This study provides suggestive evidence that the de novo mutations in fetal brain-specific enhancers play role in the aetiology of intellectual disability. CSMD1, OLFM1, and POU3F3). Most of the DNMs from ID probands showed allele-specific enhancer activity when tested using luciferase assay. Using CRISPR-mediated mutation and editing of epigenomic marks, we show that DNMs at regulatory elements affect the expression of putative target genes. Our results, therefore, provide new evidence to indicate that DNMs in fetal brain-specific enhancers play an essential role in the aetiology of ID.The genetic aetiology of a major fraction of patients with intellectual disability (ID) remains unknown. De novo mutations (DNMs) in protein-coding genes explain up to 40% of cases, but the potential role of regulatory DNMs is still poorly understood. We sequenced 63 whole genomes from 21 ID probands and their unaffected parents. In addition, we analysed 30 previously sequenced genomes from exome-negative ID probands. We found that regulatory DNMs were selectively enriched in fetal brain-specific enhancers as compared with adult brain enhancers. DNM-containing enhancers were associated with genes that show preferential expression in the prefrontal cortex. Furthermore, we identified recurrently mutated enhancer clusters that regulate genes involved in nervous system development ( Intellectual disability (ID) is a neurodevelopmental disorder characterised by limitations in intellectual functioning and adaptive behaviour . The clicis-regulatory elements, also known as transcriptional enhancers, in monogenic developmental disorders, including preaxial polydactyly (SHH) (SOX9) (TBX5) (PTF1A) (cis-regulatory elements (Previous studies have implicated noncoding mutations in long-range ly (SHH) , 6, Pier) SOX9) , congeni) (TBX5) , and pan , congenelements . Moreoveelements .Despite these precedents, efforts to implicate enhancer mutations in human diseases face numerous challenges. Importantly, it is currently not possible to readily discern functional enhancer mutations from non-functional or neutral variants based on sequence features. This can be partially addressed through experimental analysis of regulatory DNA mutations. Moreover, we still need a complete understanding of which regulatory regions and which subsequences within the regulatory regions are most likely to harbour disruptive mutations. In addition, one of the biggest challenges in interpreting mutations in regulatory regions is correctly associating regulatory elements with the potential target genes. Systematic identification of tissue-specific promoter–enhancer interaction maps would help identification of regulatory regions that are associated with disease-relevant genes.ID is a severe early-onset neurodevelopmental phenotype; hence, we hypothesised that ID could result from DNMs in enhancers that are specifically active during fetal brain development rather than the adult brain. Furthermore, more than half of the human enhancers have evolved recently; thus evolutionarily not conserved , and advIn this study, we deployed WGS analysis, integrative genomic and epigenomic studies, together with experimental functional validations to show that DNMs in patients with ID are selectively enriched in fetal brain enhancers. We further show that DNMs map to enhancers that interact with known ID genes, genes that are intolerant to mutations, and genes specifically expressed in the prefrontal cortex. Furthermore, we identify three fetal brain-specific enhancer (FBSE) domains with recurrent DNMs and provide experimental evidence that candidate mutations alter enhancer activity in neuronal cells. Using dual luciferase assay, we show that the majority of enhancer DNMs have allele-specific activity. Furthermore, using CRISPR/Cas9-mediated editing, we show that enhancer DNM results in the down-regulation of target gene expression. Our results provide a new level of evidence that supports the role of DNMs in neurodevelopmental enhancers in the aetiology of ID.We performed WGS of 63 individuals, including 21 probands with severe intellectual disability (ID) and their unaffected parents, at an average genome-wide depth of 37.6X (Table S1). We identified, on average, 4.18 million genomic variants per individual that included 3.37 million single nucleotide variants (SNVs) and 0.81 million short indels (Table S1). We focused our analysis on DNMs, including de novo CNV, as it has been shown that DNMs contribute significantly to neurodevelopmental disorders . We idenMean coverage, number of SNVs and indels in 63 whole genome sequences that include 21 intellectual disability patients.Table S1 Number of de novo mutations per ID proband from the in-house cohort.Table S2 De novo copy number variants identified in ID probands.Table S3 KAT6A, TUBA1A, KIF1A, and NRXN1, all of them previously implicated in ID . Of the 23 coding mutations, 15 were non-synonymous coding mutations or protein-truncating mutations. In four ID probands , we identified various potentially pathogenic mutations in the genes  ClinVar  ; hence, we decided to investigate potentially pathogenic mutations in disease-relevant enhancer regions. Our cohort size was relatively small, hence to increase the sample size for statistical analysis, we included 30 previously published severe ID samples in which no pathogenic protein-coding DNMs have been found using WGS  in tissues that are not relevant to ID, suggesting that the DNM enrichment observed in fetal brain enhancers is tissue-specific. When we analysed two ID cohorts, the in-house cohort (n = 17) and severe ID cohort (n = 30), separately, DNMs from both cohorts showed enrichment in FBSEs as compared with adult brain enhancers , which include 82 de novo SNVs and one de novo indel (Table S5). A total of 52 DNMs were located within FBSEs, 30 DNMs were in human gain enhancers, whereas one overlapped with both fetal brain enhancers and a human gain enhancer. First, we investigated whether in our ID cohort (n = 47), DNMs were enriched in the enhancers that are specifically active in the fetal brain or the enhancers that are active in specific subsections of the adult brain. In our ID cohort, DNMs were significantly enriched in FBSEs and human gain enhancers compared with adult brain-specific enhancers ; however, no statistically significant enrichment was observed in fetal lung and fetal small intestine enhancers (negative controls) as compared with adult lung and adult small intestine . This suggests that the statistical method used to estimate enrichment is robust and the enrichment of DNMs observed in the fetal brain enhancer is a real reflection of biology. Our results were consistent with the expectation that mutations in enhancers active during fetal brain development contribute to the aetiology of ID, a severe early-onset neurodevelopmental phenotype, rather than mutations in enhancers active in the adult brain.As our method to estimate the enrichment of DNMs in fetal brain enhancers as compared with adult brain enhancers is new, to support our findings, we also performed a two-sample Poisson rate ratio test to estimate enrichment. We found that the DNMs were significantly enriched in the fetal brain enhancers as compared with adult brain enhancers   from neuhttps://panelapp.genomicsengland.co.uk/), genes implicated in neurodevelopmental disorders in the Deciphering Developmental Disorder (DDD) project . The most robust enrichment was observed for the DDD genes . Out of 47 coding-negative ID patients in 17 patients, DNMs were observed in FBSEs associated with known neurodevelopmental disorder genes.Next, we compiled a list of genes that have previously been implicated in ID or related neurodevelopmental disorders, using four gene sets: known ID genes , 3, ID g project , and aut project . The DNMEnrichment of known ID genes in genes associated with DNM containing enhancers.Table S7 https://maayanlab.cloud/Enrichr). We observed that the target genes of DNM-containing enhancers were not only involved in nervous system development , but also predominantly expressed in the prefrontal cortex , a region of the brain that has been implicated in social and cognitive behaviour, personality expression, and decision-making .The potential functional effect of enhancer mutations is expected to be mediated through the altered expression of target genes. Recently, it has been shown that most known severe haploinsufficient human disease genes are intolerant to loss of function (LoF) mutations . We compEnrichment of loss of function-intolerant (pLI ≥ 0.9) genes in genes associated with DNM containing enhancers.Table S10 Taken together, our analysis shows that DNMs detected in severe ID patients are predominantly found in enhancers that regulate genes that are specifically expressed in the prefrontal cortex, have been previously implicated in ID or related disorders, and exhibit intolerance to LoF mutations.The human brain is a most complex tissue composed of multiple cell types and subtypes , 29. We P = 5.18 × 10−5; FDR = 4.14 × 10−4) suggesting that DNMs affecting highly interacting regulatory regions of IPCs might be functional. Interestingly, this signal was driven by DNMs overlapping promoter regions rather than enhancers (Table S11B). Therefore, we performed an enrichment analysis by selecting ATAC-seq peaks that overlap with the promoter regions (±2 kb of TSS) of protein-coding genes. Promoter regions of all four cell types were enriched for ID DNMs as compared with GoNL DNMs.Cell type-specific open chromatin (ATAC-seq) data were available for radial glia, IPCs, excitatory neurons, and interneurons from developing cortex . We did (A) Enrichment of ID DNMs in developing brain cell types. (B) ID DNMs overlapping interacting regulatory regions of IPCs. (C) Enrichment of ID DNMs in developed human brain cell types. (D) ID DNMs overlapping