First, maternal transcripts of trim46a were present at 1 cell stage whereas zygotic messages were abundant in the eyes, MHB (Midbrain-Hindbrain Boundary) and hindbrain at 24 hpf (hours post fertilization). Second, transcriptional regulatory region of trim46a contains cis-acting elements binding a transcriptional factor Foxa2. Transcription of foxa2 is positively regulated by Sonic Hedgehog (SHH), and treatment of cyclopamine, an SHH inhibitor, represses transcription of foxa2 in 4 hpf through 24 hpf embryos. Third, the transcriptional repression of foxa2 inhibited transcription of trim46a to cause developmental defects in the midbrain and MHB. Finally, spatiotemporal expression patterns of a midbrain marker otx2b in the developmental defects confirmed inhibition of SHH by cyclopamine caused underdevelopment of the midbrain and MHB at 24 hpf. We propose a signaling network where trim46a contributes to development of the midbrain and MHB via Foxa2, a downstream element of SHH signaling in zebrafish embryogenesis.TRIM46 is a RING finger E3 ligase which belongs to TRIM (tripartite motif-containing) protein family. TRIM46 is required for neuronal polarity and axon specification by driving the formation of parallel microtubule arrays, whereas its embryological functions remain to be determined yet. Expression patterns and biological functions of  Eukaryotic cells are equipped to degrade proteins via the ubiquitin-proteasome system (UPS). Proteins become degraded upon their conjugation to chains of ubiquitin where they are directed to the 26S proteasome, a macromolecular protease (Deshaies and Joazeiro TRIM46 (tripartite motif-containing 46) belongs to the large family of tripartite motif proteins that have been implicated in many biological processes including UPS, cell differentiation, transcriptional regulation and signaling pathway  and Foxa2, which controls transcription of trim46a.Ubiquitin ligase TRIM46 has been revealed as an essential element in neurogenesis as well as in the initiation and maintenance of axonal transport. TRIM46 is localized in the proximal axon, where it specifies neuronal polarity of microtubule bundles oriented with their plus-end pointing outward (Curcio and Bradke in situ hybridization (WISH) was blocked by treating them with 0.002% phenylthiourea (PTU) after onset of somitogenesis.Wild types of adult zebrafish were maintained at 28.5°C with 10 h dark/14 h light cycles. Embryos were obtained through natural spawning and raised, and staged as described previously  (http://www.ncbi.nim.nih.gov/). Amino acid sequences of trim46a and other orthologs were aligned as previously described  in different stages of embryos, forward primer; 5′-GGACTGCGTTAAATTGGACCTCTCAG-3′ and reverse primer; 5′-ATCGCTGAAACTGTGCCGTTGTTC-3′, and for β-actin (328 bp), as an internal control for this experiment, forward primer; 5′-GCCCATCTATGAGGGTTACG-3′ and reverse primer; 5′-GCAAGATTCCATACCCAGGA-3′. Upon completion of the PCR reaction, every set of reaction was confirmed with running 1% agarose gel in TE buffer using gel electrophoresis. After cloning in pGEM®T-easy vector and confirmation with the digestion process, plasmid construct was sent for sequencing to SolGent Co. Ltd. The protocols were established and applied as previously in our laboratory  were performed as described in  was performed following a standard procedure with minor modifications as described in  . Embryos were fixed in 4% paraformaldehyde (PFA) overnight, and dehydrated in 100% methanol. All images and their magnification were captured with Leica MZ16 microscopy systems.Whole-mount trim46a were obtained from National Center for Biotechnology Information (NCBI) (http://www.ncbi.nim.nih.gov/) and searched for the occurrences of binding motifs for transcription factors (TF) as defined by Transfac database  of Embryos were staged and synchronized at 4 hpf by discarding any embryos not at sphere stage, and then incubated beginning at 4 hpf until 24 hpf in embryo medium containing 20 μM and 60 μM cyclopamine respectively (LC Laboratories), diluted from a 10 mM stock in ethanol, at 28.5°C. Control embryos were treated with an equivalent concentration of ethanol. Embryos were raised at low density (no more than 50 embryos/100 mm Petri dish) to ensure developmental synchrony across individuals . First, the images were converted to 8-bit grayscale and the area to be measured was outlined, and the sum of intensity (reaction product plus background) was determined in this region of interest (ROI). Next, the outline was moved to an adjacent region of the embryo that showed no reaction product and the background intensity was determined. This was then subtracted from the ROI. Using the sum intensity and dividing by the ROI pixel area, the mean intensity of an embryo was calculated. The average mean intensity of each gene was obtained from 5 to 15 embryos in each group.To provide objective and quantitative assessments of t tests. P < 0.05 was considered to indicate a statistically significant difference. GraphPad Prism 9 software was used for data analysis.Statistically significant differences between the two groups were determined using the unpaired trim46a, an ortholog of TRIM46 in zebrafish, NCBI database was used as reference. Zebrafish trim46a  is mapped to chromosome 16 and located in NC_007127.7  which spans about 27.497 kb of nucleotides. trim46a transcript consists of 4136 bp containing the open reading frame (ORF) between 340 bp and 2661 bp, which encodes 773 amino acids (M. musculus), and human (H. sapiens) using CLUSTAL OMEGA under default parameters for multiple sequence alignment demonstrated that zebrafish Trim46a shares homology with mouse and human by 58.96% and 59.36%, respectively (data not shown).To identify trim46a to vertebrate embryogenesis, we initially examined spatiotemporal expression of trim46a in zebrafish embryos at various developmental stages. Analysis of RT-PCR (Reverse Transcription Polymerase Chain Reaction) exhibited that trim46a transcripts were abundant at 1-cell stage, and the expression persisted until 24 hpf, indicating that it is maternally and zygotically expressed (data not shown). Spatiotemporal expression patterns of trim46a were analyzed with whole-mount in situ hybridization (WISH) using trim46a-specific digoxigenin-labeled RNA probe in zebrafish embryos at 1-cell  region from the transcriptional start site and 500bp of the first intron were analyzed by Transfac database , LIM homeobox gene 3 (LHX3), Hepatocyte nuclear factor 4 alpha 1 , Forkhead box A2 (FOXA2), Forkhead box D3 (FOXD3), Octamer transcription factor 1 (OCT-1), TATA-binding protein (TBP), E2F, Meis1A:Hoxa9 and Runt-related transcription factor 1 (RUNX1) (trim46a because its DNA recognition domain is well conserved in zebrafish (-1244 bp: AAATGTTTGCTTT), mouse (-1618 bp: AGTCAATAGTGCG) and human (-458 bp: GAGGAAATAATGCG) (data not shown). Forkhead box (FOX) proteins comprise a large family of transcription factors (TFs), members of which display functional diversity and participate in cellular processes ranging from development to immunity and metabolism  analysis software. Region of interest (ROI) consists of the forebrain, midbrain and cerebellum except for the rhombomeres. Calculation of the mean intensity indicated that inhibition of SHH signaling by cyclopamine reduced level of trim46a transcripts more significantly than that of foxa2 in the ROI of the zebrafish embryos at 24 hpf  . TRIM46 induces ubiquitination of PPAR-α and regulates cell viability, cell cycle, and apoptosis of OS (Jiang et al. trim46a . Further(trim46a , which r(trim46a . Most pa


GATA4 and GATA6 promoter can increase susceptibility to AMI. In this study, we explored the relationship between GATA5 promoter and AMI for the first time, hoping to provide a new genetic basis for understanding the pathogenesis of AMI.Acute myocardial infarction (AMI) is a severe type of coronary artery disease, caused by coronary occlusion and followed by cardiac ischaemia. GATA binding protein 5 (GATA5) is an important member of GATA family and plays an important role in vascular inflammation, endothelial function, oxidative stress and cell metabolism. Previous studies have shown that the DNA sequence variants (DSVs) in GATA5 promoter was sequenced in 683 individuals (332 AMI patients and 351 controls). The transcriptional activity of the GATA5 promoter with or without DSVs in HEK-293 cells, H9c2 cells and primary neonatal rat cardiomyocytes were examined by Promega Dual-Luciferase® Reporter Assay system. Electrophoretic mobility shift assay (EMSA) was performed to explore whether the DSVs interfered with the binding of transcription factors (TFs).GATA5 promoter, eight of them evidently altered the transcriptional activity of the GATA5 promoter, five of them disrupted the binding of TFs (such as farnesoid X receptor). Furthermore, haplotype AT (across rs80197101 and rs77067995) is a dangerous haplotype of AMI. Genotype GA and allele A of rs80197101 and genotype CT and allele T of rs77067995 are the risk factors of AMI.Nine mutations have been found in GATA5 promoter can increase susceptibility to AMI. But the mechanism remains to be verified in vivo.DSVs in  Cardiovascular diseases (CVDs) are the leading cause of death globally . Acute