and affects all brain regions to a variable degree. To keep the number of animals as low as possible, we used a study design that included transient ischemia/sham, 7 different brain regions, and post-ischemic reperfusion time as independent variables. Transient ischemia resulted in an increase of Vegf and Glut1 transcripts as expected. Furthermore, transcript levels of B1b4 and E5e6a, but not E1e2, were also increased . Due to the sample number, the data on individual brain regions must be viewed as preliminary. Clearly, the effects of ischemia require further studies on the time course of response and effects in specific brain regions, as their vulnerability and response may differ. A link between hypoxia and CNS-specific PPARGC1A expression is supported by studies in the subterranean rodent Nannospalax galili. In this blind mole that survives at 3% O2 for up to 11 h as compared to 2–4 h for rats [PGC-1α, specifically the truncated NT-isoform, has been shown to be upregulated in myotubes by hypoxia, but the canonical hypoxia response pathway characterized by HIF-1α stabilization was not involved , 73. The studies , 73, HIF hypoxia . Importafor rats , CNS-spefor rats .HIFs interact with HREs with the core sequence 5′-RCGTG-3′ , but onlUpregulation of CNS-specific PGC-1α could have major implications for the pathology of cerebral ischemia as well as neuronal physiology. Our results on the interaction of HIF1A and the GT repeat size would imply inter-individual differences in the response to hypoxia. CNS-specific transcripts encode for proteins that differ from the reference protein only at the N-terminus. The N-terminal activation and repression domains appeared to be responsible for gene programs and splicing events that are specific for some PGC-1α isoforms . Our preIn conclusion, the CNS-specific, but not the RG, promoter is activated by FOXA2 via an autoregulatory loop, suggesting a role of CNS-specific transcripts in the development and maintenance of dopaminergic mid-brain neurons. The activation of the CNS-specific promoter by HIF1A supports a role of CNS isoforms in ischemia. The hypoxic/ischemic activation may differ between individuals, as an interaction is likely to occur between a variable GT repeat and HIF1A. Finally, cross-talk between the CNS and RG promoters via transcriptional co- and cross-activation may support a balanced expression of the reference protein and the CNS-specific isoforms.ESM 1(DOCX 430 kb)


The epigenome defines the unique gene expression patterns and resulting cellular behaviors in different cell types. Epigenome dysregulation has been directly linked to various human diseases. Epigenome editing enabling genome locus-specific targeting of epigenome modifiers to directly alter specific local epigenome modifications offers a revolutionary tool for mechanistic studies in epigenome regulation as well as the development of novel epigenome therapies. Inducible and reversible epigenome editing provides unique temporal control critical for understanding the dynamics and kinetics of epigenome regulation. This review summarizes the progress in the development of spatiotemporal-specific tools using small molecules or light as inducers to achieve the conditional control of epigenome editing and their applications in epigenetic research. Chemical modification patterns on histone tails and DNA collectively constitute the epigenome that dictates unique gene expression patterns and resulting phenotypes in each distinct cell type ,2,3. TheGenome-wide mapping of epigenome modifications by the next generation sequencing technologies has provided valuable information correlating specific epigenome modifications or combinations with certain transcriptional activities ,30. HoweEpigenome editing aims to write or erase specific epigenome modifications at specific gene loci to reprogram local epigenome environments and alter resulting gene expression. Programmable locus-specific DNA-targeting technologies play a pivotal role in realizing this goal ,35,36, wSeveral DNA-targeting technical platforms have been developed that have been or can potentially be applied to locus-specific epigenome editing ,41,42,43Streptococcus pyogenes, dCas9 from Neisseria meningitidis, TALEs or ZFPs and was recruited to the promoter and/or enhancer region of IL1RN, MYOD, OCT4, HS2, HBE, HBG or ICAM1 gene leading to the installation of H3K27ac and the activation of corresponding genes in human HEK293T cells ..MYOD1, CGRM2 and NEUROG2 expression in primary neurons and Neuro2a cells . I. I96]. IThe CIP method can potentially also be adapted for light inducible epigenome editing. Several light-inducible methods based on CIP have been reported using CIP inducers caged with different photosensitive groups. These caged CIP inducers  ,107,108 Staphylococcus aureus (saCas9) and Streptococcus pyogenes (spCas9) were fused individually to ABA-binding adaptor proteins PYL and ABI and targeted to two distal genomic loci mediated through corresponding sgRNAs. Adding ABA induced the juxtaposition of these two targeted chromosomal loci to form a particular chromatin spatial organization. It was demonstrated that using the CLOuD9 system, adding ABA resulted in the formation of stable but reversible chromatin loops that brought the promoter of -globin into contact with the HS2 locus control region (LCR), which led to increased Regulating the dynamic three-dimensional (3D) organization of chromatin within the cell nucleus is another layer of epigenome regulatory mechanism to control gene activities ,112,113.62 cells .Another inducible system, CRISPR-genome organizer (CRISPR-GO), was developed by combining CRISPR/dCas9 with ABA-based CIP or the Trimetho-prim-Haloligand (TMP-Htag) inducible DHFR/HaloTag system , to contCas12a, or Cpf1, has been engineered as a complementary genome editing platform along with Cas9 ,117,118.