MYCN oncogene. Epigenetic factors play a pivotal role in normal neural crest and NB development, influencing gene expression patterns critical for tumorigenesis. This review delves into the multifaceted interplay between MYCN and known epigenetic modifications during NB genesis, shedding light on the intricate regulatory networks underlying the disease. We provide an extensive survey of known epigenetic mechanisms, encompassing DNA methylation, histone modifications, non-coding RNAs, super-enhancers (SEs), bromodomains (BET), and chromatin modifiers in MYCN-amplified (MNA) NB. These epigenetic changes collectively contribute to the dysregulated gene expression landscape observed in MNA NB. Furthermore, we review emerging therapeutic strategies targeting epigenetic regulators, including histone deacetylase inhibitors (HDACi), histone methyltransferase inhibitors (HMTi), and DNA methyltransferase inhibitors (DNMTi). We also discuss and summarize current drugs in preclinical and clinical trials, offering insights into their potential for improving outcomes for MNA NB patients. Neuroblastoma (NB), a childhood cancer arising from the neural crest, poses significant clinical challenges, particularly in cases featuring amplification of the  MYCN oncogene has emerged as an adverse prognostic genetic event. MYCN-amplification (MNA) occurs in approximately 20% of NB cases. MNA is associated with an aggressive phenotype, treatment resistance, and poor prognosis [Neuroblastoma (NB), an embryonal tumor arising from the peripheral sympathetic nervous system, represents a challenging clinical entity characterized by heterogeneity in clinical presentation, prognosis, and treatment response . Among trognosis ,2. Treatrognosis ,4. Aboutrognosis .In recent years, the field of epigenetics has garnered significant attention in cancer research, unraveling the intricate regulatory mechanisms that dictate gene expression patterns beyond the DNA sequence itself. Epigenetic modifications, including DNA methylation, histone modifications, and chromatin remodeling, play fundamental roles in cellular development, differentiation, and disease pathogenesis . MoreoveMYCN locus, consequently influencing downstream gene networks involved in cell proliferation, differentiation, and apoptosis. Moreover, MYCN itself can regulate epigenetic processes in MNA NB. In this review, we summarize the latest advances in understanding the epigenetic dysregulation in MNA NB, and we highlight new therapeutic avenues targeting epigenetics for this high-risk pediatric malignancy.In the context of MNA NB, understanding the interplay between genetic and epigenetic factors is of utmost importance. Epigenetic modifications have been recognized as crucial determinants of MYCN expression levels during neural crest development and in NB development . EpigeneNeural crest cells (NCC) arise at the neural plate border during gastrulation and neurulation in the third week of human development. NCCs undergo a series of developmental processes, including specification, migration, differentiation, and maturation. These processes are tightly regulated by gene regulatory networks involving various transcription factors (TFs) activated by bone morphogenetic proteins (BMPs), fibroblast growth factors (FGFs), wingless-type (WNT), and Notch signaling pathways  9,10,1111,12. ThNB specifically originates from the sympathetic lineage of NCCs ,17. InstMYC, a paralogue of MYCN, is transcribed, maintaining the multipotent NCC progenitors [One of the master regulators of NCC development is the MYCN TF. The expression levels of MYCN vary during NCC development . During genitors . Later, genitors . After mgenitors . Indeed,genitors ,18,20,21genitors ,22. MYCNgenitors ,24. Howegenitors . MYCN results in decreased size of the entire nervous system, along with a reduction in the number of mature neurons in the spinal ganglia [Aberrations in MYCN expression influence NC cell fate decisions, impairing the normal processes of neuronal differentiation and migration. In mice, loss of  ganglia . Notably ganglia ,38. Pati ganglia . MYCN under the tyrosine hydroxylase (TH) promoter, which is active in early migrating sympathetic precursors, spontaneously develop morphologically and phenotypically similar tumors to high-risk NB in humans [The importance of MYCN in NB development is supported by findings from genetically engineered animal models of NB. Transgenic mice expressing perinatal n humans ,39,40. Tn humans ,21. In zn humans . AltogetSeveral epigenetic mechanisms involved in NC development are known to be disrupted in NB genesis. MicroRNAs (miRNAs) play important regulatory roles during NC development, where they work in coordination with other regulatory mechanisms to fine-tune gene expression and ensure the proper development of NCCs into diverse cell types, including neurons. MYCN influences the expression of multiple miRNAs and is itself subject to miRNA regulation. This feedback mechanism is disrupted in NB, where upregulated levels of MYCN coincide with altered expression of specific miRNA clusters ,29. In nMYCN gene between normal and NB cells during mouse sympathetic cervical ganglia (SCG) development (E11.5) revealed a failure in the shift of histone marks from active H3K4me3 (trimethylation of lysine residue K4 on histone H3) to repressive H3K27me3 (trimethylation of lysine residue K27 on histone H3) [Investigation of histone modifications at the tone H3) . Indeed,tone H3) .TH-MYCN mice (at E13.5) demonstrated a significant downregulation of PRC2 target genes, indicated by an increase in H3K27me3 levels around their promoters. Specific inhibition of EZH2 reversed the repression of target genes and eventually suppressed in vivo tumor growth in TH-MYCN mice [Enhancer of Zeste Homolog 2 (EZH2), a core catalytic subunit of the Polycomb Repressive Complex 2 (PRC2), is a histone methyltransferase (HMT) that represses transcription via H3K27me3 . The comYCN mice ,36. MoreYCN mice ,37  are specific genomic regions characterized by a high frequency of cytosine–guanine (CpG) dinucleotides. These regions are often associated with gene promoters, transcription start sites, and first exons. DNA methylation at CpG islands is a crucial epigenetic mechanism implicated in cell fate determination during development, generally leading to gene silencing . CASP8) and members of the Ras association domain family (RASSF) and protocadherin beta cluster (PCDHB) families have been extensively studied and linked to MNA [CASP8 by methylation is believed to allow for unhinged cell proliferation and therefore contribute to MNA NB tumorigenesis [CASP8 methylation and MNA has been questioned by other groups [MYCN has been shown to bind to numerous promoters and CpG islands (CGIs) in NB, suggesting direct control of potential tumor suppressor genes via DNA methylation ,44. Specd to MNA ,47,48,49r groups ,50. HIST1H3C) and acetyl-CoA synthetase short-chain family member 3 (ACSS3). Specifically, HIST1H3C methylation is associated with both overall and event-free survival [NR4A3) exon 3 in MNA cells, which may be associated with a better prognosis [CD44 methylation in both MNA and non-MNA NB [Moreover, MYCN single-copy and MNA NB cell lines exhibit distinct promoter methylation patterns for genes like histone H3.1 , identified 663 differentially methylated CpGs and 14 highly methylated genes associated with MNA in NB, suggesting that MNA alters the methylation landscape in NB and that this landscape differs from that in non-MNA NB [TERT (cg11625005). In addition, MNA NB showed higher TERT expression compared to non-MNA NB.Various studies suggest a CpG island methylator phenotype (CIMP) in NB, which is linked to poor survival. Both Japanese and German studies showed that MNA is linked to the CIMP+ phenotype ,56, whern-MNA NB . In addin-MNA NB . The difOverall, DNA methylation is a critical epigenetic mechanism involved in the development of MNA NB . HoweverHistone modifications, such as methylation, acetylation, phosphorylation, and sumoylation of histones, can influence the accessibility of DNA to the transcriptional machinery. Histone methylation can either activate or repress gene transcription, depending on which histone and which amino acid residue is methylated. For instance, methylation of histone H3 at lysine 4 (H3K4) is associated with gene activation, while methylation at H3K9 and H3K27 is linked to gene repression. Conversely, the addition of acetyl groups to histone tails generally results in a more open chromatin structure, facilitating increased gene transcription and is typically associated with gene activation. On the other hand, deacetylation is a hallmark of gene silencing.EP300 leads to a global loss of H3K27ac marks and loss of MYCN protein expression [MYCN has been shown to impact histone acetylation, the marker of transcriptional activation . Importapression .CD9, which contributes to the invasion and metastasis of MNA NB [NTRK1) promoter and downregulates TrkA expression, which is usually associated with spontaneous regression of NB [MYCN is known to upregulate histone deacetylases (HDACs), including HDAC1/2/5 and sirtuin-1 (SIRT1), to promote gene repression and oncogenesis in both NB cell and mouse models ,72,73. If MNA NB . UpregulEZH2, a subunit of PRC2, catalyzes the trimethylation of histone H3 lysine 27 (H3K27me3) at target promoters for gene silencing. MYCN directly interacts with EZH2 via the Myc homology box domain 3 . MYCN waMIR17HG), M-phase inducer phosphatase 1 (CDC25A), SRY-box 2 (SOX2), KIT ligand (KITLG), versican (VCAN), and syndecan 1 (SDC1), while suppressing MYCN function in both NB cells and xenograft models [Recently, KDM4B, a demethylase involved in histone modifications, was found to be highly expressed in MNA NB cells. KDM4B physically interacts with MYCN and prevents the accumulation of repressive H3K9me2/me3 marks at chromatin loci of target genes. Suppression of KDM4B leads to downregulation of major tumor genes, including miR-17-92a-1 cluster host gene  . MYCN mRNA or act via indirect pathways to influence tumor progression [Non-coding RNAs (ncRNAs) are a diverse group of functional RNA molecules that do not code for proteins but play essential roles in regulating gene expression and contributing to various cellular processes, development, and disease. MYCN has been found to exert its influence not only on protein-coding genes but also on non-protein-coding genes like microRNAs (miRNA) and long non-coding RNAs (lncRNAs). The deregulation of these genes, driven by MYCN, significantly contributes to the development and progression of NB ,81. Thesgression .MicroRNAs (miRNAs) are small single-stranded RNA molecules that regulate gene expression by binding to target messenger RNA molecules . MiRNAs In MNA NB, MYCN is known to be predominantly a repressor of tumor suppressor miRNA expression, although several miRNAs, including the miR-17-92 cluster, were found to correlate with MYCN expression ,84. Several key tumor suppressor miRNAs are shown to be repressed by MYCN in the context of MNA. Indeed, miR-184 or miR-542-5p overexpression in NB xenograft models inhibited tumor growth and metastasis ,85. MYCNMYCN promoter region [MYCN mRNA and protein levels via downregulation of let-7 miRNA in MNA but also in non-MNA NB tumors [The interaction between MYCN and miRNAs is reciprocal, as miRNAs can directly target and regulate the oncogene. In particular, miR-34a