WNT4 was shown to be involved in the selection and development of chicken follicles by upregulating the expression of follicle-stimulating hormone receptors (FSHR), stimulating the proliferation of follicular granulosa cells, and increasing the secretion of steroidal hormones. FSH also stimulates the expression of WNT4. To further explore the molecular mechanism by which FSH upregulates WNT4 and characterize the cis-elements regulating WNT4 transcription, in this study, we determined the critical regulatory regions affecting chicken WNT4 transcription. We then identified a single-nucleotide polymorphism (SNP) in this region, and finally analyzed the associations of the SNP with chicken production traits. The results showed that the 5′ regulatory region from −3354 to −2689 of WNT4 had the strongest activity and greatest response to FSH stimulation, and we identified one SNP site in this segment, −3015 (G > C), as affecting the binding of NFAT5 (nuclear factor of activated T cells 5) and respones to FSH stimulation. When G was replaced with C at this site, it eliminated the NFAT5 binding. The mRNA level of WNT4 in small yellow follicles of chickens with genotype GG was significantly higher than that of the other two genotypes. Moreover, this locus was found to be significantly associated with comb length in hens. Individuals with the genotype CC had longer combs. Collectively, these data suggested that SNP−3015 (G > C) is involved in the regulation of WNT4 gene expression by responding FSH and affecting the binding of NFAT5 and that it is associated with chicken comb length. The current results provide a reference for further revealing the response mechanism between WNT and FSH.The signaling pathway of the wingless-type mouse mammary tumor virus integration site (Wnt) plays an important role in ovarian and follicular development. In our previous study,  As a conservative pathway, Wnt signaling regulates multiple developmental processes and occurrences of disease, such as stem cell self-renewal, cell proliferation, cell fate determination and early embryonic development and differentiation ,2,3. TheWNT4 serves an important regulatory function in adult ovarian follicles. The overexpression of WNT4 in the granulosa cells of eCG-treated mice upregulates the expression of β-catenin and the key genes CYP11A1, CYP19A1 and StAR in the synthesis of gonadal steroid hormones [WNT4 affects the growth, differentiation and development of fallopian tubes and is regulated by estrogen [WNT4 was the highest in the granulosa cells of the small yellow follicles in chickens and was lower in the hierarchal follicles. WNT4 upregulates the expression of FSHR and downregulates the expression of AMH and OCLN, promotes the expression of StAR and CYP11A1, and stimulates the proliferation of granulosa cells. Additionally, WNT4 expression was stimulated by FSH. The interaction of WNT4 and FSH signaling strengthened from the prehierarchical to the hierarchical follicles during the process of follicle selection [WNT4 also participates in this process by regulating the expression of FSHR. However, the molecular mechanism by which FSH upregulates WNT4 expression is still unclear.Some Wnts and their homologous receptor components are expressed in postnatal ovaries, but their role in ovarian physiology remains unclear. Many studies have proven that hormones . In chicestrogen ,11. Our election . FSH is WNT4 transcription. First, we determined the critical regulatory cis-elements responsible for WNT4 transcription that are also responsive to FSH treatment. We identified two single-nucleotide polymorphisms (SNPs) in the 5′ regulatory region of the chicken WNT4 gene and analyzed their associations with production traits in hens. Finally, we analyzed the mechanism of SNP that is associated with chicken comb length. These results may help us to further explore the molecular mechanism by which FSH upregulates WNT4 expression. This study also laid a foundation for screening potential DNA markers in the molecular breeding of chickens for egg laying.In the current study, we investigated the regulatory mechanism of chicken For this study, we used three breeds of hen, Hy-line brown, Jining Bairi and Sunzhi, with different production performances . The hens were randomly selected from the local farm affiliated with Shandong Agricultural University. The egg- laying traits of the Jining Bairi population were recorded individually for use in association analysis. All chickens had free access to water and feed. The chickens were housed in separate cages with a daily light period of 16 h, and egg laying was monitored to determine the timing and regularity of laying. Genomic DNA was extracted from blood samples collected from the wing vein using a DNA extraction mini-kit . All sampled hens were killed by cervical dislocation immediately after oviposition, and their abdominal cavity was opened. Preovulatory follicles were carefully collected from the laying hens and placed in phosphate-buffered saline (PBS) with 1% penicillin/streptomycin for cell culture . All of 5/well. The number of cells was detected using Trypan blue. Cells were cultured at 38 °C in an atmosphere of water-saturated 5% CO2 for 24 h.The hierarchical follicles were isolated from the egg-laying hens and placed in PBS. The yolks of the follicles were carefully removed with ophthalmic forceps. The granulosa cells (GCs) were isolated from the hierarchical follicles and then dispersed by treatment with 0.25% trypsin-EDTA  at 37 °C for 10 min with gentle oscillation in a centrifuge tube. After centrifugation, the GCs were suspended in a culture medium  and subsequently seeded in 24-well culture plates at a density of 1 × 10WNT4 promoter, as well as four promoter deletion fragments, −2689/+252, −1875/+252, −1188/+252 and −535/+252, were amplified from the hen genomic DNA, where +1 was the transcription initiation site, the five promoter fragments were designed according to the chicken WNT4 gene sequence (NC_052552.1). Five forward primers contained the KpnI site at the ends, and one reverse primer located downstream of the transcription start site contained the HindIII site at the ends (the primer sequences are listed in The region from −3354 to +252 bp in the 5′-regulatory region of the chicken WNT4 promoter. The primers for the g.−3015 (G > C) mutations were designed using the −3354 to −2689 region of the WNT4 gene promoter as the template (the primer sequences are listed in Two plasmids including the wild type (pGL3-G) and the mutation type (pGL3-C) were constructed to assess the functionality of this transcription factor binding site in the WNT4 luciferase plasmids, which differed in length (800 ng/well), were added to the wells 6 h after transfection along with recombinant FSH. In another experiment, the pGL4.74 control vector , the wild-type plasmid and the mutation-type plasmid (800 ng/well) were used to transfect cultured GCs with recombinant FSH. Twenty-four hours after transfection, these cells were lysed for a luciferase activity assay.GCs were plated on 24-well plates for transient transfection experiments using Lipofectamine LTX and Plus Reagent (Invitrogen). The cells were transfected with pGL4.74 control vector , and the five Luciferase activity was measured using the Dual-Luciferase Reporter Assay System according to the manufacturer . The enzymatic activity of luciferase was measured with a luminometer . The individual values were averaged for each experiment, and the transfections were performed in at least triplicate. Empty pGL3-basic was used as the control. Luciferase activity was calculated by dividing the Firefly luciferase activity by the Renilla luciferase activity.WNT4 (−3353 to −2689), and then sequenced the amplifications. We analyzed the data, sequenced bidirectionally, using the DNAMAN program  to determine the potential SNPs within these amplifications. The primer pairs amplified the −3353 to −2689 fragments are shown in p-value were calculated using the Tools for Population Genetic Analyses software . The associations of SNPs with production traits in the Jining Bairi population were analyzed using the following general model in SPSS : Yij =μ+ Gi + ije, where Yij is the phenotypic value of traits, μ is the population mean, Gi is the fixed effect of genotype and ije is the random error effect.We used 50 Jining Bairi, Hy-line Brown and Sunzhi hens as templates for PCR amplification to the critical promoter region of www.genomatix.de) was used to predict that −3015 (G > C) would be the transcription factor binding sites of NFAT5 that regulates the transcription of WNT4 by responding to FSH. For further verification, HIH3T3 cells were seeded at a density of 1 × 106 cells/mL and incubated in DMEM with 10% FBS at 37 °C for 72 h. The nuclear extracts prepared from the cells were incubated with biotin-labeled double-stranded oligonucleotides containing the consensus sequences for NFAT5 (5′-TTTATCCCAgGGAAACCTTCACAGTGCATTC-3′) for an additional 4 h. GATA1 (5′-CACTTGATAACAGAAAGTGATAACTCT-3′) was used as a control. EMSA was performed using Non-Radioactive EMSA Kits with Biotin-Probes . The DNA-protein complex and unbound probe were electrophoresed on a 6% native polyacrylamide gel and visualized with Western blotting. The NFAT5 monoclonal antibody was used for the super shift, and standard NFAT5 was used as a positive control.Genomatix software . The total RNA was extracted from chicken small yellow follicles (6-8 mm) of 15 individuals with different genotypes using TRIzol reagent (Invitrogen). The quality of the total RNA samples was tested by gel electrophoresis and spectrophotometry. Synthesis of the cDNA was performed using a PrimeScript RT Reagent Kit with a gDNA Eraser  according to the manufacturer’s protocol. qPCR of the mRNA expression level of WNT4 was performed using an SYBR Premix Ex Taq™ II kit  on a Light Cycler 480 real-time PCR system  as follows: 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing and extension at 58 °C for 20 s. The primers were designed according to the chicken WNT4 mRNA sequence (NM_204783.2) and internal reference gene GAPDH mRNA sequence (NM_204305.2), the primer sequences are listed in −ΔΔCT relative quantification method.We further verified the effect of −3015 (G > C) mutation on p < 0.05.The experiments were performed a minimum of three times using tissues from different hens. All data are presented as the means ± SEM. The differences between groups were determined by one-way ANOVA followed by Duncan’s test in SPSS . The differences between groups were considered statistically significant when WNT4 plays an essential role in follicle selection, and FSH could regulate its expression, we set out to analyze the mechanisms regulating WNT4 transcription. The luciferase activity assay on chicken preovulatory follicle GCs transfected with different WNT4 promoter vectors  at −3015 (n = 539) represents the samples which are successfully genotyped. According to the results of KASP, the genotype and allele frequency of this SNP locus were calculated  with production records. p < 0.05), and the chickens with genotype CG had more eggs at 50 weeks of age, although the difference was not significant (p = 0.075).The statistical analysis is based on the genotype results of the Jining Bairi chicken population (WNT4 gene transcription. As shown in p < 0.001). After FSH treatment, the luciferase activity of the promoter with allele G was significantly (p < 0.01) increased than before, as well as still significantly (p < 0.01) higher than the activity with allele C.Luciferase reporter constructs of pGL3-G and pGL3-C were transiently transfected into GCs to assess