+) or ligand-inactivated sgRNA (termed ligRNA−) using theophylline or 3-methylxanthine as the small molecule inducer  or lIn addition to the strategies of using ligands or light to control the recruitment of epigenome modifiers to the targeted genomic loci, modulating the stability of dCas-fused epigenome editing effectors can potentially be another useful tool for conditional epigenome editing. Ligand-dependent destabilizing domains (DDs) have been used to regulate the stability of various POIs in cells ,123. WitAnother approach to control the level of functional dCas9-effector fusion protein and may be adaptable for conditional epigenome editing is to use ligand-dependent NS3 protease domain derived from hepatitis C virus (HCV). Inserted between dCas9 domain and the effector domain, NS3 promoted the cleavage between these two domains, unless an antiviral NS3 inhibitor BLIN-2061 was present, which inhibited NS3 activity and maintain the full length of the functional dCas9 fusion protein d. Using Advances in epigenome editing technologies have led to a technical revolution facilitating functional epigenomic research and contributing to therapeutic development for diseases caused by epigenome dysregulation. The genome locus-specific targeting enabled by ZFPs, TALEs and CRISPR/dCas offers unparalleled precision in epigenome editing unachievable by conventional genetic or pharmacological approaches. Conditional editing platforms controlled by small molecule ligands or light further open up the door for high resolution temporal manipulation and dissection of epigenome regulatory processes, which enable the quantitative analysis of the kinetics and dynamics of epigenome establishment, remodeling and maintenance. The knowledge gained from these studies and the editing tools developed for these purposes will directly contribute to the discovery and development of novel epigenome therapy strategies ,18,127.


Drosophila. Moreover, we track the CCF state for syntenic blocks between human and mouse and conclude that active state assessed by CCF is an intrinsic property of the DNA region, which is independent of local genomic and epigenomic context. Our findings establish a missing link between Hi-C normalization procedures removing CCF from the data and poorly investigated and possibly relevant biological factors contributing to CCF.Regulation of gene transcription is a complex process controlled by many factors, including the conformation of chromatin in the nucleus. Insights into chromatin conformation on both local and global scales can be provided by the Hi-C (high-throughput chromosomes conformation capture) method. One of the drawbacks of Hi-C analysis and interpretation is the presence of systematic biases, such as different accessibility to enzymes, amplification, and mappability of DNA regions, which all result in different visibility of the regions. Iterative correction (IC) is one of the most popular techniques developed for the elimination of these systematic biases. IC is based on the assumption that all chromatin regions have an equal number of observed contacts in Hi-C. In other words, the IC procedure is equalizing the experimental visibility approximated by the cumulative contact frequency (CCF) for all genomic regions. However, the differences in experimental visibility might be explained by biological factors such as chromatin openness, which is characteristic of distinct chromatin states. Here we show that CCF is positively correlated with active transcription. It is associated with compartment organization, since compartment A demonstrates higher CCF and gene expression levels than compartment B. Notably, this observation holds for a wide range of species, including human, mouse, and  The conformation of chromatin in the nucleus plays an important role in many cellular processes, including the regulation of gene transcription and DNA replication . The regcooler , the differences in experimental visibility may be explained not only by technical or experimental biases but also by biological factors. Notably, local chromatin conformation correlates with functional characteristics of the genome, such as individual histone modifications  or theirFeatures such as TADs, enriched contacts, and compartments are usually called in normalized Hi-C interaction maps . ExperimGSE63525 and GSE69013, respectively). We downloaded the processed Hi-C maps in the hic format from  as the sum of contact frequencies of each locus. To make CCF comparable between different cell lines and resolutions, we further report it as the percentage from the maximum CCF in the Hi-C map. We considered two types of CCF: whole-genome and inter-chromosomal . Inter-chromosomal CCF was analyzed separately to demonstrate that intra-chromosomal contacts do not drive our observations.Armatus algorithm , for TAIn order to identify chromatin compartments, we performed computational analysis as in Drosophila dataset (PVE for the first component of S2 cells is 0.11), probably due to Drosophila compartments being much smaller than the selected dataset resolution (1 Mb).Notably, the first principal component for human and mouse datasets demonstrated the highest proportion of variance explained (PVE of the first component ranging from 0.60 for HMEC and HUVEC cells to 0.80 for K562 cells) and had a characteristic checkerboard pattern in accordance with previous findings . We wereDrosophila  and MAGENTA (2) colors, and