mGATA5 loss-of-function mutations are closely related to congenital heart disease, atrial fibrillation, hypertension . The linked single nucleotide polymorphisms (SNPs) [g.62476317G>A (rs80197101) and g.62476223C>T (rs77067995)] were constructed as one expression vector (pGL3-62476317A+62476223T). Designated expression constructs (pGL3-DNA fragments of interesting) expressed firefly luciferase activity, and pRL-TK (25 ng) expressed renilla luciferase. The pRL-TK and empty vector (pGL3-basic) were used as internal and negative control, respectively.In order to generate expression vectors, the Hind III and Kpn I sites of pGL3-basic (Promega Corporation) were inserted into the variant and wild type DNA fragments  of GATA5 gene promoter was expressed as the ratio of firefly luciferase activity over renilla luciferase activity. Transcriptional activity of wild type GATA5 gene promoter was designed as 100%, relative transcriptional activities of the variant GATA5 gene promoters were calculated.These expression vectors were transfected into H9c2 cells , HEK-293 cells  and primary NRCMs which have been widely used in transient transfection experiments. Cells were seeded in 6-well plates and grown to 70% to 80% confluence, and then designated expression constructs (1.0 μg) were used to transfect the cells in each well. Forty-eight hours later, dual-luciferase activities of the transfected cells were examined using the Promega Dual-Luciferase® Reporter Assay system on a Promega Glomax 20/20 luminometer (both Promega Corporation). The transcriptional activity of the EMSA was carried out using the LightShift® Chemiluminescent EMSA kit  according to the manufacturer’s protocol. NE-PER® Nuclear and Cytoplasmic Extraction Reagent kit  was used to prepare nuclear extracts from H9c2 and HEK-293 cells, the concentrations of nuclear extract were measured by Bradford protein assay. Double-stranded oligonucleotide fragments (30 bp) containing DSVs were biotinylated and used as probes . Nuclearhttp://analysis.bio-x.cn/myAnalysis.php). The statistical power was analyzed by Genetic Association Study Power Calculator . All statistical tests were two-sided and P<0.05 was considered statistically significant.Results of transient transfection were independently repeated at least three times and verified by others, the value was expressed as the mean ± standard error of the mean and compared by a standard Student’s t-test. Statistical analysis was carried out by SPSS software version 19.0 , the measurement data were expressed as mean ± standard deviation. For SNPs , Hardy-Weinberg equilibrium was tested using a goodness-of-fit Chi-square, and R×C chi-square test was used to analyze the distribution difference of genotype and allele frequency between AMI and control group. The correlation between SNPs and AMI was analyzed by logistic regression analysis, which was expressed by odds ratio (OR) and 95% confidence interval (CI). The linkage disequilibrium of the SNPs was analyzed by Haploview 4.2 software. Haplotypes were analyzed by SHEsis online software . The level of high density lipoprotein cholesterol (HDL-C) in the control group was significantly higher than that in AMI group (P<0.001). Due to the use of lipid-lowering drugs in AMI patients, the levels of body mass index (BMI), low density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) in the control group were significantly higher than those in AMI group . The levels of systolic blood pressure (SBP), diastolic blood pressure (DBP) and triglyceride (TG) was no significant difference between the two groups  , diabetes mellitus (DM) and the level of age in AMI group were significantly higher than that in control group (= 0.650) . In the GATA5 promoter was sequenced in all 683 individuals and nine DSVs were identified. We re-sequenced these nine DSVs to rule out the possibility that some DSVs arose due to PCR mistakes. The frequencies and locations of these nine DSVs are summarized in X2 = 6.459, P = 0.040; X2 = 6.459, P = 0.040, respectively), while g.62476271 A>C (rs145936691) was reported in controls and AMI patients with similar frequencies (P = 0.722).The P = 0.722, P = 0.781, P = 0.722; for control: P = 0.489, P = 0.808, P = 0.489), indicating that the samples had good representativeness and came from the population that was genetic equilibrium.The results of Hardy-Weinberg equilibrium test showed that rs80197101, rs145936691 and rs77067995 were in accordance with Hardy-Weinberg equilibrium , the frequencies of GA+AA genotypes and A allele in AMI group were significantly higher than that in control group . The frequency distribution of alleles and genotypes of rs77067995 between AMI and control group was the same as that of rs80197101. For rs145936691, the frequency distribution of alleles and genotypes between AMI and control group was not significantly different .For rs80197101, there was significant difference in the distribution of rs80197101 genotypes between AMI and control group . After adjusting for age, gender, smoking, HTN, DM, HDL-C, LDL-C, TG and TC, the GA and GA+AA genotypes of rs80197101 locus were still correlated with the occurrence of AMI . As for the effect to result in AMI, the GA and GA+AA genotypes were 2.263 and 2.297 times higher than that of GG genotype, respectively. The correlation between rs77067995 and AMI was the same as that of rs80197101.Correlation between SNPs (rs80197101 and rs77067995) and AMI was analyzed by Logistic regression. The results showed that GA and GA+AA genotypes of rs80197101 locus were correlated with the occurrence of AMI . The haplotypes composed of these two SNPs were analyzed , and found that rs80197101G>A and rs77067995C>T formed two haplotypes (AT and GC). Haplotype AT exhibited higher frequencies in AMI group than in control group , while haplotype GC exhibited higher frequencies in control group than in AMI groups . Considering the two haplotypes (AT and GC) together, the difference of frequency distribution between the two groups was also statistically significant (P = 0.013).Rs80197101 and rs77067995 showed perfect linkage disequilibrium  was used to calculate the statistical power under these situations: the ratio of cases (n = 330) to controls (n = 350) was 0.943, significance level was 0.05, disease model was multiplicative, disease prevalence was 0.10, disease allele frequency was 0.2338 (159/680), genotype relative risk was 1.4. Finally, the values of statistical power was 0.863.Genetic Association Study Power Calculator  was used to predict whether and which putative binding sites for TFs were affected by DSVs. The DSV g.62476323-24 GG>AA abolished the binding site for E2F related factors (cttCGGCGgggg) and AP-2 (ccagccttCGGCGgg). The DSV g.62476197G>A modified the binding sites for ZFP161 (tcCGCGCtggccgc). The SNP g.62475977C>G (rs1294169077) abolished the binding site for farnesoid X receptor (FXR) (cccgccGGTCGccgaccca). The SNP g.62476317G>A (rs80197101) abolished the binding site for Kruppel-like factor 6 (KLF6) (gcGGGGG). The SNP g.62476223C>T (rs77067995) created the binding site for Small mothers against decapentaplegic (SMAD) (cGCCTGt). The SNP g.62476171C>T (rs1341970027) abolished the binding site for LRH-1(cgtCCTTGtcg) and Steroidogenic factor-1 (SF-1) (cgtCCTTGt) group, created the binding sites for transcription factor 7 (TCF7) related factors (tCTTTGt). The SNP g.62476123A>G (rs1435326263) created the binding sites for BEN (CTGCGgcc). The DSV g.62476046G>A modified the binding sites for AP-2 (cgcggcggGGGCGgg) and Krüppel-like factor 4 (KLF4) (ggGGCGG).Transfac program  was designed as 100%. According to our results, the transcriptional activity of the promoter-free vector (pGL3-basic) was close to zero compared with pGL3-WT, indicating that the transcriptional activity of other expression vectors we constructed were credible.Whether in HEK-293 cells, H9c2 cells or primary NRCMs, transcriptional activity of the wild-type GATA5 gene promoter (P<0.01), pGL3-62476197A, pGL3-62476123G, pGL3-62476323-24AA and pGL3-g.62476171T (identified only in AMI patients) and pGL3-62476317A+62476223T  evidently increased the transcriptional activity of GATA5 gene promoter (P<0.05). As expected, pGL3- 62476271C  did not affect the activity of GATA5 gene promoter significantly evidently repressed the transcriptional activity of yP>0.05; .GATA5 gene promoter was not tissue-specific.The transfection results of H9c2 cells  and primElectrophoretic mobility shift assay (EMSA) was performed with variant and wild-type oligonucleotides  to exploGATA5 plays an important role in CVDs as GATA5 loss-of-function mutations are closely related to congenital heart disease, atrial fibrillation, hypertension  of GATA5 gene promoter was carried out, nine mutations have been found in GATA5 gene promoter and these DNA variants may increase the susceptibility to AMI as risk factors. But the mechanism remains to be verified in vivo. Besides, this study only focused on GATA5. GATA4, GATA5 and GATA6 belong to the same subfamily of GATA family, whether they have interaction and the mechanism of their interaction in the occurrence and development of AMI remains unclear, which needs further study.In summary, the molecular genetic analysis of S1 Raw images(PDF)Click here for additional data file.


Enhancers play important roles in controlling gene expression in a choreographed spatial and temporal manner during development. However, it is unclear how these regulatory regions are established during differentiation. Here we investigated the genome-wide binding profile of the forkhead transcription factor FOXK2 in human embryonic stem cells (ESCs) and downstream cell types. This transcription factor is bound to thousands of regulatory regions in human ESCs, and binding at many sites is maintained as cells differentiate to mesendodermal and neural precursor cell (NPC) types, alongside the emergence of new binding regions. FOXK2 binding is generally associated with active histone marks in any given cell type. Furthermore newly acquired, or retained FOXK2 binding regions show elevated levels of activating histone marks following differentiation to NPCs. In keeping with this association with activating marks, we demonstrate a role for FOXK transcription factors in gene activation during NPC differentiation. FOXK2 occupancy in ESCs is therefore an early mark for delineating the regulatory regions, which become activated in later lineages. The unique spatial and temporal patterns of gene expression during development are the result of the engagement of multiple enhancers regions which drive this carefully choreographed process  differentiation and in particular its impact on the regulatory chromatin environment. We find extensive FOXK2 binding to regulatory regions in ESCs, and although this binding is associated predominantly with active histone marks, it also pre-marks regulatory regions for future activation following differentiation. By focussing on neural precursor cells (NPCs), we demonstrate a role for FOXK transcription factors in promoting NPC differentiation. Importantly, enhancer regions prebound in ESCs show elevated activation in the transition to NPCs. In this context, we uncover a transcriptional activating role for FOXK proteins in addition to their well established repressive function.4/cm2 in Matrigel coated culture plates, in mTeSR1 containing 5 ng/ml BMP4  and 25 ng/ml Activin A (PeproTech 120-14E) for 40 h. For NPC differentiation, H1 cells were induced using STEMdiff™ Neural Induction Medium (Stemcell Technologies) using the monolayer culture method for 5 or 7 days with the media changed daily.H1 ESCs were grown in mTeSR1 (Stemcell Technologies) on Matrigel (BD Biosciences) coated culture dishes. Colonies were routinely passaged using ReLeSR (Stemcell Technologies) with the media changed daily. For mesendoderm (MESE) differentiation, H1 cells were cultured until 70–80% confluent, before dissociation using TryPLE (Gibco), and seeding at a density of 4 × 10Escherichia coli dihydrofolate reductase (eDHFR) degron into the C-terminus of endogenous FOXK2 locus. Briefly, two targeting of gRNAs were designed (http://crispr.mit.edu) and ligated with pSpCas9(BB)-2A-GFP (PX458)  digested by BbsI (to create pAS4236 and pAS4237). A FOXK2-DHFR-Puro gBlock sequence was designed based on human FOXK2 genomic DNA and incorporating the HA-eDHFR-T2A-puroR cassette (DNeasy Blood & Tissue Kits (Qiagen) for PCR genotyping. Controllable protein expression in FOXK2-DHFR cell lines was detected by Western Blot using whole cell lysates from cells treated with 10 μM TMP (+TMP) or DMSO (−TMP) for 24 h. The oligonucleotides and gBlock DNA sequence used to create and validate the FOXK2-DHFR cell line are listed in hESCs containing trimethoprim (TMP)-stabilised degron-tagged endogenous FOXK2 (H1-FOXK2-DHFR) was constructed by introducing the cassette , synthesFor flow cytometric analysis of NPCs, cells were differentiated for 8 days and transfected by siRNA of FOXK1 and FOXK2 every 2–3 days. Cells were dissociated using TryPLE and washed with FACS buffer (1% BSA/PBS). Samples were stained for 30 min at 4°C with fluorochrome-conjugated CD15  or CD56  antibodies and washed 3 times using FACS buffer prior to analysis. Samples were stained using 0.25 μg/ml DAPI to exclude dead cells before collecting with BD LSR II flow cytometer (BD Biosciences) and analysed with Flowjo v10 software (BD Biosciences).® RNAiMAX (Invitrogen) according to the reverse transfection method in the manufacturer's instructions. For the first 24 h of transfection, cells were treated using the ROCK inhibitor LY27632 (Stemcell Technologies). 48 h after transfection or at the indicated times during differentiation processes, the cells were harvested for further analyses. To achieve knockdown over a 5-day period in NPCs, cells were treated for second time with siRNA 72 h after the first transfection.siRNAs for FOXK1, FOXK2 and SIN3A were ON-TARGETplus SMART pools from Dharmacon , and control non-targeting siRNAs  were used throughout. To carry out RNA interference (RNAi), cells were dissociated using TryPLE (Gibco) and transfected with 50 nM siRNA using LipofectamineWestern blots derived from whole cell lysates or immunoprecipitated proteins were visualized after incubation with primary antibodies  according to the manufacturer's instructions and transcripts detected in a one-step RT–qPCR reaction using Quantitect SYBR green reagent (Qiagen). The primer-pairs used for RT-qPCR experiments are listed in P-value <0.01 and the averaged transcripts per 10 million reads (TC10M) >1.RNA-seq was performed in triplicate. Paired-end reads were mapped to human genome hg19 by the aligner STAR with the two-pass option . A pair 6 to 5 × 106 of 5-day differentiated NPC cells were used. Bound regions were detected by quantitative PCR (qPCR) (using primers listed in t-test. The error bars represent standard error of the mean. 5 × 107 cells were used for ChIP-seq experiments. Immunoprecipitated DNA was purified with a PCR purification kit (Qiagen) and ∼5 ng of DNA were processed for sequencingChIP-qPCR and ChIP-seq analysis was performed essentially as described previously  with anthttp://broadinstitute.github.io/picard/). Then the peaks were called from each of the two replicates with the corresponding input samples as control, using MACS2  and sequenced using the Illumina HiSeq4000 platform. The paired-end reads were aligned to the human genome hg19 using Bowtie2  with theng MACS2  with theThe correlation coefficients between the two replicates of the read counts on the whole genome  was bethttp://noble.gs.washington.edu/proj/hmmseg/). The windows with the learned HMM state 3 or 4 were regarded as the signal enriched windows. We then moved the 200 bp windows by 100 bp and applied the same HMM learning process to those moved windows. The regions regarded as the signal enriched in both HMM models were used in the further analysis. We then applied the HMM models learned from the corresponding ChIP-seq sample to the input sample to identify the enriched windows in the input sample. Then we removed the signal enriched windows from the ChIP-seq sample which were also signal enriched windows in the input sample. We also filtered out the signal enriched windows if they overlap with any of the ENCODE DAC blacklisted regions. The final set of SERs were obtained from merging the filtered signal enriched windows. We then applied PeakSplitter to identify the subpeaks in SERs. We subsequently filtered these subpeaks by removing those whose normalised height (i.e. the number of reads per 10 M uniquely mapped reads at the summit of the subpeak) is less than 2. 115 134 subpeaks were selected in H1 ESCs, 101 770 peaks in mesendoderm cells and 175 684 peaks in NPCs.We noted that the peaks identified by MACS2 often contain more than one subpeak. We therefore used an alternative approach by first locating the signal enriched regions (SERs) from ChIP-seq data, using HMM learning and then identifying all the subpeaks within SERs, using the software PeakSplitter . The HMMhttps://github.com/js229/Vennerable). To filter this list for higher confident dataset-specific FOXK2 peaks, we applied further criteria: firstly, to select the subpeaks present in any particular cell type, we used a threshold of 2 on the normalised height of subpeaks in that cell type. In order to identify the subpeaks which are in one sample A but not in another sample B, we first calculated the normalised height in the sample B of the subpeaks identified in the sample A, and then computed the difference of the normalised height between the sample A and B, and finally applied a further normalisation step by dividing the height difference by normalised height in B . We then used the criteria of the final normalised difference >2 and no subpeak in dataset B within 100 bp of those subpeaks in dataset A to select the subpeaks in dataset A as those unique to A relative to B. The subpeaks in A which are regarded as being shared by A and B are those which are not unique to A relative to B as defined above and have subpeak in dataset B within 100 bp of their summits and the absolute difference between the normalised heights of the two summits respectively in datasets A and B <1. Any subpeaks which are neither unique to A relative to B (or vice versa) nor shared by A and B (as defined above) were excluded from the Venn diagrams.To identify overlapping peaks between two samples, one peak was regarded as overlapping with another peak if they overlap by at least 30% of each of their lengths. Venn diagrams showing the overlapping of peaks from different ChIP-seq datasets were created using the R package Vennerable . We then ran the chromHMM  with the following identifiers: ENCFF526QOE , ENCFF258PMU , and ENCFF461HDT . In order to assess the correlation between two ChIP-seq samples, we first divided the whole genome into non-overlapping 1 kb sections, and removed those sections overlapping with the ENCODE DAC blacklisted regions or with the regions with mappability score ≤0.2 from the ENCODE CRG alignability 100mer data. Then for each sample we calculated the number of reads on each section. Finally, using the read counts found in each section, we computed the pairwise Pearson correlation coefficients between each two samples.In order to assess the correlation between FOXK2 binding and the presence of other histone marks, we first downloaded the ChIP-seq data for histone marks in H1 ESCs from the 111 consolidated epigenomes datasets of the Roadmap Epigenomics project . The ChIIn order to compare the magnitudes of the histone marks between FOXK2 and SIN3A in H1 ESCs, we used the 54 805 FOXK2 peaks obtained by MACS2 from our ChIP-seq data and the 21,309 SIN3A peaks downloaded from the ENCODE web site. We first determined the peaks shared by both factors by taking FOXK2 peaks which overlap by at least 30% with the SIN3A peaks. The remainder were classified as either FOXK2 or SIN3A alone peaks. Then we calculated the averaged read counts of the normalised Roadmap histone mark data on these FOXK2 or SIN3A peaks (FOXK2 peaks were used for the overlapping peaks), which were used to draw the boxplots for comparing the different histone modification signal on the three sets of peaks. The lower and upper boundaries of the boxes denote the first quartile and the third quartile, and the whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box.Enrichment scores for overlaps of the FOXK2 peaks with H3K27ac peaks found in ESCs as they differentiate to NPCs over a 72-h period were calculated by first counting the number of the peaks in one set overlapping with the peaks in another set, then dividing the overlap number by the number of peaks in each of the two sets, and finally multiplying the number by 10 000 in order to avoid a too small value.To identify potential transcription factor binding sites, motif enrichment analysis was performed on the 101 bp windows centred at the summit of the FOXK2 ChIP-seq peaks, using the software Homer  with theIn order to investigate whether FOXK2 or SIN3A binding was associated with the expression levels of the nearby genes, we focussed on high confidence assignments by associating ChIP-seq peaks to genes if their promoter regions  include at least 30% of the peak length. We obtained the expression level of the genes in H1 cells from the Roadmap Epigenomic data . We thenhttps://www.R-project.org).To draw an average tag density plot of several samples over a selected set of genomic regions, we first obtained the number of reads of one sample at each nucleotide position in the regions by using the software seqMINER . We normTo associate FOXK2 binding events with potential biological functions, we used the online tool GREAT  to obtaiP-values were calculated on the resulting boxplots using a t-test. However, this indicated that, due to the large sample sizes (i.e. the number of peaks in each case) everything was significantly different between groups. This meant that any difference  between the means of the two compared groups scored as significant. However, we corrected for this by using a commonly used effect size index, the Cohen's d . FOXK1/K2 and SIN3A siRNA RNA-seq experiments in ESCs and FOXK1/2 siRNA experiments in differentiating NPCs (E-MTAB-9639). Previously published datasets are shown in de novo motif analysis  motif and both known motif analysis  and de ns Figure  identifis Figure . Howevers Figure . We nexts Figure . A high s Figure . A largeNext, to determine whether any chromatin features are associated with FOXK2 binding regions, we examined correlations between FOXK2 binding events and other ChIP-seq datasets for a range of histone modifications in H1 cells Figure . The knoCollectively, these results demonstrate widespread binding of FOXK2 in the genome of H1 ESCs that is generally associated with active chromatin and co-associated active genes. Frequent co-binding with its partner protein SIN3A occurs at these active chromatin regions.The results from H1 ESCs suggest a role for FOXK2 in the context of an active chromatin environment. To establish whether this is the case in other cells and whether this changes as cells adopt different fates, we differentiated H1 ESCs to mesendodermal cells (MESE) by culturing in BMP4 and Activin A for 40 h ,42 Figu. The celMotif analysis identified the expected FOX protein binding motifs in both the ESC-specific and MESE-specific binding regions Figure . ConversWe also associated the cell type-specific binding regions with nearby genes and asked whether these gene sets are associated with any particular biological processes. The genes associated with ESC-specific FOXK2 binding regions are associated with processes centred on neuronal differentiation and brain development Figure . A diffeThese results therefore demonstrate a dynamic change in the FOXK2 binding landscape as ESCs differentiate down the mesendodermal lineage. FOXK2 binding events are both lost and gained. The sites which are lost are characterised by association with genes involved in neuronal developmental and contain binding motifs for transcription factors that are linked with this process. Conversely, those which are gained are associated with genes involved in different developmental processes and contain binding motifs for transcription factors that drive the development of mesendoderm and developmental processes downstream from this lineage.The identification of FOXK2 binding regions that are unique to a particular cell type was intriguing, suggesting a potential dynamic role in either modifying or responding to their local chromatin environment. We therefore studied the chromatin status of FOXK2 binding regions and how these change during differentiation from ESCs to various cell lineages. First we partitioned the genome into six different chromatin states using published data  and a hiWe did however notice that there are a relatively small number of FOXK2 binding regions associated with chromatin state 6 (i.e. marked with repressive H3K27me3). One possible role of FOXK2 binding at these regions might be to prime for downstream activating events. We therefore examined the fate of this subclass of FOXK2 binding regions found in MESE cells in other lineages. Generally these regions are either retained in state 6 or converted to state 3 and become devoid of histone marks. However, in endodermal (ENDO) cells, there is a high degree of conversion to states 4 and 5 which are characterised by H3K4me1, and hence potential enhancer priming Figure . This reOur discovery that FOXK2 binding is dynamic and changes during differentiation to MESE cells also uncovered a potential role for FOXK2 binding to regulatory regions in ESCs that are primed for later activation in NPCs. One prediction of this model is that FOXK2 binding events that are apparently specific to ESCs, i.e. not retained in the MESE lineage, would be retained upon differentiation to NPCs. We therefore performed ChIP-seq for FOXK2 following differentiation of ESCs to NPCs. This approach would also enable us to identify potentially novel NPC-specific peaks for FOXK2 binding which would further test the dynamic nature of the FOXK2 cistrome.MPPED1 locus  and a loss of pluripotency markers (e.g. OCT4) . ChIP-ses Figure . De novoThe availability of FOXK2 binding datasets in three different cell types enabled us to further investigate the characteristics of different types of FOXK2 binding regions, and in particular the fate of the FOXK2 binding regions identified in ESCs. We initially focussed on the DNA binding motifs discovered in each subcategory of FOXK2 binding region. While all categories of peaks showed enrichment of FOX motifs as expected, they differed in the other enriched motifs. For example, the ESC-specific peaks contained high levels of OCT4 motifs . The ESCAs SOX motifs are enriched in FOXK2 occupied sites in NPCs, we investigated potential FOXK2 and SOX2 co-occupancy by comparing our data to SOX2 ChIP-seq data in NPCs . We overCollectively, these results lead to a model in which there is lineage-specific binding of FOXK2 alongside transcription factors that play important lineage-specific roles Figure . ImportaOur overlapping ChIP-seq datasets enabled us to identify regions that are occupied by FOXK2 in ESCs and where binding is maintained either in the MESE cells or NPCs. Such behaviour suggests either that the regions may be already active in ESCs and/or primed for activation either in MESE cells or NPCs or further more differentiated cell lineages that emerge later. We focussed on FOXK2 binding regions that are initially occupied in ESCs and retained in NPCs. These FOXK2 binding events (2231) showed a similar genome-wide distribution to NPC-specific FOXK2 peaks  and likeTo begin to understand the regulatory dynamics of the different categories of FOXK2-associated regions, we first examined the histone marks surrounding the FOXK2 peaks in ESCs and NPCs. The ESC-specific FOXK2 binding regions exhibit high levels of the activation-associated histone marks H3K27ac and H3K4me2 which are subsequently extinguished in NPCs Figure , left. IASCL1 and OLIG1 remain devoid of H3K27ac in NPCs . FOXK1/K2 and SIN3A siRNA RNA-seq experiments in ESCs and FOXK1/2 siRNA experiments in differentiating NPCs (E-MTAB-9639).gkaa1281_Supplemental_FilesClick here for additional data file.


B Cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL) is the most common pediatric cancer. Identifying key players involved in proliferation of BCP-ALL cells is crucial to propose new therapeutic targets. Runt Related Transcription Factor 1 (RUNX1) and Core-Binding Factor Runt Domain Alpha Subunit 2 Translocated To 3  are master regulators of hematopoiesis and are implicated in leukemia.We worked with BCP-ALL mononuclear bone marrow patients’ cells and BCP-ALL cell lines, and performed Chromatin Immunoprecipitations followed by Sequencing (ChIP-Seq), co-immunoprecipitations (co-IP), proximity ligation assays (PLA), luciferase reporter assays and mouse xenograft models.CBFA2T3 transcript levels correlate with RUNX1 expression in the pediatric t ETV6-RUNX1 BCP-ALL. By ChIP-Seq in BCP-ALL patients’ cells and cell lines, we found that RUNX1 is recruited on its promoter and on an enhancer of CBFA2T3 located − 2 kb upstream CBFA2T3 promoter and that, subsequently, the transcription factor RUNX1 drives both RUNX1 and CBFA2T3 expression. We demonstrated that, mechanistically, RUNX1 and CBFA2T3 can be part of the same complex allowing CBFA2T3 to strongly potentiate the activity of the transcription factor RUNX1. Finally, we characterized a CBFA2T3-mimicking peptide that inhibits the interaction between RUNX1 and CBFA2T3, abrogating the activity of this transcription complex and reducing BCP-ALL lymphoblast proliferation.We demonstrated that Altogether, our findings reveal a novel and important activation loop between the transcription regulator CBFA2T3 and the transcription factor RUNX1 that promotes BCP-ALL proliferation, supporting the development of an innovative therapeutic approach based on the NHR2 subdomain of CBFA2T3 protein.The online version contains supplementary material available at 10.1186/s13045-021-01051-z. The transcription factor RUNX1 and the transcription regulator CBFA2T3 interact.CBFA2T3 located − 2 kb upstream CBFA2T3 promoter and drives both CBFA2T3 and RUNX1 expression.RUNX1 is recruited on its promoter and on an enhancer of CBFA2T3 strongly enhances the transcriptional activity of RUNX1.A CBFA2T3-truncated protein functions as a potent inhibitor of RUNX1 and CBFA2T3 protein–protein interaction.A CBFA2T3-truncated protein dramatically inhibits RUNX1 transcriptional activity and decreases BCP-ALL lymphoblast proliferation.The RUNX1 and CBFA2T3 self-activation loop is a BCP-ALL driver loop.Runt domain that shows strong evolutionary conservation [RUNX1 gene deregulation, either by genetic alterations  or gene expression modification, is involved in many hematological malignancies, notably in ETV6-RUNX1 pre-B acute lymphoblastic leukemia (BCP-ALL) [RUNX1 (Runt Related Transcription Factor 1) is a major transcription factor of hematopoiesis. It belongs to the RUNX family of transcriptional regulators, where members, RUNX1, RUNX2 and RUNX3, share a ervation . RUNX1 gBCP-ALL) . The traBCP-ALL)  that binBCP-ALL) .CBFA2T3  belongs to the eight-twenty-one (ETO) family of chromatin-associated proteins. This family also includes Myeloid Translocation Gene 1-Related (MTGR1) and Myeloid Translocation Gene 8  . Each ofRUNX1 and CBFA2T3 are specifically upregulated in ETV6-RUNX1 BCP-ALL [BCP-ALL is the most frequent type of pediatric cancer. Several detailed studies have examined the expression of genes deregulated in ETV6-RUNX1 BCP-ALL compared to other types of leukemia and demonstrated that  BCP-ALL  suggesti BCP-ALL –21. More BCP-ALL –24.CBFA2T3 gene. Next, we characterized the protein–protein interaction domains between CBFA2T3 and RUNX1. At a mechanistic level, we identified an activation loop between CBFA2T3 and RUNX1. Finally, at a functional level, we demonstrated that a CBFA2T3-mimicking peptide, whose expression was able to disrupt this activation loop by inhibiting RUNX1 activity, results in a decrease of BCP-ALL cell proliferation. Our findings reveal a novel BCP-ALL driver loop dependent on CBFA2T3 and RUNX1.In this study, we aimed at delineating the functional and structural CBFA2T3 and RUNX1 relationship at the genome and protein levels. First, we demonstrated that RUNX1 protein upregulates Detailed experimental procedures for RNA extraction, RT-qPCR, generation of stable cell lines, luciferase assay, immunoblotting, chromatin immunoprecipitation (ChIP-Seq), ChIP-PCR and cell cycle and apoptosis assays are presented in Additional file ETV6-RUNX1 fusion gene, were collected at diagnosis, after informed consent had been obtained, in accordance with the declaration of Helsinki. The protocol was approved by the ethics committee of Rennes Hospital (France).Pre-B leukemia cell line Nalm6 (ATCC#CRL-3273TM) and REH (ATCC#CRL-8286™) were maintained in RPMI-1640 medium containing 10% heat-inactivated fetal calf serum and 1% penicillin/streptomycin. HEK293 cells (ATCC#CRL-1573TM) were maintained in DMEM/10% fetal calf serum/1% antibiotics. Bone marrow cells from B cell precursor acute lymphoblastic leukemia (BCP-ALL) patients, not presenting the t shCBFA2T3 or REHshRUNX1 cells were obtained by transduction with lentivirus MISSION pLKO.1 shRNA puromycine or neomycine resistant . Halotag-ETV6-RUNX1, Halotag-RUNX1 plasmids are described in [+NHR2 cell production.REHribed in , 26. CBFribed in  and subcCBFA2T3  or RUNX1  gene, and RUNX1-consensus motif repetition AGATTTCCAAACTCTGTGGTTGCCTT (three times repeats) as described in [pGL4.10-luc with a minimal promoter. The c-KIT enhancer luciferase plasmid is described in [For luciferase assays, genomic DNA fragments derived from the human ribed in .per nucleus were extensively described in [PLA was carried out with Duolink® In Situ Detection Reagents (Sigma Aldrich). PLA technology allows the detection of interactions between endogenous proteins, based on the detection of protein proximity. PLA uses one pair of primary antibodies; each antibody targets one of the two distinct proteins for which the proximity is studied. The antibodies used for PLA have been validated by immunofluorescence and western blot. For the validation of each PLA experiment, a negative control (only one type of antibody) and a positive control  are included. The experimental procedure, the automatic quantification and analysis of PLA dots ribed in  which isribed in , 29. To ETV6-RUNX1 (n = 3), H3K4me1, H3K4me3, H3K27ac, and CBFA2T3 in REH (n = 1) and Nalm6 (n = 1). Antibodies used are listed in Table S2. All sequencing data are available at NCBI's GEO (https://www.ncbi.nlm.nih.gov/geo/query) through #GSE109377 for RUNX1 in Nalm6 and patients[The detailed procedure is described in Additional file  patients and #GSETen percent (5µL) of protein extracts were separated from the rest for the input. The remaining input was incubated overnight with anti-Flag magnetic beads  in a total of 1 mL co-IP Lysis Buffer, on a rotation wheel at 4 °C according to the manufacturer’s recommendations. Ten µL of beads were used for each condition, with a binding capacity of 6 µg of proteins. The following day, the beads were washed with Tris Buffered Saline (TBS) and the proteins were eluted in Laemmli Buffer. Finally, the co-IP samples and their respective input were resolved on a 10% polyacrylamide gel for Western-Blotting.Proliferation assays were done by automatic counting with a Cellometer (Nexcelom). Apoptosis assay was done using Annexin V-FITC  and Propidium Iodide. For cell cycle analyses, we performed nocodazole block.scid IL2 Rgnull mice  were maintained in the ARCHE Animal Housing Center . Animal experiments were performed after authorization by the French Research Ministry, and according to European regulation. Four-week-old mice received two intraperitoneal injections of 20 μg/g busulfan  on 2 days. They were then allowed to rest for 2 days before retro-orbital injection of 100,000 cells as previously described [NOD/escribed .Statistical analyses were performed with GraphPad Prism 6 software. Statistical significance was analyzed using Mann–Whitney nonparametric tests for small sizes samples, parametric t-test for larger size samples, and Fisher’s exact test when appropriate.RUNX1 and CBFA2T3 transcripts are upregulated in ETV6-RUNX1 BCP-ALL blasts  and Nalm6 cells (that do not express ETV6-RUNX1) , we performed chromatin immunoprecipitation followed by sequencing (ChIP-Seq) with RUNX1 antibody in REH cell line. We compared with our previously published ChIP-Seqs done in Nalm6 cells [CBFA2T3 located − 2 kb upstream of CBFA2T3 promoter  as we had applied previously to human pre-B cells . RUNX1 aClearly, not all RUNX1-bound regions are shared with CBFA2T3, and not all CBFA2T3-bound regions are shared with RUNX1. This result indicates that RUNX1 and CBFA2T3 are not always in the same complex.RUNX1 and CBFA2T3 genes.Altogether, our results suggest that a complex between RUNX1 and CBFA2T3 can be poised on chromatin to regulate, and importantly activate, common target genes including NHR2 domain and the CBFA2T3NHR3−4 domain interact with RUNX1 in HEK293 cells  is necessary for binding whereas the RUNT domain and the transactivation domain of RUNX1 are not crucial for this binding  blocks the oligomerization of the RUNX1-ETO fusion protein [NHR2 domain could act as an inhibitor of the interaction between RUNX1 and CBFA2T3, since we found that CBFA2T3NHR2 is able to bind RUNX1 as well as CBFA2T3. Doing so, we observed that the ectopic expression of CBFA2T3NHR2 inhibits the endogenous interaction between RUNX1 and CBFA2T3 as assayed by proximity ligation in REH cells  slows down the proliferation dramatically more than a single depletion of either RUNX1 or CBFA2T3 , compatible with additive or cooperative effects between RUNX1 and CBFA2T3 , which is compatible with an additive effect. However, the rescued expression of RUNX1 is not sufficient to restore the proliferation delay induced by depletion of CBFA2T3 , demonstrating that RUNX1 and CBFA2T3 participate into a common pathway, at least partially. Altogether, this demonstrates that CBFA2T3 and RUNX1 bind together to regulate BCP-ALL proliferation, and also points out the importance of the role of CBFA2T3 in BCP-ALL proliferation.We further studied the impact of NHR2 on cell phenotype. Depletions of CBFA2T3 and RUNX1 by stable shRNA decrease cell proliferation suggesting that RUNX1 and CBFA2T3 sustain independently cell proliferation in leukemic cells  presented a longer survival compared to mice injected with REHshcontrol cells , the lymphoblasts present altered gene expression. Two genes are overexpressed: partners –24. TherRUNX1 expression and function in preB-cells adds to the specificity of this transcription regulator.The mechanisms of control of cell specification and differentiation rely on multimeric complexes containing transcription factors, coregulators, and additional non-DNA binding components. The CBFA2T3 transcription regulator is found in such transcriptional complexes in many hematopoietic lineages. At the onset of blood cell specification, the SCL transcription factor forms a multi-protein complex that contains CBFA2T3 and represses cardiac and paraxial cell lineages . In eryt. reported that the overexpression of CBFA2T3 in erythroid K562 cells leads to the upregulation of 667 genes and the downregulation of 598 genes [RUNX1, CBFA2T3 and c-KIT genes leads us to reconsider the paradigm of CBFA2T3 univocally viewed as a repressor. Here, we show that CBFA2T3 can strongly amplify RUNX1-induced transcriptional activity. The exact mechanism of action, including the precise co-regulators, will be explored in future investigations. Yet, we clearly demonstrated that CBFA2T3 binds RUNX1 transcription factor in an exogenous/overexpression model, but also demonstrated that this binding exists at an endogenous steady-state level in human preB cells, including BCP-ALL cell lines and BCP-ALL primary cells. These results are in agreement with previous reports in erythroid cells [In most of those CBFA2T3-multi-protein-containing complexes, CBFA2T3 is described as a repressor. Our data in BCP-ALL cells showed that, indeed, CBFA2T3 is mainly bound to repressor effectors such as NCOR, HDACs and SIN3A. Yet, we also unveiled the binding of a fraction of CBFA2T3 to activators, including CBP and P300. Fujiwara et al98 genes . It is vid cells , 24, or id cells . We provRUNX1, CBFA2T3 and c-KIT. CBFA2T3 and RUNX1 genes are also RUNX1 targets in myeloid cells [We showed, by Chip-Seq, that CBFA2T3 and RUNX1 interaction occurs at the chromatin level, and that CBFA2T3 has a strong impact on RUNX1 transcriptional activity, that is exerted on at least three genes, id cells  and in mid cells . It was id cells , and thaid cells .Prdm14-induced T-acute lymphoblastic leukemia, demonstrating its oncogenic role [. showing that downregulation of CBFA2T3 arrests G1/S cell cycle progression and attenuates in vitro and in vivo proliferation of acute myeloid leukemia cells [We next questioned the role of the interaction between RUNX1 and CBFA2T3 and the self-activation loop between RUNX1 and CBFA2T3 in leukemogenesis. First, it is well known that alteration of each protein, RUNX1 and CBFA2T3, separately, causes hematological malignancies. The fusion protein ETV6-RUNX1 sustains BCP-ALL, RUNX1-ETO is highly frequent in acute myeloid leukemia, RUNX1-MECOM leads to myelodysplasia and acute myeloid leukemia CBFA2T3-GLIS2, and RUNX1-CBFA2T3 is found in acute megakaryoblastic leukemia , 17, 46.nic role . Even thnic role  CBFβ-SMMnic role . This roia cells .RUNX1 promoter , but not on the CBFA2T3 enhancer  or on the RUNX1-consensus motif repetition  in luciferase promoter assays. This suggests that ETV6-RUNX1 can potentially play a similar activation role than RUNX1, depending on the chromatin context.We can also question why ETV6-RUNX1 fusion protein could lead to positive correlation of RUNX1 and CBFA2T3 expression. ETV6-RUNX1 results from the fusion of a repressor (ETV6) with almost the entire RUNX1 which is an activator. It has never been formally demonstrated that ETV6-RUNX1 exclusively plays a repressive role on every promoter normally controlled by RUNX1, especially in preB-cells. PLA data in REH cells done with ETV6 antibody (thus targeting ETV6-RUNX1 protein) show that ETV6-RUNX1 colocalizes with activators  as well as repressors  . At a functional level, in our hands, ETV6-RUNX1 reinforces the activation role of RUNX1 on We showed that the interaction between CBFA2T3 and RUNX1 mainly occurs by the NHR2 domain of CBFA2T3, which is also its oligomerization domain. The NHR2 domain is a protein–protein interaction motif, and is responsible for homo-oligomerization and dimerization among ETO family members , 13. EctSeveral authors have been able to prevent ETO-fusion or CBFA2T3-fusion protein oligomerization, and importantly have demonstrated a reversion of the leukemic phenotype using a NHR2-mimicking protein or small molecule inhibitors mimicking the tetramerization interface of NHR2, in particular with RUNX1-ETO fusion , 51–54 oNHR2 protein domain inhibits the interaction between RUNX1 and CBFA2T3, and results in reversion of leukemic phenotype by decreasing BCP-ALL cell proliferation. Our findings unveil the role of CBFA2T3 also as a transcriptional activator for RUNX1 in human PreB lymphoblasts, and demonstrate the existence of a new RUNX1 and CBFA2T3 driver-loop in ETV6-RUNX1 BCP-ALL leukemia. Our findings open novel potential therapeutic approach for BCP-ALL.Altogether, we described an important activation loop between the transcription factor RUNX1 and the transcription regulator CBFA2T3. We deciphered the interaction of RUNX1 with CBFA2T3, and demonstrated that addition of free CBFA2T3Additional file 1: Table S1. List of primers used in qPCR or ChIP-qPCR. Table S2: List of all antibodies used.Additional file 2: Figures S1, S2  and S3 and their legends.Additional file 3: Table S3. Analysis of Motif Enrichment for CBFA2T3 regions in REH cells. Table S4. Analysis of Motif Enrichment for CBFA2T3 regions in Nalm6 cells.


More than 50 years after the identification of RNA polymerase II, the enzyme responsible for the transcription of most eukaryotic genes, studies have continued to reveal fresh aspects of its structure and regulation. New technologies, coupled with years of development of a vast catalog of RNA polymerase II accessory proteins and activities, have led to new revelations about the transcription process. The maturation of cryo-electron microscopy as a tool for unraveling the detailed structure of large molecular machines has provided numerous structures of the enzyme and its accessory factors. Advances in biophysical methods have enabled the observation of a single polymerase’s behavior, distinct from work on aggregate population averages. Other recent work has revealed new properties and activities of the general initiation factors that RNA polymerase II employs to accurately initiate transcription, as well as chromatin proteins that control RNA polymerase II’s firing frequency, and elongation factors that facilitate the enzyme’s departure from the promoter and which control sequential steps and obstacles that must be navigated by elongating RNA polymerase II. There has also been a growing appreciation of the physical properties conferred upon many of these proteins by regions of each polypeptide that are of low primary sequence complexity and that are often intrinsically disordered. This peculiar feature of a surprisingly large number of proteins enables a disordered region of the protein to morph into a stable structure and creates an opportunity for pathway participants to dynamically partition into subcompartments of the nucleus. These subcompartments host designated portions of the chemical reactions that lead to mRNA synthesis. This article highlights a selection of recent findings that reveal some of the resolved workings of RNA polymerase II and its ensemble of supporting factors. RNA polymerase II (Pol II) is an essential, multi-subunit, DNA-dependent, nucleotidyltransferase. In eukaryotes, Pol II is the one of three nuclear RNA polymerases. It is responsible for unspooling the genetic program in the form of protein-coding mRNAs and some small non-coding RNAs. Pol II’s activity is highly regulated at a number of steps, many of which impact the process of getting Pol II to transcription initiation sites. Pol II’s ability to disengage from the promoter and become committed to elongating nascent RNA is another complex regulated process, as is Pol II’s ability to complete that primary transcript. Finally, transcription termination, which involves Pol II choosing where to stop polymerization and releasing its RNA product, can be modulated. Understanding the regulation and intricacies of all three stages of the transcription cycle—initiation, elongation, and termination—has been the focus of studies for decades, yet new aspects of these processes continue to be revealed. Several of the newest developments are described here.3. Cryo-electron microscopy (cryo-EM) refined and expanded these initial structural models, as well as those of the comparably large TFIIH and TFIID proteins, which are the most complex of the general initiation factors that Pol II uses to locate promoters and initiate transcription5. Even larger assemblies of Pol II in which it is bound to some of its general initiation factors have also been studied using cryo-EM16. More recently, cryo-EM has been exploited to study more elaborate transcription complexes, including Pol II associated with its chromatin template, its RNA product, and some attendant proteins that guide it through the transcription cycle. Reports that provided mechanistic insight into these functionally important assemblies will be described in this section.A major advance at the turn of the 21st century was the development of atomic-level structural maps of the approximately 0.5 megadalton Pol II enzyme by using x-ray crystallographyet al. resolved seven intermediates of Pol II in the act of encountering, and progressively moving through, a nucleosome with the aid of elongation factor TFIIS17. This elongation factor activates a nascent transcript nuclease activity in Pol II that aids in its passage through obstacles to elongation19. Cryo-EM analysis showed that Pol II stalls at the entrance to the nucleosome but that the nucleosome remains structurally unchanged. Further translocation allows it to penetrate the DNA–histone interaction in a fashion that peels DNA off the histone octamer. Complete passage through the nucleosome was not monitored, but extrapolation from these structures suggests that Pol II could proceed into a form in which nucleosomal DNA is looped over polymerase or in which the nucleosome has lost a subset of histones; both are intermediates previously proposed to exist during Pol II transit20. The addition of the general elongation factors Elf1 and Spt4/5  synergistically lowered the barrier of Pol II entry into the nucleosome by interposing themselves between the enzyme and the nucleosome21. Pol II advanced further as the nucleosome disk became tilted while the collection of bound elongation factors acted as a chisel to displace DNA from the nucleosome. This effectively averts Pol II from getting trapped between the DNA and histones. As more structures of Pol II–elongation factor complexes are solved, it is likely we will learn that different elongation factors and different combinations of factors help Pol II surmount the nucleosome obstacle by distinct mechanisms.An age-old question in the transcription field has been: what happens when template-engaged Pol II encounters a nucleosome? Nucleosome-wrapped duplex DNA is an impediment to Pol II, particularly given the enzyme’s need to transiently separate the DNA strands. Certain elongation factors, however, can facilitate nucleosomal readthrough. Kujirai 24. Cryo-EM analysis now reveals structural details of how these two proteins engage elongating Pol II25. Importantly, the positions of the docked elongation factors appear to be mutually exclusive with the initiation factors that recruit Pol II to DNA, delineating a structural variance that differentiates the pre-initiated and initiated states. Prior work showed that DSIF provides a DNA clamp and an RNA clamp that preserve the transcription bubble and guide the RNA through its exit tunnel on Pol II27. The consequence of NELF addition is to render the complex elongation-incompetent, possibly because the complex gains an anomalous positioning of the DNA:RNA hybrid substrate that is unsuitable for templating or allowing access and retention of nucleoside triphosphate substrates in the active site25. These proteins may also occlude the binding of other positively acting elongation factors. Recent findings enabled by a NELF-depletion technique in cultured human cells indicated that NELF’s restriction of RNA extension by Pol II is not the only yoke put on polymerase but that an additional unidentified constraint is apparent before the fully elongation-competent enzyme is liberated28. Since NELF-associated pol II is an intermediate to productive elongation, the absence of NELF may also have precluded any necessary downstream positive events from taking place.Pol II assembles into a large pre-initiation complex where it engages a region of nucleosome-free DNA abetted by a collection of general initiation factors. RNA chain polymerization is accompanied by Pol II’s disengagement from this tight complex. This is quickly followed by Pol II’s association with elongation factors that stabilize the just-initiated Pol II, thereby holding it in a paused condition analogous to an idling automobile. The decision to abort elongation, versus complete the primary transcript, is a determinant of transcriptional output and a regulated event. This process may be necessary for the nascent RNA capping machinery to engage Pol II while awaiting the maturation of the complex mediated by a specific set of kinases. Two factors key to generating the paused state are the multi-subunit proteins, negative elongation factor (NELF) and DSIF, whose biochemical activities as Pol II elongation modulators were identified many years ago29. These include positive elongation factor b (P-TEFb), a cyclin-kinase22 that phosphorylates and releases NELF from the complex31, and the RNA polymerase-associated factor (Paf1) complex33, whose mode of action in stimulating elongation is complicated and unresolved. Also, the association of Spt6, one of a number of Spt transcription factors that were revealed in a landmark productive and penetrating genetic suppressor screen , was studied34. An activated elongation complex containing DSIF, PAF, and Spt6 was assembled with P-TEFb and ATP29. The action of the P-TEFb kinase facilitates the replacement of pause-stabilizing NELF with the PAF complex, thereby removing NELF’s ability to misposition the nascent RNA:DNA hybrid and limit substrate NTP accessibility, as described above. Stimulation by PAF and Spt6 could be due to these proteins’ coating of the surface of Pol II and an allosteric conformational change that may facilitate elongation by assisting template annealing behind the enzyme. Spt6 engages with the activated elongation complex near the RNA exit site following P-TEFb’s specific phosphorylation of Pol II. PAF and Spt6 appear to remodel the extent of DSIF’s clamping of DNA and RNA at their respective exit channels. This kinase is profligate indeed, as it can phosphorylate all the components described in these Pol II–“plus” structures: DSIF, NELF, PAF, Spt6, and polymerase itself. This places the kinase at the hub of coordinating the elongation complex’s gain and loss of proteins at an important regulated step of transcription possessed by many genes, namely, promoter escape. A recent cryo-EM study of the activated complex with trapped Rtf1, an otherwise dissociable subunit of the PAF complex, suggested that Rtf1 provokes an additional conformational change that may enhance Pol II translocation, thereby facilitating elongation35.The cryo-EM analysis of transcribing Pol II was extended by assembling elongation complexes in the presence of a variety of well-studied, biochemically characterized proteins involved in elongation37. The rate of firing during a burst, or the length of the interval between bursts, is variable and subject to regulation.Over the years, most investigations studying Pol II have examined readouts from populations of polymerase molecules and cells. The advent of high-resolution microscopy and biophysical techniques has allowed the behavior of individual molecules or templates to be observed. These advances have been particularly valuable for populations of nucleic acid polymerases because individual molecules can asynchronously occupy distinct phases of their polymerization cycle. In fact, some molecules may be altogether inactive at any point in time. Thus, a surprising heterogeneity in performance can be observed in otherwise identical molecules; that is, the various polymerases or templates in a population can carry out different functions and unless each can be resolved, a population average is obtained. One such heterogeneity is transcriptional bursting. This is seen when a single promoter releases a volley of polymerase initiations which alternates with relatively quiescent intervalset al. used both single-cell and ensemble methods to show that biological stimuli accelerate Pol II pause release and bursting and, somewhat counterintuitively, not Pol II recruitment rates38. In other words, Pol II is driven to a promoter during bursting in contrast to the idea that bursting results from pre-loaded polymerases.The development of single-molecule techniques has led to advances in understanding the molecular basis for bursting and its regulation. Recently, Bartman 39. This analysis suggested that enhancers control burst frequency, consistent with early models that enhancers increase the probability of Pol II firing41. Promoters appeared to govern burst size, and TATA-containing sequences directed larger bursts than promoters lacking the TATA consensus, again as suggested some years ago43. These findings imply that genotypic and cell type differences can yield alternative burst sizes, and hence gene output, as a function of the selection of proteins that engage a locus as a consequence of its DNA sequence.Using computational modeling and single-cell RNA sequencing, investigators were able to dissect burst size and frequency underlying genome-wide transcription44. This approach enabled the study of transcription of single genes in individual living cells with the ability to follow Pol II and its transcription factors in compartments of extremely small volumes. For the Nanog gene, bursting was accompanied by clustering of Pol II with the Sox2 and Brd4 proteins at the locus in a manner consistent with looping of the enhancer and promoter thereby forming a bridge between enhancer-bound proteins in contact with promoter-bound Pol II. The size of the Brd4 cluster correlated with burst frequency similar to the relationship described above for enhancer-powered bursting. Further development of this technology promises the possibility of watching in real time a single Pol II molecule, and its support factors, advance through the entire transcription cycle.Bursting has also been explored by using sophisticated computational and optical nanoscopy techniques aided by target-locking and background suppression methods45. A well-studied paradigm is the case of cytoplasmic stress granules, a non-membrane-delimited compartment that is a site of RNA sequestration and metabolism. The LCDs of RNA-binding proteins support liquid–liquid phase separation and contribute to the assembly of these compartments.An active area of research has been the examination of how subcellular compartments form from self-assembling proteins that contain low-complexity domains (LCDs) This concept has been extended to the machinery that activates and carries out transcription, including Pol II and the Mediator complex, as well as proteins that co-transcriptionally modify and terminate the primary transcript (reviewed in et al.50 showed that ubiquitination of histone H2B lysine 123, a modification associated with active chromatin, is stimulated through biomolecular condensation mediated by the LCD of a specific ubiquitination complex-associated protein. Through the organizing principle of phase separation, the ubiquitination apparatus becomes co-localized with nucleosomal H2B in what the authors refer to as a “reaction chamber”50. This process operates broadly across the nucleosomes of the body of genes in a process that is poorly understood.Two notable extensions of the concept were reported in the last year in findings that emphasized how condensate formation operates across the transcription cycle. Gallego et al.51 provided evidence from mammalian cells that Pol II with a hypo-phosphorylated CTD joins Mediator condensates established through enhancer sequences. Once the CTD becomes phosphorylated, Pol II is dislodged from that condensate and elongating Pol II forms a condensate with splicing factors in a spatially separate locale. The overall importance of these condensation reactions requires further study. Future work will be needed to validate what seems to be a recurring theme of a handoff of Pol II between a chain of condensates. By linking condensates, various parts of the mRNA biogenesis pathway could be connected by what is effectively a substrate channeling mechanism.Meanwhile, Guo 53. In this durable, three-decade-old proposal, the precursor transcript is endonucleolytically cut just after Pol II transcribes the polyadenylation signal into nascent RNA54. The 3′ terminus of the upstream piece is polyadenylated and becomes the mature mRNA. Importantly, Pol II continues extending the downstream fragment until an exoribonuclease engages the still-emerging transcript and begins hydrolyzing it in the 5′-to-3′ direction while advancing toward Pol II, effectively using the RNA as a “trail of breadcrumbs” to home in on and chase down the still-translocating polymerase. The nuclease acts as a torpedo by contacting, and ultimately displacing, Pol II from the template, thereby terminating transcription. A key feature of the model is the kinetic competition in which the digesting nuclease “catches” elongating polymerase.There has been continued progress in dissecting the polyadenylation-coupled termination process carried out by Pol II. A prevailing idea for how Pol II terminates transcription at the end of protein-encoding genes is the so-called “torpedo” model55 that phospho-Spt5, which piggybacks on Pol II starting at DSIF’s engagement with the paused polymerase56, is de-phosphorylated just after Pol II transcribes the polyadenylation site58. This switches Pol II, which accelerated out of the promoter region because of Spt5’s phosphorylation60, into a slower elongating form which facilitates the nuclease’s ability to overtake Pol II and trigger termination55. The exact mechanism by which de-phosphorylation of Spt5 slows Pol II remains to be elucidated. An interesting related finding is that the initial loading of Spt5 onto Pol II at the promoter was found to employ the c-Myc proto-oncoprotein60, which has been long studied as a DNA-binding transcription factor but is also known to play a general role in elongation64. c-Myc’s effect on normal and pathological changes in gene expression could be operating at least in part through a widespread boosting of the output of active genes through Spt5’s elongation-stimulating activity.We now learn68. The resulting loops use specific proteins to section chromatin into transcriptionally active and inactive segments69. In 2018, Heinz et al. showed how transcription can remodel the boundaries of these looped domains70. By exploiting a viral protein that broadly disrupts host transcription termination, the authors found that readthrough by Pol II of the end of the transcription unit resets loop boundaries, seemingly plowing the proteins off chromatin and making active regions out of formerly inactive ones. In contrast, inhibition of elongation led to re-association of distant sequences and compaction, thereby switching a formerly active region to an inactive one. Thus, while chromatin is generally remodeled to become permissive for transcription, transcription can also remodel the three-dimensional organization of chromatin.Aided by powerful molecular techniques, we have learned much about the physical basis for the functional arrangement of chromatin during the last decade. Distant portions of the chromosome that contact each other segregate chromatin into functionally distinct topologically associating domains71, we are still learning about the enzyme, its auxiliary factors, and the environment in which it operates. A large international research effort armed with a sophisticated toolbox of experimental methods has revealed many of the detailed mechanistic steps that Pol II employs to find, transcribe, and disengage from eukaryotic genes. Many of the participating protein and nucleic acid components have been identified, and important regulatory post-translational modifications have been characterized. Biophysical techniques enabling the study of single Pol II molecules, and cryo-EM which has provided relatively high-resolution structures of very large complexes, have propelled this decade’s progress. These approaches and their future refinement should continue to be fruitful in placing transcribing Pol II in its three-dimensional nuclear location and in characterizing the dynamics of what proteins and nucleic acids enter and exit the transcription domain and how the act of transcription remodels the chromatin/nuclear environment. A goal will be to describe the numerous dynamic steps of the transcription cycle, including the co-transcriptional events that engage the elongation complex, with the objective of filling out our picture of this fundamental pathway in its cellular context.A half century after RNA Pol II was first extracted and purified from eukaryotic cells


The prevalence of diabetes and its related complications are increasing significantly globally. Collected evidence suggested that several genetic and environmental factors contribute to diabetes mellitus. Associated complications such as retinopathy, neuropathy, nephropathy and other cardiovascular complications are a direct result of diabetes. Epigenetic factors include deoxyribonucleic acid (DNA) methylation and histone post-translational modifications. These factors are directly related with pathological factors such as oxidative stress, generation of inflammatory mediators and hyperglycemia. These result in altered gene expression and targets cells in the pathology of diabetes mellitus without specific changes in a DNA sequence. Environmental factors and malnutrition are equally responsible for epigenetic states. Accumulated evidence suggested that environmental stimuli alter the gene expression that result in epigenetic changes in chromatin. Recent studies proposed that epigenetics may include the occurrence of ‘metabolic memory’ found in animal studies. Further study into epigenetic mechanism might give us new vision into the pathogenesis of diabetes mellitus and related complication thus leading to the discovery of new therapeutic targets. In this review, we discuss the possible epigenetic changes and mechanism that happen in diabetes mellitus type 1 and type 2 separately. We highlight the important epigenetic and non-epigenetic therapeutic targets involved in the management of diabetes and associated complications. DN. DN15]. Studies on promoter DNA methylation on gene expression were done: (1) promoters with low CpG density were common among tissue-specific genes and were found to be methylated without any link to gene activity; (2) promoters with high CpG density were mostly found with housekeeping genes and were non-methylated irrespective of promoter activity; (3) promoters with intermediate CpG density were intermediary methylated and promoter methylation correlated with gene expression .Studies showed that non-methylated CpG are present at the beginning of transcription near to SXY boxes binding transcription factors and methylation in this region results in suppression of transcription .Hypermethylation is commonly related to the gene silencing, and hypomethylation leads to increased gene expression. Various reports are mentioned on distinct methylation level of genes related to the T1D patients . The difde novo methylation of DNA, which results in the transcription activation. The interaction of the DNMT