is the most extensively studied miRNA that directly regulates MYCN expression and acts as a tumor suppressor by inducing NB cell apoptosis ,64. MiR-r region . Other mr region ,91. Inder region . LIN28B B tumors ,93.MYCN status [The interaction between MYCN and different miRNAs varies depending on their specific characteristics, and the expression of miRNAs is thereby influenced by N status . Indeed,N status . MYCN direct binding to miRNAs loci at the proximal region is thought to be the mechanism for activation or repression of miRNAs in NB, as observed with the miR-17-92 cluster for example . As mentOverall, miRNAs are considered great biomarker candidates, and their modulation holds potential as a novel therapeutic strategy for NB treatment. Several reviews extensively describe the role of miRNAs in NB and their involvement in prognosis, drug response, and resistance ,83,98. SLncUSMycN is located at the 2p 130-kb amplicon that is co-amplified with MYCN and is found to upregulate MYCN mRNA expression via binding to NonO protein. Knocking down of lncUSMycN expression was shown to reduce MYCN at the mRNA and protein level, thereby inhibiting MNA NB cell proliferation and tumor growth in in vitro and in vivo NB models, respectively [SNHG1) is also linked to MNA NB. SNHG1 might downregulate miR338-3p, which in turn leads to PLK4 overexpression, thus promoting proliferation, migration, and invasion [DEPDC1B [LncRNAs are a class of RNA molecules that are longer than 200 nucleotides which are often associated with chromatin-modifying complexes. Although very little is known about the role of lncRNAs in NB, a study identified “non-coding RNA expressed in aggressive neuroblastoma”  and lncUSMycN, as being associated with aggressive MNA NBs and poor prognosis  136,137137. LncUinvasion . Another[DEPDC1B . In addi[DEPDC1B .Moreover, transcribed ultraconserved regions (T-UCRs), a novel subgroup of lncRNAs, have been discovered to be abnormally expressed in NB. These RNA transcripts are highly conserved across human, rat, and mouse genomes. Specific UCR expression profiles can be correlated with prognosis in high-risk patients and MNA status in NB .MYCN opposite-strand (MYCNOS) lncRNA, specifically MYCNOS-01 and 02, were found to regulate MYCN protein expression [MYCN promoter, resulting in increased MYCN expression. This positive regulation of MYCN by MYCNOS-02 leads to suppressed differentiation and increased growth, invasion, and metastasis of NB cells. On the other hand, silencing of MYCNOS-01 in MNA NB cell lines had a similar effect to MYCN reduction, suggesting the potential of these two lncRNAs as therapeutic targets [Furthermore, the pression . MYCNOS- targets ,142.Circular RNAs (circRNAs) are a type of RNA molecule where the 3′ and 5′ ends are joined together in a closed-loop structure. This circular formation is produced via a mechanism known as back-splicing, where a downstream splice donor site is joined to an upstream splice acceptor site, resulting in a circRNA molecule. They have been implicated in various cellular processes, including acting as microRNA sponges and regulating gene expression. ARID1 SWI/SNF tumor suppressor gene, which is almost always mutated in MNA NB, as described in In a study characterizing circRNAs in NB cell lines, seven were located within MNA regions and were upregulated: hsa_circ_0003287, hsa_circ_0008083, hsa_circ_0052767, hsa_circ_0000978, hsa_circ_0117720, chr2:15467874|15567918, and hsa_circ_0008261 . ImportaMYCN [Enhancers are DNA sequences that can increase the transcriptional activity of nearby genes. Super enhancers (SEs) have a higher density of TF binding sites compared to typical enhancers. They are particularly associated with genes that control cell fate and cell-type-specific functions. The formation of SEs involves the binding of TFs, co-activators, and chromatin regulators to the DNA. This assembly of regulatory proteins leads to a highly accessible and active chromatin state, facilitating robust and specific gene expression, including oncogenes such as MYCN . As mentMYCN . These cMYCN .Indeed, the activation of MYCN in NB occurs via a process known as “enhancer hijacking”, where structural rearrangements or translocations bring distal regulatory elements into proximity with other genes, resulting in enhanced expression . MNA in Using genome-wide profiles of H3K27ac in primary and relapsed NB patients, Gartlgruber et al. identified four epigenetic subtypes driven by SEs. Specifically, three of these subtypes are characterized by ADRN-specific signatures that align with known clinical groups: MNA, non-MNA high-risk, and non-MNA low-risk NBs . The fouTo summarize, MNA allows stabilization of the CRC, thereby activating the MYCN transcriptional regulatory network and promoting an immature neuroblast cell state ,154. DisMYC SE in acute myeloid leukemia cells, highlighting its critical role in SE regulation. In MNA NB, the elevated levels of MYCN promote increased histone acetylation, thereby creating more binding sites for bromodomain proteins. Due to their involvement in cancer-related processes, BET proteins have garnered significant attention as potential therapeutic targets. Several small molecule inhibitors targeting BRD4 specifically have been developed and tested in preclinical and clinical studies, showing promise as anti-tumor agents in NB [The bromodomain and extraterminal (BET) subfamily consists of proteins that possess bromodomains, which recognize and bind to acetylated lysine residues on histones. These proteins, including BRD2, BRD3, BRD4, and BRDT, play important roles in regulating gene transcription and are involved in various cellular processes linked to chromatin remodeling. BRD4 is known to have two distinct enzymatic functions: kinase and HAT activity. Indeed, BRD4 plays a crucial role in regulating transcription via the phosphorylation of various interacting partners, including RNA polymerase II (RNAPII) and MYC. BRD4 also acetylates nucleosomes and influences chromatin architecture via its HAT activity . Moreovets in NB ,158,159.Chromatin remodeling complexes depend on ATP to mobilize and restructure selected nucleosomes. These multi-subunit protein complexes can be divided into four subfamilies: switch/sucrose non-fermentable (SWI/SNF), imitation switch (ISWI), chromodomain helicase DNA-binding (CHD), and inositol 80 (INO80) chromatin remodelers. These complexes are conserved across eukaryotes and are involved in dynamic changes of nucleosome architecture, allowing better or less access to DNA by various cellular machinery, including TFs and RNA polymerases. SWI/SNF subunits are often associated with cancer, including NB [ARID1A encodes a subunit of the canonical BAF complex and is deleted on one allele in at least 87% of NB cases with loss of chromosome 1p. The chromosome 1p36 region is almost always missing in MNA NB, resulting in at least one-third of NB cases harboring haploinsufficiency for ARID1A. Using a transgenic MYCN zebrafish model of high-risk NB, researchers demonstrated that disrupting ARID1A enhances MYCN-induced cell proliferation in the SA lineage. In ARID1A homozygous mutant MNA NB cell lines, the differentiation status shifted from adrenergic to mesenchymal, resulting in increased invasiveness. This transition is orchestrated by modifications in enhancer-driven gene expression via the modification of binding sites for both BAF and PBAF complexes [SMARCE1 is located on chromosome 17q, which is frequently gained in NB and correlates with MNA and poor prognosis. High SMARCE1 expression is found to correlate with poor prognosis and is necessary for the proliferation and survival of MNA NB cells. Moreover, not only does MYCN directly upregulate SMARCE1 transcription by binding to its promoter, but both proteins interact to regulate MYCN target genes. These results underscore the possibility for SMARCE1 to modify nucleosome structure and chromatin accessibility, aiding in the transcriptional regulation of MYCN. Altogether, targeting chromatin modifiers belonging to the SWI/SNF complexes could help restore normal gene expression patterns in MNA NB [Mammalian SWI/SNF complexes are part of three subfamilies: canonical BAF, polybromo-associated BAF, and non-canonical BAF. Mutations or alterations in uding NB ,146. ARIomplexes . SMARCE1n MNA NB .CHD5 is also encoded at 1p36, and it is frequently lost or silenced in high-risk NB [CHD5 is often observed in high-risk NB, including MNA NB. However, this is irrespective of MYCN amplification [-risk NB . CHD5 is-risk NB . CHD5 fufication .To date, there are not many studies on epigenetic drug targets for MNA NB. So far, the major classes of epigenetic drugs that have shown promise in MNA NB are histone deacetylase inhibitors (HDACi) and BET inhibitors (BETi), mostly in preclinical studies. A comprehensive summary of the existing literature on epigenetic drugs and their corresponding targets in the context of MNA NB is provided in A compound screen performed by Krstic et al. revealed that NB cells are vulnerable to various classes of epigenetic regulators, and they showed that C646, a CBP/p300 HAT inhibitor, is specifically effective in reducing the viability of NB cells with MNA .TH-MYCN NB mouse model have been conducted. Indeed, a positive feedback loop between MYCN and the HDAC SIRT1 has been described, and treatment with the SIRT1 inhibitor cambinol was able to reduce tumorigenesis in the transgenic NB model [As already mentioned above, MNA in NB leads to the suppression of tumor suppressor genes via the recruitment of DNMTs and increased expression of HDACs. Promising preclinical studies using HDAC inhibitors in the NB model . AdditioNB model . Other HDAC inhibitors, such as MS-275, BL1521, and vorinostat (SAHA), were shown to decrease the cell viability of several NB cell lines ,166,167.Other drugs targeting HDAC1/2/8, which are associated with poor prognosis in NB, have sparked interest . In factGRHL1) gene was identified as an early response gene following HDACi treatment, which is usually repressed by MYCN via HDAC3. This TF, conserved throughout evolution, plays a crucial role in the development of the nervous system in Drosophila [Romidepsin (FK228), an HDAC1/2i FDA-approved drug, has been shown to be specifically potent in MNA NB cells by increasing histone acetylation and thereby cell death via caspase-dependent apoptosis . Furtherosophila . Importaosophila .One HAT inhibitor, JQAD1, has been recently investigated in pre-clinical trials on MNA NB cells . JQAD1 iAltogether, these findings highly suggest that targeting HDACs and, to a lesser extent, HATs, could be a potential approach to treat MNA NB.As mentioned in TH-MYCN mouse model with no related adverse effect [For example, GSK343 significantly reduced tumor growth in a MNA xenograft mouse model without noticeable toxicity . Nonethee effect . GSK126 e effect ,175, bute effect . Lastly,e effect . MYCN oncogene. Moreover, EHMT2 inhibition was found to synergize with doxorubicin, further hampering cell proliferation [In an immunology-related study, Seier and al. suggested exploring the use of H3K9 euchromatic histone-lysine methyltransferase (EHMT) inhibitors in combination with EZH2 inhibitors as an immunomodulation strategy for MNA NB treatment. Essentially, EHMT1 and EHMT2 were identified as key epigenetic factors involved in the malignancy of MNA NB and suppressors of interferon signaling. Inhibiting EHMT