whether the SNP could change the effect of WNT4 responses to FSH. An electrophoretic mobility shift assay (EMSA) was performed to identify this transcription factor. As shown in Analysis with Genomatix revealed that SNP (G > C) at −3015 may affect the binding site of NFAT5, which may be related to the regulation of WNT4 expression, we examined the mRNA level of WNT4 in chicken follicles of different genotypes. The results showed that WNT4 mRNA levels were detected in both the CC, CG and GG chicken small yellow follicles. The mRNA levels from the GG genotype were higher than that from the CC and CG genotypes (p < 0.01), but there was no significant difference between the CC and CG genotypes  on enotypes .WNT4, a member of the Wnt family, also plays a key regulatory role in the development and differentiation of the female reproductive system [WNT4 plays a significant role in ovarian follicle selection in chickens, and FSH treatment significantly increased the expression of WNT4 in GCs [WNT4 can enhance the stimulation of FSH during the follicular dominance phase [WNT4 and FSH. However, the molecular mechanism of the interaction between them has not been investigated. In our study, we investigated the critical regulatory regions affecting WNT4 transcription and then identified a single-nucleotide polymorphism (SNP) in this region, in order to provide a reference for further elucidating the regulatory relationship between WNT4 and FSH.In recent years, researchers have made considerable progress in determining the roles of Wnt family members in mammalian ovarian and follicular development. The expression of Wnt family members and their downstream signal elements have been detected in the different grade follicles and corpus luteum of rats, mice, humans and cattle, and they also play a certain regulatory role in follicular development ,17,18,19e system . Our pre4 in GCs . In cowsWNT4 was the key regulatory region that responded to FSH stimulation and affected chicken WNT4 gene transcription. We identified SNP−3015 (G > C) in this region as the working site. Two studies showed that the SNPs of the WNT4 gene can affect its expression and function. SNP rs7521902 in WNT4 is significantly correlated with the pathogenesis of endometriosis [WNT4 is associated with body mass index (BMI) variations in the Han Chinese population [WNT4 promoter that contained SNP−3015 (G > C), and we found that SNP−3015 (G > C) was significantly associated with comb length in chickens. A recent study has shown that the Wnt signaling pathway is related to comb growth and development in chickens [We found that the 5′ regulatory region from −3354 to −2689 of pulation . We idenchickens . Comb sichickens ,25. HereWNT4 gene, and the results showed that this SNP significantly affected the promoter activity. The promoter with allele G had higher luciferase activity and mRNA levels than the promoter with allele C, and allele G responded more actively to FSH stimulation. The transcription factor prediction and EMSA experiment showed that SNP−3015 (G > C) in this key regulatory region was the binding site of the transcription factor NFAT5. When this site is G, it has binding activity. NFAT5 is a transcription factor that participates in many biological processes, including cell differentiation, cellular migration and embryonic development [WNT4 expression through NFAT5 binding, in turn affecting the Wnt signaling pathway and chicken follicle selection. However, although NFAT5 binding may be involved in the regulation of WNT4 expression, it does not exclude that the expression of WNT4 may be regulated by other unknown factors, and the regulation of FSH may also be related to these unknown factors. About this possibility, further in-depth investigation is needed in our next studies.We investigated the effect of SNP−3015 (G > C) on the promoter activity and expression of the chicken elopment ,27,28. Melopment ,30,31. Melopment ,33. In helopment . In miceelopment . CombineWNT4 promoter region responded to FSH stimulation and appeared to be regulated by NFAT5. The SNP was found to be significantly associated with chicken comb length. These data provide a reference for the further elucidation of the relationship between FSH and WNT4 in chicken follicles.In summary, SNP−3015 (G > C) in the


The ETO-family transcriptional corepressors, including ETO, ETO2, and MTGR1, are all involved in leukemia-causing chromosomal translocations. In every case, an ETO-family corepressor acquires a DNA-binding domain (DBD) to form a typical transcription factor—the DBD binds to DNA, while the ETO moiety manifests transcriptional activity. A directly comparative study of these “homologous” fusion transcription factors may clarify their similarities and differences in regulating transcription and leukemogenesis. Here, we performed a side-by-side comparison between AML1-ETO and ETO2-GLIS2, the most common fusion proteins in M2-and M7-subtypes of acute myeloid leukemia, respectively, by inducible expression of them in U937 leukemia cells. We found that, although AML1-ETO and ETO2-GLIS2 can use their own DBDs to bind DNA, they share a large proportion of genome-wide binding regions dependent on other cooperative transcription factors, including the ETS-, bZIP- and bHLH-family proteins. AML1-ETO acts as either transcriptional repressor or activator, whereas ETO2-GLIS2 mainly acts as activator. The repressor-versus-activator functions of AML1-ETO might be determined by the abundance of cooperative transcription factors/cofactors on the target genes. Importantly, AML1-ETO and ETO2-GLIS2 differentially regulate key transcription factors in myeloid differentiation including PU.1 and C/EBPβ. Consequently, AML1-ETO inhibits, but ETO2-GLIS2 facilitates, myeloid differentiation of U937 cells. This function of ETO2-GLIS2 is reminiscent of a similar effect of MLL-AF9 as previously reported. Taken together, this directly comparative study between AML1-ETO and ETO2-GLIS2 in the same cellular context provides insights into context-dependent transcription regulatory mechanisms that may underlie how these seemingly “homologous” fusion transcription factors exert distinct functions to drive different subtypes of leukemia. In the classical model of gene transcription, binding of a single transcription factor to a target gene is sufficient to recruit cofactors and RNA polymerases to initiate transcription, and the physical property of this transcription factor can largely determine the activation versus repression transcriptional states. However, in fact, most  genes are regulated by an array of multiple transcription factors, which cooperatively bind to the regulatory DNA elements and thereby set a unique transcriptional context . The actTranscription factors and cofactors are the most common targets of chromosome translocations in leukemia. The produced fusion transcription factors play vital roles in leukemogenesis, and they can usually determine leukemia subtypes by regulating cell differentiation . NotablyIn this study, we sought to perform a side-by-side comparative study between AML1-ETO and ETO2-GLIS2. AML1-ETO is the product of t, the first chromosomal translocation ever discovered , and is Previous studies from our group and others have shown that AML1-ETO can oligomerize through its NHR2 domain and then nucleate a stable multi-protein complex, AETFC, mainly involving NHR1- and NHR2-meidated interactions with E proteins  and other transcription factors and cofactors . SimilarTherefore, we designed to ectopically express AML1-ETO and ETO2-GLIS2 in the U937 leukemia cell line and perform ChIP-seq and RNA-seq assays for a comparative transcriptomic analysis, as well as accompanied cell functional studies. The U937 cell line is a human immature monoblast cell line that can be induced to differentiate into heterogenous populations of cells in monocyte and macrophage lineages . This ceThe stable cell lines were constructed by transduction of U937 cells with a retroviral vector system and by selection of single clones. In brief, the N-terminal HA-tagged AML1-ETO and ETO2-GLIS2 cDNAs were subcloned into a pRetrox-Tet3G vector that was equipped with puromycin resistance. The retroviruses were produced by co-transfection of 293T cells with the pRetrox-Tet3G and the psPAX2 and pMD2.G helper vectors. U937 cells were infected with filtered virus-containing supernatant of the 293T cultures, in the presence of 8 μg/ml polybrene , and centrifuged at 1200 × g, 37°C for 90 min. Infected cells were selected with 1 μM puromycin at 12–24 h after infection. The survived cells were then subjected to a limiting dilution method in 96-well plates to obtain single cell clones of stable cell lines. To avoid the leaky expression before induction, the stable cell lines were grown in RPMI 1640 medium  supplemented with 10% Tet-free fetal bovine serum .Cells were washed with cold PBS for three times and then lysed in RIPA buffer  with 1% protease inhibitor  and phosphatase inhibitor . Total protein level of each sample was quantified with the BCA protein assay kit , and 20 μg of total protein was loaded per well for SDS-PAGE electrophoresis and membrane transfer. An anti-HA rabbit monoclonal antibody  was used to detect the HA-tagged AML1-ETO and ETO2-GLIS2 proteins, and an anti-β-Actin mouse monoclonal antibody  were used to detect β-Actin as a loading control. HRP-conjugated anti-rabbit IgG  and anti-mouse IgG  were used as secondary antibodies.Cell viability was determined with Cell Counting Kit-8 (CCK-8) . 5,000 cells were inoculated in each well of 96-well plate and cultured for certain time at 37°C. 10 μl CCK-8 solution was added into each well and incubated for 2–4 h. The absorbance at 450 nm, which reflected the dehydrogenase activity in living cells, was determined with a microplate reader.For cell cycle analysis, the cells were washed with pre-chilled PBS and fixed with 70% ethanal at −20°C. The fixed cells were resuspended with PBS, treated with RNase, stained with propidium iodide (PI) , and subjected to flow cytometry analysis with Phycoerythrin (PE) channel. Cell apoptosis was analyzed with the Annexin V-FITC/PI Apoptosis Detection Kit . The cells were washed with pre-chilled PBS and resuspended with binding buffer. The Annexin V-FITC and PI Staining Solution were added and incubated with the cells at room temperature for 10–15 min, and the stained cells were diluted with binding buffer and subjected to flow cytometry analysis with the PE and FITC channels within 1 h.ChIP-seq analysis was performed as described previously . The estThe U937 stable cell lines #4 and #14 that contain AML1-ETO and ETO2-GLIS2, respectively, as well as a control cell line containing an empty vector, were treated with 5 μg/ml doxycycline for 12 and 48 h and were subjected to RNA-seq analysis. RNA was extracted with the TRIzol Reagent . TruSeq RNA Sample Preparation Kit, version2 (Illumina) was used to generate libraries. Illumina NovaSeq 6000 was used for sequencing. Clean sequenced reads were aligned to hg38 by HISAT2 (2.0.5) with default parameters. Aligned SAM files were handled using samtools (1.7). Aligned reads were extracted by Htseq (0.9.1) with default parameters. Number of reads and mapping rate are shown in GAPDH forward-GATTCCACCCATGGCAAAT; GAPDH reverse-GACAAGCTTCCCGTTCTCAG; RHOU forward-GCTACCCCACCGAGTACATC; RHOU reverseGGCTCACGACACTGAAGCA; SPI1 forward-GTGCCCTATGACACGGATCTA; SPI1 reverse-AGTCCCAGTAATGGTCGCTAT. The expression levels of the genes were normalized against the internal control GAPDH.TRIzol Reagent  was used to lysis U937 cells for isolating total RNA, and PrimeScript RT reagent kit with gDNA Eraser  was