promoter regions of developed human brain cell types.Table S11 P = 0.0073; FDR = 0.012; Table S11C) and neuronal cells  showed enrichment after multiple test corrections. On the contrary, all four human brain prefrontal cortex cell types showed significant enrichment for ID DNMs compared with GoNL DNMs in promoter regions after correcting for multiple tests. A total of 44 DNMs overlapped with the promoter regions of at least one of the four human brain cell types (Table S11D). Interestingly, the majority (70%) of the DNMs overlapped with the promoters that were active in all four cell types and 88.63% overlapped with promoters that were active in three or more cell types.Of four human brain prefrontal cortex cell types , enhancer regions of only microglia . The presence of three enhancer clusters with recurrent DNMs within the cohort of 47 ID probands was significantly higher than expected (permutation test P = 0.016). All three genes  play a role in the nervous system development . It has been shown that enhancers that regulate genes important for tissue-specific functions often cluster together . Therefoclusters . To testing DNMs . In shorelopment , 36, 37.CSMD1, OLFM1, and POU3F3 enhancer DNMs. We found three individuals with CSMD1 enhancer DNMs, five patients with DNMs in the OLFM1 enhancer cluster, and 15 ID patients with DNMs in the POU3F3 enhancer cluster. Only the enhancer cluster associated with POU3F3 showed statistically significant enrichment for DNMs in the GEL cohort as compared with the expected number of DNMs estimated using the previously defined framework for DNMs . Next, we extracted human phenotype ontology terms of the patients with DNMs in enhancers of these genes. Of three probands with CSMD1 enhancer DNMs, two showed delayed speech and language development, delayed motor development, microcephaly, and seizures. All five probands with OLFM1 enhancer DNMs showed delayed speech and language development, whereas three showed autistic behaviour and delayed motor development. Nine out of 15 probands with POU3F3 enhancer DNMs showed autistic behaviour, global developmental delay, and delayed speech and language development, whereas eight probands showed delayed motor development. Heterozygous mutations in POU3F3 protein-coding regions have been recently implicated in ID  has sequenced 6,514 patients with intellectual disability. In GEL, no pathogenic coding variants were found in 3,169 ID patients for which WGS data of unaffected parents were available (trio WGS). We analysed DNMs from these 3,169 samples to find additional evidence supporting for DNMs  . Our results suggest that the enhancer DNMs from ID probands were more likely to affect the binding sites of neuronal transcription factors and could influence the regulation of genes involved in nervous system development through this mechanism.Enhancers regulate gene expression through the binding of sequence-specific transcription factors (TFs) at specific recognition sites . DNMs co(A) Effect of ID DNMs located in fetal brain enhancers on transcription factor-binding site. (B) Tissue expression enrichment analysis of TFs of which, the binding sites were disturbed by DNMs in ID patients.Table S12 CSMD1 enhancer cluster had two DNMs (chr8:g.2177122C>T and chr8:g.2411360T>C) in two unrelated ID probands . Both DNMs yielded a gain of enhancer activity compared with the WT allele (OLFM1 enhancer cluster (chr9:g.137722838T>G and chr9:g.137333926C>T) from two unrelated ID probands  caused a loss of activity . Altogether, we selected 11 enhancer DNMs (Table S13) and investigated their functional impact in luciferase reporter assays in the neuroblastoma cell line SH-SY5Y. Of the 11 enhancers containing DNMs, 10 showed significantly higher activity than the negative control (empty vector) in at least one allelic version , indicating that they do indeed function as active enhancers in this neuronal cell line . AmongstT allele . By contactivity . FurtherList of DNMs tested using dual-luciferase reporter assay.Table S13 SOX8 (positive strand) and LMF1 (negative strand) genes , by guideRNA  from a family 14 probands results in a loss of enhancer activity . The prod) genes , suggestpromoter . To inveguideRNA  mediatedSOX8 enhancer was active in HEK293T cells (SOX8 gene showed a significant (P = 0.0301) reduction in expression levels; however, no difference was observed in the expression levels of the LFM1 gene  in the mutant clone as compared with the WT. However, the level of H3K4me1 was not altered , H3K27ac (t test P = 0.0751) and H3Kme1 (t test P = 0.444) levels were not altered in the mutant cells as compared with WT. We, therefore, conclude that the enhancer harbouring DNM located 138,665 bp downstream of the SOX8 promoter regulates the expression of SOX8 in neuronal cells.The presence of mono-methylation of histone H3 at lysine 4 (H3K4me1) and acetylation of histone H3 at lysine 27 (H3K27ac) is a strong indicator of active enhancers. The histone mark H3K4me1 can be observed at both active and inactive enhancers but the H3K27ac mark is observed only at active enhancers. Hence, we investigated H3K4me1 and H3K27ac levels at DNM containing SOX8 enhancer mutation from the family 14 ID proband indeed leads to reduced activity of the enhancers and, in turn, reduced the expression of the SOX8 gene. Further experimental analysis is required to establish the SOX8 enhancer DNM as a cause of ID in this patient. However, our study provides strong evidence that SOX8 enhancer DNM might be a causal variant in this patient.Taken together, our analysis suggests that the Despite the current widespread use of WGS, the true burden of pathogenic mutations in enhancers is unknown. This is mainly because of an inability to predict the pathogenicity of enhancer mutations based on sequence features. The aggregation of a minority of pathogenic mutations with the majority of benign regulatory mutations nullifies any signal from pathogenic mutations in non-protein-coding genomic regions in disease cohorts. It is noteworthy that in protein-coding regions of the genome, only protein-truncating variants and damaging missense variants, but not other protein-coding mutations, show significant enrichment in neurodevelopmental disorders , 42, 43.CSMD1, OLFM1, POU3F3) and found that this enrichment was significant relative to expectations. One of them (POU3F3) was recently shown to harbour pathogenic heterozygous mutations in patients with ID (CSMD1 and OLFM1 show high pLI scores (pLI = 1), indicating that they are intolerant to loss of function (LoF) mutations and are dosage-sensitive. More than 72% of the genes that are intolerant to loss of function mutations have not been assigned to any human diseases despite the strong evidence of constraint .WGS was performed on the Illumina X10 at Edinburgh Genomics. Genomic DNA (gDNA) samples were evaluated for quantity and quality using an AATI, Fragment Analyzer, and the DNF-487 Standard Sensitivity Genomic DNA Analysis Kit. Next-generation sequencing libraries were prepared using Illumina SeqLab-specific TruSeq Nano High Throughput library preparation kits in conjunction with the Hamilton MicroLab STAR and Clarity LIMS X Edition. The gDNA samples were normalised to the concentration and volume required for the Illumina TruSeq Nano library preparation kits, and then sheared to a 450-bp mean insert size using a Covaris LE220 focused-ultrasonicator. The inserts were ligated with blunt-ended, A-tailed, size selected TruSeq adapters and enriched using eight cycles of PCR amplification. The libraries were evaluated for mean peak size and quantity using the Caliper GX Touch with an HT DNA 1k/12K/HI SENS LabChip and HT DNA HI SENS Reagent Kit. The libraries were normalised to 5 nM using the GX data, and the actual concentration was established using a Roche LightCycler 480 and a Kapa Illumina Library Quantification kit and Standards. The libraries were normalised, denatured, and pooled in eights for clustering and sequencing using a Hamilton MicroLab STAR with Genologics Clarity LIMS X Edition. Libraries were clustered onto HiSeqX Flow cell v2.5 on cBot2s, and the clustered flow cell was transferred to a HiSeqX for sequencing using a HiSeqX 10 Reagent kit v2.5.Preprint), creating a gvcf file. The gvcf files of all the individuals from the same family were merged and re-genotyped using GATK GenotypeGVCFs producing a single VCF file per family.De-multiplexing was performed using bcl2fastq (2.17.1.14), allowing 1 mismatch when assigning reads to barcodes. Adapters were trimmed during the de-multiplexing process. Raw reads were aligned to the human reference genome (build GRCh38) using the Burrows–Wheeler Aligner (BWA) mem (0.7.13) . The dupVariant Quality Score Recalibration pipeline from GATK , 51, 52 The DNMs were called using the GATK Genotype Refinement workflow. First, genotype posteriors were calculated using sample pedigree information and the allele accounts from 1,000 genome sequence data. Next, the posterior probabilities were calculated at each variant site for each trio sample. Genotypes with genotype quality (GQ) < 20 based on the posteriors were filtered out. All the sites at which the parents' genotype and the child's genotype with GQs ≥ 20 for each trio sample were annotated as the high confidence DNMs. We identified, on average, 1,527 DNMs in 21 probands (∼73 DNMs per proband) with GRCh38 assembly. Only high confident DNMs that were novel or had a minor allele frequency less than 0.001 in 1,000 genomes project were selected for further analysis. This resulted in the removal of 143 DNMs, resulting in a total of 1,384 DNMs (∼66 DNMs per proband).Because most of the publicly available datasets, including epigenomic datasets, are mapped to human genome assembly version hg19, we lifted over all the DNM coordinates to hg19 using the liftover package. The liftover resulted in the