inactive/repressed chromatin, comprized of DARKGRAY (6), DARKBLUE (7), LIGHTBLUE (8), LIGHTGRAY (9).The states for the lication . Based op-values are calculated for the observed correlations in the real data.To characterize correlation patterns, we used two approaches. First, we calculated the Pearson correlation coefficients between CCF and chromatin state proportions in each region of the whole genome. To further validate the findings, we used Stereogene , a tool LiftOver tool (Syntenic regions (size 2 Mb and larger) were obtained from the Mouse Genome Informatics database (MGI) . The regver tool . The conAs active transcription requires binding of RNA polymerase and a variety of transcription factors, increased gene expression is intuitively associated with loose packaging of chromatin and thus better accessibility to the Hi-C reaction and higher CCF. At the same time, active chromatin is involved in a larger number of interactions, including distant regulatory ones. Thus, one might expect regions with high CCF to show high gene expression levels and regions with low CCF to exhibit low gene expression. To validate this hypothesis, we constructed a whole-genome Hi-C map combined with the functional state plot showing the distributions of chromatin state proportions for each genomic region –1C. IndeChromosomes are known to segregate into two mutually exclusive types of chromatin, referred to as “A” and “B” compartments . Active To get a more precise estimate of dependencies between CCF and chromatin states, we visualized the growth of chromatin state percentages at increasing CCF .To further validate the result, we calculated the correlations between each of the chromatin states and CCF  enablingDrosophila cell line S2 and mouse cell line CH12-LX. For Drosophila and mouse, the chromatin state annotations  and MAGENTA (2)  , which aENTA (2) .Drosophila  for a genomic region as the number of contacts for a region in a non-normalized Hi-C map. We then analyze CCF properties, including correlation with biologically meaningful signals such as chromatin compartments, transcriptional activity, and chromatin states.We observe that for human cells, large CCF is predictive of active chromatin and compartment A. This result holds for multiple resolutions of the Hi-C data and several human cell types. We also have used the Stereogene approach  to demonDrosophila and mouse, suggesting broad generalizability of our conclusions. Notably, we use human and mouse Hi-C that were mapped by Drosophila Hi-C that was mapped by Moreover, positive correlation of CCF with active chromatin states holds for To further separate the biologically relevant signal of CCF from possible technical artifacts, we have considered confounding factors that might affect our analysis. GC-content is a well-known source of variability in the genomic coverage for sequencing experiments, Hi-C, in particular . We haveOne of the first observations obtained using Hi-C method was the tendency of small chromosomes to interact with each other while being more active . Thus, CSurprisingly, we have observed that CCF of small and large chromosomes differs. We suggest that this difference might happen not because of the chromosome size, but because of the intrinsic properties of the regions. First, we have confirmed it by downsampling large chromosomes to the size of small ones. Second, we have compared CCF in syntenic regions between the human and mouse genomes and observed that CCF does not change after translocation between large and small chromosomes.There are still some other possible technical confounding factors that might contribute to the CCF properties, such as the density of restriction fragments in a genomic bin, mappability of the region, chromatin openness as assessed by DNase-seq or ATAC-seq . These fImportantly, all these observations do not allow us to introduce a causative link between chromatin activity and CCF. We also do not account for the evolutionary history and sequence conservation of corresponding regions, which might reveal the reasons for our cross-species observations. Extensive further research is required to shed the light on these problems.Nevertheless, our results allow to suggest that removal of CCF in the IC procedure is currently understudied. CCF contains biologically relevant information that is not affected by GC-content and chromosome size. Currently, the effect of removal of this information on calling of Hi-C features, such as TADs, enriched contacts, and compartments, has not been studied. We propose to take the Hi-C normalization step with caution and interpret Hi-C features that are robust to the removal of CCF and present in both non-normalized and normalized maps.Drosophila). Surprisingly, these properties are inherited and preserved between syntenic regions of human and mouse genomes. We conclude that the importance of CCF is underestimated, and it should be removed from Hi-C maps with caution.In this work, we dissociate the technical and biological signal that is removed by the iterative correction (IC), one of the most widely used methods of Hi-C data normalization. For that, we study cumulative contact frequency (CCF) defined as the number of contacts for a genomic region in a non-normalized Hi-C map. We demonstrate that CCF has significant biological properties, such as correlation with chromatin compartments, transcriptional activity, and active chromatin states. These properties are independent of GC-content and chromosome sizes. They can be generalized to a broad range of species (human, mouse, and 10.7717/peerj.9566/supp-1Supplemental Information 1Click here for additional data file.