used to synthesize cDNA. TB Green Advantage qPCR premixes  was used for qPCR reaction on an ABI Prism 7500 Sequence Detection system (Applied Biosystems). The sequences of primers were listed as following: Phorbol-12-myristate-13-acetate  induced differentiation of U937 cells to monocyte/microphage lineages was performed as described previously . To inveTo directly compare AML1-ETO and ETO2-GLIS2 in the same cellular context, we used U937 cells to generate stable cell lines, in which the expression of AML1-ETO or ETO2-GLIS2 could be induced by adding doxycycline (DOX) in the cell culture medium. An HA-tag was inserted in the N-terminus of each protein to allow detecting and precipitating these proteins using the same protocol and condition. As a negative control, a stable cell line containing the empty vector was also generated. Immunoblot analysis validated that, in two AML1-ETO cell lines (#4 and #16) and two ETO2-GLIS2 cell lines (#14 and #17), the HA-tagged AML1-ETO and ETO2-GLIS2 proteins, respectively, could be properly expressed upon induction with doxycycline for 12 and 48 h .1-to-S phase transition in the cell cycle. Furthermore, PI and Annexin V staining and flow cytometry analysis of the cells showed that the induced expression of AML1-ETO and ETO2-GLIS2 caused considerably increased numbers of apoptotic cells  with an anti-HA antibody under the same experimental conditions and peak calling algorithm. As a result, 25,266 peaks of AML1-ETO and 11,280 peaks of ETO2-GLIS2 were identified . Manual We then performed a comprehensive analysis of the different classes of peaks for their enrichment of transcription factor binding motifs. In particular, four Runt motifs and a GLIS motif were found to be specifically enriched in the AML1-ETO only and ETO2-GLIS2 only peaks, respectively . This reMoreover, the results of a direct comparison of the motif enrichment patterns among the three classes of peaks revealed that 1) the ETS motifs are enriched in all three classes of peaks; 2) the bZIP motifs are enriched in the overlapped and the AML1-ETO only peaks; and 3) the bHLH motifs are enriched only in the overlapped peaks . Based oRHOU; To understand how AML1-ETO and ETO2-GLIS2 regulate the expression of target genes, we performed an RNA-seq analysis of the cells (stable cell lines #4 and #14) with and without doxycycline induction. We also used the empty vector control cell line in this analysis to exclude the genes that might be regulated by doxycycline treatment. As a result, we identified 935 upregulated genes and 1,016 downregulated genes by AML1-ETO . This raTo explore the mechanism of how AML1-ETO functions as either repressor or activator on different target genes, we reasoned that, as a transcription factor capable of recruiting both corepressor  and coacSPI1 gene) and C/EBPβ (encoded by the CEBPB gene). Interestingly, we found that AML1-ETO and ETO2-GLIS2 both bind to, but differentially regulate these key transcription factor genes (SPI1 is tightly regulated by three interplayed regulatory elements: an upstream regulatory element (URE) can directly interact with, and enhance the activities of, the conventional proximal promoter (PrPr) and an intragenic antisense promoter (AsPr); while PrPr drives the transcription of PU.1 coding mRNA, AsPr drives an antisense noncoding RNA, which may negatively regulate the PrPr activity; meanwhile, URE itself also drives a noncoding RNA termed LOUP, which can facilitate these enhancer-promoter interactions. Our ChIP-seq results revealed that AML1-ETO and ETO2-GLIS2 bind to URE at similar levels, but AML1-ETO has a much lower binding strength to PrPr and AsPr, compared with ETO2-GLIS2  than that of the AML1-ETO cells (26.9%) upon PMA treatment  this immature monoblast cell line provides a proximate myeloid transcriptional context; 2) it maintains the potential of myeloid differentiation; and 3) it has been widely used for analyzing many leukemogenic fusion proteins, thus providing useful information for reference and comparison. Therefore, this well-defined U937 cell line serves as a type of “cellular test tube” for this comparative study. Using this system, we identified similarities and differences between AML1-ETO and ETO2-GLIS2 for their binding and regulation of target genes, as well as functions in regulating cell differentiation.Our results clearly demonstrate that, although AML1-ETO and ETO2-GLIS2 can use their own DNA-binding domains to bind DNA, they share a large proportion of genome-wide binding regions. Further analysis suggests that the binding of AML1-ETO and ETO2-GLIS2 to the genome can be facilitated by several families of cooperative transcription factors. Notably, these cooperative transcription factors also show marked preference to different fusion proteins on certain binding regions, which is likely determined by the specific interaction mechanisms between the cooperative transcription factors and the fusion proteins through different domains/moieties. For example, the bHLH-family transcription factors E proteins specifically interacts with the ETO/ETO2 moieties of both AML1-ETO and ETO2-GLIS2, therefore these E proteins can determine some binding regions shared by AML1-ETO and ETO2-GLIS2, but not those bound exclusively by each. In contrast, as the bZIP-family transcription factors C/EBPα interacts with AML1-ETO through the AML1 moiety , it can SPI1 and CEBPB are of particular interest because they encode the most important transcription factors, PU.1 and C/EBPβ, respectively, in monocyte/macrophage differentiation. Because these analyses were performed side-by-side in the same cellular context and experimental conditions, it would not be difficult to use this system to further clarify the more detailed mechanisms, for example, by making mutations in the fusion proteins or in the regulatory elements of the target genes. Although being analyzed in the U937 cells, these regulatory mechanisms are unlikely restricted to U937 cells but may be applicable in broader myeloid differentiation and leukemogenic processes. Therefore, this type of “cellular test tube” may provide a useful tool to explore previously unknown mechanisms. Furthermore, because the U937 cell line has been widely used to study many leukemogenic fusion proteins, a retrospective comparison with previous studies would also be helpful to understand the new ones. For example, the differentiation promoting effect of ETO2-GLIS2 is strikingly reminiscent of a similar effect of MLL-AF9 observed previously (It is unexpected and interesting to find that AML1-ETO and ETO2-GLIS2 have opposite functions in regulating myeloid differentiation of U937 cells. Although the U937 cell line does not represent the appropriate stages of hematopoiesis when these fusion proteins occur or transform the cell to leukemia, the opposite effects of cell differentiation induced by AML1-ETO and ETO2-GLIS2 still reflect mechanistical differences in regulation of the genes essential for myeloid differentiation. The differential regulation of eviously , which iIn summary, this study is the first direct comparison between AML1-ETO and ETO2-GLIS2 in the same cellular context. The results reveal the similarities and differences between them in their binding and regulation of target genes, as well as exerting cellular functions. In principle, the similarities enable them to be cooperated with the same families of other transcription factors and thereby share a large proportion of genome-wide binding regions, whereas the differences promote them to differentially regulate gene expression and cellular functions through transcriptional context-dependent regulatory mechanisms. The new mechanisms and insights may underlie how these seemingly “homologous” fusion transcription factors exert distinct biological functions and drive different subtypes of leukemia.


In contrast, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.Enhancers and promoters predominantly interact within large-scale topologically associating domains (TADs), which are formed by loop extrusion mediated by cohesin and CTCF. However, it is unclear whether complex chromatin structures exist at sub-kilobase-scale and to what extent fine-scale regulatory interactions depend on loop extrusion. To address these questions, we present an MNase-based chromosome conformation capture (3C) approach, which has enabled us to generate the most detailed local interaction data to date (20 bp resolution) and precisely investigate the effects of cohesin and CTCF depletion on chromatin architecture. Our data reveal that  Chromosome conformation capture (3 C) techniques have captured largescale 3D genome architecture. Here the authors present their “Tiled-MCC” approach for generation of 3 C data across megabase-scale loci at very high (up to 20 bp) resolution, which allowed them to observe nano-scale chromatin structures and investigate how these structures depend on cohesin and CTCF. Since these elements can be separated by large genomic distances, the specificity of enhancer–promoter interactions is dependent on the 3D organization of the genome in the nucleus2. Mammalian chromosomes are organized into compartments and topologically associating domains (TADs)6. Compartments reflect the separation of euchromatin and heterochromatin3, whereas TADs are formed by an active process of loop extrusion halted by boundary elements8. Accumulating evidence has identified cohesin as the key loop extruding factor and CTCF-binding elements as the major boundaries in mammalian cells9.Mammalian gene expression patterns are controlled by enhancers, which form specific interactions with the promoters of their target genes to transfer activating signals10, these domains are thought to be relevant for the regulation of the specificity of enhancer–promoter communication. Previous studies have shown that perturbations of CTCF-binding elements at TAD boundaries result in changes in chromatin structure, which can lead to rewiring of enhancer–promoter interactions and ectopic activation of genes by enhancers that are normally located in a neighboring TAD19. However, more recent genetic dissections of TAD boundaries have demonstrated that the impact of TAD rearrangements on gene expression is context-dependent and sometimes very mild, thus questioning the importance of TADs for gene regulation23. Due to potential redundancy between CTCF-binding elements, it is challenging to deduce the general function of CTCF and TADs in the regulation of enhancer–promoter specificity based on perturbations of individual TAD boundaries.As interactions between enhancers and promoters predominantly occur within TADs24. Using such approaches, it has been shown that depletion of cohesin, its loading factor NIPBL, and CTCF result in large-scale changes in the 3D organization of the genome and a loss of TADs31. However, surprisingly, the effects of these depletions on gene expression are relatively mild. It has been suggested that loop extrusion is only required for mediating very long-range enhancer–promoter interactions, which could explain why only a small subset of genes is misregulated upon the depletion of cohesin and CTCF33. Furthermore, it has recently been proposed that the relationship between enhancer–promoter interaction strength and gene activity is not linear35. Another possible explanation is that loop extrusion is particularly important for regulatory interactions in the context of dynamic gene expression changes, as has been suggested based on studies of the functions of cohesin and CTCF during neuronal differentiation and upon inflammatory signaling37.The development of acute protein degradation approaches