to the H3K4 on the DNA at promoter level leads to transcription repression. The interaction of H3K9 with DNA methylation recruited with ubiquitin-like, containing PHD and RING finger domains, 1 (UHRF) protein which increases the attachment of the DNMTs to the DNA, which results in transcription repression [Histones undertake the post-translational modifications on particular residues of amino acids at N-terminal of the histone protein. The importance of the modification depends upon type of modification , degree of modification  and on the position of the modification. The defined modification is exactly recognized by the remodeling of chromatin protein which eventually modifies the degree of chromatin condensation. The level of gene expression based upon the territory of the modification , combination and pattern of modification . Histonepression   in the type 1 diabetes susceptible genes, HLA class II histocompatibility antigen, DRB1 β chain (HLA-DRB1) and HLA-DQB1, was found to be higher in T1D patients as compared with the normal individuals . The metviz miR – 152, 30a-5p, 81a, 24, 148a, 210, 27a, 29a, 26a, 27b, 25, 200a. Moreover, miRNA-25 was positively correlated with glycemic control  (P=0.0035) and negatively correlated with residual β-cell function (P=0.0037), several miRNAs were also linked to apoptosis and β-cell function [miRNAs control the gene expression by influencing the stability and translation of messenger RNAs (mRNAs) via straight inhibition and degradation of mRNA. Epigenetic changes of miRNAs have been related to the cell cycle modification with alteration in immune reaction. The reports of miRNAs expression in the Treg cells of type1 diabetes patients showed over-expression of miRNA-146a and under-expression of miRNA 20b, 31, 99a, 100, 125b, 151 and 365 which resulted in direct involvement of miRNA in the immune response regulation that leads to T1D . In animfunction .In spite of this, the monogenic configuration of diabetes remains unidentified. Molecular diagnosis has raised awareness about the genes that control β-cell function and their total analysis will further identify the fundamental pathways involved in the pathogenesis of diabetes ,35. The In the case of early-onset diabetes (EOD) is described by significant dissimilarities between the frequency of haplotypes and HLA alleles. Hathout et al. , studiedFrom these findings, it is cleared that compilation of epigenetic changes with genetic and environments factors plays a critical role in pathogenesis of T1D . Moreovein utero either before or after the famine [The frequency of T2D has increased significantly from several decades, estimated to extend to 366 million around the world by 2030 . Mendelie famine . Anothere famine . IUGR reEpigenetic modification of genome leads to permanent proliferation of gene expression from one generation to another generation . Mainly In vivo lysine group present at amino-terminal may be mono-di and tri-methylated. This gives an extra level of regulation [Histone modification in eukaryotes cells contains four histone proteins, i.e. H2A, H2B, H3 and H4. The lysine residue of this protein can be easily modified by several mechanisms such as acetylation, methylation, phosphorylation, glycosylation and adenosine diphosphate (ADP) ribosylation. The main mechanism involved in the epigenetic modification is acetylation and methylation at lysine residue on amino-terminal. In the epigenetic modification the main mechanism involved is acetylation and methylation at the lysine residue that is present on amino-terminal. The increased acetylation of a lysine residue on amino-terminal induces increased transcription, whereas, decreased acetylation induces repression of transcription . In methgulation . Recentlgulation . These egulation ,74. Incrgulation  . In studies it was found that methylation at lysine 9 anticipated in changes of DNA methylation ,76. It hA study by Raychaudhary et al. , found tEpigenetic modification mainly contains Histone and DNA modifications, which leads to severe phenotype changes—this part is mostly entertained because of these events, are inherently reversible and this is advantageous for selecting the epigenetic targets. In respect to environmental and genetic stress, epigenetic changes may proceed ahead or backward, after withdrawal of stress it reverts back to original state. Recent studies has shown that histone modification mainly contribute toward methylation of CpG island, which relate the several epigenetic changes and regulations . This reFocus on new and novel management for diabetes mellitus, the probability of utilizing the reversibility of epigenetic modifications for regulation of glucose metabolism has been fascinated. In facts, the several novel small molecules have been established with epigenetic activity . These mNow it is well known that T2D mellitus does not contain only genetic factors that related to etiology of disorder . Beside Diabetes mainly developed due to lack of insulin release from the β cells and the abnormally high production of glucagon from the α cells of pancreas. Several studies have been found that DNA. Methylation plays the vital role in functioning of pancreatic islet cells . The imp310 [It is a member of the sirtuin family, also called as NAD-dependent deacetylase and evaluate in bacteria . The sev310 . In macr310 ,107. Fur310 . SIRT1 i310 . Hence, 310 . SIRT1 a310 . Also, S310 . This de2+ signaling pathway and also activate the PGC-1α in adiponectin receptor 1 knockout mice [Therefore, SIRT1 can increase the insulin resistance via promoting oxidation of free fatty acid and biogenesis of mitochondria through deacetylation of PPAR-α and PGC-1α activation of skeletal muscles. Therefore, the PGC-1α significantly increased the expression of GLUT4 which resulted in transport the glucose C2C12 murine myotubes . Adiponeout mice . Adiponeout mice , which r254 and Arg24 [Protein tyrosine phosphotase 1B (PTP-1B) comes under the family of PTP enzyme family having 435 amino acids, encoded by PTpn1 genes with a molecular weight of 50 kDa ,119. Prond Arg24 , althougnd Arg24 . PTB1B cnd Arg24 . Hence, The management of diabetes and its complications is not an easy task. The management of diabetes can be resolved by identifying the novel therapeutic targets and discovery of new drugs several hormones are involved in glucose homeostasis in the human body such as incretin, glucagon-like peptide-1 (GLP-1) and glucose-dependent insulin trophic peptide (GIP). The incretin is degraded by dipeptidyl peptidase-IV enzyme, inhibition of this enzyme leads to lowering of the blood glucose level . Over-exThe primary hormones involved in metabolism is GLP1, after taking a meal, GLP1 is released with GIPs which increased the release of insulin from β cells and inhibits the glucagon secretion . In suscPPAR-γ is a nuclear receptor (Type 2) mainly found in adipose tissue , identifγ agonists in vivo in the rat [The identified a novel protein resistin which is involved in insulin resistance. Rosiglitazone was the first thiazolidinedione drug which decreased the expression of resistin but the proper mechanism was unknown . Pyruvat the rat . The fin the rat  . The sodium-glucose transport transfers the glucose from lumen to blood against the concentration gradient. Mainly sodium-glucose linked transporter 1 (SGLT1) and 2 (SGLT2) is the main co-transporter in the lumen of the kidney which facilitates the re-absorption of glucose. Inhibition of these two co-transporters leads to decreases in the re-absorption of glucose to blood and promote glycosuria. In normal human beings, 95% filtered glucose is reabsorbed by these transporters, which helps to maintain glucose homeostasis .In T2D patients, these transporters are up-regulated which increase the transportation of glucose into the blood. Currently, SGLT2 inhibitors have their anti-hyperglycemic effect on various animal models . SGLT1 rin vitro and in vivo. lncRNA-p3134 is associated with glucose metabolism and insulin signaling in pancreatic β cells and its circulating level was high in diabetic patients [Long non-coding RNAs (lncRNAs) are a group of RNAs ranging from 200 nts to 100 kb in length and possess a potentiality of limited protein-coding and functionality as regulatory genes at the epigenetic, transcriptional and post-transcriptional levels. In last several years, scientific evidences are totting up to show that lncRNAs can regulate a variety of important biological processes and are also involved in development of a variety of diseases, including metabolic diseases like diabetes and other diseases like depression. In diabetes, some recent works has provided the evidence that lncRNAs are often dysregulated during pancreatic cell differentiation, as well as by hyperglycemia and other related growth factors. They can also play role in diabetic complications by incorporating changes in inflammation, fibrosis, ER stress, mitochondrial dysfunction and oxidant stress. Few studies suggested the involvement of lncRNAs in diabetes and diabetes related complications by showing that a number of human SNPs associated with type 1 diabetes and T2D are located within the lncRNA loci . Arnes epatients . Its ovepatients . In highpatients ,186 suggpatients . MoleculProc. Natl. Acad. Sci. U.S.A.116:7455–7464, 2019) demonstrated a novel circRNA cPWWP2A associated with diabetic retinopathy (DR) that can play a role in DR-induced retinal vascular dysfunction through up-regulating the expression of Angiopoietin 1, Occludin and SIRT1 by acting as ceRNA interacting with miR-579. In another study it has been observed that four circRNAs  out of the five most abundant in human islets, showed noticeable associations with diabetes status. CircCIRBP showed a role in insulin secretory index in isolated human islets whereas circ CIRBP and circRPH3AL displayed altered expression of elevated fatty acid in treated EndoC-βH1 cells. CircCAMSAP1 was also found to be interfere with T2D status in human peripheral blood. No associations between genotype at T2D risk loci and circRNA expression were identified.CircRNA, a type of closed circular RNA molecule belongs to the ever-growing class of naturally occurring noncoding RNAs (ncRNAs). CircRNAs are covalently closed transcripts that are mostly originated from precursor-mRNA by back-splicing which is a non-canonical event. CircRNAs are mainly generated from exons or introns, by two kinds of splicing, exons reverse splicing of exons and introns . They arCollective evidence suggests that well-elaborated epigenetic mechanism might be involved in altering the gene expression to influence the etiology of diabetes mellitus and associated complications. Diabetic stimuli activate several epigenetic modifications which may be a key mechanism underlying metabolic memory. Therefore, most of epigenetic factors and mechanism involved in gene modification via upstream signal transduction pathways and other pathways are not known yet. This is the main reason behind the rapid growth and energetic field for identification of new mechanism and therapeutic targets involved in epigenetic changes. Another reason is that the anti-diabetic medication has potential adverse and side effect on the patient such as lactic acidosis, severe hypoglycemia, edema and heart attack and urinary tract infections . Several