loss of 123 DNMs as they could not be mapped back to hg19 resulting in a set of 1,261 DNMs (60 DNMs per proband). All the variant coordinates presented in this article are from hg19 human genome assembly.DNM annotations were performed using Annovar . To acceTo detect structural variants (SV), we used four complementary SV callers: BreakDancer v1.3.6 , Manta vTo detect de novo SV, we used SV2 v1.4.1 . SV2 is Roadmap Epigenomic Project  chromHMMGSE63649.Human gain enhancers published previously by Reilly et al  were dowWe downloaded DNMs identified in the healthy individuals in genomes of the Netherland (GoNL) study  from theRoadmap Epigenomic Project  gene expThe size of enhancer regions differs widely between tissues. Furthermore, the mutability of the tissue-specific enhancer region differs significantly. The mutability of FBSEs, human gain enhancers, and adult brain-specific enhancers was estimated using the previously defined framework for DNMs . The fraFor example, the background mutation rate for FBSEs is 0.970718 and we observed 53 DNMs. Similarly, the background mutation rate for the adult brain subsection angular gyrus is 0.680226 and we observed 22 DNMs. Because of the difference in background mutation rate, we cannot directly compare the number of DNMs between fetal brain enhancers and angular gyrus. Hence, we normalised the observed number of DNMs in angular gyrus enhancers to a background mutation rate of 0.970718 using the following formula.t test.Similarly, we normalised the observed number of mutations from all adult brain subsections and human gain enhancers to a background mutation rate of 0.970718 (Table S6) so that we could perform a valid comparison between the observed number of mutations from various enhancer sets. The significance level between DNMs observed in FBSEs (n = 2) and adult brain enhancers (n = 8) was calculated using a two-tailed P-value.The enhancer clusters were randomly shuffled 1,000 times. We estimated the number of enhancer clusters with more than one mutation for each iteration. Then, we counted the number of times when more than or equal to two mutations were observed in three or more enhancer clusters. This number was then divided by 1,000 to calculate the The R Bioconductor package motifbreakR  was usedThree different methods were used to predict the potential target genes of enhancers.Chromosome conformation capture (Hi-C) comprehensively detects chromatin interactions in the nucleus; however, it is challenging to identify individual promoter–enhancer interactions using Hi-C because of the complexity of the data. In contrast, promoter capture Hi-C (PCHi-C) specifically identifies promoter–enhancer interactions as it uses sequence capture to enrich the interactions involving promoters of annotated genes . The sigFor an enhancer to interact with a promoter, both promoter and enhancer need to be active in specific cells at a specific stage. To identify promoter–enhancer interactions, all the active promoters in the fetal brain (as defined by chromHMM segmentation) were extracted. Promoter–enhancer interactions occur within TAD; hence, promoters located within the same TAD as that of a DNM-containing enhancer were used for analysis.P-value. The interactions with an adjusted P-value less than 0.01 were considered a significant interaction between the enhancer and promoter.For each enhancer and promoter, H3K27ac counts were extracted from all tissues for which H3K27ac data were available in the Roadmap Epigenomic Project  ChIP-seqFinally, if any enhancers remained unassigned to a gene using these approaches, they were assigned to the closest fetal brain-expressed genes within the TAD. A gene with an expression level more than or equal to 1 TPM in the Roadmap Epigenomics Project fetal brain RNA-seq data was considered to be expressed in the fetal brain.To test if enhancer-associated genes were enriched for genes previously implicated in neurodevelopmental disorders, three different gene sets were used: 1) intellectual disability (ID) gene list published in the review by Vissers et al  intellec; and (3)http://amp.pharm.mssm.edu/Enrichr/). The list of genes was uploaded to the Enricher web interface. Enrichr enables users to submit lists of human or mouse genes to compare against numerous gene set libraries of known biological function, such as pathways, diseases, or gene sets regulated by transcription factors. The matching gene sets are ranked by different methods that assess the similarity of the input gene set with all other gene sets in each library . The list of genes was uploaded to the Enricher web interface. Enricher uses the data from the ARCHS4 project . The significance of enrichment was tested using a hypergeometric test in R.The probability of loss of function intolerance (pLI) scores for each gene was downloaded from Exome Aggregation Consortium (ExAC) browser (The developing human cortex cell type ATAC-seq and PLAC-seq data published in  were dowNeuroblastoma cell line (SH-SY5Y) was maintained in DMEM/F12 media (Gibco), 1% penicillin–streptomycin, 10% fetal bovine serum, and 2 mM L-glutamine. The HEK293T cells were maintained in DMEM, 10% FCS, and 1% penicillin–streptomycin.Enhancer and control regions (500–600 bp) were amplified from human genomic DNA from HEK293T cells using Q5 High-Fidelity Polymerase (New England Biolabs). Amplified fragments were cloned into pGL4.23 plasmid (Promega), which consists of a minimal promoter and the firefly luciferase reporter gene. These regions were mutagenised to introduce the DNMs of interest using the Q5 Site-Directed Mutagenesis kit (New England Biolabs) using non-overlapping primers. pGL4.23 plasmids containing putative enhancer DNA were sequence-verified and transfected, together with a Renilla luciferase–expressing vector  into SHSY-5Y cells using Lipofectamine 3000 (Invitrogen) following the manufacturer’s protocol. Firefly and Renilla luciferase activities were measured 24 h after transfection using the Dual-Luciferase Reporter Assay System  as per the manufacturer’s instructions. Primers used to amplify genomic DNA and for mutagenesis are provided in Table S14.List of primers used to amplify genomic DNA and for site-directed mutagenesis for dual-luciferase reporter assay.Table S14 SOX8 enhancer element, cells were co-transfected with gRNA  plus 4 U/ml of Proteinase K (Thermo Fisher Scientific) for at least 2 h at 55°C with agitation. Then, 0.4× vol of 5 M NaCl were added to the mixture and centrifuged at max. speed for 10 min. DNA in the supernatant was precipitated with 1× vol of isopropanol. After centrifugation, the pellet was washed with 70% ethanol and air-dried for half an hour. The DNA was resuspended in water and incubated for at least 1 h at 37°C with agitation.RNA was extracted using the RNeasy Mini Kit (QIAGEN), and cDNA was produced with the First Strand cDNA Synthesis Kit . qPCR reactions were performed with SYBR Green Master Mix  and run on a CFX96 Real-Time PCR machine (Bio-Rad). Relative gene expression values were calculated with the –ΔCt method, using GAPDH as a housekeeping gene for normalisation. Oligonucleotides used for qPCR are provided in Table S16.List of primers used to quantify gene expression by qPCR and for ChIP–qPCR.Table S16 H9 human embryonic stem cells hESCs (WiCELL) were differentiated to NPCs by adding Gibco PSC Neural Induction Medium . After 7 d of induction, the cells were passaged in an NPC proliferation medium containing Advanced DMEM/F-12 - 24.5 ml , Neurobasal – 24.5 ml .CRISPRi using dCAS9-Sid4x is performed as previously described in  with theList of guideRNA used for CRISPRi experiments.Table S17 5 NPCs were plated onto one well of a six-well plate precoated with geltrex  in the NPC proliferation medium . ∼24 h after plating, 625 ng of respective guide RNAs were diluted into 250 μl opti-MEM  and 1875 ng dCAS9-pSid4X. 10 μl of TransIT-X2 reagent  was added to the above mix and thoroughly mixed and incubated at room temperature for 20 min. 260 μl of the final transfection mix was then added onto one well of a six-well plate containing 2.5 ml media. The plate was rocked to mix and incubated for 24 h. 48 h after transfection, 0.5 μg/ml puromycin  was added to the media. Cells were harvested with accutase  after 24 h of puromycin selection and taken for RNA isolation and RT-qPCR.For NPCs transfection, 7.5 × 105 NPCs were plated onto one well of a 24-well plate precoated with 100 μg/ml PLO  and 5 μg/ml lamin . ∼24 h after plating, NPC proliferation medium was replaced with 750-500 μl neuronal maturation medium . 3 d after the start of neuronal differentiation, 200 ng of the respective guide RNAs were diluted into 50 μl opti-MEM and 600 ng of dCAS9-pSid4X. 2 μl of TransIT-X2 reagent was added to the above mix and thoroughly mixed and incubated at room temperature for 20 min. 52 μl of the final transfection mix was then added onto one well of a 24-well plate containing 0.5 ml neuronal maturation medium. The plate was rocked to mix everything and incubated for 24 h. 24 h after transfection, 0.5 μg/ml puromycin was added to the media. Surviving transfected cells after puromycin selection were harvested with accutase after 48 h of antibiotic selection and taken for RNA isolation and qPCR.For CRISPRi in NPC-derived neuronal cells, 10t test.All luciferase experiments, gene quantification using qPCR, and H3K4me1 and H3K27a levels using ChIP–qPCR were done in three biological replicates. The significance level was calculated using a two-tailed We used R package p.adjust to calculate false discovery rate (FDR) to correct for the multiple hypothesis testing.The WGS data are not publicly available because they contain information that could compromise research participant privacy/consent. However, WGS data and variant calls that support the findings of this study are available on request from the corresponding author (SS Atanur).