has made it possible to investigate the impact of genome-wide depletions of key architectural proteins on genome organization and gene expression32 and imaging approaches40 with relatively low resolution. Although these studies have given important insights into the roles of cohesin and CTCF in large-scale genome organization, the functions of these proteins in regulating chromatin interactions at a smaller scale remain unresolved. To address this, it is important to analyze the effects of cohesin and CTCF depletion on chromatin architecture with approaches that enable analysis at very high resolution and sensitivity and which can detect potentially subtle changes in fine-scale chromatin structures, including the specific regulatory interactions between enhancers and promoters which control gene expression. This would contribute to a better understanding of the roles of loop extrusion and TADs in gene regulation and the relationship between genome structure and function.A limitation of the current studies is that the effects of depletion of cohesin and CTCF on chromatin architecture have been analyzed with genome-wide Chromosome Conformation Capture (3C)41 are limited in their resolution by sequencing depth, library complexity, and the distribution of the recognition sites of the restriction enzymes used for chromatin digestion42. The latter can be overcome by the use of micrococcal nuclease (MNase), which digests the genome largely independent of DNA sequence. MNase digestion was initially implemented in the Micro-C approach in yeast44. Micro-C has recently been adapted for use in mammalian genomes and has demonstrated a better signal-to-noise ratio compared to Hi-C and the potential to generate contact matrices at very high resolution47. However, since Micro-C is a genome-wide approach, its sensitivity and resolution are limited by sequencing depth. We have recently developed a 3C-based method that combines MNase digestion with an oligonucleotide-based enrichment approach and enables deep, targeted sequencing of chromatin interactions with viewpoints of interest48. This Micro-Capture-C (MCC) approach can generate 3C interaction profiles at base-pair resolution. Here, we present an extension of the MCC method, based on the integration of an enrichment strategy that involves dense tiling of capture oligonucleotides across large regions of interest. This Tiled-MCC approach allows for the generation of regional contact matrices at very high resolution (up to 20 bp) and can therefore resolve local nano-scale chromatin structures with unprecedented detail.Conventional 3C methodscis-regulatory elements, but that cohesin does not strongly contribute to these localized interaction patterns. Together, our findings provide insights into the role of loop extrusion in regulating fine-scale chromatin architecture and enhancer–promoter interactions.We have used Tiled-MCC to study the effects of cohesin and CTCF depletion in four well-characterized gene loci in mouse embryonic stem (mES) cells. We show that cohesin depletion in these regions leads to a subtle reduction in enhancer–promoter interactions, which is associated with a small decrease in gene expression. CTCF depletion does not generally reduce interactions between the investigated enhancers and promoters, but can lead to rewiring of interactions and ectopic gene activation in some cases. At the level of nano-scale structures, we find that CTCF mediates local insulation within 48. To achieve this, MCC combines the following features: (1) permeabilization of cells using digitonin instead of traditional detergents, thus maintaining cellular and nuclear architecture; (2) digestion with MNase instead of restriction enzymes; (3) high library complexity and efficient enrichment of viewpoints of interest, thus allowing for deep, targeted sequencing; (4) direct sequencing and bioinformatic reconstruction of ligation junctions.We have recently developed the MCC approach, which is capable of generating base-pair resolution 3C data for selected viewpointsHowever, since the MCC viewpoints are narrow 120 bp), MCC data do not define complete 3D structures of regions of interest. We have therefore extended the MCC approach and combined it with an enrichment strategy based on capture oligonucleotides tiled across large genomic regions of interest Fig. . For the20 bp, MC49, Capture Hi-C50, and Targeted Chromatin Capture51. However, an important difference is that Tiled-MCC is based on MNase digestion, whereas the existing approaches use restriction enzymes for chromatin digestion. Approaches based on restriction enzyme digestion are limited in their resolution by the distribution of the recognition motifs in the genome, because the ligation junctions (based on which chromatin interactions are identified) can only be formed at sites where the chromatin has been cut. For enzymes with a 4 bp recognition motif (such as DpnII), the theoretical resolution limit is 256 bp; for enzymes with a 6 bp recognition motif (such as HindIII), the resolution is theoretically limited to 4096 bp. However, due to the irregular distribution of the recognition motifs, bin sizes of 1–2 kb and 5–10 kb, respectively, are usually required to avoid “empty bins” for which information on chromatin interactions is missing due to the absence of restriction sites. Since MNase digests the genome largely independent of sequence, ligation junctions can be formed more uniformly across the genome. MNase digestion can therefore overcome the barrier in resolution imposed by restriction enzymes. Consequently, Tiled-MCC is able to generate contact matrices at a higher resolution compared to Tiled-C  uncover the same features without artifacts  or CTCF27 can be rapidly degraded via an auxin-inducible degron (AID). To examine enhancer–promoter interactions in the absence of cohesin, we produced Tiled-MCC matrices of four well-characterized gene loci in RAD21-AID mES cells. We initially focused our analyses on the region containing the Sox2 gene and its super-enhancer53. Consistent with previous reports28, cohesin depletion results in a near-complete loss of TAD structure and long-range interactions between CTCF-binding sites . These genes interact with 2–8 individual enhancer elements, resulting in a total of ~30 enhancer–promoter pairs. We find that the strength of the investigated enhancer–promoter interactions is decreased upon cohesin depletion, with the exception of the interactions between the weak enhancer–promoter pairs that span a CTCF boundary , we observe a decrease in enhancer–promoter interactions and gene expression at the relatively small Pou5f1 and Nanog loci upon cohesin depletion, across distances of ~20 and ~50 kb, respectively.It has been suggested that cohesin is particularly important for mediating very long-range interactions between enhancers and promoters26. However, our data suggest that cohesin is particularly important for mediating the robustness of enhancer–promoter interactions. It is therefore perhaps not surprising that only a subset of genes is misregulated upon cohesin depletion, since the loss of cohesin might only affect genes whose expression is critically dependent on the robust formation of tissue-specific enhancer–promoter interactions. This is consistent with the reported importance of cohesin for regulating gene expression upon stimuli and during differentiation37.In our study, we have focused on gene loci that are specifically expressed in mES cells. We find consistent reductions in enhancer–promoter interactions and gene expression upon cohesin depletion in the investigated loci. This appears to be in contrast with reported mild effects on gene expression upon loss of cohesin or NIPBLcis-regulatory elements with unprecedented resolution. These analyses reveal distinct nano-scale structures, characterized by very fine-scale compartmentalization and enriched interactions between accessible regions of chromatin within promoters and super-enhancers. Furthermore, we find that binding of the CTCF protein results in strong local insulation in a cohesin-independent manner. This suggests an additional role for CTCF in  chromatin organization, which is not mediated via loop extrusion. Further investigation is required to identify the underlying mechanisms, which might involve the recruitment of other factors or the strong impact of CTCF binding on the arrangement of nucleosomes61.In addition to a detailed investigation of the effects of cohesin and CTCF depletion on selected enhancer–promoter interactions, the Tiled-MCC approach has enabled us to identify localized interaction patterns at cis-regulatory elements, which are independent of loop extrusion. Furthermore, we show that loop extrusion is not essential for enhancer–promoter interactions, but an important mechanism to regulate the robustness and specificity of regulatory interactions.Taken together, our high-resolution data identify the existence of complex nano-scale structures within 29, and CTCF-AID-eGFP27 mouse embryonic stem (mES) cells were cultured on plates pre-coated with 0.1% gelatin  in Glasgow Modified Essential Medium  supplemented with 10% fetal bovine serum , 0.01 mM 2-Mercaptoethanol , 2.4 mM L-glutamine , 1× non-essential amino acids , 1× sodium pyruvate  and 20 ng/ml recombinant mouse leukemia-inhibitory factor . Cells were grown at 37 °C and 5% CO2. Half of the media was exchanged daily and cells were passaged every 2 days by trypsinization .Wild type (E14), RAD21-mAID-eGFP2) medium containing 500 μM indole-3-acetic acid  freshly prepared before use. RAD21-mAID-eGFP cells were treated with auxin for 6 hr. Since complete depletion of CTCF takes longer to achieve62, CTCF-AID-eGFP cells were treated for 48 hr, with exchanging auxin-containing media after the first 24 hr. Alkaline phosphatase testing for the assessment of pluripotency before and after auxin treatment was performed using the Alkaline Phosphatase Detection Kit .RAD21-mAID-eGFP and CTCF-AID-eGFP mES cells were passaged and plated 2 days before auxin treatment. On the treatment day, cells were first washed once with phosphate-buffered saline (PBS) and supplemented with an equilibrated  with a protease inhibitor cocktail  and 250 U/µL benzonase  and rotated for 1 hr at 4 °C. After centrifugation at maximum speed, whole-cell extract in the supernatant was measured using the Bio-Rad Protein Assay . In all, 15 µg of protein extracts were mixed with NuPAGE LDS Sample Buffer , supplemented with 50 mM DTT , and loaded on a NuPAGE 4–12% Bis-Tris gel . Proteins were transferred to a PVDF membrane using the XCell Blot Module  for 1 hr at 30 V. Membrane blocking was performed in 5% blotting grade milk powder  dissolved in PBS-0.05% Tween-20 (PBS-T) for 1 hr at room temperature. After rinsing the membranes with PBS-T, incubation with primary antibodies was performed as follows: the membrane was cut into two pieces, one piece corresponding to the higher protein ladder size for the incubation of RAD21 and CTCF primary antibodies , and the second piece with lower protein ladder size for the incubation with the loading control (Histone H3: anti-histone H3 antibody (HRP), Abcam, ab21054, 1:5000). All primary antibodies were diluted in 2% PBS-T and incubated at 4 °C overnight. The next morning, membranes were washed 3× with PBS-T for 5–10 min each and incubated with secondary antibody (RAD21 and CTCF: Goat Anti-Rabbit IgG H&L (HRP), Abcam ab205718) in PBS-T in a 1:5000 dilution for 1 hr at room temperature, washed again 3× and analyzed on the Intas ChemoCam Imager HR.RAD21-mAID-eGFP and CTCF-AID-eGFP mES cells were grown in 25 cm48. In all, 10 million cells per biological