enhances the expression of Th1-chemokines, therefore facilitating T-cell infiltration into the tumor microenvironment and improving responsiveness to immune checkpoint blockade therapy . Indeed,feration . MYC oncogene, making BET inhibitors effective in MYC-dependent cancers. By reducing the expression of oncoproteins like MYC, they effectively hinder the growth of malignant cells [MYCN canonical promoter by competitively binding to acetyl lysine sites, resulting in the downregulation of MYCN and its transcriptional outputs. In NB cells, JQ1 treatment leads to cell cycle arrest, apoptosis, and increased differentiation. Importantly, cell lines harboring MNA were found to be more sensitive to JQ1 than the non-amplified ones. Promising results were also observed in in vivo studies using MNA NB xenograft models, showing a significant decrease in tumor volume and increased overall survival without noticeable toxicity [Over the last few years, targeting BET family proteins has emerged as an eminent strategy in the treatment of cancers and other diseases. These proteins have a close association with the regulation of the nt cells . To datent cells . JQ1 distoxicity ,181.Building upon these findings, three JQ1 derivatives  are currently under investigation, with BMS-986158 currently entering phase I in clinical trials (NCT03936465). OTX015, an orally available inhibitor of BRD2/3/4, demonstrates potent inhibition of tumor growth and increased overall survival in MNA NB cells and xenograft models. OTX015 has been found to exhibit specific activity against MYCN target genes, and this activity is correlated with high levels of MYCN expression and MNA in several NB cell lines . Another small molecule BETi, GSK525762, exhibits anti-proliferative effects and cytotoxicity in NB cell models regardless of MYCN status, suggesting alternative mechanisms of action. A phase I study involving hematological and solid tumor cancers has shown therapeutic activity and tolerability of GSK525762, paving the way for potential translation to NB clinical studies . HoweverAdditionally, preclinical studies exploring combinations of BET inhibitors with other therapeutic agents, such as phosphoinositide 3-kinase (PI3K), cyclin-dependent kinase 7 (CDK7), mitogen-activated protein kinase (MEK), HDAC, and Aurora A inhibitors, have shown synergistic effects, and potential to overcome resistance, and reduce treatment-related toxicities ,187,188.Only very few hypomethylating agents have been tested so far in preclinical and phase I clinical trials against NB. Decitabine and 5-azacytidine are DNMT inhibitors (DNMTi) primarily used in the treatment of myelodysplastic syndromes (MDS). Decitabine was found to epigenetically activate miR-34a when combined with retinoic acid, resulting in MYCN downregulation in acute myeloid leukemia patients and cultured cells . This isRecently, two less toxic DNMTi, SGI-1027 and nanaomycin A, displayed enhanced NB cell death when combined with doxorubicin, compared to doxorubicin alone. Even though this effect was MYCN-independent, cell lines harboring MNA were at least 20 times more sensitive to the DNMT3b inhibitor nanaomycin A . In lineA table showcasing the comprehensive range of epigenetic drugs currently being explored in preclinical and clinical trials for the treatment of MNA NB is summarized in Half of the children with high-risk NB do not respond to current treatments and relapse. Genotoxic chemotherapy is the mainstay of therapy for high-risk NB as we lack targeted treatments for this aggressive pediatric malignancy. Therefore, better and less toxic treatments are desperately needed. Targeting epigenetic machinery is a promising approach as MYCN regulates epigenetic processes. Epigenetic dysregulation is a hallmark of cancer, including NB, and contributes to NB development, tumor growth, and progression. By specifically targeting the aberrant epigenetic modifications, epigenetic drugs have the potential to restore normal gene expression patterns, inhibit tumor growth, and overcome drug resistance. The major classes of epigenetic drugs that have so far shown promise in MNA NB are histone deacetylase inhibitors and BET inhibitors, with a more recent focus on drug combination therapies.Epigenetic drugs offer the advantage of potentially greater specificity, targeting cancer cells while minimizing damage to normal cells. They can potentially enhance the efficacy of conventional chemotherapy and reduce the reliance on highly toxic agents, thereby mitigating some of the side effects associated with genotoxic treatments in young patients. Additionally, exploring epigenetic drugs in high-risk NB may provide alternative treatment options for patients who do not respond well to standard chemotherapy or who experience disease relapse. However, further research and clinical trials are needed to establish their safety, efficacy, and optimal use in the context of high-risk NB. Efforts are also being made to identify biomarkers that can help predict patient response to epigenetic drugs and guide personalized treatment decisions. MYCN-driven NB. Moving forward, further research into novel treatment targets involving epigenetic-related molecules and their interactions with MYCN holds promise. By focusing on these avenues, we can advance our understanding and potentially discover more effective therapies for


Cohesin regulates gene expression through context-specific chromatin folding mechanisms such as enhancer–promoter looping and topologically associating domain (TAD) formation by cooperating with factors such as cohesin loaders and the insulation factor CTCF. We developed a computational workflow to explore how three-dimensional (3D) structure and gene expression are regulated collectively or individually by cohesin and related factors. The main component is CustardPy, by which multi-omics datasets are compared systematically. To validate our methodology, we generated 3D genome, transcriptome, and epigenome data before and after depletion of cohesin and related factors and compared the effects of depletion. We observed diverse effects on the 3D genome and transcriptome, and gene expression changes were correlated with the splitting of TADs caused by cohesin loss. We also observed variations in long-range interactions across TADs, which correlated with their epigenomic states. These computational tools and datasets will be valuable for 3D genome and epigenome studies. Enhancer–promoter looping and topologically associating domain are at the base of chromatin structures. Here the authors present a computational workflow in which multi-omics datasets are compared systematically to explore how three-dimensional (3D) structure and gene expression are regulated by cohesin and related factors. Cohesin colocalizes with the CCCTC-binding factor CTCF to function as an insulator3. In contrast, a small proportion of cohesin binds the genome independently of CTCF, regulating gene expression with tissue-specific transcription factors5. At least a subset of CTCF-independent cohesin mediates chromatin interactions between enhancer and promoter sites of active genes with mediator complexes6. Cohesin also participates in transcription elongation machinery by interacting with RNA polymerase II (Pol2)7. Mutations in the cohesin loader NIPBL and cohesin core subunits have been found in the human cohesinopathy Cornelia de Lange syndrome (CdLS), a multisystem developmental disorder8, and in cancers10. Although the molecular mechanisms of CdLS are not fully understood, the function of cohesin as a gene expression regulator is considered to be important11.The cohesin complex is crucial for gene transcription and chromatin folding in mammalian cells13. Briefly, chromosomes are folded into megabase-scale TADs, the boundaries of which are strongly enriched for cohesin and CTCF14. TADs can be nested, and interactions between TADs (interTAD interactions) are rarer than those within TADs (intraTAD interactions)16. The depletion of cohesin or the cohesin loader NIPBL causes a dramatic loss of TADs and chromatin loops throughout the genome18, whereas CTCF depletion affects TAD boundaries and loops more locally19. These observations can be explained by the “loop extrusion” model, in which cohesin translocates along chromatin until it encounters CTCF, resulting in the formation of TADs20. This model also explains the depletion effects of cohesin unloading factors (WAPL and its binding partners PDS5A and PDS5B22), which prevent the release of cohesin from DNA and cause loop extension, resulting in the appearance of longer loops than usual24. The loop extension would be due to the “passing through” of CTCF sites by cohesin due to temporal dissociation of CTCF23 or the physical clustering of neighboring CTCF boundaries called “traffic jam“25. Importantly, such extended loops are rare but also occur in wild-type cells25. This suggests that the dynamics of TAD and loop formation is not absolute, but rather stochastic, and depends on the amount of cohesin on chromatin as a result of its continuous loading and unloading 26. Cohesin turnover is also critical for the proper regulation of gene expression; its loss by the WAPL depletion resulted in the loss of cohesin localization and loops near several genes, leading to loss of gene expression27. A mutation in the cohesin deacetylase HDAC8 has also been found in CdLS, where increased cohesin acetylation results in low efficiency of cohesin turnover28.Recent studies using whole-genome chromatin-conformation capture (Hi-C) have established the role of cohesin in the 3D organization of the genome12. Comprehensive loss of TADs/loops or loop extension has a limited impact on gene expression and does not cause the spread of histone modifications30. Moreover, cohesin and CTCF also localize within TADs without forming boundaries31. These results suggest a more complicated set of rules for chromatin structure formation and gene expression regulation by cohesin and related factors than the current models present. Although each cohesin-related factor has been studied using different cell lines by different laboratories, a study to explore how cohesin and its related factors collectively or individually regulate chromatin folding, gene expression, and the epigenome is needed.Despite extensive efforts, the detailed mechanism that results in hierarchical chromosome organization and the functional relationships that result in transcription regulation still need to be clarifiedTo this end, we have generated 3D genome, transcriptome, and epigenome data before and after depletion of cohesin and related factors and conducted a large-scale comparative multi-omics analysis. To analyze our dataset, we developed a computational workflow to systematically compare multi-omics datasets, the main component of which is a package named CustardPy Fig. . Custard8. To minimize potential secondary effects, we evaluated the similarity and variability in depletion effects among factors rather than determining the function of each factor individually. We also generated a sample treated with the BET bromodomain inhibitor JQ1, as the bromodomain protein BRD4 is reported to interact with NIPBL and to be mutated in CdLS32.Here we used human retinal pigment epithelium (RPE) cells to avoid the effects of aneuploidy or other genomic rearrangements. We used short interfering RNA (siRNA) to deplete cohesin (Rad21), cohesin loaders (NIPBL and