Knowledge gaps remain on how nucleosome organization and dynamic reorganization are governed by specific pioneer factors in a genome-wide manner. In this study, we generate over three billons of multi-omics sequencing data to exploit dynamic nucleosome landscape governed by pioneer factors (PFs), FOXA1 and GATA2. We quantitatively define nine functional nucleosome states each with specific characteristic nucleosome footprints in LNCaP prostate cancer cells. Interestingly, we observe dynamic switches among nucleosome states upon androgen stimulation, accompanied by distinct differential (gained or lost) binding of FOXA1, GATA2, H1 as well as many other coregulators. Intriguingly, we reveal a noncanonical pioneer model of GATA2 that it initially functions as a PF binding at the edge of a nucleosome in an inaccessible crowding array. Upon androgen stimulation, GATA2 re-configures an inaccessible to accessible nucleosome state and subsequently acts as a master transcription factor either directly or recruits signaling specific transcription factors to enhance WNT signaling in an androgen receptor (AR)-independent manner. Our data elicit a pioneer and master dual role of GATA2 in mediating nucleosome dynamics and enhancing downstream signaling pathways. Our work offers structural and mechanistic insight into the dynamics of pioneer factors governing nucleosome reorganization. Here the authors provide a multi-omic study of the nucleosome landscape in LNCaP cells and observe nine functional nucleosome states each with characteristic nucleosome footprints. Upon androgen stimulation, they observed changes in these nucleosome states accompanied by changes in binding and function of pioneer factors, including GATA2. The dynamic nucleosome reorganization is the interplay among nucleosome, wrapped DNA, and nucleosome-binding factors such that nucleosomes sterically occlude their wrapped DNA from DNA-binding factors and ATP-dependent chromatin remodelers unwrap nucleosomal DNA or slide nucleosomes to reposition along DNA4. Recent genome-wide nucleosome mapping highlighted functionally important regular nucleosomal arrays as their array regularity is often aligned at biological features6. In contrast, impaired genic arrays are correlated with increased cryptic transcription with suppressive activities7. High-resolution mapping studies suggested that DNA contains information specifying the position of nucleosomes called positioning code which facilitates the shunting of nucleosomes from one array to another by chromatin remodeling machines10. Some studies also showed nucleosomes are subject to extensive reversible post-translational modifications that can alter the local chromatin structure poised for activation and transcription12. Other studies further elucidated coordinated and antagonistic functional relationships between nucleosome remodeling and modifying machineries15.Nucleosome organization  plays a central role in gene regulation17, GATA families20, PAX715, HOXB1321, and P5322 can access target DNA sequences on nucleosomes. A paradigm of competence for transcription is thus established that nucleosome-binding properties of PFs can engage in the assembly of regulatory factors on the DNA by either opening the chromatin locally, positioning nucleosomes, or enabling intrinsic cooperative binding effects among other TFs24, or directly recruiting other chromatin modifiers and coregulators25. Despite many advances in the understanding of nucleosome dynamics, there still remain knowledge gaps on how nucleosome organization and dynamic reorganization are governed by specific pioneer factors in a genome-wide manner.A group of special transcription factors (TFs) called pioneer factors (PFs) such as FOXA families28, including ours29, have found both FOXA1 and GATA2 act as PFs to trigger the androgen-induced androgen receptor (AR) signaling pathway. It is believed that both PFs can recruit chromatin modifiers, chromatin remodelers, and chaperone molecules to establish an “open” chromatin environment or a nucleosome-free region (NFR) to facilitate the accessibility for other factors, then initiate subsequent regulatory events. However, the pioneer capacity of GATA2 is inconclusive. Several studies illustrated that GATA2 is simply a non-pioneer TF30 but others demonstrated it is indeed a pioneer TF19. To fully understand the detailed pioneer capacities of GATA2 in regulating nucleosome organization in androgen stimulated prostate cancer cells, it is critical to use integrative approaches combining high-resolution genomic techniques (ChIP-exo and MNase-ChIP-seq) and computational analyses to examine the relationship among GATA2 and nucleosome organization.Many studiesIn this study, we conducted high-resolution ChIP-exo, MNase-seq, and MNase-ChIP-seq in LNCaP cell model under Vehicle (Veh) and 5α-dihydroxytestosterone (DHT)-treated conditions and generated over three billions multi-omics sequencing data. Here, we exploit the landscape of dynamic nucleosome footprints and quantitatively define functional nucleosome states based on histone marks, genomic regions, nucleosome positioning, spacing, and regularity. We further identify GATA2-associated dynamic nucleosome state switching upon DHT treatment. Using various in vitro assays, we demonstrate that GATA2 initially functions as a PF binding at the edge of a nucleosome in an inaccessible crowding array. Under the DHT-treated condition, GATA2 reconfigures inaccessible to accessible nucleosome state and subsequently, it acts as a master transcription factor either directly or to recruit signaling-specific TFs to enhance oncogenic Wnt/β-catenin signaling in an AR-independent manner. Our work thus elicits a noncanonical GATA2 pioneer model, providing a structural and mechanistic insight into the dynamics of pioneer factors governed by nucleosome reorganization.r value of 0.95 , the Proximal (−5 Kb to −1 Kb away from TSS), and the Distal (−50 Kb to −5 Kb away from TSS). We observed a distinct distribution of nucleosomes enriched with different histone marks in three genomic regions Fig. . For exa34, therefore, we focused on thoroughly examining the functionality of S2, S3, S4, S6, and S7 and their relationship with FOXA1, GATA2, and other TFs and coregulators in the downstream analyses.Indeed, when further incorporating various genomic features with the trajectory, including histone marks, the number of the grouped nucleosomes, genomic location, degree of positioning, regularity score, and average spacing, we were able to define nine functional states, S1–S9 Fig. . S1 was 38  and DHT-treated LNCaP cells and used ePEST to identify GATA2 binding borders at one-base resolution 41 Fig. . GATA2 b 41 Fig. . By plot 41 Fig. . Two exa 41 Fig. .Fig. 4GA42  we identify the PF/TF-associated condensed nucleosome states; (2) we identify the PF/TF binding borders within the nucleosomes; (3) we determine whether the PF/TF is accompanying the nucleosome switches under at least two biological conditions; and (4) we perform competitive nucleosome-binding assays to validate the pioneering capacity. Although our approach is able to define the pioneer functionality of any TFs, we are cautious that the PF functionality and capacity should be interpreted tightly with the specific biological context. Nevertheless, our approach highlights the importance of utilizing the high resolution of high throughput genomic data in elucidating the pioneer function of TFs.Remarkably, we found almost half (42.1%) of GATA2 was bound on nucleosomes  in untreated LNCaP cells and 51.8% of these GATA2-associated nucleosomes were switched to more accessible nucleosomes or free nucleosome regions in DHT-treated cells Fig. , suggest27 have demonstrated the pioneer functionality of GATA2 in hormone-induced prostate cancer cells, all of these studies emphasized on the pioneering role of GATA2 in activating or enhancing AR-dependent gene transcription. By contrast, our results illustrated a pioneer function for GATA2 regulation in which it regulates oncogenic Wnt/β-catenin signaling by circumventing AR signaling. Our finding that GATA2 exerts an AR-independent functionality in promoting aggressive prostate cancer is consistent with a previous study that GATA2 regulates a core subset of clinically relevant genes in an AR-independent manner43.Despite that previous studiesIn summary, we provided a quantitative model of defining functional nucleosome states to the community. We also conducted a detailed examination of the pioneer capacity of GATA2 in regulating dynamical nucleosome reorganization in hormone-induced prostate cancer cells, and further implicated GATA2-mediated Wnt/β-catenin signaling in conferring aggressiveness in prostate cancer. Our work may provide a rationale for targeting GATA2 downstream signaling as a therapeutic strategy to treat advanced prostate cancer. Our work also offers a structural and mechanistic insight into the dynamics of pioneer factors governed by nucleosome reorganization.44. In brief, LNCaP cells were exposed to 10 nM DHT or DMSO (Veh) for 4 h. Mono-nucleosomes with solubilized chromatin was achieved by MNase digestion of 2 min at 37 °C, then immunoprecipitated with antibody-conjugated magnetic beads. DNA is phenol extracted and ethanol precipitated. Libraries were prepared from isolated DNA and sent for sequencing on the Illumina HiSeq3000 at the UTHSA sequencing core. All samples were performed in biological replicates. Antibodies include: H3K4me1 (ab8895) 1:500 dilution, H3K4me2 (ab7766) 1:250 dilution, H3K27ac (ab4729) 1:500 dilution, H3K27me3 (ab6002) 1:250 dilution, H3K36me3 (ab9050) 1:250 dilution, H3K79me2 (ab8898) 1:250 dilution from Abcam . H3K4me3 (07-473) 1:500 dilution, H3K9me3 (17-10242) 1:250 dilution from Millipore (Upstate).MNase-ChIP-seq and MNase-seq protocols were performed according to previous studies21: Cells were fixed with 1% formaldehyde for 10 min at room temperature and chromatin was sonicated and incubated overnight with 2–4 μg antibodies against GATA2 1:250 dilution with biological replicates. T4 DNA polymerase, T4 PNK, and Klenow DNA Polymerase were used together for end polishing. The ligation step was performed with 1 mM dithiothreitol. Protein A Dynal magnetic beads were washed using modified RIPA buffer  followed by Tris pH 8.0 twice during each step. The library was amplified with only 10–12 cycles and prepared without gel-based size selection. Paired-end sequencing (50 bp) was performed by Illumina HiSeq2500.A modified ChIP-exo protocol for TFs was performed as following stepsoc for 30 min in a thermomixer with shaking at 1000 rpm. Transposed fragments were then purified with a Zymo DNA Clean and Concentrator-5 Kit. All libraries showed sufficient amplification after the five pre-amplification cycles and were quantified using the KAPA Library Quantification Kit. Libraries were then sequenced using Illumina Novaseq 6000 at the Duke sequencing core. All samples were performed in biological replicates.About 50,000 cells were resuspended in cold ATAC-seq resuspension buffer . Cell nuclei were then prepared by incubation in 50 μl of ATAC-seq resuspension buffer containing 0.1% NP-40, 0.1% Tween-20, and 0.01% digitonin on ice for 3 min. After centrifugation, nuclei were resuspended in 50 μl of transposition mix , 16.5 μl PBS, 0.5 μl 1% digitonin, 0.5 μl 10% Tween-20, and 5 μl water), and incubated at 37 MNase-seq, MNase-ChIP-seq, and ChIP-ePENS sequencing datasets were generated in LNCaP cells with both Veh and DHT-treated conditions. Raw sequence reads were aligned against the human genomic sequence (hg19) using bowtie2 (version 2.2.8) with -v 3 -k 2 -m 1 -I 20 -X 400 for ChIP-seq data. Only uniquely mapped reads were used for further downstream analysis.31, which uses a Laplacian of Gaussian convolution model to obtain smooth estimates of the stringency of nucleosome positioning, on MNase-seq data to detect nucleosome centers (dyads) and robustly estimate the degree of positioning. The kernel bandwidth w is a key parameter to control the smoothness of the stringency profile. We initially chose w = 30 as suggested in ref. 39 to conduct the calculation, then adjust it to make sure that it provides sufficient smoothing for the particular data without sacrificing the sharpness of the positioning estimate. We assigned nucleosomes to 21,319genes with their longest isoforms in which each gene was divided into three genomic regions: Promoter region of −1 Kb to 1 Kb around the transcriptional start site (TSS), Proximal region of −5 Kb to −1 Kb upstream of TSS and Distal region of −50 Kb to −5 Kb upstream of TSS. We used MACS244 to identify enriched peaks of various histone marks using nonmodel and shift options in order to remove a technical bias and peak shifting for ChIP-seq data.We applied iNPSThe degree of positioning describes how well the nucleosome is positioned in the cells’ population and the regularity score indicates the periodical feature of a nucleosome array, which is measured by calculating power spectral density with an interpolating method and Welch’s method for the nucleosome states’ array.i represents the nucleosome positioning and spacing, as Si is the peak area of nucleosome Ni, Wi is the width of nucleosome position, ω is the weight for the spacing factor, μ is the average spacing in a specific area and di is the actual spacing between nucleosomes. βi represents the histone mark state factor, as i is the relative number of reads of nucleosome Nii+1 and N1 to Ni. If γ among calculated nucleosomes is lower than 10%, the nucleosomes were merged into the same group.For quantitatively defining the nucleosome states, we utilized the following features related to nucleosomes, nucleosome positioning and spacing, histone marks, and the similarity ratio between continuous nucleosomes.We defined nucleosome dyad position, degree of positioning, regularity score, and histone peak signals as grouped nucleosome profiles. We then performed K-means clustering on grouped nucleosome profiles to obtain distinct classes of nucleosome states.40 and depending on a statistical evaluation of Chernoff inequity on exo-5′-end reads and r-scan statistic method for peak-calling on son-3′-end reads, Border-calling was conducted specifically within these binding regions and borders were finally assigned into each individual binding site by a graph-based strategy. A GATA2-associated gene was defined as the closest gene of GATA2 border bindings and each GATA2 border pair was assigned to only one gene according to the order of the following criterion: gene body region (TSS~ TES of a gene), promoter region (TSS~ −1 Kb upstream of a gene), proximal region (−5 Kb ~ −1 Kb upstream of a gene), then distal region (−50 Kb ~ −5 Kb upstream of a gene) and no associated gene.Border-calling of ChIP-ePENS data was conducted by ePEST (version 1.0) with the parameter of -D True -p 1e-8 -R 25 -c 0.05 -k 2.0 -o. The ePEST algorithm was specifically designed for ChIP-ePENS2(|folder change | ) >1 and p values < 0.05. ATAC-seq data of LNCaP cells were downloaded from the Gene Expression Omnibus (GSE105116). ATAC-seq peaks were called using HOMER (version 4.10) find peaks localSize 50000 -size 150 -minDist 50 –fragLength 0 -style dnase. Differential accessibility was called using DESeq2 and hyper- and hypo-accessible peaks were defined with a | log2 FC | > 1 and an adjusted p value < 0.01.RNA-seq data were aligned by STAR (version 2.5.3) with default parameters. The differentially expressed genes were performed by HTseq-count (version 0.9.1) and DESeq2 (version 1.10.1) with thresholds of log® Select siRNAs of GATA2 were obtained from Thermo Fisher Scientific . Each sample was performed in triplicates with siRNA of different targets. For transfection of siRNA oligos, cells were seeded in six-cell plates with Lipofectamine® RNAiMAX Transfection Reagent for 48 h. The knockdown efficiency was detected by qPCR.Silencer22: In vitro nucleosomes were generated from H2A/H2B dimer and H3.1/H4 tetramer (NEB). Synthesized double-stranded DNA sequences were mixed at equal molar amounts and then added to histones at octamer/DNA molar ratios of 1.5:1 in 2 M NaCl. Nucleosomes were reconstituted through salt gradient dialysis and further purified by 7–20% sucrose gradient centrifuge and concentrated by 50,000 centrifugal filter units . The protein-nucleosome-binding assays were carried out with the purified nucleosomes mentioned above and human full-length recombinant GATA2 protein  in a 7 μL DNA binding buffer and incubated for 30 min. Protein binding was analyzed by non-denaturing polyacrylamide gel electrophoresis with ethidium bromide staining.The nucleosome-binding assay was performed as following45. Briefly, LNCaP cells were lysed and chromatin was extracted by adding lysis buffer to cell pellets and incubating for 10 min. Chromatin pellets were centrifuged and resuspended in a nuclease reaction mix and then added to a stop solution and incubated with Proteinase K. Accessible fragments were then purified by DNA binding columns and gene targets were analyzed with PCR by comparing the nuclease-treated condition and the no-nuclease control.Open chromatin assay was performed using a Chromatin Accessibility Assay Kit (ab185901) from Abcam Supplementary InformationPeer Review FileDescription of additional Supplementary FileSupplementary Data 1Supplementary Data 2Supplementary Data 3


The Hedgehog signaling pathway functions in both embryonic development and adult tissue homeostasis. Importantly, its aberrant activation is also implicated in the progression of multiple types of cancer, including basal cell carcinoma and medulloblastoma. GLI transcription factors function as the ultimate effectors of the Hedgehog signaling pathway. Their activity is regulated by this signaling cascade via their mRNA expression, protein stability, subcellular localization, and ultimately their transcriptional activity. Further, GLI proteins are also regulated by a variety of non-canonical mechanisms in addition to the canonical Hedgehog pathway. Recently, with an increased understanding of epigenetic gene regulation, novel transcriptional regulators have been identified that interact with GLI proteins in multi-protein complexes to regulate GLI transcriptional activity. Such complexes have added another layer of complexity to the regulation of GLI proteins. Here, we summarize recent work on the regulation of GLI transcriptional activity by these novel protein complexes and describe their relevance to cancer, as such GLI regulators represent alternative and innovative druggable targets in GLI-dependent cancers. The Hedgehog (HH) signaling pathway orchestrates both embryonic development and tissue homeostasis in adults . ImpoThe canonical HH signaling pathway refers to GLI activation through a HH-Patched 1 (PTCH1)-SMO axis . In the There are three GLI proteins: GLI1, 2, and 3 . Among tIn addition to canonical HH signaling, GLI activity is also regulated via mechanisms independent of the SHH-PTCH1-SMO axis . Such noThe transcription of GLI-target genes is achieved by cooperation between GLI proteins and other transcriptional co-factors, including general transcription machinery, epigenetic regulators, and other transcription factors . Many ofMultiple components of the general transcription machinery associate with GLI proteins to regulate their transcriptional activity, including the preinitiation complexes, Mediator, and Cohesin . Though The recruitment of RNA polymerase II (POLII) requires the stepwise assembly of multiple-protein preinitiation complexes, which generally starts with the recruitment of Transcription factor II D (TFIID) by specific transcription factors (reviewed in ). TFIID Mediator is a large protein complex comprised of 26 subunits that is reversibly associated with the functionally independent four-subunit Mediator kinase module (MKM) . MKM is Aside from its well-known function in chromosome segregation, Cohesin can also cooperate with CCCTC-binding factor (CTCF) to enhance gene transcription by forming chromatin loops that enable long-range communication between enhancers and promoters ,123. In Epigenetic regulation refers to the modifications of histones or DNA that regulate chromatin structure and gene expression . These mGLI1 promoter to activate its transcription [GLI1 [ST3GAL1 [Bromodomain-containing 4 (BRD4) is a well-established epigenetic activator that recognizes histone acetylation, a hallmark of active gene transcription . It assocription . SOX2-mecription ,90,91. Ton [GLI1  and ST3G[ST3GAL1 . Importa[ST3GAL1 ,118,128 GLI1 [GLI1 promoter to inhibit BRD4 recruitment and the subsequent expression of GLI1 [GLI1 and GAS1 [GLI1 and GAS1 expression and, as a result, attenuates GLI activity [GLI1 transcription [Histone acetylases (HATs), histone deacetylases (HDACs), Protein Arginine Methyltransferase 5 (PRMT5), and SET Domain Containing 7 (SETD7) are categorized together in this section because they modify both histones and GLI proteins with opposing effects on GLI activity . SpecifiGLI1 . AnotherGLI1 . In contGLI1 . ConsistGLI1 . In addi of GLI1 . Two repand GAS1 ,78. PRMTactivity ,78. Simicription .Interestingly, HATs, HDACs, PRMT5, and SET7 can also modify GLI activity directly via directly posttranslational modifying GLI proteins . Though Polycomb repressive complex 2 (PRC2) is an epigenetic inhibitor, functioning through mono-, di- and tri-methylating H3K27 . It is lSwitch/sucrose non-fermentable (SWI/SNF) complexes function to enhance the transcription of specific genes by sliding nucleosomes along genomic DNA or ejecting nucleosomes from DNA to expose that region of DNA . SWI/SNFPTCH1 and HHIP, are transcriptionally repressed by DNMT1 [A Ubiquitin like with PHD and ring finger domains 1 (UHRF1)-DNA methyltransferase 1 (DNMT1)-proliferating cell nuclear antigen (PCNA) complex functions in DNA methylation inheritance, copying the CpG methylation pattern from the template strand into the newly synthesized DNA strand during replication . Importaby DNMT1  or the Uby DNMT1 , respectIn general, gene expression is regulated by the cooperative binding of multiple transcription factors (TFs). This enables precise gene expression regulation and cooperative gene activation . In thisSerum response factor (SRF) was identified as a GLI co-activator, based on its enriched chromatin occupancy at GLI-target genes and its selective overexpression in SMO-inhibitor-resistant BCC . In BCC,JUN itself [JUN, JUNB, JUND, and FOSL2 is enriched [JUN, JUNB, JUND, and FOSL2 levels or treatment with AP-1 inhibitors (T5224 and SR11302) decreases GLI1 expression and viability in BCC cells ([Activator Protein 1 (AP-1) is a heterodimeric transcription factor complex composed of proteins belonging to the JUN  and FOS families  . AmN itself . This GLN itself . In SMO-enriched . ConsistC cells . The SMtivation . In conttivation . The reptivation . How difGLI1 locus with FOG1 , and FOG1 knockdown relieved this inhibition of GLI1 expression [Six GATA binding proteins (GATA1-6) have been identified, which fall into two subgroups: GATA1/2/3 and GATA4/5/6 . While Gpression . Thus, jWhile SUFU is not a transcription factor, we have included it in this section because of its ability to repress GLI transcriptional activity in the nucleus. SUFU associates with all three GLIs ,101 to rATOH1 is overexpressed in SHH-MB, where it co-occupies similar genomic loci as GLI2 [NANOG knockdown can decrease GLI1 expression [Besides the well-established multi-protein GLI transcriptional complexes discussed thus far, two additional GLI transcriptional co-factors  and Nanog homeobox (NANOG)) have been described.  as GLI2 . Consist as GLI2 . NANOG i as GLI2 . Furtherpression , suggestpression .In this review, we have focused on the regulation of GLI-mediated transcription, the final step in the HH signaling pathway. We have described a series of multi-protein transcriptional complexes capable of activating or repressing GLI transcriptional activity in a context dependent fashion. Although we categorized GLI transcriptional cofactors into distinct complexes, recent models of transcriptional regulation have suggested that a high local concentration of transcription complexes, including the general transcription machinery, epigenetic regulators, and TFs, may all be present in dynamic condensates or hubs (reviewed in ). Thus,


Clostridium difficile) and for the pilM  riboswitches and their respective di-nucleotides c-di-GMP and c-GAMP. Binding capability showed a 25 nucleotide (nt) long window for pilM and a 61 nt window for Cd1. Within this window, binding affinities ranged from 35 μM to 0.25 μM spanning two orders of magnitude for Cd1 and pilM showing a strong dependence on competing riboswitch folds. Experimental results were incorporated into a Markov simulation to further our understanding of the transcriptional folding pathways of riboswitches. Our model showed the ability to predict riboswitch gene regulation and its dependence on transcription speed, pausing and ligand concentration.Riboswitches are gene regulatory elements located in untranslated mRNA regions. They bind inducer molecules with high affinity and specificity. Cyclic-di-nucleotide-sensing riboswitches are major regulators of genes for the environment, membranes and motility (GEMM) of bacteria. Up to now, structural probing assays or crystal structures have provided insight into the interaction between cyclic-di-nucleotides and their corresponding riboswitches. ITC analysis, NMR analysis and computational modeling allowed us to gain a detailed understanding of the gene regulation mechanisms for the Cd1 ( Riboswitches are RNA regulatory elements that modulate gene expression. Metabolites of low molecular weight act as inducer molecules that bind to riboswitches. The majority of riboswitches are located in the 5′-untranslated region (5′-UTR) of mRNAs . The indRiboswitches contain two parts, an aptamer domain for ligand recognition and an expression platform for gene regulation. The regulatory power of riboswitches stems from distinct conformational transitions induced upon ligand binding. In case of transcriptional riboswitches, regulation has been shown to occur during transcription either by formation of a terminator state or avoidance of this conformation. These non-terminator conformations are commonly summarized as antiterminator conformation . DependiVibrio cholera and Clostridium difficile  . Externaifficile ,11. c-diormation ,12. Seveifficile . During reatment ,18. The and TcdB . In the proteins . Interfeproteins .et al. were recently able to demonstrate that c-GAMP riboswitches bind c-di-GMP using a stably protonated adenine in the ligand binding pocket . The plasmids were transformed in E.coli DH5α cells and grown in LB medium. The harvested plasmids were sequenced. The plasmids were PCR-amplified to generate DNA templates for transcription. 2′-methoxy modified reverse primers were used in the transcription to generate specific lengths of RNA with high 3′-end homogeneity, the 2′-OMe-modified nucleotides are marked with square brackets in ®  using Phusion polymerase. Sample integrity was ensured through native and denaturing PAGE (Plasmids were purchased from eurofins® containing the full-length native sequence of the c-di-GMP-sensing riboswitch Cd1 from ing PAGE .in vitro using T7 RNA polymerase. DNA template PCR mixtures were directly applied for transcription without further purification. Transcription mixtures contained transcription buffer (250 mM Tris–HCl pH 8.1), 2 mM spermidine, 20 mM dithiothreitol (DTT), 10 or 20% (v/v) DMSO, NTPs were added in sequence adjusted amounts up to 6 mM for the most occurring, 15 to 30 mM Mg(OAc)2, 1 u/ml yeast inorganic pyrophosphatase (New England Biolabs) and 40 μg/ml T7 RNA polymerase. DMSO and Mg(OAc)2 concentrations were optimized for preparative transcriptions .All transcriptions were conducted et al. in 3–10 kMWCO (molecular weight cut-off) centrifugal concentrators . These NMR buffer conditions were chosen instead of the standard potassium phosphate buffer regularly used for nucleic acid NMR, since c-di-GMP is known for its ability to form G-quadruplexes with K+ ions in solution. before NMR and ITC measurements.13C/15N-labeled c-di-GMP provided by Heiko Keller  or 3 mm NMR tubes for automation (Bruker). Bruker AV600, AV700, AV800, AV900 and AV950 spectrometers, equipped with cryogenic probes were used for NMR experiments. Data were processed with Bruker Topspin 3.5 (Bruker Biospin) and Sparky 3.14  or 50 mM KCl (pilM), 5–10% v/v Drky 3.14 . In 1H 1 spectra , water s spectra .kon and koff rates was carried out using the equilibration time curve (ETC) fitting of the KinITC software . The RNAs were provided in a concentration range from 50 to 80 μM in a measurement volume of 205 μl. Individual measurement condition for every ITC measurement are shown in apo state with a ligand free aptamer, the terminator for pilM and the antiterminator for Cd1 and as third state a holo state where ligand is bound to aptamer. The kinetics of transitions between states are modelled by first order rate equations. The three states for each length are interconnected through a total of six rates. The apo holo transitions are described by the corresponding kon and koff rates determined in ITC measurements. In ITC data collected for numerous pilM riboswitch transcript lengths, a constant kon rate was observed. This finding was extrapolated to the Cd1 riboswitch and also for states which did not allow an accurate kon rate determination due to low affinity. The koff rates were derived by dividing the KD with the constant kon rates. Constructs without measured KD were interpolated using the mean of adjacent KD values. The apo terminator/antiterminator transitions used the difference in formed base pairs to derive transition rates. This procedure utilized the stability predictions published by Fürtig et al.  = 9.5 ppm, δ(13C) = 156 ppm) lies outside the typical chemical shift region. Additional cross peaks from G62 to A28 enabled an unambiguous assignment of the palindromic region.The pilM riboswitch features a more stable P1 stem compared to the Cd1 riboswitch. An almost complete imino assignment could be accomplished through a combination of gous RNA  with andgous RNA . The NMRgous RNA . The ide14–75 aptamer showed that almost all imino signals of the entire pilM aptamer domains originate from nucleotides found in the P2 and P3 stem . Th. Thet al. Figure . Our mod. Figure . The sta. Figure . As a co. Figure . In the terature . To estiterature  and C. Uterature .For the pilM simulation we find that at low c-GAMP concentration the change in transcription speed results in the population of 80–100% terminator fold and absence of the holo state Figure . Under hV. cholera  and a holo population of 80% for pilM and 95% for Cd1 for high ligand concentration (100 μM), respectively. Beyond the impact of ligand concentration, we investigated the impact of transcription speed, pause sites and the relevance of the rate of closing base pairs. Transcription speed markedly affects the regulatory function of both riboswitches, always favoring the ligand-bound states at low speeds. This is in line with the idea that lower speeds allow for a longer presence of binding-competent states that increase the likelihood of forming a stable and refolding-resistant ligand-bound RNA complex. Based on the ITC derived koff rates, the stable ligand-bound RNA complexes have half-life times ranging from 25 to 190 s. These stabilities make ligand release during the short transcription time windows unlikely. Interestingly, the authors of the translational Vc2 riboswitch assume that it is also kinetically controlled due to the short lifetime of mRNAs in prokaryotic cells (Our Markov modeling of the Cd1 and the pilM riboswitches provide a unifying model for the gene regulatory function of the two riboswitches despite their opposite functions to be either an ON- or an OFF-switch. We could show high sensitivity to ligand concentration within the ranges of changes in concentration associated with lifestyle changes reported in the literature . Both riic cells . They fuic cells . The pilic cells . Adding apo to terminator and antiterminator folds. These rates are usually in the ns range (Within the conformational transitions in riboswitches, the rate of base pair closing is by far the fastest conformational step during RNA refolding. Base pair closing drives the transition from ns range ,66. Accoet al. and Steinert and Sochor et al. to discuss different structural arrangements (TA structure in a time window of 1.2 s, the Cd1 riboswitch has a 3.0 s window presenting the PA structure. Next, pilM starts to fold metastable PT[AH] terminator after 0.4 s compared to 1.5 s for Cd1 until transiently stable P[AT] antiterminator folding occurs. We differentiate between different degrees of RNA structure stability. While metastable structures can be readily refolded through ligand addition, transiently stable structures must be stable throughout the transcription process to enable the riboswitch function. The transiently stable Cd1 antiterminator structures will refold to form terminator after extended periods of time. At high ligand concentration, even before the terminator PT[AH] or antiterminator fold P[AT] can occur, the two riboswitches reach different levels of holo state population. The Cd1 riboswitch reaches 95% holo state population [PA] before the antiterminator conformation is folded. PilM on the other hand reaches 55% holo population [PTA] before terminator fold can occur. This results in pilM having a contested apo state PTA at the point of decision that even in the high ligand regimes folds into 20% terminator P[TAH], compared to the 5% antiterminator fold P[ATH] observed for Cd1. These results show that an early uncontested population of the holo state is important for both riboswitches to function in their allocated timeframe. Interestingly, the large difference in the time available for ligand binding does not result in large population differences of their holo states at full length challenging the idea that a larger time frame increases the likelihood of ligand binding under these conditions. Yet, there remain uncertainties in our chosen parameters. There are no data available on transcription speed and on life style-dependent CDN concentration in addition to local concentration effects at the site of transcription due to known strong localization of cyclases and phosphordiesterases at bacterial poles (The Markov model yields a comprehensive picture of the timeframes necessary for riboswitch-regulated gene transcription Figure . We use ngements ,37,67. Tal poles .TA]H/PTAH ON signal while being OFF according to the simulations.Riboswitch switching efficiency is imperfect, and we refer this erroneous signal transfer as leakiness. The additional time observed for Cd1 caused by the transcription of the antiterminator sequences reduces the amount of leakiness Cd1 signaling linked to structures forming that do not correspond to the correct ligand-induced switch state. Our model predicts a remarkably low leakiness for both riboswitches in the absence of cognate ligand. The Cd1 riboswitch will signal under 2% OFF signal as [PA][TH]/PA[TH] while being ON and the pilM riboswitch will only signal around 6% [Pin vitro single-round transcription assays for pilM (This low leakiness was also observed in for pilM . The leaIn summary, in this contribution, we delineate the secondary structures of several transcript lengths through NMR spectroscopy. The measurement of binding affinities and kinetics of the transcript lengths allow us to incorporate these biophysical data into a cotranscriptional model. This model predicts the regulatory output as function of transcription speed, base pair closing speed, pausing and concentration changes of the inducer molecules, resulting in an understanding of the key nucleotides and the temporal dynamics of state population required for riboswitch function. These biophysical studies provide a benchmark to understand the coupling of kinetics to function of kinetic riboswitches and their transcriptional intermediates.http://unafold.rna.albany.edu/?q = mfoldmFold: http://www.analyticalultracentrifugation.com/sedphat/download.htmSedphat: http://biophysics.swmed.edu/MBR/software.htmlNITPIC: https://www.cgl.ucsf.edu/home/sparky/Sparky: https://www.bruker.com/service/support-upgrades/software-downloads/nmr/free-topspin-processing/nmr-topspin-license-for-academia.html.Topspin: gkac514_Supplemental_FileClick here for additional data file.


Given the importance of the epithelial-to-mesenchymal transition in tumor progression and the pivotal role of ZEB1 as a transcriptional repressor during this process, the regulation of ZEB1-targeted gene expression is an active area of investigation. Diverse signaling pathways converge to induce ZEB1 activity, but few studies have focused on the enzyme-dependent modifications of ZEB1 that occur after its translation from mRNA . In addition to outlining the current knowledge in the field, we outline several questions regarding the PTM-mediated regulation of ZEB1 that remain unanswered. Thus, the areas of research covered in this review paper will provide a noteworthy conceptual advancement in our understanding of ZEB1′s biological function, as well as its post-transcriptional and post-translational regulation. Furthermore, the review will enhance the development of treatment strategies by identifying knowledge gaps in ZEB1′s regulatory mechanisms that could potentially be targeted to prevent and treat metastasis in cancer patients.Zinc finger E-box binding homeobox 1 (ZEB1) is a pleiotropic transcription factor frequently expressed in carcinomas. ZEB1 orchestrates the transcription of genes in the control of several key developmental processes and tumor metastasis via the epithelial-to-mesenchymal transition (EMT). The biological function of ZEB1 is regulated through pathways that influence its transcription and post-transcriptional mechanisms. Diverse signaling pathways converge to induce ZEB1 activity; however, only a few studies have focused on the molecular associations or functional changes of ZEB1 by post-translational modifications (PTMs). Due to the robust effect of ZEB1 as a transcription repressor of epithelial genes during EMT, the contribution of PTMs in the regulation of ZEB1-targeted gene expression is an active area of investigation. Herein, we review the pivotal roles that phosphorylation, acetylation, ubiquitination, sumoylation, and other modifications have in regulating the molecular associations and behavior of ZEB1. We also outline several questions regarding the PTM-mediated regulation of ZEB1 that remain unanswered. The areas of research covered in this review are contributing to new treatment strategies for cancer by improving our mechanistic understanding of ZEB1-mediated EMT. Cancer is the second leading cause of mortality in the United States . MetastaCDH1 gene, which codes for E-cadherin and acts at cell–cell adherens junctions, and the miR-200 family genes, which both repress and are repressed by ZEB1 to create a double-negative feedback loop . ThUbiquitin is a small protein that regulates the processes of other proteins. During ubiquitination, a ubiquitin molecule binds to a substrate protein to modulate its functionality. Ubiquitination is a reversible process with the subtraction of ubiquitin molecule(s) catalyzed by deubiquitinating enzymes (DUBs or deubiquitinases) . The abeThe sumoylation pathway results in the reversible attachment of a small ubiquitin-like modifier (SUMO) to a lysine residue in the protein. Intriguingly, sumoylation significantly influences the function of transcription factors even when a minor portion of the protein is sumoylated . PolycomThe acetylation of proteins is catalyzed by histone acetyltransferases, including P300, CBP, and PCAF, which contain acetyl-transfer capability of the substrate. Inversely, histone deacetylases (HDACs) remove acetyl groups to modulate protein function, interaction, and stability. Several studies have acknowledged that transcription, signaling pathways, and diverse intracellular events are controlled by the acetylation status mediated by histone acetyltransferases and HDACs . The abiCDH1 transcription. Instead, ZEB1 deletion decreases histone acetylation on the promoters of Dll4 and NOTCH1, thus suppressing their transcription and downregulating NOTCH signaling. NOTCH signaling is a critical regulator of vascular development, migration, lumen formation, and cell differentiation [ZEB1 has a prominent role in the regulation of bone metabolism, promoting both angiogenesis and osteogenesis. ZEB1 also associates with P300 and CBP in bone endothelial cells and co-occupies the promoters of Dll4 and NOTCH1 to foster their histone acetylation, which subsequently activates the transcription of Dll4 and NOTCH1. Interestingly, in bone endothelial cells, ZEB1 deletion does not affect ntiation . Taken tCDH1 and the miR-200 family, and increase the expression of mesenchymal proteins, such as vimentin. In breast cancer, ZEB1 represses the transcription of the CDH1 gene, by targeting the E-box of the gene promoter [CDH1 promoter caused the recruitment of the CtBP to ZEB1’s CID, which ultimately suppresses CDH1 transcription and promotes tumor progression and metastasis . On the basis of our own and others’ studies, we propose that post-translational mechanisms play a prominent role in defining ZEB1 interactions to regulate cell fate and tissue-specific outcomes. The roles that phosphorylation, acetylation, ubiquitination, sumoylation, and other modifications have in regulating the behavior of ZEB1 require additional investigation.Given its numerous protein partners and target genes, ZEB1 is a pleiotropic transcription factor whose function largely depends on cellular, genetic, and epigenetic factors. Protein–protein interaction is one of the main classes of therapeutic targets for cancer. The chromatin configuration  and the presence or absence of tissue-specific cofactors strongly influence the function of ZEB1 as a transcriptional repressor and/or activator. This may explain why targeting ZEB1 remains a challenge, as ZEB1 promotes complementary or synergistic effects in some cells, but apparently has opposite functions in others. Although CtBP, BRG1, and NuRD work independently as ZEB1 corepressors, each efficiently represses E-cadherin ,14,15. M−/−) cells also stimulate EMT [CBP and P300, through their acetyl-transfer activity, participate in the transcriptional regulation of ZEB1 by contributing to the formation of protein complexes. However, the contribution of P300 in tumor progression and metastasis is controversial. Some studies have shown that elevated P300 levels lead to EMT , whereaslate EMT ; the varlate EMT . Thus, aFinally, ZEB1 is predicted to have a molecular weight of 125 kDa; however, numerous groups report that ZEB1 instead exists in two different species—a 125 kDa and 190,250 kDa band by gel electrophoresis. This observation supports the hypothesis that ZEB1 is subject to extensive PTMs, including acetylation, phosphorylation, and ubiquitination. However, these modifications do not entirely account for the large inconsistency between the molecular weights of ZEB1. The current lack of structural data for ZEB1 hamper drug development to target the protein or its interactions. In addition to PTMs, dimerization is a common mechanism that can unlock novel functions, promote stability, or enhance DNA binding . TranscrHerein, we highlighted the current understanding of the biological function of ZEB1, as well as its post-transcriptional and post-translational regulation. Several questions regarding ZEB1’s regulation by PTMs remain unanswered, and our goal is to further elucidate the novel molecular mechanisms of ZEB1. We identified numerous reports that solidified the role of ZEB1 as a transcriptional repressor and/or activator by the recruitment of cofactors. ZEB1, through the direct action of PTMs, promotes complementary or synergistic effects in some cells but has a seemingly opposite role in others. Studies that identify all of ZEB1’s interacting partners and any PTMs that might modulate its function and molecular associations are required to elucidate ZEB1 protein stability, interaction, and dimerization with an eye towards developing treatment strategies to combat metastasis.


Circular RNAs (circRNAs) are a class of closed circular non-coding RNAs widely exist in eukaryotes, with high stability and species conservation. A large number of studies have shown that circRNAs are abnormally expressed in various tumor tissues, and are abundant in plasma with long half-life and high specificity, which may be served as potential tumor biomarkers for early diagnosis, treatment and prognosis of malignant tumors. However, the role of circRNAs is still poorly understood in gastric cancer. This article reviews the research progress of circRNAs in gastric cancer in recent years so as to explore the relationship between circRNAs and the occurrence and the development of gastric cancer, and provide new ideas for the diagnosis and treatment of gastric cancer. CircRNAs are mainly divided into four categories according to their sources: exonic circRNAs (ecircRNAs) , introniExonic circRNAs can be formed by reverse splicing of a single exon or multiple exons. The specific splicing methods are mainly divided into the following two models: the lasso-driven circularization model  and the Most exon-derived circRNAs are mainly formed through a lasso-driven circularization model. The mRNA precursor (pre-mRNA) is partially folded during transcription, bringing the distance between originally non-adjacent exons closer, resulting in exon skipping, thereby the crossed region forms the circRNA intermediates, and then forms the so-called exon circRNA through splicing.During primary transcription, the intron regions on both sides of the primary mRNA(pri-mRNA) transcription product are complementary to each other due to the presence of reverse complementary sequences, such as ALU repeat elements, resulting in complementary pairing of introns and mediating the reverse splicing of exons and folding them together, and then partially cutting off introns to form some exonic circular RNAs.The formation of intronic circRNAs is formed by the splicing of introns, and can be divided into group I introns, group II introns, and nuclear pre-mRNA introns  according to different splicing methods. Most commonly, intron splicing during exonic circRNA formation is mediated by nuclear pre-mRNA introns, whereas intron circRNA formation is associated with class I introns and class II introns .CircRNAs formed by intron cyclization can be produced in two ways: one is that an exogenous guanine nucleotide attacks the 5’ splice site, excises the 5’ exon and connects to the intron, and then the 3’ hydroxyl end of the excised 5’ exon attacks the 3’ splice site, releases the linear intron and joins the exons, and finally, the linear intron releases the 3’ tail to form a 2’-5’ junction intronic circular RNAs. The other is to first release the 3’ exon, and then the 2’ hydroxyl end of the intron attacks the 5’ splice site to generate circRNA..An exogenous guanine nucleotide is used as a nucleophile to attack the 5 ‘ end splicing site, the exon at the 5’ end is excised by transesterification, and the exogenous guanine and intron are connected to each other, while the 3’ hydroxyl (-OH) above the cleaved 5’ terminal exon attacks the 3’ terminal splice site, resulting in the release of the linear intron by excision, the exons are connected to each other. Then the released linear intron removes its own 3’-terminal tail, and the remaining 2’-OH and 5’-terminal splice sites are connected to form a phosphodiester bond to generate an intronic circRNA , 11 . On the one hand, RBPs can promote the formation of circRNAs by binding to target sites in the flanking intronic regions of pre-mRNA. For example, the splicing factor Quaking promotes circRNA formation by binding to targets upstream and downstream of circularized flanking intron regions on SMARCA5 pre-mRNA . The immIn addition, circRNA formation occurs in synergistic transcription and post-transcriptional coupling with Pol II transcription , 19. A s.Some circRNAs contain miRNA binding sites, and act as competing endogenous RNAs (ceRNAs) to negatively regulate miRNA activity by competing miRNA binding sites, thereby reducing the inhibitory effect of miRNAs on their downstream target genes. The most representative example is the antisense transcript of cerebellar degeneration-related protein-1 (CDR1as), also known as ciRS-7, which contains 74 selectively conserved miRNA-binding sites and acts as a molecular sponge for miR-7. Another circular RNA circSRY, exists specifically in mouse testis tissue, contains 16 binding sites for miR-138 , 23. AnoBesides acting as miRNA sponges and specific inhibitors of target miRNAs, circRNAs can also act as miRNA reservoirs to stabilize or activate miRNA functions. For example, ciRS-7 can be considered as miR-7 reservoir. After miR-671 cleaves ciRS-7, miR-7 is released in large quantities and its activity increases rapidly, consequently resulting in the effective inhibition of miR-7 targets. At this time, ciRS-7 becomes a “miR-7 reservoir” and activates miR-7 function . ERβ rep.RBP is a class of proteins widely involved in gene transcription and translation in organisms, and the interaction with RBP can be considered as an important part of the function of circRNA. RBPs function in splicing, processing, folding, stability and localization of circRNAs by interacting with the circularized splice junctions of circRNAs . For exa.Nuclear EIciRNAs and ciRNAs may be involved in the transcriptional regulation of genes. Studies shown that the transcription of the parental gene was inhibited after knockdown of EIciRNA. Mechanistic studies have shown that EIciRNAs such as circEIF3J and circPAIP2 form EIciRNA-U1 snRNP complexes by interacting with U1 small nuclear ribonucleoprotein (U1snRNP), and interacte with RNA polymerase II (Pol II) on the promoters of EIciRNA parent genes to promote transcription of the parental gene. In addition, when the above RNA-RNA interaction is blocked, the interaction between EIciRNA and Pol II is disrupted, resulting in a subsequent decrease in the transcription of the EIciRNA parental gene . BesidesAlthough circRNAs have always been defined as non-coding RNAs, some circRNAs have internal ribosome entry site elements (IRES)  or prokaMechanistic studies have shown that CircRNA can not only bind to mRNA, but also act as a regulator of mRNA translation and stability , 40. For.In addition to the above mechanisms, circRNAs may also initiate the translation process through N6-methyladenosine (m6A) modification. CircRNAs are rich in m6A consensus motifs, and a single m6A site is sufficient to drive translation initiation. One study found that m6A-driven circRNA translation was widespread through ribosome sequencing analysis and mass spectrometry detection, and many endogenous circRNAs had translation potential   is one of the most common malignant tumors of the digestive system in the world, with higher morbidity and mortality in our country . AlthougThe expression of some circRNAs is up-regulated in gastric cancer Table 1.Some circRNAs are down-regulated in gastric cancer. For example, circRNA LARP4 is lowly expressed in gastric cancer tissue, and its expression level is significantly correlated with the pathological stage and overall survival rate of gastric cancer patients . Hsa_cirin vitro by acting as a miR-155 molecular sponge, and can promote tumor growth in vivo  Conception and design: QZ. (II) Administrative support: QZ. (III) Provision of study materials or patients: P’AD, PY, PL, YT. (IV) Collection and assembly of data: P’AD, PY. (V) Data analysis and interpretation: P’AD. (VI) Manuscript writing: All authors. (VII) Final approval of manuscript: All authors.This work was supported by the Cultivating Outstanding Talents Project of Hebei Provincial Government Fund (No.2019012); Hebei public health committee county-level public hospitals suitable health technology promotion and storage project (No.2019024); Hebei University Science and Technology Research Project (No.ZD2019139)The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.