replicate were washed once with PBS, dissociated with trypsin, and resuspended in 10 ml of culture media. Fixation was performed by incubating cells with formaldehyde to a final concentration of 2% on a roller mixer for 10 min at room temperature. The reaction was quenched by adding cold glycine to a final concentration of 130 mM. Samples were centrifuged at 300 rcf for 5 min at 4 °C, the pellet was washed once with cold PBS, centrifuged again, and resuspended in 1 ml of cold PBS containing 0.005% digitonin .The preparation of MCC libraries was performed as previously described2). The reaction was then incubated for 1 hr at 37 °C on an Eppendorf Thermomixer at 550 rpm, after which it was quenched with 5 mM of ethylene glycol-bis(2-aminoethylether)-N,N,N′,N′-tetraacetic acid . The quenched reaction was subsequently centrifuged for 5 min at 300 rcf and the supernatant was carefully discarded. The pellet was resuspended in 1 ml of PBS with 5 mM EGTA. 200 µl was removed as a control for MNase digestion, from which DNA was extracted using the DNeasy Blood and tissue kit  and digestion efficiency was assessed using the Agilent D1000 TapeStation . MNase reactions that yielded ~180 bp fragments, corresponding to mono-nucleosomes with linkers, were taken further for the subsequent reactions.Crosslinked and permeabilized cells were centrifuged at 300 rcf for 5 min. The supernatant was carefully removed without disrupting the pellet, which was subsequently resuspended in 900 µl nuclease-free water. Cells were then split equally into three digestion reactions, such that each reaction contained 3–4 million cells. Titration of different MNase concentrations  was performed for each aliquot with MNase concentrations ranging from 30–60 Kunitz U in a total reaction volume of 800 µl containing low-calcium MNase buffer  supplemented with dNTPs  at a final concentration of 400 μM each and 2.5 mM EGTA. T4 Polynucleotide Kinase , DNA Polymerase I Large Fragment , and T4 DNA ligase  were added to final concentrations of 200 U/ml, 100 U/ml, and 300 U/ml, respectively. The reaction was incubated on an Eppendorf Thermomixer at 550 rpm for 2 hr at 37 °C, followed by a 16-h incubation at 20 °C. Decrosslinking of chromatin was performed using proteinase K (included in the Qiagen DNeasy blood and tissue kit) at 65 °C for >4 hr, which was followed by DNA extraction using the Qiagen DNeasy Blood and tissue kit. The ligation product, referred to as 3C library hereafter, was assessed using the Agilent D1000 TapeStation. A successful ligation is indicated by a significant increase in the fragment size > ~370 bp.Sonication of 3C libraries was performed using 3–5 μg per library on a Covaris S220 Focused Ultrasonicator with the following conditions: 250–300 s: duty cycle 10%; intensity 5; cycles per burst 200, to yield an average fragment size of 200 bp. The sonication quality was assessed using the Agilent D1000 TapeStation. The DNA was purified using Ampure XP beads . To maximize library complexity, the addition of sequencing adapters was parallelized in triplicate reactions such that each reaction contained 1–2 μg of sonicated 3C library. NEB Ultra II  reagents were used following the manufacturer’s protocol with the following deviations: (1) 2–3× the number of adapters was used; (2) all Ampure XP bead clean-up reactions were performed with a DNA sample:bead ratio of 1:1.5; (3) to maximize library complexity and yield, the PCR was performed in triplicate per ligation reaction using the Herculase II PCR reagents . The parallel library preparations and PCR reactions were subsequently pooled for each reaction.49 (https://oligo.readthedocs.io/en/latest/). The panels of double-stranded capture oligonucleotides were ordered from Twist Bioscience (Custom probes for NGS target enrichment).Tiled-MCC capture panels were designed to densely cover regions of interest, with oligonucleotides that are 70 nucleotides in length and have an overlap of 35 nucleotides. The sequences were designed and filtered for repetitive sequences using a python-based oligo toolThe enrichment procedure was performed using the Twist Hybridization and Wash Kit , Twist Universal Blockers , and Twist Binding and Purification Beads . Per hybridization reaction, up to 8 amplified and indexed libraries were multiplexed to a final amount of 1.5 µg in a single 0.2-ml PCR strip-tube. Library pools were dried completely in a vacuum concentrator at 45 °C. Dried DNA was resuspended in 5 µg of mouse Cot-1 DNA and 7 µl of Twist Universal Blockers. In a separate PCR 0.2-ml strip-tube, the probe solution was prepared by mixing 20 µl of Twist Hybridization Mix with 1–2 µl of oligonucleotides and the final volume was adjusted with nuclease-free water. To prepare both the probe solution and the resuspended indexed library pool for hybridization, the probe solution was heated to 95 °C for 2 min in a PCR thermal cycler with the lid at 105 °C, then immediately cooled down on the ice for 5 min. While the probe solution was cooling down on the ice, the library pool was heated following the same conditions for 5 min. After equilibrating both mixtures to room temperature for 1–2 min, the probe solution was added to the library pool, and 30 µl of Twist Hybridization Enhancer was added last. The capture reaction was incubated at 70 °C for 16 hr in a PCR thermal cycler with the lid heated to 85 °C. The hybridization reaction was subsequently mixed with Streptavidin Binding Beads for 30 min at room temperature on a shaker. Washing with Twist Wash Buffers 1, 2, and 3 was performed following the Twist Target Enrichment Protocol. Post-hybridization PCR was performed with 11–12 amplification cycles. The enriched library was purified with pre-equilibrated Twist DNA Purification Beads at a ratio of 1:1.8 DNA to beads. DNA quantification and QC validation were performed using the Qubit dsDNA Broad Range Quantification Assay  and the Agilent Bioanalyzer Broad Range DNA kit , respectively.Libraries were sequenced using the Illumina NovaSeq and NextSeq 550 platforms with 150 bp paired-end reads. Depending on the quality of the MCC libraries, sequencing 100–200 million reads per enriched Mb per pooled sample is sufficient for data at 500 bp resolution.48 (https://github.com/jojdavies/Micro-Capture-C). The main scripts are available for academic use from the Oxford University Innovation Software Store (https://process.innovation.ox.ac.uk/software/p/16529a/micro-capture-c-academic/1).Tiled-MCC analysis was performed using the MCC pipeline63 (v.1.2.11). Reads were then mapped to the DNA sequences corresponding to the enriched regions of interest with the non-stringent aligner BLAT64 (v.35), using a custom-made file containing the sequences of the regions of interest as reference. Based on the mapping by BLAT, the reads in the FASTQ files were split into two or more reads corresponding to the chimeric fragments, and into different files based on the region, they mapped to, using the MCCsplitter.pl script. Uninformative reads that did not contain a sequence that mapped to any of the enriched regions of interest were discarded. The split reads in the FASTQ files were subsequently mapped to the mm10 reference genome using Bowtie265 (v.2.3.5). The aligned reads were further processed using the MCCanalyser.pl script. PCR duplicates were removed based on the sonicated ends and ligation junction and allowing for a wobble of ±2 bp. Unique ligation junctions were identified if the fragment ends were less than 5 bp apart in the original read and separated by mapping with BLAT and Bowtie2. The orientation of the reads was considered to enable precise identification of the ligation junction.Briefly, adapter sequences were removed using Trim Galore  and paired-end reads were reconstructed using FLASH52. The large-scale contact matrices (500 bp resolution) are shown on a linear scale, whereas the fine-scale contact matrices (20–50 bp resolution) are shown on a log scale. To generate density plots, the unique ligation junctions were filtered for a minimum distance ≥10 bp and plotted in python as a scatter plot with a color code defined by the local density of the data points.To generate contact matrices, the unique ligation junctions were converted into raw matrices, which were balanced using ICE normalizationThe data presented in the manuscript represent the averages of nine replicates for WT samples, four replicates for untreated RAD21-AID samples, six replicates for auxin-treated RAD21-AID samples, and six replicates for auxin-treated CTCF-AID samples. For direct comparisons between Tiled-MCC matrices, the data were downsampled to the lowest number of filtered, unique ligation junctions per condition or replicate. The RAD21-AID and CTCF-AID cell lines are derived from the same strain as the WT cells and contact matrices from the untreated RAD21-AID samples are nearly identical to the WT samples (Supplementary Fig. 46 as files listing valid pairs in the mm10 reference genome, which were converted into ICE-normalized52 contact matrices using HiC-Pro66.The Micro-C data were downloaded from GSE13027567. ChIP-seq data for CTCF, RAD21, and H3K4me1 were downloaded from GSE3020368, GSE9445230, and GSE2784469, respectively. ChIP-seq data for H3K27ac and H3K4me3 and DNase I hypersensitivity data in mES cells were accessed via ENCODE70.Alignment to the mm10 reference genome and processing of ChIP-seq data was performed using the NGseqBasic pipelineP values, and significance scores for genes of interest in RAD21-AID29 and CTCF-AID27 mES cells were extracted from the original processed data files shared by the authors of the respective articles.The normalized read counts, Further information on research design is available in the Supplementary informationPeer Review FileReporting Summary


Arabidopsis transcription factor LSH10, a member of the ALOG protein family, interacts with OTLD1 in living plant cells. Loss-of-function LSH10 mutations relieve the OTLD1-promoted transcriptional repression of the target genes, resulting in their elevated expression, whereas recovery of the LSH10 function results in down-regulated transcription of the same genes. We show that LSH10 associates with the target gene chromatin as well as with DNA sequences in the promoter regions of the target genes. Furthermore, without LSH10, the degree of H2B monoubiquitylation in the target promoter chromatin increases. Hence, our data suggest that OTLD1-LSH10 acts as a co-repressor complex potentially representing a general mechanism for the specific function of plant histone deubiquitinases at their target chromatin.Histone ubiquitylation/deubiquitylation plays a major role in the epigenetic regulation of gene expression. In plants, OTLD1, a member of the ovarian tumor (OTU) deubiquitinase family, deubiquitylates histone 2B and represses the expression of genes involved in growth, cell expansion, and hormone signaling. OTLD1 lacks the intrinsic ability to bind DNA. How OTLD1, as well as most other known plant histone deubiquitinases, recognizes its target genes remains unknown. Here, we show that  The plant histone deubiquitinase, OTLD1, interacts with the Arabidopsis transcription factor, LSH10, and both transcriptionally regulate a set of common target genes. Histone modifications not only regulate gene expression in response to diverse environmental signals such as stress2, pathogen attack3, temperature5, and light6 but play an important role in plant development and morphogenesis [reviewed in10].Growing in a dynamic environment, plants must deal with numerous biotic and abiotic stress sources. Histone modifications that underly epigenetic regulation of gene expression help plants