Mau2), cohesin unloaders , a boundary element (CTCF), and a cohesin acetyltransferase (ESCO1) individually and carried out two co-depletions  forms the typical chromosome structure, inactive chromosome X (Xi) is partitioned into two megadomains, the boundary between which is affected by depletion of cohesinncy Fig. . Xi showIn summary, our Hi-C analysis showed consistent tendencies with previous studies, confirming its reliability, and provided multiple new findings of diverse depletion effects on chromatin folding.Next, we explored the depletion effect on gene expression and ChIP-seq peaks. We detected 2000–7000 differentially expressed genes (DEGs) for each sample  based on the overlap of up- and downregulated genes among depletions  to examine the overlap of the DEG loci with the ChIP-seq peaks and chromatin loops  would be more persistent even under siNIPBL and siRad21 than non-acetylated sites. This tendency can be observed in Rad21 peaks in siRad21, i.e., since stronger Rad21 peaks remain more under siRad21, the clusters where Rad21 peaks are enriched under siRad21 indicate containing more stronger Rad21 peaks at TSSs. The clusters enriched for Smc3ac and Rad21 under siRad21 are highly correlated . Loops mediated by Smc3ac were enriched in upregulated DEGs associated with siNIPBL and siPDS5B , whereas clusters not enriched for Smc3ac  were downregulated  add to the diversity of this pattern of dysregulation.Figure 42 among samples  values comparing ChIP reads of control and siRNA samples under spike-in normalization43. As reported in previous studies19, broad histone marks  were not substantially affected, suggesting that the loss of TAD boundaries does not cause these histone modifications to spread. In contrast, H3K27ac marks were largely perturbed after NIPBL and Rad21 depletion, indicating that these marks can be affected by changes in intraTAD or interTAD interactions.We next explored the genomic regions that changed significantly after depletions across the genome to investigate the correlation between the depletion effect on the epigenome and chromatin folding Figs.  and S8a.20.We next investigated the binding of cohesin and several related factors. Strikingly, we observed a broad decrease in cohesin binding after siRad21 and siNIPBL  but does not control the amount of cohesin on chromatin.We also evaluated cohesin enrichment from an epigenomic perspective using “extended ChromHMM”18, this depletion effect was more region specific and was not symmetrical  approachcts Fig. . We scancts Fig. . Some ofion Fig. . The sto46 for a distance of 500 kbp–5 Mbp and did observe a striking difference among depletions ns Figs.  and S10ans Figs.  and S10bk = 5; Fig. We also tested whether there is a region-wide depletion effect between TADs and the relationship to the epigenome. For this, we annotated all TADs with epigenomic marks and then classified all TAD pairs based on their relative change in interactions . To circumvent these limitations, we focused here on the context-specific variation associated with the depletion of these factors by investigating the similarity and variability of the depletion effects. We delineated the dominant factors for boundaries and interTAD interactions. In future experiments, this computational strategy should provide more insights as additional samples are analyzed using various conditions and other factors.It should be noted that the siRNA system we used in this study has several limitations, such as incomplete depletion and potential secondary effects. For example, cohesin- and CTCF-independent boundaries may be lost after extreme depletion  supplemented with Penicillin-Streptomycin-l-Glutamine Solution (Wako), 10% fetal bovine serum (Biosera), and 20 mM HEPES-KOH (pH 7.4). All siRNA transfections were performed using Lipofectamine RNAiMAX (Thermo Fisher Scientific) and the manufacturer’s protocol 2 or 3 days before sample preparation, with a final RNA duplex concentration of 50 nM. The siRNA sequences are shown in Supplementary Data 52. For inhibition of BET family proteins, cells were treated with JQ1  for 6 h at a 1 μM final concentration. We labeled JQ1-treated and the corresponding control samples as JQ1(+) and JQ1(–), respectively.We used the siRNA system for depletion, as the auxin-inducible degradation system reduces protein levels even in the absence of auxin, which is unsuitable as a controlS-KOH pH .4. All s53, H3 lysine-4 trimethylation (H3K4me3), H3 lysine-9 trimethylation (H3K9me3), H3 lysine-36 trimethylation (H3K36me3), and Pol2ser2 were provided by Dr. Kimura . We also used antibodies against Rad21, Smc3ac, and ESCO1, which were described previously52. Antibodies against NIPBL , Mau2 , CTCF , BRD4 , AFF4 , Pol2  and H3 lysine-27 trimethylation  were used for ChIP. Antibodies against NIPBL , α-tubulin , WAPL , PDS5A , PDS5B , and CTCF  were used for immunoblotting. The mouse monoclonal antibody against Mau2 was generated using a synthetic peptide corresponding to residues 596–613 (PVQFQAQNGPNTSLASLL) of human Mau2 and used for immunoblotting. Antibody dilutions for immunoblotting were 1:500 (NIPBL and ESCO1) and 1:1000 .Antibodies used for ChIP and immunoblotting were as follows. Antibodies against histone H3 lysine-27 acetylation (H3K27ac)2; 10 mM sodium butyrate; 0.25% Triton X-100; 1 mM dithiothreitol; 1× cOmplete protease inhibitor cocktail [Roche]) as described28. The resulting lysate was mixed with SDS-PAGE sample buffer  and boiled for 5 min. The proteins were analyzed with a Mini-PROTEAN Tetra Vertical Electrophoresis Cell (Bio-Rad) following the manufacturer’s protocol.Cells were lysed with lysis buffer . Fixed cells were permeabilized in Hi-C lysis buffer  on ice. The cells were treated with 100 U of MboI (New England Biolabs) for chromatin digestion, and the ends of digested fragments were labeled with biotinylated nucleotides followed by ligation. After DNA reverse crosslinking and purification, ligated DNA was sheared to a size of 300–500 bp using a Covaris S2 focused-ultrasonicator . The ligated junctions were then pulled down with Dynabeads MyOne Streptavidin T1 beads (Thermo Fisher Scientific). The pulled-down DNA was end-repaired, ligated to sequencing adaptors, amplified on beads, and purified using a Nextera Mate Pair Sample Preparation kit (Illumina) and Agencourt AMPure XP (Beckman Coulter). DNA was then sequenced to generate paired-end 150-bp reads using the Illumina HiSeq-2500 or X Ten system.We used the in situ Hi-C protocol as described in Rao et al.https://custardpy.readthedocs.io). It is written in Python3.7 and is available using the Docker system (https://www.docker.com/). CustardPy is designed to compare multiple Hi-C samples to evaluate the variation of depletion effects across multiple proteins .We developed various custom scripts for this study, most of which were integrated into the Hi-C analysis pipeline CustardPy . We then generated contact map files with square root vanilla coverage (VC_SQRT) normalization. We used 25-kbp resolution maps unless otherwise described. We called TADs using the Juicer tools Arrowhead command. Because the obtained TADs can be nested, we also generated a list of non-overlapping TADs by segmenting the genome based on all TAD boundaries. TAD boundaries were defined as edges for all annotated TADs. Loops were called at 5-kbp, 10-kbp, and 25-kbp resolution by the Juicer tools HiCCUPS command. To obtain peak-overlapping loops  and extracted loops for which both anchor sites overlapped with the peaks. High-resolution data that combined all replicates were generated by mega.sh script provided in Juicer. Eigenvector (PC1) values for compartment analysis were calculated by HiC1Dmetrics44 because the Eigenvector command in Juicer tools sometimes failed. For allele-specific Hi-C analysis of chromosome X, we obtained single-nucleotide polymorphism data for RPE cells from Darrow et al.56, which was then converted to genome build hg38 by the liftOver tool (https://genome-store.ucsc.edu/). We modified the diploid.sh script provided by Juicer and made interaction map files for active and inactive chromosome X.Sequenced reads were processed using Juicer version 1.5.7 and Juicer tools version 1.9.9ops Fig. , we used57. siRad21 and siNIPBL were most affected by this fact, because almost all TADs and loops were depleted after these treatments. Therefore, our analysis focused on the variation of depletion effects across samples to capture the context-specific tendency, rather than translating the biological meaning of increased/decreased interactions.The samples and mapping statistics are summarized in Supplementary Data 58, which internally implements QuASAR59 and HiCRep60. Because of the large computational complexity involved, we used only chromosomes 21 and 22 with a 50-kbp bin for the quality evaluation. We confirmed that all of our Hi-C data had sufficient quality  for APA plots, plots of average TAD data, and saddle plots. We used GENOVA62 to calculate the compartment strength. The Hi-C matrices with ChIP-seq distributions were visualized using CustardPy commands.To evaluate the quality and reproducibility of our Hi-C data, we used 3DChromatin_ReplicateQC46 implemented in HOMER (http://homer.ucsd.edu/homer/). SIMA assembles information for multiple occurrences of each feature, providing an overview of Hi-C interactions associated with a genomic feature between each pair of specified domains. In this study, the genomic features included the ChIP-seq peak list , and TSSs of DEGs . Domains of interest were defined as TAD lists, and the distance between two TADs was specified to be <5 Mbp, <2 Mbp, or 2 to <5 Mbp with ‘-max -min’ parameters. By comparing the background model, we obtained an enrichment score, representing the degree to which a genomic feature pair was enriched in the Hi-C interactions between two TADs. To compare differences in enrichment scores between cohesin-knockdown and control Hi-C samples, we used the paired Wilcoxon signed-rank test to calculate the p-value and effect size for each genomic feature pair, as described30. We used Cytoscape v3.8.2 (https://cytoscape.org/) to visualize the results.To explore interactions between specific chromatin features , we used a SIMA42. In brief, the insulation score was calculated at a resolution of 25 kbp as the log-scaled relative contact frequency across pairs of genomic loci located around the genomic positions from 100 kbp to 1 Mbp. The 500-kbp distance was used in the single insulation score analysis. For the classification of insulation boundaries into six types, we used the following criteria based on this 500-kbp insulation score:Tins and if(siCTCF – control) > Tins: “all-dependent”if(siNIPBL – control) > Tins or if(siRad21 – control) > Tins: “cohesin-dependent”else if(siNIPBL – control) > Tins: “CTCF-dependent”else if(siCTCF – control) > Tins: “CTCF-separated”else if(control – siCTCF) > Tins or if(control – siRad21) > Tins: “cohesin-separated”else if(control – siNIPBL) > Tins and if(siCTCF – control) <Tins and if(siCTCF – control) < Tins: “robust”else if(siNIPBL – control) <where we set Tins, the threshold of the insulation score, as 0.13. We excluded siMau2 as a criterion because it had a smaller effect than siNIPBL and siRad21 on the insulation score. We excluded chromosomes X and Y and the mitochondrial genome from this boundary analysis. The obtained six boundary types are summarized in Supplementary Data CustardPy calculates a multi-scale insulation score as described44. DRF measures the bias in the relative interaction frequency M = log(CsiRNA) – log (Ccontrol) between regions up- and downstream of each genomic region, where C is a normalized contact matrix. Therefore, the DRF can be calculated bylmin and lmax indicate the range of the interaction. In this study, we set lmin = 500 kbp and lmax = 2 Mbp.CustardPy can calculate the DRF that identifies the directional bias of depletions on interTAD interactionsTDRF or <–TDRF, where TDRF refers to the DRF threshold. We set TDRF = 0.7 in this study. The obtained differential DRF regions are summarized in Supplementary Data To obtain differential DRF regions, we classified Hi-C samples as “cohesin and loaders,” “cohesin unloaders,” and “others (including control)”. We then calculated the average DRF values and 99% confidence intervals (CIs) for each region. We used these values to identify the regions that satisfied the following criteria: the 99% CI ranges of “cohesin and loaders” and “others” did not overlap, and the average DRF value of “cohesin and loaders” was > 63 with the following options “SortedByCoordinate --quantMode TranscriptomeSAM --outSAMattributes All”. The samples and mapping statistics are summarized in Supplementary Data 64 with the option “--estimate-rspd --strandedness reverse”. We used DESeq265 to identify DEGs . We focused on protein-coding genes to avoid the effects of repetitive non-coding RNAs.Total RNA was isolated using Trizol (Thermo Fisher Scientific) and a Nucleospin RNA kit (Macherey-Nagel). rRNA was removed with the Ribo-Zero Gold rRNA Removal kit (Illumina), followed by sequencing library preparation with the NEBNext Ultra Directional RNA Library Prep kit for Illumina (New England Biolabs). Single-end 65-bp reads were sequenced by the Illumina HiSeq-2500 system. Sequenced reads were mapped to the human reference sequence (GRCh38) by STAR version 2.7.3a66 for the GO enrichment analysis.To mitigate the indirect effect and the technical variances, we generated the list of DEGs by merging the top-ranked 1000 DEGs from each pairwise comparison between each siRNA and the controls. For clustering RNA-seq samples based on DEGs, we used the Simpson index to compare the binary overlap of DEGs. We did not adopt quantitative clustering using the z-score of gene expression level because we prepared the RNA-seq samples by three different experiments, each with control samples, and the quantitative analysis would be more affected by the technical variations due to sample preparation. For clustering DEGs, we generated DEG vectors of each depletion containing {1: upregulated, −1: downregulated, 0: not included} and created a matrix by combining the vectors of all depletions. We applied K-means clustering to the matrix and classified the DEGs into 20 clusters. We used clusterProfiler7. In brief, ~8 × 106 RPE cells were crosslinked with 1% formaldehyde for 10 min at room temperature, followed by an additional 5 min with glycine in PBS added at a final concentration of 125 mM. Fixed cells were lysed in LB1  on ice. The lysate was washed sequentially with LB2  and LB3  on ice. The lysate was resuspended in LB3 and sonicated using a Branson Sonifier 250D (Branson) for chromatin shearing . In addition, lysate containing fragmented chromatin was also prepared from ~2 × 106 mouse cells (C2C12) with the same procedures. Human cell lysate and mouse cell lysate  were combined (~4:1 ratio) and incubated with protein A or G Dynabeads (Thermo Fisher Scientific) conjugated with 2 μg of the relevant antibodies for 14 h at 4 °C. The beads were then washed five times with cold RIPA wash buffer  and once with cold TE50 . Material captured on the beads was eluted with TE50 containing 1% SDS. The eluted material and input were incubated for 6 h at 65 °C to reverse crosslinks. They were then treated with 100 ng RNaseA (Roche) for 1 h at 50 °C, followed by treatment with 100 ng Proteinase K (Merck) overnight at 50 °C. The input and ChIP DNA were then purified with a PCR purification kit (Qiagen). DNA from the ChIP and input fractions was end-repaired, ligated to sequencing adaptors, amplified, and size-selected using NEBNext Ultra II DNA Library Prep kit for Illumina (New England Biolabs) and Agencourt AMPure XP (Beckman Coulter). DNA was then sequenced to generate single-end 65-bp reads using the Illumina HiSeq-2500 and NextSeq 2000 systems.Chromatin preparation for ChIP was performed as described67 with default parameters. Quality assessment was performed with SSP version 1.2.268 and DROMPAplus version 1.12.169. Spike-in read normalization, peak calling, and visualization were performed with DROMPAplus. The mapping statistics, quality values, and scaling factors for spike-in normalization are summarized in Supplementary Data 10(p) scores of ChIP/input enrichment (--showpenrich 1 option), which is recommended for distinguishing the signal from the noise70.Reads were aligned to the human genome build hg38 and mouse genome build mm10 using Bowtie2 version 2.4.1n = 1000) that compared the relative overlap frequency against the background distribution. As a background, we used all DEGs obtained by DESeq2  for DEGs and all boundaries (7421) for the six types of boundaries. We randomly picked up the same number of genes or boundaries from the background in each permutation and generated the frequency distribution. For the boundary analysis, we counted DEGs and the peaks that overlapped within 50 kbp of them.To compare the overlapping ratio of TSSs of DEGs and ChIP-seq peaks, Hi-C loops, and insulation boundaries, we implemented a permutation test (2(NsiRNA/Ncontrol) where NsiRNA and Ncontrol indicate the total number of fragments mapped within the interTAD regions between a TAD pair (with ≤2 Mbp between them) annotated with the epigenomic marks. We calculated scores for each TAD pair and applied k-means clustering (k = 5). Then we calculated the z-score–normalized fraction of epigenomic status for the TAD pairs included in each cluster to estimate the epigenomic-dependent depletion effect of interTAD interactions.For interTAD interaction comparisons, we extracted all TAD regions with widths of >100 kbp and annotated them using the epigenomic marks  if the marks covered >40.0% of the TAD length. To avoid a low read coverage at long-range distances and the technical effect derived from the different resolutions of Hi-C matrices, we used the log-fold change log44. In this study, we added CTCF, TAD boundaries, and compartment information in addition to five core histone marks  to ChromHMM and annotated 15 chromatin states.Our previous study showed that several one-dimensional metrics for Hi-C data are effective for annotating chromatin states in detailFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting SummarySource Data


E. coli K-12 MG1655 genomic DNA using Escherichia coli RNAP holoenzyme (including σ70) and yielded 3226 transcription start sites, 2167 of which were also identified in in vivo studies, and 598 were new. Many new promoters not yet identified by in vivo experiments might be repressed under the tested conditions. Complementary in vivo experiments with E. coli K-12 strain BW25113 and isogenic transcription factor gene knockout mutants of fis, fur, and hns were used to test this hypothesis. Comparative transcriptome analysis demonstrated that ROSE could identify bona fide promoters that were apparently repressed in vivo. In this sense, ROSE is well-suited as a bottom-up approach for characterizing transcriptional networks in bacteria and ideally complementary to top-down in vivo transcriptome studies.The global characterization of transcriptional regulatory networks almost exclusively uses in vivo conditions, thereby providing a snapshot on multiple regulatory interactions at the same time. To complement these approaches, we developed and applied a method for characterizing bacterial promoters genome-wide by in vitro transcription coupled to transcriptome sequencing specific for native 5′-ends of transcripts. This method, called ROSE (run-off transcription/RNA-sequencing), only requires chromosomal DNA, ribonucleotides, RNA polymerase (RNAP) core enzyme, and a specific sigma factor, recognizing the corresponding promoters, which have to be analyzed. ROSE was performed on  Most bacteria must quickly adapt to changing environmental conditions, such as temperature, pH, osmolarity, or nutrient availability. Gene expression can be regulated at every step, from transcription to post-translational processing; however, transcription and, more accurately, transcription initiation is the first and most widely regulated step in bacteria . One impE. coli, seven sigma factors have been described: the primary sigma factor σ70 and six alternative sigma factors . σ70 is the housekeeping sigma factor responsible for the transcription of most growth-related genes. The consensus sequence of σ70-dependent promoters (5′-TTGACA-17-TATAAT) is well known and highly conserved among σ70-dependent promoters. However, the −10 region, which occurs about seven base pairs upstream of the transcription start site, shows a higher level of conservation than the −35 region in E. coli [In Bacillus subtilis, the ROMA method was successfully applied to E. coli and was used to disentangle the overlapping σ70 and σ38 regulons [The run-off transcription-microarray analysis (ROMA) method has been described for genome-wide analysis of transcription regulated by sigma factors . Here, pregulons . ROMA alROMA also has limitations, including the lack of single-nucleotide resolution and transcriptional read-through at convergently oriented genes, possibly leading to false-positive signals. MacLellan et al. observed the extended transcription of up to 10 kb downstream of the active promoters . AlthougE. coli core RNA polymerase with the ECF sigma factors of Corynebacterium glutamicum [Streptomyces coelicolor. Since there are several technical differences between ROSE and RIViT-seq, especially with the preparation of the primary transcript libraries and the determination of transcription start sites (TSSs), we like to keep the name ROSE and refer to the technical differences compared to RIViT-seq and their implications in more detail below. ROSE and RIVit-seq can be regarded as different flavors of a ‘bottom-up’ approach to transcription regulatory networks in bacteria. Therefore, they are ideally complementary to ‘top-down’ transcriptome analysis in vivo, e.g., by comparing wildtype and transcription factor mutant strains.We developed run-off transcription/RNA-seq (ROSE) to overcome these limitations. ROSE employs genome-wide in vitro transcription with isolated RNA polymerase and genomic DNA. In vitro transcribed RNA analysis includes the preparation of native 5′-end-specific transcript libraries  and subsutamicum . A similutamicum . RIViT-sE. coli RNA polymerase holoenzyme with σ70 to characterize bacterial transcriptional regulatory networks of E. coli. In this study, we demonstrate the power of ROSE by using the native E. coli K-12 MG 1655, cultivated overnight on solid lysogeny broth medium [E. coli RNA polymerase buffer . Template DNA and reconstituted RNAP holoenzyme were incubated at 37 °C for 15 min, followed by the addition of NTPs to a final concentration of 200 nM each to start the transcription reaction. After 60 min, in vitro run-off transcription was terminated by five-minute incubation at 65 °C. Template DNA was digested with DNase I  immediately after in vitro transcription . In vitro transcribed RNA was purified using a Qiagen RNeasy MinElute Kit, including a second DNase digestion, and eluted in nuclease-free water. At least three independent in vitro transcription reactions were combined to obtain sufficient RNA for transcriptome sequencing. Nucleic acid concentration and purity were determined with an Xpose spectrophotometer. RNA quality and size distribution were checked on an Agilent Bioanalyzer 2100 using the RNA Pico assay. PCR amplification has ruled out residual DNA contamination with specific primers binding to the E. coli genome. Genomic DNA from h medium , was isofur), #10443 (Δfis), and #9111 (Δhns). Deletion strains were cultivated in liquid LB broth containing 50 mg/mL kanamycin in shaking flasks . After inoculation to an OD600 of 0.05 from an overnight liquid culture, 2 mL cell suspensions were harvested at an OD600 of 0.8 in the exponential growth phase and immediately frozen in liquid nitrogen and stored at −80 °C. For the wildtype strain, another sample was taken after cells reached the stationary growth phase.The following transcription factor deletion mutants were derived from Coli Genetics Stock Center (CGSC): #7636 (BW25113), #8758  genome sequence using bowtie2 v2.3.0 [High throughput single-end sequencing (1 × 75 bp) was conducted on Illumina MiSeq . Reads were quality trimmed using Trimmomatic v0.3.5  with thehttps://users.soe.ucsc.edu/~kent/improbizer/improbizer.html (accessed on 24 May 2023) or MEME v4.1.2 [After identification of transcription start site (TSS) positions with ReadXplorer v2.2.3 , upstreaE v4.1.2 . FinallyE v4.1.2 . For hig70-saturated form of E. coli RNA polymerase (Eσ70). In contrast to ROMA and RIViT-seq [The ROSE method was developed based on ROMA , which uIViT-seq , templatAlthough ROSE and RIViT-seq aim for a genome-wide in vitro-transcriptome, the two approaches have distinct technical differences  were extracted for further analysis. All 3226 putative promoter sequences were subjected to motif enrichment analysis using Improbizer . Two disendently . As expeIn contrast, a −35 consensus motif, namely a conserved ttGA about 35 nucleotides upstream of the transcription start site, was derived from 2922 promoter sequences. A total of 2838 putative promoter sequences contained both −10 and −35 regions. Only 11 sequences did not resemble either the −10 or the −35 consensus sequence. 70 recognizes natural σ70-dependent promoters in vitro with high specificity and initiates transcription at well-defined nucleotides. Transcription initiation occurred preferentially at purine bases (A/G), which was observed in 81.0% (50.3% A and 30.7% G) of the detected promoters. Interestingly, the base directly upstream of the TSS at position −1 prefers pyrimidine bases, with 77.3% of the promoters harboring T (41.3%) or C nucleotides (36.0%) at the respective position. Both findings align with in vivo transcriptional profiling studies, reporting similar nucleotide preferences of 78.6% purine bases at +1 and 80.2% pyrimidine bases at −1 [It is apparent that Eσes at −1 ,20.Escherichia coli K-12 MG1655 contains 4146 genes organized in 2376 transcription units. In total, 1523 transcription units are monocistronic, whereas 853 operons have more than one gene [E. coli, including experimentally determined transcriptional start sites of the strain K-12 MG1655. In a subset of the database, TSSs are assigned to the different sigma factors and provided with a level of evidence , depending on the informative value of the method for TSS identification. For the following comparison, only those TSSs were considered to belong to the classes “Confirmed” or “Strong”. In addition, to cope with different experimental methods of TSS identification and issues of the template, such as the degree of supercoiling, a deviation of three nucleotides in either the upstream or downstream direction has been allowed to compare two TSS positions. The mapped TSSs show a clear peak, with 64.7% having zero and 7.0% having one nucleotide deviation in either direction  were also identified in the ROSE-Eσ70 experiment. A total of 30 TSSs found in our ROSE experiment are assigned to other sigma factors in the database with no affiliation to σ70. In total, 25 of these TSSs are classified as σ38-, and the other five as σ32-dependent promoters; however, it is known that the consensus sequence of σ38-dependent promoters is similar to the σ70 consensus sequence, and a clear distinction between both promoter sets cannot be made [70 but listed as σ38-dependent in RegulonDB were compared to those of σ38-dependent promoters that ROSE did not recognize. Again, 50 nt upstream of the TSSs have been extracted and analyzed for conserved motifs. Comparing the resulting motifs clearly shows differences, mainly in the −10 regions. The presumed σ38-dependent promoters show conserved bases at all positions from −12 through −7 (TATACT), whereas in the σ38-dependent promoters not detected by ROSE-Eσ70, only the bases at −12, −11, and −7 are conserved (TANNNT). Additionally, there is a C at position −13, upstream of the −10 region, described earlier as a distinct sequence characteristic in σ38-dependent promoters [38-dependent promoters is a highly conserved GC at positions −33/−32 (ttGC), occurring in most σ38-dependent promoter sequences, with a higher conservation of the TT at position −35/−34 in the promoters present in ROSE-Eσ70  of the overlapping TSSs, while ROSE-Eσ70 and Thomason et al. had an exact match at 86.2% (±1 bp: 7.6%) of the TSSs.Comparing the three TSS datasets showed that 2006 (62.2%) of the TSSs detected by ROSE were also determined by Thomason et al., while 168 further TSSs are confirmed by the study included in RegulonDB. A set of 755 TSS was contained in all three datasets . Again, E. coli genome but not described in existing TSS mapping studies. The second class includes those that are repressed or not activated under standard in vivo testing conditions. In total, 2303 transcriptional start sites detected by ROSE-Eσ70 are yet undescribed, according to RegulonDB. Thomason et al. identified 1254 of those TSSs in vivo. The remaining 1049 upstream regions were subjected to motif enrichment analysis using Improbizer [70 consensus sequence. Due to this, it can be speculated that these promoters were repressed under the conditions tested in the in vivo studies. Manual inspection showed regulator binding sites around many of these promoters, suggesting that transcription from those promoters is prevented in vivo by known transcriptional regulators, such as H-NS, Fur, or Fis. We performed in vivo experiments for each of the three regulators with defined transcription factor knockout mutants from the KEIO collection [hns, JW0669 for Δfur, and JW3229-1 for Δfis. Sequencing on Illumina MiSeq yielded, on average, 0.91 million reads per library  and ftnA (b1905) are both negatively regulated by H-NS, a global transcriptional silencer [E. coli genes [stpA has a TSS at position 2,798,556 and a perfect σ70-like −10 region (TATAAT). The gene ftnA has a TSS at position 1,988,682 and has a complete σ70-like promoter (TTGCAA-16-TATAGT). Both genes showed no transcription in the wildtype strain, but transcriptional activity was measured in the Δhns knockout strain and in the ROSE approach  has already been described for genomic position 4,605,777 in the E. coli MG1655 genome. According to EcoCyc, there are two ferric uptake regulator (Fur) binding sites in the vicinity of the transcription start site of yjjZ [70-like consensus sequence (TTGCAA-18-TATGAT), Thomason et al. did not detect a transcription start site for yjjZ, suggesting efficient transcriptional repression in vivo. This has been validated in our in vivo experiment, where the wildtype strain has shown no activity of the yjjZ gene; however, in the Δfur knockout strain, transcription from the σ70 promoter is measurable. Moreover, the TSS has been identified clearly in vitro (1494 read starts) using the ROSE method. (see fepA (b0584) [fepA promoter has a σ70-like consensus sequence (TTGCAG-14-TATTAT) and was not detectable in vivo in the wildtype strain; however, both the Δfur knockout strain  and ROSE  show transcriptional activity for the gene fepA  ,34  for the gene in vivo. In the knockout strain, the amount of read starts was five times higher than in the wildtype strain, demonstrating the higher transcription of glcC in the absence of the Fis regulator; however, the most read starts and the strongest transcription of the gene were identified by the in vitro ROSE approach (493 read starts) (see aer (b3072), which shows a clear transcription start site in ROSE, the Δfis knockout strain at position 3,219,346, and that it is harboring a σ70-like consensus sequence (TTGTGC-19-TAACAT). This transcription start site is also described in the publication of Thomason et al. but is not defined in RegulonDB. Nevertheless, RegulonDB contains a Fis binding site with an unknown function upstream of the aer gene  and a −35 sequence (TTAACT). As stated in rts) see . Anotheraer gene C. ROSE a aer see . ndh of E. coli expresses the NADH dehydrogenase II. The corresponding promoter Pndh is located at position 1,165,992 of the genome and is harboring a standard σ70-like consensus sequence (TTGGTA-21-TATTCT). This gene is negatively regulated by multiple transcription factors, such as FNR [2+ [ndh, no transcription was detectable in the E. coli wildtype strain or the single knockout strains in vivo. However, the ROSE method showed a distinct TSS at the known position of Pndh with over 200 read starts  of E. coli can be activated and repressed by the transcriptional regulator AraC in vivo, depending on the availability of arabinose [araBAD operon by ROSE-Eσ70, suggesting that CRP and/or AraC activation is critical for transcription initiation at pBAD. Another instance is the σ70-dependent promoter of csiE (b2535), known to be activated by both CRP and H-NS in vivo [70. The σ70-dependent promoter of alkA is activated upon binding to Ada, a DNA repair protein, which is a critical component of the adaptive response [70, obviously requiring activation by Ada. In conclusion, ROSE robustly and comprehensively identifies bona fide promoters and those potentially repressed under in vivo conditions. It also allows drawing conclusions from negative results, predicting efficient activation in vivo.A different type of σrabinose ,40. It irabinose ,42,43. S in vivo ,45. Dualresponse ,47. The 70 regulon of E. coli K-12 MG1655.In this study, we developed the ROSE method for genome-wide in vitro transcriptional profiling and validated it by exploring the σLike ROMA , ROSE isIn vitro transcription analyzed by genome-wide methods, as in ROMA , RIViT-sE. coli σ70 regulatory network proved consistent with in vivo-based transcriptome studies. Accordingly, 2174 of 3226 TSSs (67.4%) identified by ROSE were also described earlier in comprehensive reference studies [E. coli RNAP showed high specificity with only 11 detected TSS lacking a typical σ70 promoter motif. Interestingly, ROSE-Eσ70 data also contained TSS earlier assigned to other sigma factors . Apart from possible dual recognition in vivo, linear template DNA confirmation could have facilitated this issue, as the σ70-containing holoenzyme is known to preferentially initiate transcription on more highly supercoiled DNA [leu-500 promoter from E. coli [24 or σ38 for transcription initiation. Despite this, ROSE-σ70 was able to identify several novel potentially regulated promoter sites for all three tested transcription factors. Interestingly, our research also revealed the presence of genes, like yjjZ, which were believed to be regulated by one of the tested factors but did not exhibit any read starts in our in vivo experiments. Nevertheless, these genes were shown to have noticeable TSSs in ROSE. These are more complex promoters with multiple repressor binding sites, and numerous knockouts would be needed to identify this TSS in vivo. For example, the promoter Pndh of the gene ndh is repressed by different regulators like FNR [2+ [ndh gene and demonstrated the method’s power due to its minimalistic construction (araBAD operon or the promoter of the gene csiE.The ROSE analysis of the  studies ,22, whililed DNA ,49; howeiled DNA ,51,52. Tlike FNR , Fur-Fe2 FNR [2+ , and Nsrfactors σ, σ32. Aptruction , howeverE. coli genomes. For example, the E. coli RNAP holoenzyme has also been used successfully for in vitro transcription of promoters from other bacteria [E. coli RNAP core enzyme with sigma factors of Streptomyces coelicolor is possible for genome-wide in vitro transcription studies. However, problems might arise if the interaction of the organism-specific RNAP core enzyme with distinct promoter motifs or sigma factors is crucial for transcription. Therefore, homologous RNAP complexes have been isolated and functionally tested for a broad spectrum of bacteria like Bacillus subtilis [Pseudomonas aeruginosa [Mycobacterium tuberculosis [Corynebacterium glutamicum [Therefore, expanding ROSE appears possible by adding regulators, such as transcription factors or metabolite effectors, to directly investigate their influence on transcriptional regulation. This has, for instance, been demonstrated for the regulator protein DksA  and the bacteria ,57. Furtbacteria  demonstrsubtilis , Pseudomruginosa , Mycobacrculosis  or Corynutamicum  and can The global in vitro transcription method ROSE presented in this study is the perfect addition to classical global in vivo and local in vitro transcription assays due to its simplicity and wide range of possible applications. It can be used to identify the primary effects of different sigma factors and their binding motifs with single-nucleotide resolution. We are expanding the technology by transferring it to other bacteria and adding regulatory proteins and small molecules.


Pairs of sense and antisense transcriptions that are adjacent at their 5′ and 3′ regions are called divergent and convergent transcription, respectively. However, the structural properties of divergent/convergent transcription in different species or RNA biotypes are poorly characterized. Here, we developed CCIVR2, a program that facilitates identification of both overlapping and non-overlapping antisense transcripts produced from divergent/convergent transcription whose transcription start sites (TSS) or transcript end sites (TES) are located within a specified region. We used CCIVR2 to analyze antisense transcripts starting around the sense TSS (from divergent transcription) or ending around the sense TES (from convergent transcription) in 11 different species and found species- and RNA biotype-specific features of divergent/convergent transcription. Furthermore, we confirmed that CCIVR2 enables the identification of multiple sense/antisense transcript pairs from divergent transcription, including those with known functions in processes such as embryonic stem cell differentiation and TGFβ stimulation. CCIVR2 is therefore a valuable bioinformatics tool that facilitates the characterization of divergent/convergent transcription in different species and aids the identification of functional sense/antisense transcript pairs from divergent transcription in specified biological processes. Most mammalian promoters lack TATA elements but are CpG rich. Consistently, many instances of bidirectional transcription have been identified in mammals3. Bidirectional transcription is known as divergent transcription5, which typically generates short (50–2000 nucleotide) and relatively unstable upstream antisense RNAs, termed transcription start site-associated (TSSa)-RNAs, upstream antisense RNAs (uaRNAs), and PROMoter uPstream Transcripts (PROMPTs)7. Interestingly, this divergent transcription is hypothesized to be a driving force for the generation of new genes. In this model, initially short and unstable TSSa-RNA/uaRNAs/PROMPTs, evolve into longer and more stable genes by gaining U1 sites and losing polyadenylation signals (PASs)8. Indeed, some transcripts from divergent transcription are a protein-coding mRNA and a long non-coding RNA (lncRNA) that contain relatively long exon/intron structures and polyA sequences10, and some of these lncRNAs function to regulate gene expression12. However, the functions of many transcripts from divergent transcription are largely unknown.A gene corresponds to a single transcription unit starting from a promoter, which may or may not contain TATA elements. In promoters with TATA elements, the TBP complex binds to the TATA element in a direction-dependent manner, thereby determining unidirectional transcription. In contrast, in promoters with CpG-rich elements instead of TATA elements, the TBP complex binds in both directions via CpG-binding transcription factors, such as Sp1, resulting in bidirectional transcription14. Among alternative polyA sites, a functional PAS is sensed by CPA factors causing slowing of Pol II transcription15. The transcribed PAS is cleaved and a polyA sequence is added by CPA factors to generate the mRNA end sequence, called the transcript end site (TES). Transcription continues through the PAS and is completed by structural changes in Pol II 16 and/or degradation of the co-transcriptionally cleaved transcript (CoTC sequence) by Xrn2, which causes Pol II to detach from the RNA and terminate transcription (torpedo model)18. This detaching point of Pol II is called the transcription termination site (TTS) and is usually located several kilobases after the PAS, although some transcripts terminate shortly after the PAS. Pairs of transcription consisting of one sense transcription and one antisense transcription that are initiated from different gene regions and overlap in the 3′ region are called convergent transcription. The overlap region at the 3′ end of sense/antisense pairs can function in regulating the expression of partner genes through polymerase collisions and double-strand RNA formation, which can mask the binding of miRNAs20. Although convergent transcription and the mechanism of its regulation have been reported, structural characteristics of convergent transcription in different species or RNA biotypes are poorly defined.The single transcription unit of a gene terminates at the terminator. Termination of transcription by eukaryotic RNA polymerase II (Pol II) proceeds in a stepwise manner. The largest subunit of Pol II contains a unique carboxyl-terminal domain (CTD) consisting of a repeated heptameric sequence (YSPTSPS). During transcription elongation, the second serine in the heptad repeat is phosphorylated and this enhances interaction with the 3′-end cleavage and polyadenylation (CPA) complexcomprehensive cis-NATs identifier via RNA-seq data) that can identify cis-NATs with their structural features including EB, FO, HH, and TT, based on locational information21. However, CCIVR can only identify overlapping antisense transcripts and not non-overlapping transcripts, such as nearby head-to-head (nHH) and nearby tail-to-tail (nTT) transcripts, which may function in collaboration with their partner genes.Natural antisense transcripts (NATs) encode sequences that are complementary to RNA transcripts of genes. Unlike trans-NATs, whose partner genes are other genomic loci, cis-NATs are transcribed from the opposite strand of their partner genes and overlap completely or partially with their partner genes. Cis-NATs can be classified into four different types according to their structural characteristics. In the embedded type (EB), the entire transcription unit of the antisense gene is embedded in the transcription unit of the sense gene. In the fully overlapped type (FO), the antisense transcription unit covers the entire sense gene. In the head-to-head (HH) type, the sense and antisense genes partially overlap only at their 5′ ends, while in the tail-to-tail (TT) type the genes partially overlap only at their 3′ ends. We previously developed a simple and convenient program termed CCIVR  and 3′ (i.e. nTT) non-overlapping antisense transcripts. Furthermore, in CCIVR2, the user can specify the region where antisense transcription start sites (TSSs) or transcript end sites (TESs) are located to identify both overlapping and non-overlapping antisense transcripts within the region. CCIVR2 can comprehensively identify antisense transcripts from divergent transcription, including those with known functions. CCIVR2 therefore facilitates the characterization of TSSs and TESs in sense/antisense gene pairs and aids the identification of candidate functional antisense transcripts from divergent transcription.21; however, CCIVR can only identify overlapping and not non-overlapping antisense transcripts, which may be functional. To identify and investigate antisense transcripts that do not overlap with the sense transcript but that are transcribed nearby and faraway, we developed CCIVR2. We distinguished non-overlapping antisense transcripts adjacent to a sense transcript (within 5 kb) as “nearby” and those more distant as “faraway” . CCIVR2 can be run in one of three modes  and plus (i.e. downstream) positions relative to the TSSs/TESs of the sense transcripts. Therefore, non-overlapping antisense transcripts can be extracted when the range is set in the minus position (i.e. upstream) in Mode 2 and in the plus position (i.e. downstream) in Mode 3. Conversely, if the range is set to the plus position in Mode 2 or to the minus position in Mode 3, it is also possible to extract the overlapping cis-NATs as well as the non-overlapping antisense transcripts beyond them. After setting the range, the input data is divided into two groups of gene sets according to whether the genes are on the plus ( +) or minus (−) strand. Then, the antisense transcripts that match the conditions are sequentially extracted from each strand in two parts: plus-to-minus extraction and minus-to-plus extraction. The antisense transcripts extracted in each part are listed as gene pairs in a transient dataset. Finally, these two transient datasets are combined to generate an output file that contains all antisense transcript data  is shown in Fig. 21). As in previous CCIVR analyses21, we chose 11 genetically well-studied representative model organisms for CCIVR2 analysis. Each species has various types of RNA . Therefore, we first analyzed the data by using all types of RNA . The regions to be analyzed comprised 20 sections of 500 bp in the interval from 5 kb upstream to 5 kb downstream, and the percentage of genes possessing antisense transcripts to the total number of genes in each particular region was calculated.We next attempted to comprehensively extract antisense genes with TSSs within 5 kb upstream to 5 kb downstream of the TSS of a sense gene . Similarly, we also attempted to comprehensively extract antisense genes with TESs within 5 kb upstream to 5 kb downstream of the TES of a sense gene (i.e. convergent transcription including both overlapping and non-overlapping) , likely because of the presence of large amounts of piRNAs specific to the species . To avoid the influence of species-specific RNA types, we performed a series of such analyses focusing on protein-coding genes only. The results confirmed divergent and convergent transcription to have the trend of protein-coding genes having a high percentage of sense/antisense pairs around their TSS/TSS and TES/TES contact points . However, for some species the trend was less remarkable. For example, in . HoweverDanio rerio, Xenopus tropicalis, Gallus gallus, Mus musculus, and Homo sapiens, we expanded the range of analysis to reflect the gene length of each species. We found that the trend was observed in all species . This indicates that the trend to avoid duplication of antisense transcription within the gene body of a protein-coding gene is common to many species. Therefore, CCIVR2 facilitates analysis of transcription start and end point characteristics of sense/antisense pairs based on gene sequence information.In these species, whose genomes and gene lengths are relatively short, the abundance of antisense TSS-harboring genes decreased significantly in the regions downstream of sense genes’ TSSs (i.e. 5' end of the gene body of the sense genes)  of Huh-7 human liver cancer cells in response to TGFβ 27. Based on RNA-seq data, we defined “expressing genes” among both coding genes and lncRNAs as those with a TPM greater than 1 at least one of the time points and not 0 at any time point  differentiation via embryoid body (EB) formation Fig. A26 and eint Fig. C and TGFint Fig. B and humint Fig. E. We theint Fig. D and TGFint Fig. C and humint Fig. F. TherefWe then investigated whether the correlation of the expression pattern of each gene from a sense/antisense pair varies with the distance between their TSSs and TESs by performing Spearman correlation coefficient analysis using RNA-seq samples of differentiating mouse ESCs. For the analysis, both protein-coding and lncRNA expressing genes were used Fig. B. We extTrp53/Wrap53 and Ppil4/Ginm1 as representative non-overlapping sense/antisense transcript pairs from divergent transcription whose expression was positively or negatively correlated, respectively. Wrap53 is an antisense-oriented transcript of Trp53, which encodes the tumor suppressor protein p53. Wrap53 enhances the level of p53 mRNA and protein in human cells23. Consistent with this, we found their coordinated expression was synchronously up-regulated and then down-regulated upon ESC differentiation , is transcribed from upstream of the Ppil4 promoter, and its expression was negatively correlated with the expression of Ppil4 . This included the lncRNA/coding pair, PCAT6/KDM5B, and the coding/lncRNA pair, LHX1/LHX1-DT, which functions in malignant tumor cell growth. PCAT6 enhances proliferation and EMT in gastric cancer (GC)29, and KDM5B enhances EMT in lung cancer cell line, A54930, and in hepatocellular carcinoma31. LHX1 is involved in modulating EMT induction in uterine corpus endometrial carcinoma32, and LHX1-DT enhances cell proliferation in breast cancer33. These coordinated gene expression changes that were up-regulated by TGFβ stimulation were also confirmed by qRT-PCR . In addition to these sense/antisense transcript pairs from divergent transcription, some of which have been reported their functions and regulation of expression, many others with unknown functions were identified  and Precision Run-On (PRO) sequencing data, and GenomicRanges40 allows detection of overlapping and neighboring genes from annotated genomic information. While these tools provide reliable methods for such analyses, CCIVR2 has the unique feature of being able to comprehensively extract sense/antisense pairs along with annotation of the type of antisense transcript within a specified range. This feature of CCIVR2 allows structural analysis of divergent/convergent transcription and screening for functional antisense transcripts in specified processes.In this study, we developed a program called CCIVR2, which is an extension of our previously reported program, CCIVR8. Indeed, we observed many protein-coding genes and lncRNAs in divergent pairs . This hypothesis is also supported by the low number of lncRNA/lncRNA divergent pairs we observed . It is not known whether the positively correlated gene expression is merely the result of the same regulation for both genes or whether there is a positive feedback regulation mechanism between them. Further investigations are required to clarify the mechanism of expression regulation between divergently transcribed gene pairs by suppressing the functions of RNAs using approaches such as morpholino antisense oligonucleotides (mASOs), RNA interference (RNAi), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas7-1142 and by controlling the expression of target genes using CRISPR-mediated transcriptional interference/CRISPR-mediated transcriptional activation (CRISPRi/CRISPRa)43.In this study, we analyzed divergent transcription using CCIVR2. In mammals, relatively unstable and short transcripts without a polyA tail, called TSSa-RNAs/uaRNAs/PROMPTs, are transcribed in the opposite direction to protein-coding genes from promoters with CpG rich elements. It is hypothesized that these expressed loci have evolved into lncRNAs and protein-coding genes over a long period of timeirs Fig. , whereasved Fig. . Further16 and by degradation of the cotranscriptionally cleaved transcript by Xrn2, which causes Pol II to detach from the RNA and terminate transcription (torpedo model)18. In addition, R-loops can form at G-rich terminator elements and promote RNA interference-dependent formation of a repressive chromatin environment, such as the repressive histone mark H3K9me2, which reinforces Pol II pausing and transcription termination in mammalian protein-coding genes44. Therefore, the mechanism of transcription termination on the sense strand may affect the transcription termination on the antisense strand through formation of a repressive chromatin environment. In general, many Pol II-transcribed genes exhibit gradual termination profiles over several kilobases after the PAS, while others terminate immediately after the PAS15. Therefore, in addition to sharing the PAS, the transcription termination mechanism may also be involved in selecting the TESs of convergent transcript pairs. A long read-through after a TES can affect the expression of neighboring genes, for example because of Pol II collision. Our findings may indicate a mechanism to avoid this, and further molecular investigations focusing on individual sense/antisense transcript pairs sharing their TESs are warranted.In this study, we also analyzed convergent transcription using CCIVR2. We found that the number of convergently transcribed gene pairs whose TESs are proximately located is relatively increased in mouse and human Fig.  and S4. Here, we have introduced an original program, CCIVR2, that facilitates the analysis of overlapping and non-overlapping antisense transcripts as well as divergent/convergent transcription. We believe that CCIVR2 will drive the study of overlapping and non-overlapping antisense transcripts to elucidate their mechanisms of action and functions in numerous biological processes in various species.https://plants.ensembl.org/index.html; Arabidopsis thaliana: TAIR10.51), Ensembl Fungi , and Ensembl . Only the gene information listed in the feature column of the GTF files was extracted and converted to a CSV file using Python (ver 3.8.8) and one of its modules, gtfparse (ver 1.2.1). To avoid the analysis of duplicate genes, only The European Nucleotide Archive (ENA) for N. crassa and PomBase for S. pombe were used as their gene_source. For the other species, every gene_source was used for CCIVR2 analysis because no duplication was observed. The number of genes was counted from gene_id and not GeneSymbol because we found that every gene was unique in gene_id but that this was not the case for some genes in GeneSymbol. The CSV formatted input files for the CCIVR2 analysis, containing all RNA biotypes or only protein-coding genes, were applied to a python script "autoccivr2.py", which allows both Mode 2 and Mode 3 of the CCIVR2 analysis to be performed with different ranges on multiple datasets simultaneously. The number of antisense pairs with different RNA biotype combinations, such as with lncRNAs and pseudogenes, was counted using Python scripts "count_cg_lr.py" and "count_cg_pg.py", respectively. The phylogenetic tree was generated using phyloT-v2 (https://phylot.biobyte.de)45.GTF files from 11 species were downloaded from Ensembl plant . The SRA files for the TGFβ response in Huh-7 cells (accession number: DRA01352)27 were downloaded from the DNA Data Bank of Japan (DDBJ). Low-quality RNA-seq reads and adaptor sequences were removed using Trim Galore!. Sequence reads were aligned to the mouse reference genome (GRCm39.105) or human reference genome (GRCh38.107) using STAR (version 2.7.9a)34 allowing up to three mismatches. For each gene, TPM was calculated using RSEM (version 1.3.3)22. Differential expression analysis  was performed using DESeq235. The expressing genes were sorted using the Excel sorting function, with the criteria that the TPM was greater than 1 at least one of the time points and not 0 at any time point.The SRA files for ESC differentiation (accession number: GSE07528)gplots" (ver 3.1.1) function.The CSV formatted input files containing expressing genes with their TPM and statistical analysis (i.e. padj) were applied to a Python script "autoccivr2.py", and the number of antisense pairs with different RNA biotype combinations, such as protein-coding genes and lncRNAs, was counted using a Python script "count_cg_lr.py". The input files were subjected to CCIVR2 analysis with the following conditions: . Spearman correlation coefficient analysis between the sense and antisense transcripts was performed using the Excel functions combined with "RANK" and "CORREL". Bins of 100 sense/antisense pairs  in the region 5 kb upstream and downstream relative to the sense TSS/TES were identified and visualized by a violin plot using a Python method "violin" from one of its packages, plotly (ver 5.11.0). The Steel–Dwass multiple comparison test was performed using a Python method "posthoc_dscf" from one of its packages, scikit_posthocs (ver 0.7.0). Heatmaps for selected positively and negatively correlated genes were generated using R (ver 4.1.1) with "2. Recombinant hTGFβ1  was added to a final concentration of 10 ng/ml for TGFβ stimulation.Human hepatoma cell line Huh-7 (JCRB0403) was purchased from JCRB Cell Bank , grown in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum (Sigma-Aldrich) and 1 × penicillin/streptomycin  at 37 °C under an atmosphere containing 5% COGADPH.Total RNA was purified using an RNeasy Mini Kit (Qiagen). For RT-qPCR, cDNA was prepared using SuperScript II reverse transcriptase (Invitrogen) with random primers (Invitrogen). RT-qPCRs were performed in duplicate using Thunderbird SYBR qPCR mix  with the primers listed in Supplemental Table S1 on a StepOnePlus Real-Time PCR system . The standard curve method was used for quantification and expression levels were normalized against Supplementary Information.