to adapt to environmental changes and serve as an environmental memory of the transcription11. However, because histone-modifying enzymes often do not possess a DNA binding ability, they are not sufficient to trigger transcriptional regulation of the specific target genes. To achieve this regulation, histone-modifying enzymes are thought to function in complexes with transcription factors that contain DNA-binding domains. This DNA binding capacity is thought to direct histone-modifying enzymes, as components of the histone modifier-transcription factor complexes, to the target promoters. Indeed, in plants, different transcription factors have been shown to act in concert with such diverse histone modifiers as histone methyltransferases12, histone acetyltransferases13, histone demethylases14, and Polycomb repressive complexes that promote histone trimethylation and monoubiquitylation15. Yet, whether transcription factors can also facilitate the function of plant histone deubiquitinases, one of the major types of histone-modifying enzymes, remains unknown.Covalent modifications of histones can activate or repress transcription by altering the chromatin structure17. Our previous studies have shown that OTLD1 mainly functions as a transcriptional co-repressor  by associating with the target chromatin and deubiquitylating histone 2B (H2B) at the occupied regions, thereby promoting the erasing or writing of euchromatic histone marks19. Specifically, monoubiquitylation of H2B represents one of the molecular reactions that mediate epigenetic regulation of plant physiology from leaf and root growth to seed dormancy to circadian clock to photomorphogenesis24. The erasure of the monoubiquityl marks from the histone molecules by histone deubiquitinases facilitates changes in histone methylation and acetylation, bringing about transcriptional repression or activation of the corresponding genes26. Consistent with this role in epigenetic regulation of transcription, OTLD1 can interact and crosstalk with the histone demethylase KDM1C to coordinate histone modification and transcriptional regulation of the target genes28. But how is OTLD1 directed to the target gene promoters? To address this question, we endeavored to identify a putative DNA-binding protein that recognizes OTLD1 and targets it to the sites of its biological function.OTLD1, which belongs to the ovarian tumor (OTU) deubiquitinase family, represents one of the few characterized plant histone deubiquitinasesOSR2, WUS, ABI5, and ARL. The expression of these genes was elevated in the LSH10 loss-of-function mutants and suppressed in the gain-of-function lines. Specific interaction between LSH10 and OTLD1 was demonstrated by two independent fluorescence imaging techniques in living plant cells. The binding of LSH10 to the chromatin and DNA sequences of the OSR2, WUS, ABI5, and ARL promoters was also demonstrated. The LSH10 loss-of-function plants also exhibited enhanced H2B monoubiquitylation in the target promoter chromatin. Taken together, these observations suggest that LSH10 functions as a transcription factor that interacts with the OTLD1 co-repressor for transcriptional control of a common set of target genes.Here, we report that LSH10, a member of the ALOG (Arabidopsis LSH1 and Oryza G1) protein family, interacts with OTLD1 and participates in transcriptional repression of the OTLD1 target genes 29, and two independent clones encoding the LSH10 protein were identified as a putative interaction partner of OTLD1  and fluorescence resonance energy transfer (FRET). BiFC experiments shown in Fig. 30. In SE-FRET, protein interaction results in the transfer of the excited state energy from the GFP donor to the RFP acceptor without emitting a photon, producing the fluorescent signal with an emission spectrum similar to that of the acceptor. AB-FRET, on the other hand, detects and quantifies protein interaction from increased emission of the GFP donor when the RFP acceptor is irreversibly inactivated by photobleaching. Figure 31.Next, the physical interaction of LSH10 with OTLD1 was studied LSH10 and OTLD1 genes in different organs systematically throughout the plant, i.e., rosette and cauline leaves, stems, flowers, and roots of wild-type plants using reverse transcription-quantitative RT-PCR (RT-qPCR) analysis. Figure LSH10 was expressed most prominently in roots, less in stems and rosette leaves, and at the lowest relative levels in flowers and cauline leaves  were obtained from ABRC (www.arabidopsis.org/abrc/) and the homozygous mutant lines, designated lsh10-1 and lsh10-2, were generated. The lsh10-1 and lsh10-2 mutants contained a single T-DNA insertion in the exon and 5’UTR of the LSH10 gene, respectively . We selected 2–4 conserved motifs of the intergenic regions of each of these genes  to detect its presence. To correlate the physical association of LSH10 with the target chromatin and the binding of LSH10 to the target promoter sequence, the qChIP primers were designed to overlap several EMSA probes  promoter40. The transcription factor ALF (ABI3-like factor) is thought to recruit a histone acetyltransferase (HAT) activity to the phaseolin promoter in the bean (Phaseolus vulgaris)41. Transcription factors BPC1 (BASIC PENTACYSTEINE 1), BPC6, and AZF1 (ARABIDOPSIS ZINC FINGER 1) interact with the components of Polycomb repressive complexes PRC2 and PRC1 and recruit them to the GAGA and telobox motifs to promote histone 3 (H3) trimethylation and H2A monoubiquitylation43. Transcription factors VAL1 (VP1/ABI3-like 1) and VAL2 interact with the PRC1 component LHP1 and recruit it to the BY motifs44 whereas NAC  transcription factors NAC050 and NAC052 interact with and recruit the histone demethylase JMJ14 to its target genes45. This relatively short list of plant histone-modifying enzymes that are recognized and recruited by transcription factors does not include histone deubiquitinases, one of the main histone modifiers for which the mechanism of recruitment is poorly understood.Epigenetic regulation of transcriptional outcomes heavily relies on the action of histone-modifying enzymes that function as writers and erasers of euchromatic and heterochromatic marks. Most of these enzymes, however, lack DNA binding capabilities and, therefore, are unable to recognize their target chromatin. Instead, this targeting most likely is mediated by DNA-binding transcription factor proteins that recognize specific histone-modifying enzymes and recruit them to their sites of function. For example, the transcription factor SUF4 (SUPPRESSOR OF FRIGIDA4) likely recruits the histone methyltransferase EFS (EARLY FLOWERING IN SHORT DAYS) and the PAF1-like complex to the floral inhibitor 48 suggest that the ALOG proteins may function as specific transcription factors. Our present understanding of the biological functions of different members of the ALOG protein family is very limited. Several ALOG/LSH (LIGHT-SENSITIVE SHORT HYPOCOTYLS) genes have been characterized in Arabidopsis. The initially identified Arabidopsis LSH gene is LSH1, the protein product of which is involved in the light-dependent regulation of hypocotyl development49. Subsequently, LSH3 and LSH4 were shown to suppress the differentiation of organs, such as cotyledons, leaves, and flowers51, LSH8 was reported to have a role in the regulation of the ABA signaling pathway52, and LSH9 was shown to regulate hypocotyl elongation by interacting with the temperature sensor ELF354. The function of LSH10, however, remained unknown, and our observations here suggest that it can function as a transcriptional repressor that interacts with the co-repressor histone deubiquitinase OTLD1 for its functional recruitment to promoters of several of its target genes.Here, we began filling this gap in our knowledge by detecting the specific interaction between the OTLD1 histone deubiquitinase and the LSH10 protein of Arabidopsis. LSH10 belongs to the ALOG family of proteins, such as DUF640 (domain of unknown function 640), known to act as key developmental regulators in land plants. The presence of DNA binding motifs, transcriptional regulation activity, nuclear localization, and homodimerization abilityin planta, BiFC established the subcellular localization of the LSH10-OTLD1 complexes in the nucleus whereas FRET indicated that, in this complex, the interacting proteins are positioned <10 nm from each other. The likely function of LSH10 in these complexes was investigated using LSH10 loss-of-function and gain-of-function plant lines, both of which indicated that LSH10 acts as a transcriptional repressor and that its target genes include four of the five genes known to be repressed by OTLD119. Specifically, LSH10 repressed the OSR2, WUS, ABI5, and ARL genes whereas the transcription of the GA20OX gene was not affected by LSH10. Consistent with this activity, LSH10 is associated with the chromatin of the promoter regions of each of the OSR2, WUS, ABI5, and ARL genes in plant cells. In the LSH10 loss-of-function plants, these promoter regions were enriched for monoubiquitylated H2B, suggesting impairment in the OTLD1 recruitment to the target chromatin. Furthermore, LSH10 directly associated with the DNA sequences of these promoter regions. The DNA binding activity of LSH10 detected experimentally is supported by the alignment of the secondary structure of LSH10 predicted by AlphaFold56  were obtained from ABRC (www.arabidopsis.org/abrc/). For genetic complementation, gain-of-function lines of the lsh10-1 mutant, the Arabidopsis LSH10 cDNA was amplified using primers listed in Supplementary Data 60 by the LR reaction using the Gateway LR Clonase II . The recombinant plasmids were subsequently introduced into Agrobacterium tumefaciens strain GV3101, and transformed into the lsh10-1 mutant plants by the floral dip method61. The transgenic plants were selected on MS medium supplemented with hygromycin (30 mg/l) and timentin (100 mg/l) and confirmed by PCR and RT-qPCR using primers listed in Supplementary Data The Nicotiana benthamiana plants, wild-type Arabidopsis thaliana (ecotype Col-0) plants, and the mutant lines were grown on soil in an environment-controlled chamber at 22 °C under long-day conditions (16-h light/8-h dark cycle at 140 μE sec−1m−2 light intensity) as described previously19. The use of these plant species is helpful because the consistent findings in different systems bolster the robustness of the reported data.OTLD1, LSH10, LSH4 cDNAs, and the Tobacco mosaic virus methyltransferase domain (MT) coding sequence were amplified using primers listed in Supplementary Data N. benthamiana plants by agroinfiltration. Free CFP was expressed from pPZP-RCS2A-DEST-ECFP-C162. For subcellular localization, LSH10 was fused with CFP by transferring its coding sequence by Gateway cloning from pDONR207 into pPZP-RCS2A-DEST-ECFP-N162. The fluorescence signal was recorded 2 days after infiltration using a confocal laser scanning microscope  with a 40X oil immersion objective and CFP and YFP filters.Arabidopsis LSH10 and OTLD1 were transferred by Gateway cloning from pDONR207 into pPZP-RCS2A-DEST-EGFP-N162 and pPZP-RCS2A-DEST-ERFP-N1 to generate the donor vector p35S::LSH10-GFP and the acceptor vector p35S::OTLD1-RFP, respectively. For positive control, ERFP was amplified from pPZP-RCS2A-DEST-ERFP-N1 using primers listed in Supplementary Data N. benthamiana plants via agroinfiltration. The FRET signal was detected and recorded by confocal microscopy using a 40X oil immersion objective.The coding sequences of For sensitized emission, a set of three confocal images of the same field of view was taken using the following channel settings: the GFP channel for excitation and emission of the donor chromophore , the RFP channel for excitation and emission of the acceptor chromophore , and the FRET channel for excitation of the donor and emission of the acceptor chromophores ; the RFP channel emission filter for was set to separate the acceptor signal from the FRET signal. The ImageJ plug-in PixFRET software was used to generate corrected images of SE-FRET efficiency after subtraction of spectral bleed-through. The bleed-through values for the donor and the acceptor were obtained from images of cells expressing each of the constructs alone  and donor channel (GFP) were captured simultaneously before and after photobleaching. %AB-FRET was calculated as the percent increase in GFP emission after RFP photobleaching using the followingEq.  and Hexa-random primers. RT-qPCR was performed using Power SYBR Green PCR master mix   and a QuantStudio™ 3 Real-Time PCR System (Applied Biosystems by Thermo Fisher Scientific). The thermocycler program consisted of pre-denaturation at 95 °C for 10 min followed by 40 cycles at 95 °C for 15 s and 60 °C for 1 min. Each sample was analyzed in 8 biological replicates and 3 technical repeats for each, and the relative expression values were calculated using the 2−ΔΔCt formula63, where Ct indicates cycle threshold, i.e., the number of PCR cycles required for the signal to become detectable above the background. A. thaliana Actin, Sand, and EF1a were used as reference genes. An averaged Ct value from all three of these genes was also used for the normalization of data in the calculation of fold changes in expression of the endogenous LSH10 and OTLD1 genes in different organs of the wild-type plants using the ΔCt-method as described64. Specific primers used in these experiments are detailed in Supplementary Data Plant genomic DNA was extracted using the DNeasy-like Plant DNA Extraction Protocol. RNA from plants was extracted using TRIzol  according to the manufacturer’s instructions. The RNA was used as a template for cDNA synthesis lsh10-1/LSH10-His6 and wild-type plants were harvested and cross‐linked with 1% formaldehyde (v/v) for quantitative chromatin immunoprecipitation (qChIP) assays. The qChIP experiments were performed using an EpiQuikTM Plant ChIP Kit . According to the manufacturer’s instructions, the chromatin from the plant cells was extracted and sheared; the length of sheared DNA fragments was 200–1000 bp. 5 µl of the crude chromatin extracts were saved for use as an input control, and 100 µl were added into the microwell immobilized with the antibodies  as the negative control, specific rabbit anti-His-tag antibody  or anti-monoubiquityl-histone H2B (Lys-120) . DNA was released from the antibody-captured protein-DNA complex, reversed, purified through the specifically designed F-Spin Column, and then amplified by qPCR as described above. Input Ct values were adjusted for the dilution factor and ΔCt was calculated by normalizing Ct values to the adjusted input based on the Eq.  according to the manufacturer’s protocol. The probes were designed by two iterations of motif-based sequence analysis of the intergenic regions of the ARL, WUS, ABI5, and OSR2 genes for conserved motifs using the Multiple Expectation Maximizations for Motif Elicitation (MEME) tool (https://meme-suite.org/meme/). The biotin-labeled and unlabeled oligonucleotides corresponding to both strands of the selected sequences . Error bars represent SEM of independent biological replicates.Statistical significance was determined by paired two-tailed Student’s t-test using Excel Microsoft 365 software. Further information on research design is available in the Peer Review FileSupplementary MaterialDescription of Additional Supplementary FilesSupplementary DataNR reporting summary


Il12b can be disrupted by selective inhibition of the cohesinopathy-associated BET bromodomain BD2. Our data identify discrete steps in enhancer-mediated inducible gene expression that differ in cohesin-dependence, and suggest that cohesin and BD2 may act on shared pathways.Innate immune responses rely on inducible gene expression programmes which, in contrast to steady-state transcription, are highly dependent on cohesin. Here we address transcriptional parameters underlying this cohesin-dependence by single-molecule RNA-FISH and single-cell RNA-sequencing. We show that inducible innate immune genes are regulated predominantly by an increase in the probability of active transcription, and that probabilities of enhancer and promoter transcription are coordinated. Cohesin has no major impact on the fraction of transcribed inducible enhancers, or the number of mature mRNAs produced per transcribing cell. Cohesin is, however, required for coupling the probabilities of enhancer and promoter transcription. Enhancer-promoter coupling may not be explained by spatial proximity alone, and at the model locus  Here the authors show inducible genes and enhancers are regulated mainly by transcriptional burst frequency and that this is coordinated in single cells and individual alleles. Cohesin, which is important for inducible gene expression, is largely dispensable for regulating enhancer burst frequencies; however, it is required for coupling burst frequencies of inducible enhancers and promoters. However, the acute depletion of CTCF or cohesin from mouse or human cell lines deregulates only a limited number of genes at steady-state, notably direct targets of CTCF promoter binding7, and boundary-proximal genes that show altered transcriptional bursting8, respectively. By contrast to steady-state gene expression, loss of cohesin severely disrupts inducible transcriptional programmes in macrophages9 and neurons10, as well as oestrogen-responsive genes11.Cohesin cooperates with CTCF in organising the genome into self-interacting domains that each contain a small number of genes and enhancers, thus potentially facilitating enhancer–promoter cooperation12. Mechanistically, the engagement of anti-microbial pattern recognition receptors drives the recruitment of transcription factors, chromatin remodellers, and cofactors, including histone acetyltransferases. BET  epigenetic reader proteins interact with acetylated lysine residues via N-terminal tandem bromodomains termed BD1 and BD2 (ref. 13) and recruit Mediator and RNA polymerase to initiate eRNA transcription14. Enhancer activation culminates in target gene transcription12 by facilitating recruitment of the transcriptional machinery to promoters14 and by increasing the frequency of promoter bursting15. Transcription is typically discontinuous and can be described in terms of the frequency and the size of transcriptional bursts19. Both the size24 and the frequency28 of transcriptional bursts have been linked to inducible gene expression in different experimental systems. How cohesin contributes to the regulation of transcriptional bursting during immune gene activation remains unknown.Inducible genes in macrophages are paradigmatic for the core transcriptional response to microbial signalsHere we address the transcriptional parameters that underlie the activation of inducible genes and enhancers in macrophages and determine steps within the process that are cohesin-dependent or -independent. We use single-molecule RNA fluorescence in situ hybridisation (smRNA-FISH) to quantitatively assess the fraction and intensity of enhancer and target gene transcription as well as the coordination between the transcriptional activity of enhancers and promoters at the single-molecule level. smRNA-FISH indicates that inducible immune genes and the activity of associated enhancers are regulated primarily by an increase in the fraction of actively transcribing alleles, and that inducible enhancer activation is tightly coupled to an increased probability of target gene transcription in wild-type cells. We find that cohesin is dispensable for activation-induced increases in the probability of enhancer bursting, and that the number of mature mRNAs produced per transcribed target gene allele is largely independent of cohesin. However, cohesin is critical for coupling the probabilities of enhancer and target gene bursting. Finally, we present evidence that the role of cohesin in enhancer–promoter coupling may not be explained by spatial proximity alone and demonstrate potential convergence of cohesin and BD2, the second BET protein bromodomain, in the activation of inducible genes.P < 10e-16; Supplementary Fig. Il12b we adopted a high throughput smRNA-FISH approach that allows the quantification of transcript copy numbers and transcriptional bursts in thousands of individual macrophages during the first 2 h of the response to LPS  with RNA-seq (as a measure of transcript levels). RNA-seq and GRO-seq were highly correlated . These enhancers were actively transcribed in LPS-activated macrophages . We recorded eRNA and mRNA transcriptional bursts for five inducible enhancers and associated genes in a total of 38,093 cells  alleles encoding the cohesin subunit RAD21 by 4-OHT-mediated activation of ERt2Cre in bone marrow-derived mature macrophages9. This approach removed 90–99% of Rad21lox/lox alleles . Comparison of RNA-seq with GRO-seq showed that this reduction in inducible immune gene expression occurs predominantly at the transcriptional level in both resting and LPS-activated macrophages   Fig. . There wed) Fig. . This anIrf- and Stat- family transcription factors are dysfunctional in cohesin-deficient macrophages9. This is illustrated here for the Ifnb1 L2 enhancer  were transcribed to at least wild-type levels in Rad21−/− macrophages (adj. P > 0.05). Enhancers associated with Il12b, Prdm1, Peli1 and Egr2 were efficiently induced in Rad21−/− as well as wild-type macrophages  or lost completely (Prdm1) in the absence of cohesin  as well as contact domains . In contrast, as observed for the genes investigated by smRNA-FISH, cohesin-deficient macrophages showed a reduced correlation between the transcription of enhancers and genes located in the same TADs  or contact domains . The difference in the correlation between enhancer and gene transcription between wild-type and cohesin-deficient macrophages was significant at the level of both TADs  and contact domains . This analysis indicates that cohesin couples the activity of inducible macrophage enhancers to inducible macrophage genes genome-wide. Taken together with the smRNA-FISH data, these results show that the loss of cohesin uncouples transcriptional burst fractions of inducible genes from transcriptional burst fractions of inducible enhancers in innate immune cells and that enhancer-mediated gene activation involves discrete steps characterised by selective reliance on cohesin.We next asked whether cohesin has a role in coupling the activity of inducible enhancers to target gene transcription genome-wide. To this end, we analysed the LPS-induced transcription of canonical macrophage enhancers44, and many loci that undergo enhancer–promoter looping during active transcription can do so in the absence of cohesin across significant genomic distances of ~100 kb (refs. 49). Based on these and other considerations discussed more fully below, we set out to test the validity of the spatial proximity model of cohesin action in our experimental system.A straightforward interpretation of these results would be that cohesin mediates enhancer–promoter coupling simply by decreasing the proximity between enhancers and promoters in 3D nuclear space. Although appealing, the view of cohesin as a mere provider of spatial proximity faces significant challenges: direct enhancer–promoter looping is neither strictly required, nor is it sufficient for the activation of target genesEgr2 enhancer was slightly closer to the Egr2 target gene in cohesin-deficient macrophages (genomic distance ~61 kb), and vice versa for Peli1 (genomic distance ~43 kb). No significant differences were found for the Prdm1 and Il12b enhancer-gene pairs . These data show that transcriptional bursts can occur across a range of different enhancer–promoter distances. Furthermore, within the resolution limits of our experimental setup, enhancer–promoter distances are similar in wild-type and cohesin-deficient macrophages, suggesting that enhancer–promoter distances alone may not explain the role of cohesin in the functional coupling of enhancers and promoters.Analysis of smRNA-FISH data showed that enhancer and promoter bursts occurred over a wide range of distances Fig. , top. Ev52, and (ii) either BET inhibition or targeted degradation of BRD4 abrogate transcription but leave enhancer–promoter contacts intact53. We used the BD1-selective small molecule inhibitor GSK778, which largely phenocopies pan-BET inhibitors, as well as the BD2-selective inhibitor GSK046, which has more limited effects on steady-state transcription54. BD1 and BD2 inhibitors primarily affected burst fractions rather than burst intensities  human mutations in cohesin genes and in BRD4 cause related pathologyies Fig. . Selectiges Fig. . Interesons Fig. . Selectiges Fig. . Hence, 39, and DNA damage and repair have been proposed as one potential pathway linking mutations in cohesin-related genes to a CdLS-associated Y430C mutation in the BD2 of BRD4 (Ref. 55). In rapidly dividing mouse embryonic stem cells, corresponding BD2 mutations caused a delay in cell cycle progression, susceptibility to DNA damage, and delayed DNA damage repair55. We, therefore, examined cell cycle- and DNA damage-related pathways in wild-type and Rad21−/− macrophages.Cohesin is essential for genome integrity during the cell cycleRad21−/− macrophages  revealed no major differences between wild-type versus Rad21−/− macrophages  by demonstrating that cohesin loss altered the frequency of cells that express inducible immune gene transcripts, rather than the level of expression per cell. Consistent with these results, smRNA-FISH showed that loss of cohesin predominantly reduced burst fractions, while burst intensities were largely unaffected.Here we dissect the regulation of burst parameters in inducible gene transcription and the cooperation between enhancers and their target genes. We show that inducible immune genes and enhancers in primary mouse macrophages are regulated predominantly by the fraction of transcriptional bursting, while burst intensities remain largely unchanged. We extend previous observations that inducible immune genes show heterogeneous expression at the single cell level by scRNA-seq  and cohesin10, these findings point to similarities between cohesin and BD2. To complement the inhibitor data presented here, future experiments will address the impact of BD2 mutations on transcriptional burst parameters.Selective inhibition of the BD2 bromodomain by GSK046 reduced gene transcription, but not enhancer activity at the 50. Strikingly, a missense mutation in the BD2 bromodomain of BRD4 causes CdLS-like developmental abnormalities51. Cell cycle delay, susceptibility to DNA damage, and defective DNA repair have been proposed as shared pathways triggered by mutations in cohesin-related genes and in BD2 (Ref. 55). Our analysis shows that in quiescent macrophages, loss of cohesin does not trigger cell cycle- or DNA damage-related pathways. Our results do not question the observation that mutations in cohesin-related genes and in BD2 can lead to cell cycle aberrations and DNA damage in rapidly proliferating cells55. They do, however, show that neither cell cycle aberrations nor DNA damage are required for the shared effects of cohesin and BD2 on transcription.Human CdLS is mainly associated with mutations in cohesin-related genes66, as well as modulation of eRNA transcription and enhancer activity68. The availability of selective BD inhibitors and the identification of BD2 mutations in human disease51 provide a path for future studies to link specific BET functions to specific protein domains. Such studies will be motivated not only by the desire to dissect the regulation of transcription but also by the relevance of BET proteins to human development52 and cancer65. In this regard, mapping the mechanisms that functionally link BET proteins and cohesin is important for understanding the selective advantage of somatic cohesin mutations in haematopoietic malignancies and solid tumours69. In summary, our results delineate cohesin-dependent and -independent steps in enhancer-driven gene activation. Cohesin functionally couples the burst frequencies of inducible enhancers and promoters by mechanisms beyond spatial proximity.BET proteins can impact transcription by a variety of mechanisms, which include RNA polymerase pause release and transcriptional elongationRosa26-ERt2Cre Rad21WT/WT or Rad21lox/lox mice9 on a mixed C57BL/6 129 background were cultured in complete DMEM medium , 20% L929-conditioned media. Cre was induced on day 4 by 200 nM 4-hydroxy tamoxifen (Sigma-Aldrich H7904). Macrophages were stimulated on day 7 with 10 ng/ml of LPS from Salmonella typhosa (Sigma-Aldrich L7895). Where indicated, 1 μm GSK778 or GSK046 or carrier (DMSO) were added at the same time as LPS.Mouse work was performed according to the Animals (Scientific Procedures) Act Mouse work was done under a project licence issued by the UK Home Office, UK following review by the Imperial College London Animal Welfare and Ethical Review Body (AWERB). Bone marrow cells from Cell lysates were separated by SDS-PAGE on 7.5% gels  and transferred to nitrocellulose membranes . Membranes were blocked with 5% milk in Tris-buffered saline (TBS) with 0.1% Tween-20 and incubated with primary antibodies against RAD21  and Tubulin  in TBS 3% milk overnight at 4 °C, washed 3x in TBS, incubated with Alexa fluor 680 goat anti-rabbit (Life Technologies A21109 1/10.000) to detect RAD21 or Alexa fluor 680 goat anti-mouse  to detect Tubulin for 1 h at 4 °C, washed 3x in TBS, imaged (LI-COR Biosciences) and analysed .5 cells and kept in a cell culture incubator at 37 ◦C overnight. Coverslips were washed with PBS and fixed for 5 min with 300 μL ice-cold 100% methanol at room temperature followed by three washes with PBS and stored at 4 °C overnight. Cells were permeabilised with 200 μL/sample 0.5% Triton X-100 in PBS for 10 min, incubated with 10% serum for 30 min, and primary antibody incubation was performed at 4 °C overnight using RAD21 (Abcam ab154769) 1:500 in 10% serum. Cells were washed three times with PBS and secondary antibody incubation was performed with goat α-rabbit 488 (Invitrogen A11034 1:500) for 1 h at room temperature. Confocal stacks were acquired in Leica SP8 microscope with a voxel size of 0.144 × 0.144 × 0.99 μm, ×40 oil objective). For the analysis, nuclei and cell outlines were identified from maximum projections by CellProfiler v2.2.0 (ref. 70) as primary and secondary objects. To normalise variable levels of background, RAD21 expression for each cell was defined as the mean nuclear intensity of the antibody channel minus the cytoplasmic mean. Example images were filtered to remove speckle background noise using a CellProfiler filter.Sterile 13 mm coverslips were seeded with 2 × 105 per 13 mm coverslips or 1 × 105 per well in eight well chamber slides , kept in a cell culture incubator at 37 ◦C overnight, and activated with LPS where indicated. In experiments using exon probes, cells were stained with CellTracker  to facilitate the assignment of cytoplasmic smRNA-FISH signals to individual cells. Locus-specific exon, intron or enhancer smRNA-FISH probes  with specific image-based transcriptomics modules70. A size filter was applied to avoid scoring cell clusters as individual cells. Outlines were visually inspected, and then loaded in FISHQuant v3a (ref. 73). FISHQuant counted mRNA molecules by fitting 3D Gaussian in the 3D stacks.To increase throughput, chamber slides were imaged with a Leica TCS SP5 inverted microscope with a dry 20 × /0.75 NA with a pixel size of 189 nm and a z-step size of 500 nm. The combination of a dry objective, chamber slides and MatrixScreener software allowed imaging of up to eight samples per night. The Leica TCS SP5 was used in resonant mode with a bidirectional scan, with a line average of 8–16 to reduce noise. Imaging fields were selected automatically, with human supervision only to ensure that cells were present. Fluorescence reflection was used for automatic focusing. Nuclei and cell outlines were identified from maximum projections using CellProfiler v2.2.0 modules as primary and secondary objectshttps://github.com/IreneRobles/TSSs). The expected co-occurrence of transcriptional bursts was calculated as the probability of one multiplied by the other. TSS-TSS distances were determined based on xy coordinates. Chromatic aberration was measured at centroid distances using 0.1 µm TetraSpeck™ microspheres and was 90.8 nm between 488 and 633 nm (Ifnb1 - Ifnb1 L2 enhancer), 102.3 nm between 561 and 488 nm (Il12b - Il12b HSS1 enhancer and Peli1-Peli1 enhancer), and 41.4 nm between 561 and 633 nm (Prdm1 - Prdm1 enhancer). In each case, the chromatic aberration was smaller than the xy pixel size of 189 nm, and the data was not corrected for chromatic aberration.During a transcriptional burst, active TSSs accumulate signals that are brighter than individual transcripts. Burst intensities were estimated as the ratio between the integrated intensity of pixels at the TSS and the same area around averaged individual transcripts. Enhancers and TSSs were assigned to alleles based on distance (1 μm) using a custom Julia script .To infer mean μ and variance σ74) with arguments '–library-type fr-first strand -b2-very-sensitive -b2-L 25' with gene annotation from Ensembl version 67. Read counts on genes were summarised using HTSeq-count (ref. 75). The RUVseq Bioconductor package was applied with RUV k = 3  for ERCC spike-ins and replicate samples as controls. Differentially expressed genes were identified by DESeq2 (ref. 77).100 bp paired-end RNASeq reads were aligned to mouse genome mm9 using Tophat2  and the genome references for human , mouse , and dog . The analysis of spike-in control RNA content allowed us to identify empty wells and barcodes with more than 15% of reads mapping to spike-in artificial transcripts were discarded. Cells with less than 60% of reads mapping to the reference genome or more than 2 × 106 total reads were discarded. Gene quantification was performed using UMI-corrected transcript information to correct for amplification biases, collapsing read counts for reads mapping on a gene with the same UMI . Only unambiguously mapped reads were considered. Genes not expressed in at least 5% of the cells were discarded.WT and Rad21-deficient macrophages were sorted by flow cytometry into 386 well plates, and MARS-Seq librariesPearson correlation tests were done with the cor.test function in R. To test significant changes in event frequency across replicates we used a Cochran–Mantel–Haenszel test with replicate as strata variable in R. Bonferroni correction for multiple testing was implemented in MutipleTesting.jl Julia package. To test significant changes in distribution values across replicates, we used one-way ANOVA with Tukey HSD correction in R. To test whether the relationship between two variables differs between conditions we implemented two-way ANOVA in R.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary