The ubiquitous family of dimeric transcription factors AP-1 is made up of Fos and Jun family proteins. It has long been thought to operate principally at gene promoters and how it controls transcription is still ill-understood. The Fos family protein Fra-1 is overexpressed in triple negative breast cancers (TNBCs) where it contributes to tumor aggressiveness. To address its transcriptional actions in TNBCs, we combined transcriptomics, ChIP-seqs, machine learning and NG Capture-C. Additionally, we studied its Fos family kin Fra-2 also expressed in TNBCs, albeit much less. Consistently with their pleiotropic effects, Fra-1 and Fra-2 up- and downregulate individually, together or redundantly many genes associated with a wide range of biological processes. Target gene regulation is principally due to binding of Fra-1 and Fra-2 at regulatory elements located distantly from cognate promoters where Fra-1 modulates the recruitment of the transcriptional co-regulator p300/CBP and where differences in AP-1 variant motif recognition can underlie preferential Fra-1- or Fra-2 bindings. Our work also shows no major role for Fra-1 in chromatin architecture control at target gene loci, but suggests collaboration between Fra-1-bound and -unbound enhancers within chromatin hubs sometimes including promoters for other Fra-1-regulated genes. Our work impacts our view of AP-1. O-tetradecanoylphorbol-13-acetate (TPA)-responsive element , ATF-containing dimers preferentially bind to cAMP-responsive element (CRE) whereas MAF-containing dimers bind either MARE I or MARE II motifs that are extensions of TRE and CRE motifs, respectively (AP-1 is a ubiquitous family of dimeric transcription factors (TF) that was identified >30 years ago  and was reported to contribute to invasion in TNBCs  for two main reasons. First, Fra-1 is overexpressed in most TNBCs where it plays a central part in their aggressiveness ,19,31–36in TNBCs , we also2 atmosphere at 37°C as previously described . They were cultured in Dulbecco's modified Eagle's medium containing 10% fetal calf serum and penicillin/streptomycin (100 μg/ml each) in a humidified 5% COThe anti-Fra-1 (sc-376148X and sc-28310X), anti-Fra-2 (sc-13017X), anti-Pol II (sc-55492) and anti-GAPDH (sc-25778) antibodies were from Santa Cruz Biotechnology, as well as control IgGs (sc-2025). The anti-Fra-1 sc-376148X antibody was used in the ChIP-seq experiments of Figures For RNAi-mediated Fra-1 and/or Fra-2 depletion, we used pools of 3 siRNA directed to each protein  and low siRNA concentrations to minimize potential off-target effects without altering on-target ones see ref , we inclRNAs were extracted using the Qiagen miRNeasy kit according to the supplier's specifications. On-column DNAse I digestions were performed as described in the user's guide. Three independent replicates were prepared for each condition  and controlled for purity and integrity using the Agilent 2100 Bioanalyser with RNA 6000 nano LabChipkits (Agilent technologies). Only RNAs with neither signs of contamination nor of degradation (RIN > 9) were further processed to generate amplified and biotinylated sense-strand cDNA targets using the GeneChip WT PLUS Reagent kit from Affymetrix according to the manufacturer's specifications. After fragmentation, cDNA targets were used to probe Affymetrix GeneChip Human Gene 2.0 ST arrays, which were then washed, stained and scanned according to Affymetrix instructions . Importantly, RNA level changes were compared, not only to control cells transfected with equivalent amounts of control siRNA (siCTL), but also to cells that solely received the transfection reagent (NT) to eliminate false positive genes more stringently also see n = 3 each) files generated after array scanning were imported into the Partek Genomics Suite 6.6 (Partek) for preprocessing consisting of estimating transcript cluster expression levels from raw probe signal intensities. Analyses were performed using default Partek settings. Resulting expression data were then imported into R (http://www.R-project.org/) for further analysis. First, nonspecific filtering was applied to remove transcript clusters with no specified chromosome location. Then, box plots, density plots, relative log expressions (RLEs), and sample pairwise correlations were generated to assess the quality of the data. They revealed no outlier within the series of hybridizations. Principal component analysis (PCA) was also applied to the data set. The first two components of the PCA were able to separate samples, except the non-transfected controls, according to the conditions. Thus, the siRNA transfection was considered as the unique source of variability. Finally, the LIMMA package  (https://david.ncifcrf.gov/summary.jsp).A total of 21 CEL   and onlyet al.  of 1/100 diluted samples were measured and A280 nm-adjusted to 0.13 with the nuclei lysis buffer. For immunoprecipation of H3K4me1, H3K4me3 and H3K4me1, 150 μl of chromatin and 4.5 μl of the corresponding antibodies were used, whereas 400 μl of chromatin and 6.5 μl of antibody were used for CTCF immunoprecipitation and 600 μl of chromatin and 20 μg of the corresponding antibodies were used for that of Fra-1, Fra-2, Pol II and p300/CBP. Independent duplicates were prepared for all samples, and sequenced by the MGX genomic platform (Montpellier) using the Hi-seq2500 Illumina sequencer, except for the p300/CBP ChIPs where low amounts of chromatin were obtained and where the replicate samples were pooled before library preparation. p300/CBP ChIP-seqs conducted on MDA-MB-231 cells transfected with siCTL or siFra-1 were sequenced using the NovaSeq sequencer from Illumina. Data were aligned to the Homo sapiens Hg19 genome using the BWA-backtrack software, version 0.7.12–1039. Aligned reads were then processed using the R package PASHA (https://bioviz.org). Thresholding parameters are presented in Chromatin immunoprecipitations (ChIPs) were performed as previously described ,41 on MDge PASHA  and the et al. , 10 μl of anti-Fra-2 antibody (D2F1E), 6 μl of anti-p300/CBP antibody (Ab14984) and 3 μg of control IgG (sc-2025) were used per ChIP. Immunoprecipitated chromatin was purified as described in Moquet-Torcy et al.  and Supp6 cells and 720 U of the T4 DNA HC ligase (Life Technologies: EL0013). 3C libraries were sonicated . Samples were indexed to allow multiplexing using Illumina paired-end sequencing adaptors  and the Herculase II polymerase (Agilent: 600677). Oligonucleotide capture was then performed using 70-mer biotinylated DNA oligonucleotides designed at each of the studied target gene promoters (http://apps.molbiol.ox.ac.uk/CaptureC/cgi-bin/CapSequm.cgi)  . The hyb package  using th package .http://homer.ucsd.edu/homer/ngs/peakMotifs.html). ChIP-seq peak sequences were compared to randomly selected genomic fragments of the average region size and matched for GC content (findMotifsGenome.pl mybed.bed hg19 output_directory -size given).Motif enrichment was analyzed using HOMER  and the maximum scores within each sequence were used as predicted variables. An equal number of sequences was considered for each class (Fra-1 and Fra-2 peaks). 70% and 30% of all sequences were considered for learning and testing, respectively. We used the seqLogo R package  seqLogo Bioconductor, https://bioconductor.org/packages/seqLog) to reconstruct two PWMs from all occurrences of NFE2 with maximum scores in PF1 or PF2 peaks. To model the signal at F1F2 peaks, we first computed the log ratio log10(Y1/Y2) where Y1 and Y2 are the signals for Fra-1 and Fra-2, respectively. As for the classification, the maximal scores in each sequence calculated with all PWMs in the Jaspar database (662 PWMs) and all dinucleotide rates were used as predictive variables. For both classification and regression, a LASSO (Least Absolute Shrinkage and Selection Operator) regression was built using the cv.glmnet function from the glmnet R package, with 10-fold cross-validation and lambda.min.To run TFcoop , we firsHuman MDA-MB-231 cells have been employed in this study, as they constitute the most widely used and documented reference TNBC cell line model. Indeed, they show high fidelity with cells found in primary breast tumors with respect to, not only genomic and transcriptional aberrations , but alsFirst, we identified the transcriptomes regulated by Fra-1 and/or Fra-2. This was the first transcriptomic comparison of these two TFs within the same cellular context. MDA-MB-231 cells were subjected to RNAi to downregulate Fra-1 and Fra-2 either individually or together and mRNA level variations were characterized using Affymetrix GeneChip Human Gene 2.0 ST arrays  correspond to unambiguous TRE/AP-1 motifs . Calling for enriched regions . This revealed two interesting features. First, Fra-1 tends to bind regions richer in AT , TFcoop nevertheless unveiled limited, though notable, differences in Fra-1 and Fra-2 bindings at F1F2 sites . Consistently with the variables selected in the PF1 and PF2 classification task  in PF1, PF2 and F1F2 peaks  . TherefoThus, altogether, our data provide evidence that, in MDA-MB-231 cells, Fra-1 and Fra-2 show DNA-binding preferences at certain genomic sites. The molecular bases of this preference lie primarily in (limited) sequence variations within AP-1 TFBSs and, to a lesser extent, in the AT-content of the concerned regions. Importantly, in their vast majority, F1F2 peaks reflect neither the presence of several AP-1 TFBSs bound by the two Fra proteins nor direct binding of one Fra protein at the AP-1 TFBS and indirect protein-protein-mediated binding of the other Fra at another location within these peaks. Rather, they reflect the heterogeneity of the cell population we used in our experiments with turnover of Fra-1- and Fra-2-containing AP-1 dimers at the unique AP-1 site lying in these peaks.candidate active promoters (cAP), the 3 kb domains centered on RefSeq-annotated TSSs corresponding to protein-coding genes marked by H3K27ac, (ii) candidate inactive promoters (cIP), similar domains devoid of H3K27ac, (iii) candidate active enhancers (cAE), the regions marked by both H3K4me1 and H3K27ac but devoid of cAPs and cIPs, and (iv) candidate inactive enhancers (cIE), the regions marked by H3K4me1 but with low H3K27ac content and without cAPs and cIPs , nor well-defined reporting guidelines for these elements  and/or Fra-2 (90.5%) peaks fell into open chromatin domains . MoreoveThus, taken together, our data strongly suggested that Fra-1 and Fra-2 bind preferentially to candidate active regulatory elements in MDA-MB-231 cells with a much stronger propensity to bind enhancers than promoters.We next characterized further the molecular features of Fra-1- and/or Fra-2-bound cAEs (8388 elements) and cIEs (1730 elements) . The twoper se a good predictor of putative enhancer activity. Finally, p300/CBP mean signals at cAEs were the highest at F1F2 and PF1 peaks while very low at PF2 peaks  were stronger at cAEs than at cIEs and this was also the case for H3K4me1 and H3K4me3 marks Figure . Howevers Figure , which ss Figure ,41,56.Taken together, the above data pointed to marked molecular heterogeneity of candidate enhancers whatever the category of elements considered . Importantly, in addition to being more numerous, cAEs containing F1F2 peaks also displayed, on average, stronger marks of activity  than those containing PF1 and PF2 peaks.Next, we addressed whether Fra-1 could regulate p300/CBP recruitment at F1F2 cAEs. To this aim, we first compared p300/CBP signals in ChIP-seq experiments performed on MDA-MB-231 cells transfected with siCTL to those in cells where Fra-1 was down-regulated by siFra-1. Seventy-six percent of the p300/CBP peaks formerly found at F1F2 cAEs in MDA-MB-231 cells grown under standard culture conditions (see above) were also detected in MDA-MB-231 cells transfected with the control siRNA, indicating robust p300/CBP binding sites . RNAi-inTo confirm that Fra-1 can actually impact p300/CBP recruitment at certain F1F2 cAEs and, at the same time, address whether Fra-2 could also affect this recruitment, we ChIP-qPCR-assayed p300/CBP levels at 3 types of F1F2 cAEs after RNAi-induced Fra-1- or -Fra-2 down-regulation, i.e. at sites where p300/CBP recruitment was either increased, decreased or not affected by siFra-1 in our ChIP-seq experiments. As expected, Fra-1 and Fra-2 signals were found strongly reduced at all tested peaks upon transfection of their respective siRNAs and, in all cases, we confirmed the effects of siFra-1 on p300/CBP signals seen in ChIP-seq experiments , three were complementarily regulated by the two Fra proteins  and the others 28 were preferentially regulated by Fra-1 (EDN1 (downregulated by Fra-1) and RPSAP52 (upregulated by Fra-1) are presented in Figure To get insights on the regulation of Fra-regulated genes in MDA-MB-231 cells, we next aimed at identifying and characterizing the enhancers interacting with their promoters. We focused on Fra-1-regulated genes, as they are more numerous and were shown to have stronger expression changes in RNAi experiments than those controlled by Fra-2 Figure , which qby Fra-1 . Resultsby Fra-1 . The merpromoter-interacting regions (PIRs). For all genes, the most frequent interactions occurred in cis, i.e. on the same chromosome, within relatively confined regions that, overall, overlapped with topologically-associating domains (TAD) defined by Hi-C in other cell contexts  contained more than 1 ATAC-seq peak, with a total of 159 peaks. Among these 159 ATAC-seq peaks located within PIRs, 99 held histone marks of candidate active enhancers. This strongly suggested regulatory functions for the latter regions. In addition, 44% (44/99) of these open chromatin sites showing histone marks associated with transcription activity were bound by Fra-1 Figure . MoreoveFOSL1, CDCP1, SSFA2, PITPNC1, RPSAP52, HK2, MYO10, SOX9,SRGAP1) and 11 Fra-1-downregulated genes  had their promoter regions interacting with at least one Fra-1-bound PIR displaying active enhancer histone marks (FOSL1 (which encodes Fra-1) deserves a special mention, as it is known to be subjected to positive autoregulation in certain tumors where the Fra-1 protein is stabilized and transcriptionally activated due to multiple phosphorylations . Among them, however, PDE2A and ZNF114 might be genuine transcriptional targets of Fra-1, as our ChIP-seq indicated binding of Fra-1 at a cAE located in the viewpoint-proximal region for each of them. Their situation is reminiscent to that of HMGA1 that we have recently shown to be directly upregulated by Fra-1 in MDA-MB-231 cells via an intronic Fra-1-bound enhancer located close to the promoter . MoreoveKRT80 gene, harboring the promoter of the KRT81 gene; FOSL1 gene containing the CFL1 and MUS81 gene promoters; Finally, the analysis of PIRs also revealed that some of them harbor gene promoters of two possible types. On the one hand, some of these promoters belonged to genes shown to be regulated by Fra-1 in our transcriptomic analysis  a large fraction (24/35) of the genes we have studied are direct transcriptional targets of Fra-1 in MDA-MB-231 cells, (ii) the promoters of these genes can interact with a variable number of (very) distal enhancers showing marks of putative activity that may, or not, recruit Fra-1, (iii) their expression may result from the combined action of Fra-1-bound and non-Fra-1-bound enhancers and (iv) certain Fra-1-regulated genes may share common regulatory elements with (at least) another Fra-1-regulated gene located within the same TAD.We finally addressed how long-range enhancer/promoter interactions of Fra-1-regulated genes could be controlled. Due to its major role in the control of chromatin architecture ,60, we f(Δ Capture-C) did not reveal any drastic global changes in NG Capture-C signals between the two conditions but revealed limited and subtle signal modulations at various places  or Fra-2 (PF2) that, most often, also show low but detectable ChIP-seq signals for the other Fra protein. On the other hand, they also identified TFBSs binding both Fra-1 and Fra-2 efficiently (F1F2) with, however, some differences in relative signal intensities between the two proteins. So far, no difference in DNA motif-binding preference has ever been reported between Fra-1 and Fra-2. However, our herein machine learning analyses revealed that the two Fra proteins can show differential binding efficacies, not only at PF1 and PF2 peaks, but also at F1F2 peaks, although in a less pronounced manner. The identifiable molecular bases of binding preferences lay primarily in (limited) sequence variations between AP-1 TFBS motifs, and, to a lesser extent, to the AT-content of the surrounding regions. Importantly, even though our findings reveal that TF’s preferred combinatorics do not distinguish Fra-1 and Fra-2 bindings in MDA-MB-231 cells, they do not rule out the possibility of cooperation between Fra-1 or Fra-2, on one side, and other TFs, on the other side . MoreoveChIP-seq-  and ATAC-seq data indicated that Fra-1 and Fra-2 bind unfrequently to promoter regions and very frequently to candidate active enhancers located distally from promoters. This strengthened the currently emerging notion that AP-1 would most often behave as a ‘remote command’ with a major role for long-range interactions in its transcriptional actions and not as a ‘local switch’ proximal to gene TSSs –53. SecoIn this context, it is interesting to note that our RNAi/ChIP-seq experiments indicate that Fra-1 may have positive, negative or no effect on p300/CBP recruitment, depending on the F1F2 cAE considered. Along this line, fine analysis  of the Fper gene proposed by others  one single AP-1 motif is detectable at the vast majority of ChIP-seq peaks, (ii) the Fra proteins cannot homodimerize or heterodimerize between them and (iii) Fra-1 and Fra-2 ReChIP experiments conducted at certain F1F2 peaks argue against co-binding of Fra-1 and Fra-2 at the same sites, the most parsimonious explanation is that Fra-1- and Fra-2-containing AP-1 dimers turn over at these sites with our ChIP-seq data reflecting the average of Fra-binding events occurring in the population of analyzed cells. Moreover, we observed diverse transcriptional outputs  and different target gene configurations, in particular in numbers and positions of Fra-1- and/or Fra-2-binding PIRs, implying that the transcriptional actions of Fra-1 and Fra-2 at play are necessarily diverse. Taking this into account, different reasons for dynamic recruitment of Fra-1 and Fra-2 at the same TFBFs can be considered. For example, for genes complementarily regulated by the two Fra proteins but with only one PIR harboring a F1F2 peak, successive bindings of Fra-1- and Fra-2-containing AP-1 dimers might explain the complementary transcriptional effects of the two proteins, though the mechanistical reasons for AP-1 dimer turnover and differences in transcriptional outputs still need to be elucidated. For genes complementarily or redundantly regulated by the two Fra proteins with multiple PIRs harboring PF1-, PF2- and/or F1F2-containing cAEs, encounters of different PIRs at regulatory hubs would also provide a straightforward explanation. In contrast, the situation of genes preferentially regulated by Fra-1 that we analyzed by NG Capture-C is more puzzling, as most of the Fra protein-binding sites in the PIRs were of the F1F2 types. One possibility might be that only Fra-1 exerts a transcriptional action at these genes whereas Fra-2 remains neutral from a transcriptional standpoint for reasons that remain to be clarified. Alternatively, it cannot be ruled out that Fra-2 transcriptional potential is just masked by the much higher abundance of Fra-1. Whatever the explanation, future work will aim at clarifying these issues.As Fos family proteins can bind to DNA only in the form of heterodimers , an impoIn conclusion, we have investigated here how Fra-1 regulates its target genes in a TNBC context due to its crucial role in the aggressiveness of this tumor type. Consistently with its pleiotropic tumorigenic effects, it appeared to modulate positively and negatively a wide array of genes with however moderate effects on most of them. In a number of cases, this is achieved in coordination with Fra-2, which is the other Fos family member expressed in these tumors albeit at a much lower degree and involves, at least at certain sites, differences in AP-1 motif recognition by Fra-1 and Fra-2. Our data also strongly suggest that Fra-1 can be a direct activator or repressor depending on the gene, essentially via binding to remote gene enhancers. Fra-1-recruiting enhancers most probably collaborate with non-Fra-1-recrutiting enhancers within chromatin hubs that may, at least in certain cases, include more than one Fra-1-regulated gene promoter. Finally, at least at the Fra-1 regulated gene loci we studied, Fra-1 is unlikely to be a major actor of overall chromatin 3D architecture control. As diverse AP-1 components play major roles in many cancer types, a major question will be to clarify whether the above conclusions also apply to them.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146825).Transcriptome, ChIP-seq and NG Capture-C data are available on the GEO database, accession numbers GSE146825 .Fra-1 and Fra-2 ChIP-seq data are available on GEO database, accession number GSE132098 (p300/CBP ChIP-seq data in MDA-MB-231 cells transfected with either the control siRNA or the siRNA directed against Fra-1 are available on GEO database, accession number GSE163304.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163304.gkab053_Supplemental_FilesClick here for additional data file.


FMR1 gene is epigenetically inactivated following the expansion over 200 triplets of a CGG repeat (FM: full mutation). FMR1 encodes the Fragile X Mental Retardation Protein (FMRP), which binds several mRNAs, mainly in the brain. When the FM becomes methylated at 10–12 weeks of gestation, the FMR1 gene is transcriptionally silent. The molecular mechanisms involved in the epigenetic silencing are not fully elucidated. Among FXS families, there is a rare occurrence of males carrying a FM, which remains active because it is not methylated, thus ensuring enough FMRPs to allow for an intellectual development within normal range. Which mechanisms are responsible for sparing these individuals from being affected by FXS? In order to answer this critical question, which may have possible implications for FXS therapy, several potential epigenetic mechanisms have been described. Here, we focus on current knowledge about the role of DNA methylation and other epigenetic modifications in FMR1 gene silencing.Among the inherited causes of intellectual disability and autism, Fragile X syndrome (FXS) is the most frequent form, for which there is currently no cure. In most FXS patients, the  Fragile X syndrome  is the most common monogenic cause of inherited intellectual disability (ID). In the general population, the frequency of FXS males is approximately an estimated 1:4000, and that of affected females is 1:7000 [FMR1 was cloned in 1991, and it is located on the long arm of the X chromosome (in Xq27.3), where the folate sensitive fragile site FRAXA was originally described in affected individuals [FMR1 contains a polymorphic CGG repeat typically composed of 20–40 triplets. Based on the number of the CGG repeat, two major pathological alleles may be recognized: premutation (PM) alleles, which present with a number of triplets between 56 and 200, and full mutation (FM) alleles, for which the CGG repeat expands over 200 units. Cytosines of the FM allele are methylated, including those of the CpG island in the promoter region (methylated full mutation: MFM), thus silencing the gene. Premutation alleles, which are not methylated, confer an increased risk for both Fragile X Tremor Ataxia Syndrome , a neurodegenerative form of parkinsonism, and Fragile X Premature Ovarian Insufficiency , a causative form of premature menopause (before 40 years of age). Premutated females are also at risk of further CGG repeat expansion, generating FM alleles during oogenesis, and of having FXS children. FXS is almost exclusively caused by an MFM of the FMR1 gene, with other mutations of the gene being very rare. The DNA methylation follows the CGG expansion, leading to the transcriptional silencing of the gene. As a consequence of both dynamic mutation and epigenetic modification, Fragile X Mental Retardation Protein (FMRP) translation is absent or reduced (loss-of-function mutation) despite the persistence of an intact coding sequence of the gene. The absence of FMRP, an RNA binding protein mainly expressed in the brain, causes FXS. These three different clinical conditions associated with CGG instability are also known as FRAXopathies or/and Fragile X-related disorders [FMR1 gene, while FXTAS is mainly associated with a toxic gain-of-function effect of the FMR1 transcript [The causative gene ividuals ,5. The gividuals . In the me in Xq2.3, whereisorders . FXS is anscript .FMR1 gene contains approximately 56 CpG dinucleotides (CpG island), consensus sites for USF1/USF2 (E-Box) and α-Pal/Nrf-1 transcription factors, which positively regulate FMR1 transcription [FMR1 expression [FMR1 promoter region includes three initiator-like (InR) sequences, located about 130 nucleotides (nt) upstream the CGG stretch, lacking the canonical TATA box. The canonical transcription start site of the FMR1 gene is located approximately 50 nt from the CGG repeat. As the size of the CGG repeat expands into the PM range, the transcription start site is shifted further to the upstream sites [The promoter region of the cription ,12. Nextpression . The FMRam sites .FMR1 promoter, thus allowing for normal transcription. This methylation boundary is lost in MFM alleles, which are completely methylated in this region. The mouse genome conserves a similar methylation boundary, even if human and mouse genomes are only 46.7% identical in the 5′ region upstream the FMR1 gene. In the methylation boundary region binding sites for various nuclear proteins are located, including CTCF (CCCTC-binding factor), a well-known insulator. The binding of CTCF to its sites probably prevents the spreading of cytosine methylation towards the FMR1 promoter [Furthermore, a methylation boundary region is located around 650–800 nucleotides upstream the CGG repeat. Normal alleles present an upstream methylated region and downstream unmethylated CpGs up to the promoter .FMR1. Four major levels of epigenetic control should be considered to affect FMR1 gene transcription regulation: (1) cytosine methylation, (2) histone modifications, (3) chromatin remodeling, and (4) action of RNA transcripts.DNA methylation is not the only factor regulating the expression of de novo DNA methyltransferases and establish the normal methylation pattern during embryonic development [Fmr1 KO mice the level of MeCP2 protein in the cerebral cortex is increased, contributing to the Fxs locomotor hyperactivity phenotype. In MeCP2 KO mice, the FMRP levels are reduced, suggesting an interplay between the activities of these two proteins [The first DNA methylation at CpG dinucleotides constitutes the most important epigenetic mechanism used to transcriptionally silence the gene. This reaction is catalyzed by DNA methyltransferases (DNMTs): DNMT1 is responsible for maintaining DNA methylation by acting with its partner UHRF1 and presents a higher affinity for hemimethylated CpG dinucleotides than for unmethylated DNA . Its actelopment ,21. In telopment . Many ofelopment  and the elopment . In humaelopment . Methylaproteins . DNA metproteins .FMR1 gene silencing is not yet fully understood, its silencing seems to require all four levels of epigenetic regulation. Silenced alleles derived from FXS individuals present with a heterochromatic, non-permissive structure of the FMR1 locus, while transcriptionally active alleles, such as those of normal and PM carriers, exhibit a euchromatic, permissive configuration. In addition to cytosine methylation, which is the main factor in blocking transcription, FXS cells display histone modifications. Histone deacetylases (HDACs) catalyze histone 3 (H3) and 4 (H4) hypoacetylation; the enzyme histone methyl-transferase (HMT) Set9, in association with coactivator complexes, demethylates lysine 4 on histone 3 (H3K4) [FMR1 gene, recognized as the cause of FXS.Though the critical mechanism of 3 (H3K4) ,29,30; P3 (H3K4) ), and (b3 (H3K4) . Lastly,3 (H3K4) . TogetheFMR1 locus, FMR1 mRNA containing premutated CGGs tends to form secondary structures such as hairpins. These structures may cause the stalling of the 40 S ribosomal subunit, contributing to the translational deficit in PM alleles [FMR1 promoter region. Histone marks are similar to those of normal alleles: H3 and H4 are acetylated, trithorax group enzymes tri-methylate H3K4 (H3K4me3); H3K27 is di-methylated (H3K27me2), while H3K9 remains partially methylated. These UFM alleles may represent the epigenetic status of FXS cells before FMR1 gene silencing, at around 11 weeks of gestation [FMR1 alleles, including UFM carriers, is reported in Contrariwise, the acetylation levels of H3 and H4 in PM alleles are 1.5–2 times higher than those of normal controls  and are  alleles  and favo alleles . The tra alleles ,38,39. TFMR1 gene generated since its discovery in 1991, further evidence regarding epigenetic mechanisms that lead to its transcriptional silencing is needed. Chromatin remodeling factors are likely to be involved. One of these is CTCF, which binds to the FMR1 locus using four different binding sites [FMR1 transcription, with no changes in the DNA methylation status of the FMR1 promoter, both in normal and UFM cell lines [FMR1 transcription levels [Despite the great amount of information about the ng sites ,42. CTCFll lines , which sll lines . Though n levels , its oveFMR1 gene is indicative of a locus exposed to a complex transcriptional regulation. Most of our genome is not translated into proteins, while noncoding RNA (ncRNA) transcripts are particularly abundant in the brain [FMR1 locus. The most abundant antisense transcript, known as FMR1-AS1, is not expressed in FXS cells and is overexpressed in PM cells, exactly like the sense transcript [FMR1-AS1, consistently expressed at high levels in the brains of unaffected individuals, undergoes alternative splicing, polyadenylation, and exportation to the cytoplasm. Its transcription arises from two alternative promoters: one overlaps with the FMR1 bidirectional promoter and the other is mapped in the second intron of the FMR1 gene. Notably, the latter promoter is mostly used by PM and UFM cells, and it consequently spans the CGG expansion [FMR1 gene is spliced in normal and PM alleles using a non-consensus CT to AC splicing site. A specific alternative splicing of intron 2, also demonstrated in PM alleles, arises from a non-consensus CT–AC site. Interestingly, a homologous transcript to human FMR1-AS1 is present in the mouse genome, suggesting a conserved cellular function for the antisense transcript [The fine epigenetic control exerted on the he brain . Some ofanscript . FMR1-ASxpansion . Furtheranscript .FMR4, was identified by Khalil and colleagues. FMR4 arises in the antisense orientation with respect to the FMR1 locus and is particularly expressed in adult frontal cortex and hippocampus. Again, its expression is similar to that of FMR1 mRNA: FXS alleles do not transcribe FMR4, while PM alleles present slight overexpression compared to normal levels of transcription. Transfection experiments were performed in HEK293 cells to functionally correlate both transcripts: siRNAs against the FMR4 transcript and against FMR1 mRNA were transfected to downregulate transcription, while vectors that included their sequence were employed to overexpress them. None of these transfection experiments showed modifications of expression of the other transcript and/or functional correlation between both transcripts [An additional ncRNA, known as nscripts .FMR5 and FMR6 represent two additional ncRNAs arising from the FMR1 locus [FMR5 is a lncRNA transcribed 1 kb upstream of the FMR1 transcription start site in the sense orientation. Its transcriptional start site maps in the methylated region upstream of the methylation boundary. It is expressed in several human brain areas of unaffected and FXS individuals, as well as PM carriers. FMR6 is transcribed from the antisense strand overlapping with the 3′ UTR and the last three exons of the FMR1 gene. It is silenced in FXS and PM carriers. Moreover, FMR6 splices the two introns present between the exons 15–16 and 16–17, as for FMR1 mRNA. The splicing mechanism recognizes non-canonical consensus sites, as documented for the splicing of FMR1-AS1 [R1 locus . FMR5 isFMR1-AS1 .FMR1 transcription, though their exact role has not yet been clarified. For other loci lncRNAs or antisense transcripts induce the assembly of proteins involved in heterochromatin organization or direct the recruitment of silencing machinery [FMR1 gene through lncRNAs arising from the locus itself, acting either in cis, as demonstrated for the Kcnq1-Kcnq1ot1 gene cluster [in trans, as described for the Hox-HOTAIR gene clusters [FMR1 gene may be silenced by these complexes.The antisense transcripts may represent intermediaries in the epigenetic regulation of the achinery . One cou cluster , or in tclusters . This hyCEBPA gene, involved in hematological malignancies. The DNA methylation levels of the CEBPA locus and the transcriptional levels of an lncRNA of CEBPA, known as ecCEBPA (extra coding CEBPA), are inversely correlated. It was demonstrated that ecCEBPA transcript interacts with DNMT1, preventing CEBPA methylation and promoting CEPBA-mRNA transcription. This functional DNMT1–RNA interaction involves several loci, including FMR1 [FMR1 locus. Understanding the mechanisms that regulate the interaction between FMR1 locus and DNMT1 could uncover new targets for potential treatments. A representative model of DNMT1 interaction with DNA and transcription at the FMR1 locus is reported in Recently, it has been demonstrated that specific ncRNAs bind to DNMT1; these RNAs have been defined as DNMT1-interacting RNAs, depicting a new class of transcripts . A genoming FMR1 . These rBAMBI gene, which is a negative regulator of transforming growth factor β (TGF-β). They observed a decrease in R-loop formation that exposes the BAMBI promoter to the DNA methylation induced by DNMT1. After BAMBI silencing, the TGF-β signaling pathway is activated [FMR1 locus [FMR1 locus are associated with an increase of R-loop formation detected by a S9.6 anti-RNA:DNA hybrid antibody that has the ability to specifically bind R-loops. Through a DOX-inducible episomal FMR1 vector, it was secondarily demonstrated that R-loop formation is directly proportional to transcriptional activity. Taken together, these experiments support the assumption that there is a direct correlation between CGG repeat expansions and R-loop formation at the FMR1 locus [Recently, Cheung and colleagues showed that RNA:DNA hybrid formation promotes transcription by preventing methylation-induced silencing. The authors studied the ctivated . When thctivated ,54. Thesctivated . Recent R1 locus . R-loopsR1 locus . R-loop R1 locus —that infR1 locus . At a reR1 locus . R-loopsR1 locus ,61. SeveR1 locus ,62,63. IR1 locus . RecentlR1 locus .Fmr1-knockout models mimic some biochemical, histological, and behavioral findings of FXS patients. Though in vivo studies offer an important experimental tool for characterizing the cellular roles of FMRP, to date there have been no animal models that recapitulate the human process of CGG expansion and methylation. Human embryonic stem cells (hESCs) are a powerful tool in disease modelling thanks to their ability to recapitulate the early stages of human development [To further explore how the lack of FMRP causes the FXS phenotype, several FXS animal models have been developed, such as those of mouse , rat 6767, zebraelopment . Howeverelopment .FMR1 gene undergoes a heterochromatinization process, which involves Polycomb complex, G9a protein, and specific DNA methylation that seems to be guided by a Dicer-mediated siRNA mechanism. Following Dicer cleavage, local methyltransferase SUV39H, which mediates the H3K9me3, is responsible for the heterochromatinization of the FMR1 locus [FMR1 inactivation during development are not yet understood.hESCs show a unique epigenetic pattern critical for their self-renewal and pluripotency, as they display a largely permissive chromatin configuration with low levels of compact heterochromatin . FurtherR1 locus . HoweverFMR1 expression, and the presence of FMRP was characterized at different stages of gestation [FMR1 locus is responsible for the repression of FMR1 transcription and that methylation occurs early in embryogenesis, at around 11 weeks of gestation [Chorionic villi samples from full mutation fetuses were tested for estation . Apparenestation .FMR1 showed normal levels of transcription and translation, which means that the promoter region was unmethylated and exhibited active chromatin features.In line with these findings, in hESC lines obtained from a preimplantation FXS embryo , FMR1 shFMR1 mRNA level was observed. This decline occurred with the targeted multistep process of heterochromatinization involving firstly histone H3 methylation at lysine 9 or 27 and then DNA methylation [FMR1 locus in hESC and in corresponding differentiated cells. In a recent study [FMR1 silencing was monitored during in vitro neuronal differentiation over 60 days. Neurons derived from FXS hESCs showed a progressive decrease of the FMR1 mRNA level until it was totally absent after 51 days post neuronal induction. In particular, in differentiated FXS cells, the FMR1 promoter showed an increase of the repressive H3K9me3 mark, related to loss of the active H3K4me3 mark. Moreover, it was demonstrated that FMR1 mRNA was directly involved in silencing the FMR1 promoter [FMR1 mRNA includes the transcribed CGG-repeat sequence as part of the 5′ UTR and may hybridize with the complementary CGG tract of the FMR1 gene to form an RNA:DNA duplex. Using a small molecule, labeled 1a, the interaction between mRNA and the CGG-repeat portion of the FMR1 gene was found to be disrupted, thus preventing promoter silencing. These results provide a link between trinucleotide-repeat expansion and transcription, as well as a form of RNA-directed gene silencing mediated by RNA:DNA hybrid formation. A direct role of active transcription in the epigenetic silencing of the FMR1 gene was proved by the knockdown of FMR1 mRNA in FXS hESCs, where FMR1 gene is expressed and its promoter region retains histone marks typical of transcriptionally active chromatin. Epigenetic machinery may be triggered at the locus by secondary structures, such as hairpin, formed by CGG repeats in mRNA. The small molecule 1a may unfold and linearize these secondary structures, thus binding the repeating G–G internal loops in the RNA hairpin and inhibiting its thermal melting, preventing FMR1 silencing [FMR1 transcription in the presence of CGG expansion in the range of FM.Upon the spontaneous differentiation of FXS-hESC lines, a downregulation of hylation . Figure nt study , FMR1 sipromoter . Brieflyilencing . HoweverFMR1 promoter is partially methylated, ranging from 24% to 65%. These methylation levels remain stable in culture and are directly related to an increase in heterochromatin epigenetic marks. As observed in previous studies [FMR1 expression level was identical to that of control hESC lines. The results obtained via the bisulfite DNA sequencing of single colonies suggest a wide variability in methylation levels of full mutation FMR1 alleles. It follows that clones among the same cell lines were a mixture of cells that showed either the hypermethylation or hypomethylation of the FMR1 promoter. This is in line with the existence of methylation mosaicism among affected individuals [FMR1 transcription, whereas in originally unmethylated FXS hESCs lines, FMR1 is repressed during differentiation.In the same year, Avitzour et al. (2014) established and characterized various full mutation FXS hESCs lines from affected embryos . In some studies , in compividuals . FurtherFMR1 mRNA was produced [Further support to these findings came from the study of other FXS hESC lines containing a combination of larger methylated and smaller unmethylated full mutation alleles. However, after extended propagation in culture, it was observed that the unmethylated alleles were totally methylated and no produced .FMR1 gene remains inactive and shows histone modifications typical of inactive heterochromatin. This observation further confirms that FMR1 silencing occurs early during embryogenesis, and it highlights the epigenetic differences between ESCs and iPSCs in FXS modelling [An alternative and valid approach to modelling early stages of FXS pathogenesis is represented by iPSC lines. Though iPSCs closely resemble embryo-derived hESCs, the odelling ,81. WhilFMR1 gene methylation and silencing, how can the existence of UFM carriers be explained? Considering that FXS patients produce transcripts and R-loops at the first stage of development when the transcription is still active, which are the differences in epigenetic regulation between FXS and UFM individuals? A recent study conducted on iPSCs derived from two unrelated UFM individuals showed that lack of methylation of the FMR1 promoter is maintained during the neuronal differentiation of these cells. However, FMR1 became silenced in a small proportion of iPSC clones, with CGG expansion exceeding 400 repeats, thus indicating that the threshold for DNA methylation in UFM cells is higher than the classical 200 CGG repeat. The methylation of large CGG expansion (over 400 repeats) suggested that UFM individuals preserve the intrinsic ability to methylate their mutated FMR1 gene, but they require larger CGG repeat expansions than those observed in FXS patients. Vice versa, very few iPSC clones derived from FXS patients became unmethylated and transcriptionally active at the FMR1 locus [FMR1 gene, possibly due to an artefact of the technical procedure [If the expansion of >200 CGGs represents the main condition triggering R1 locus . In anotrocedure .FMR1 transcription, selectively targeting the epigenetic modifications at the FMR1 locus [FMR1 gene, generated a single double strand break (DBS) [FMR1 gene, resulting in two flanking DSBs [FMR1 promoter, the upregulation of active histone marks, and the expression of the gene. These changes were also maintained throughout differentiation into mature neurons [FMR1 gene [FMR1. The results showed that the specifically targeted demethylation of CGG repeats by fusing the Cas9 nuclease to the catalytic domain of the TET DNA demethylase was sufficient to reactivate FMR1 and to partially recover FMRP expression. Upon in vitro differentiation, the reactivation of FMR1 was maintained in edited neurons, which displayed a normal FMR1 expression and restored electrophysiological properties [FMR1 based on transient transfection. The authors combined the Cas9 nuclease with VP16 transcriptional activation domain in order to robustly enhance FMR1 transcription and increase FMRP levels [Recent works employed the CRISPR/Cas9  system to reactivate R1 locus ,85,86,87ak (DBS) . In the FMR1 locus. Moreover, the CRISPR/Cas9 editing system could eventually lead to a therapeutic approach in patients with FXS, albeit with caveats regarding the safety and accuracy of this method.Taken together, these results suggest that dynamic epigenetic changes in undifferentiated cells demonstrate the possibility of using multiple complementary strategies to obtain the reactivation of the FMR1 gene, one can act on the epigenetic mechanisms causing its transcriptional inactivation. In CNS cells, and specifically at the synapses, FMRP mainly acts as protein synthesis repressor. The effect of a lack of FMRP on mGLuR signaling [FMR1 expression. The epigenetic approach is based on the plasticity of epigenetic signatures, which may be reverted from heterochromatin markers in FXS-silenced alleles to more permissive markers typically described in transcriptionally active cells. Cytosines can be demethylated using 5-azacytidine (5-azaC) or, more efficiently and specifically, 5-aza-2-deoxycytidine (5-azadC). This latter compound is an analog of deoxycytidine and is incorporated into DNA during cell divisions blocking DNA methyltransferases, particularly DNMT1 [FMR1 gene by treating FXS lymphoblastoid cells with 5-azadC [FMR1-mRNA and, in a fraction of treated cells, FMRP. The observed discrepancy between mRNA and protein levels could have been due to the lower translational efficiency in presence of CGG expansion. A synergistic effect on FMR1 reactivation was obtained by the combined treatment with 5-azadC and several HDAC inhibitors such as butyrate, phenylbutyrate, and trichostatin A. Treatments with HDAC inhibitors alone did not induce any significant reactivation of the FMR1 gene [FMR1 locus [FMR1 gene. Like other HDAC inhibitors, these drugs weakly reactivate the FMR1 gene in FXS cells; in a few cells, they induce a modest reactivation, while other cell lines exhibit no reactivation. Moreover, most inhibitors of HDAC enzymes are cytotoxic, thus limiting their use in clinical trials for FXS [Two different approaches can be proposed to treat FXS: (1) to compensate for the lack of FMRP, one can intervene on pathways in which the protein is involved; (2) to reactivate the expression of the otherwise intact ignaling  has promignaling ,90. Howely DNMT1 . Our gro 5-azadC . The reaMR1 gene . EvidencR1 locus , as was R1 locus . Additio for FXS .FMR1 promoter, effectively converting a methylated FXS into an UFM. Notably, the effect of 5-azadC on genomic DNA methylation is not indiscriminate; rather, it is restricted almost exclusively to the FMR1 promoter. To support this point, using a microarray screening of 10,814 transcripts, Suzuki et al. (2002) showed that only 51 genes are transcriptionally upregulated after 5-azadC treatment and 23 genes are overexpressed after trichostatin A treatment [FMR1 gene lasts for several weeks in vitro [FMR1 reactivation [Treatments of FXS cells with 5-azadC have shown that this compound is able to induce increased histone acetylation and H3K4 methylation levels, while H3K9 methylation is only partially reduced . These ereatment . We alsoin vitro . A relevin vitro , althougin vitro . Furthertivation . Though FMR1 gene. Valproic acid (VPA), an FDA-approved drug to treat seizures and mood disorders, demonstrated a (modest) reactivating effect on FMR1 gene by acting as histone acetylator without a DNA demethylation effect [FMR1 gene in vitro, as demonstrated for other similar compounds [Further experiments have corroborated the feasibility of an epigenetic intervention to reactivate the mutant n effect . Clinican effect . A signin effect , a naturompounds .FMR1 inactivation [FMR1-reactivating compounds are evaluated for their additive and long-term effects. Higher FMR1 reactivation levels and lower DNA methylation levels of the FMR1 promoter in FXS transplants were obtained by the combined use of 5-azadC and DZNep (an SAH hydrolase inhibitor) than by treatment with 5-azadC alone. Interestingly, a small reduction of FMR1 methylation levels was obtained by the use of DZNep alone, which may indirectly inhibit histone modifications. Unlike the chemicals used in other studies [FMR1 reactivation, thus diminishing the potentially toxic side effects of this demethylating agent used in monotherapy. A platform for the analysis of FMR1-reactivating treatments in vivo was also developed on two humanized animal models carrying human FXS-affected cells: one implied the generation of a differentiated human FXS transplant in immunocompromised mice and the second involved the transplantation of a specified population of human FXS neural progenitor cells into mouse brains. In both cell types, 5-azadC administration was able to restore FMR1 expression, representing a proof of principle for FMR1 demethylation in an in vivo model [FMR1 alleles. These results also demonstrated that H3K9 methylation precedes DNA methylation and that the removal of DNA methylation with 5-azadC is necessary to obtain the optimal effect of HMT inhibitors on FMR1 gene expression [In a recent paper, an in vivo platform of FXS iPSCs and differentiated FXS transplants was developed to screen small-molecules and to quickly evaluate the ability of tested compounds to target tivation . FMR1-re studies ,107, thevo model . Lastly,pression .FMR1 suggests the reversible nature of Fragile X syndrome. A substantial body of work has already helped to clarify the pathogenic mechanism that underlies FXS, although further experiments are necessary to elucidate the fine epigenetic regulation of the FMR1 gene. Again, the phenotype is mainly characterized by neurodevelopmental disorder with ID and behavioral problems ranging from mild to moderate without any structural defects of tissues or organs. However, the effective and stable correction of a genetic defect continues to be a stimulating challenge, even in light of encouraging results obtained by the above-mentioned clinical trials in different cohorts of FXS patients. For future therapeutic approaches, it will be necessary to have a deeper understanding of the pathophysiology underlying this condition.The monogenic nature of FXS makes this rare genetic disorder more amenable than others to pharmacological or molecular therapy. In fact, virtually all FXS patients have the same dynamic mutation, which does not involve the open reading frame of the gene. The epigenetic regulation of


Background: Nucleus accumbens-associated protein 1 (NAC1) has multifaceted roles in cancer pathogenesis and progression, including the development of drug resistance, promotion of cytokinesis, and maintenance of “stem cell-like” phenotypes. NAC1 is a transcriptional co-regulator belonging to the bric-a-brac tramtrack broad (BTB) family of proteins, although it lacks the characteristic DNA binding motif of the BTB family. The formation of higher-order transcription complexes likely depends on its interaction with other DNA-binding co-factors.Results: NAC1 interacts with BCL6 via its C-terminal BEN domain and forms a complex that binds the promoter region and activates transcription of the NAC1 target gene, FOXQ1. NAC1 and BCL6 were coordinately upregulated. Our analysis also identified a novel function of NAC1 in attenuating BCL6 auto-downregulation in ovarian cancer. Lastly, we found a significant overlap among NAC1- and BCL6-regulated genes in tumor cells, suggesting that NAC1 and BCL6 coordinately control transcription in cancer.Conclusions: The results of this study provide a novel mechanistic insight into the oncogenic roles of NAC1 and underline the importance of developing the NAC1/BCL6-targeted cancer therapy.Methods: Using the Cistrome database and Chromatin Immunoprecipitation (ChIP) analyses, we identified BCL6 as a potential NAC1- interacting molecule. Co-immunoprecipitation (Co-IP), luciferase reporter assay, immunohistochemistry and microarray analysis were performed to analyze the interaction between NAC1 and BCL6 and the mechanisms by which they regulate the downstream genes including FOXQ1. Nucleus accumbens-associated protein 1 (NAC1) is a transcriptional co-regulator that belongs to the bric-a-brac tramtrack broad (BTB) family of proteins. Formation of higher-order transcription complexes likely depends on its interaction with other DNA-binding co-factors . NAC1 meNAC1 is overexpressed in several cancer types including cervical, endometrial, and ovarian cancers , 14, 15 BCL6 gene [BCL6 has been linked to disruption of its negative autoregulation, leading to its anomalous upregulation and possible involvement in pathogenesis [We hypothesize that NAC1 regulates gene expression through formation of a transcription complex with BCL6, another BTB family member . BCL6 prCL6 gene . In diffogenesis , 23. BCLogenesis –26. Moreogenesis .We have found that BCL6 is a co-factor in NAC1-containing transcription complexes. NAC1 interacts with BCL6 via its C-terminal BEN domain and forms a transcription complex that binds to the promoter region of the NAC1 target gene, FOXQ1. Moreover, we showed that NAC1 and BCL6 are coordinately upregulated in ovarian cancer, and NAC1 plays an important role in attenuating BCL6 autoregulation. Lastly, we observed a significant overlap among NAC1- and BCL6-regulated genes in tumor cells. The results of this study provide a novel mechanistic insight into the oncogenic roles of NAC1, and underlines the importance of developing NAC1/BCL6-targeted cancer therapy.FOXQ1, a gene directly regulated by NAC1. Previously, we demonstrated that NAC1 is essential and sufficient for activation of FOXQ1 transcription and that the role of NAC1 in cell motility is mediated, at least in part, by FOXQ1. NAC1 knockdown decreased FOXQ1 expression and promoter activity. In silico analysis revealed a significant co-up-regulation of NAC1 and FOXQ1 in ovarian carcinoma tissues. We identified three putative BCL6 binding motifs within the FOXQ1 promoter region at positions A (-1000), B (-800), and C (-150) upstream of the transcription start site , B (-800), and C (-150)  see . Each pr150) see , suggestFOXQ1. We transfected HEK293T cells with constructs containing the coding sequences of BCL6-Flag and with either full-length NAC1-V5 or each of the five NAC1 deletion mutants , but express a truncated, dysfunctional NAC1 (N130) that combines with and inactivates endogenous NAC1 when cultured in the absence of doxycycline (system on) [FOXQ1 promoter  when the expression of NAC1, BCL6, or both genes was suppressed  . We founpromoter . To furtppressed   . Furthermore, the correlation between NAC1 and BCL6 expression was verified using the GEPIA , which is based on a large sample cohort from TCGA databases   . Moreove2.5e-07) . All of BCL6 transcription relies on its interaction with other BTB/POZ corepressors [The negative auto-regulation of pressors . In viewBCL6 after 48 h (P=0.041) (BCL6 transcription (P=0.016) (BCL6 transcription was observed in OVCAR-3 (P=0.0089), SKOV3 (P=0.0216) cells, and in the taxane-resistant derivate line SKOV3TR (P=0.0002), as compared to the cells transfected with a control siRNA (BCL6 transcript was reduced by 50% in the spleen of Nacc1-/- mice (P=0.0068) (We induced ectopic overexpression of NAC1 by transfecting OSE4 cells with V5-tagged NAC1. Overexpression of NAC1 resulted in a significant increase in the transcription of P=0.041) . In OVCAP=0.016) . Similarol siRNA –5E. Of r=0.0068) . NAC1 exBCL6 promoter in ovarian cancer cells. The results showed that BCL6 bound to its own promoter, and more interestingly, NAC1 was enriched in the chromatin immunoprecipitate . Then, we used a competition assay in which the expression of BCL6 was assessed in OVCAR-3 cells ectopically expressing the NAC1 C-terminal 186 amino acids (NAC1-C186), which comprise the minimum NAC1 domain capable of binding BCL6. We found that BCL6 expression decreased by approximately 50% when cells were transformed with NAC1-C186-V5, in comparison with the cells transfected with the control construct pcDAN6 (P<0.0001)  . These dTo identify the NAC1/BCL6 transcription-complex target genes, we performed transcriptome analysis in OVCAR-5 cells that constitutively overexpress NAC1 and BCL6. We compared transcriptional profiles of cells transfected with a NAC1 siRNA, BCL6 siRNA, or control siRNA. The efficiency of knockdown was tested by qPCR 72 h after transfection. There was a significant down-regulation of NAC1 and BCL6 in the cells transfected with their respective targeting siRNAs in comparison with the control siRNA (P<0.0001) . FollowiAlthough the functions of NAC1 are mainly mediated through its transcriptional regulatory activity, the precise mechanisms remain incompletely understood , partly FOXQ1. Indeed, BCL6 binds to all three putative BCL6 binding motifs within the FOXQ1 promoter. Binding of BCL6 to these motifs depends on NAC1, as the expression of the truncated NAC1 clone N130  prevents BCL6 from binding to FOXQ1 promoter, and downregulations of both NAC1 and BCL6 result in reduced FOXQ1 promoter activity. Chromatin immunoprecipitation sequencing (ChIP-seq) might help define a more detailed mechanism underlying the coordinated transcriptional control of all downstream target genes by NAC1 and BCL6. Based on the results of this study, we propose a mechanism in which NAC1 and BCL6 form a regulatory complex and collaborate to regulate transcription factors including FOXQ1 , and were maintained in RPMI-1640 media  supplemented with 10% (v/v) fetal bovine serum  and penicillin/streptomycin (Invitrogen). SKOV3clitaxel . HeLa N1Nacc1-/- mice were obtained as described by our group [Mice were housed and handled according to approved protocol MO12M405) and guidelines of the IACUC of Johns Hopkins University. 05 and guhttp://cistrome.org/~jian/motif_collection/databases/Cistrome/pwm/M01171.pwm), and was then converted to a position weight matrix with background base frequencies calculated according to different promoter sequences [Promoter sequences were retrieved with the use of Biostrings and BSgenome packages of Bioconductor 2.12 . The posequences . OccurreChIP extract preparation followed a previously described method with minor changes . BrieflyRNA was extracted using RNAeasy kit . First strand cDNA was synthesized using the iScript cDNA synthesis kit . Relative transcript expression levels were measured using the CFX96 Real-Time PCR Detection System (Bio-Rad), and quantified by fluorescence intensity of SYBR Green I (Invitrogen). The primers used in this study are summarized in HEK293T cells were used for Co-IP as previously described by our group [HeLa cells were first transfected with NAC1-targeting and/or BCL6-targeting siRNA or control siRNA. After 24 h, cells were transfected with control pGL3 plasmid or pGL3-FOXQ1 promoter firefly luciferase constructs, and the pRL-Renilla reporter plasmid  using Lipofectamine LTX and Plus Reagent (Invitrogen). The firefly and renilla luciferase activities were measured 24 h after transfection using a luminometer  and the Dual-Glo luciferase reagent (Promega). Firefly luciferase activity was normalized to the renilla luciferase activity.A total of 51 formalin-fixed and paraffin-embedded ovarian high-grade serous carcinoma samples were obtained from the Department of Pathology at the Johns Hopkins Hospital, Baltimore, Maryland. Paraffin tissues were arranged in tissue microarrays (TMA) to ensure that tissues were stained under the same conditions. An in-house mouse monoclonal anti-NAC1 antibody, the specificity of which has been previously confirmed by Western blotting , was usehttp://gepia2.cancer-pku.cn/; retrieval date, August 22, 2019), an online software based on the TCGA databases [To further confirm the co-expression of NAC1 and BCL6 gene, we performed an analysis using GEPIA (atabases .https://www.ingenuity.com) for downstream effects analysis. This analysis is based on expected causal effects between input genes and biological functions. The expected causal effects are derived from the literature compiled in the Ingenuity Knowledge Base, and allow a prediction for each function based on the pattern of gene expression modulation.OVCAR-5 cells were transfected with either NAC1 siRNA, BCL6 siRNA, or control siRNA, and were harvested at 72 h. Total RNA was isolated using the RNeasy Plus Mini Kit (Qiagen), and RNA samples were hybridized onto the Human HT-12 v4 Expression BeadChip . By comparing global gene expression profiles among siNAC1-treated, siBCL6-treated and control cells, we prioritized a set of genes for analysis with a false discovery rate <0.002 at either time point. Genes that were upregulated or downregulated were defined using the regulation status of the time point with the lowest FDR. Genes with a >1.5-fold change compared with the control group were further analyzed by the Ingenuity Pathways Analysis System  and guidelines of the IACUC of Johns Hopkins University. This work involving human samples has been approved by The Ethics Committee of the Johns Hopkins University. Written informed consent was obtained from all the human participants involved in the study.Supplementary FiguresSupplementary Table 1


Despite the distinct roles these cell types play in the neural circuit, the molecular correlates remain unknown because of the difficulty of isolating rare neuron subtypes. To address this issue, we developed a new viral affinity purification strategy, Cre-Specific Nuclear Anchored Independent Labeling, to isolate Cre recombinase-expressing (Cre+) nuclei from the adult mouse brain. Applying this technology, we performed targeted assessments of the cell type-specific transcriptomic and epigenetic effects of dopamine depletion on PV+ and PV– cells within three brain regions of male and female mice: GPe, striatum, and cortex. We found GPe PV+ neuron-specific gene expression changes that suggested increased hypoxia-inducible factor 2α signaling. Consistent with transcriptomic data, regions of open chromatin affected by dopamine depletion within GPe PV+ neurons were enriched for hypoxia-inducible factor family binding motifs. The gene expression and epigenomic experiments performed on PV+ neurons isolated by Cre-Specific Nuclear Anchored Independent Labeling identified a transcriptional regulatory network mediated by the neuroprotective factor Hif2a as underlying neural circuit differences in response to dopamine depletion.Neuron subtype dysfunction is a key contributor to neurologic disease circuits, but identifying associated gene regulatory pathways is complicated by the molecular complexity of the brain. For example, parvalbumin-expressing (PVSIGNIFICANCE STATEMENT Cre-Specific Nuclear Anchored Independent Labeling is an enhanced, virus-based approach to isolate nuclei of a specific cell type for transcriptome and epigenome interrogation that decreases dependency on transgenic animals. Applying this technology to GPe parvalbumin-expressing neurons in a mouse model of Parkinson's disease, we discovered evidence for an upregulation of the oxygen homeostasis maintaining pathway involving Hypoxia-inducible factor 2α. These results provide new insight into how neuron subtypes outside the substantia nigra pars compacta may be compensating at a molecular level for differences in the motor production neural circuit during the progression of Parkinson's disease. Furthermore, they emphasize the utility of cell type-specific technologies, such as Cre-Specific Nuclear Anchored Independent Labeling, for isolated assessment of specific neuron subtypes in complex systems. PV+ neurons constitute roughly half of all neurons in the GPe, a key contributor to pathogenic hypersynchrony of basal ganglia nuclei under the dopamine-depleted conditions of PD   . Additiols of PD .+ neurons and PD, the molecular correlates of disease in these and other neuron populations are understudied because of technical barriers. Bulk tissue RNA sequencing (RNA-seq) (Despite the associations between PVRNA-seq)  and the RNA-seq) , 2015 ar+ neurons  and a cohort of female littermates (N = 3), all adult heterozygous Pvalb-2A-Cre mice, were transduced with cSNAIL AAV to label PV+ neurons. After 2-3 weeks of incubation, the animals underwent surgery for either a 6-OHDA injection to induce DD  or a comparable saline injection . This control allowed us to minimize possible confounding effects from the surgical procedure and isolate the effects of dopamine depletion itself. Three to five days after surgery, tissue from the cortex, striatum, and GPe was collected and sorted into labeled (PV+) nuclei and unlabeled nuclei via affinity purification with cSNAIL. The six nuclei populations from each animal were then divided for ATAC-seq or RNA-seq. For more metadata information, see Extended Data The main experiments were designed to assess gene expression and chromatin accessibility signatures in cSNAIL-isolated cell populations from mice with normal and impaired dopamine production. A cohort of male littermates , amino acids 208-757, that retains the ability to localize to the nuclear envelope in a manner that is resistant to detergent tissue homogenization and is similar to the variant described by Sun1. (3) The Sun1GFP fusion gene included one copy of superfolder GFP instead of two. (4) We adjusted the Cre-dependent mechanism from a loxp-flanked stop codon to the double-inverted orientation (DIO) system. In the initial cSNAIL genome, the Sun1GFP gene is in the reverse orientation with respect to the promoter such that the gene cannot be expressed. When the virus encounters Cre recombinase protein (Cre), it acts on the double-flanking lox sites to excise the Sun1GFP gene and reinsert it in the correct orientation for transcription (see A).Several modifications were made to the INTACT system to allow it to be delivered using AAV. (1) We used an N-truncated version of the tion see A.10.1523/JNEUROSCI.1634-20.2020.f2-1Figure 2-1Sample metadata per animal. Details about the mice, treatments, and tissue collection for molecular analyses are provided. Download Figure 2-1, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f2-2Figure 2-2Additional properties of the mouse motor cortex snRNA-seq data. A. Cluster annotations per cell of the snRNA-seq data with UMAP embedding. B. Pvalb gene expression is highest within the Pvalb interneuron cluster. Download Figure 2-2, TIF file.10.1523/JNEUROSCI.1634-20.2020.f3-1Figure 3-1Differentially expressed genes between PV+and PV–populations in each brain region. All genes with DESeq2 padj < 0.01 & |log2FoldDifference| > 1 are reported. Download Figure 3-1, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f3-2Figure 3-2Differentially accessible ATAC-seq peaks between PV+and PV–populations in each brain region. All peaks with DESeq2 padj < 0.01 & |log2FoldDifference| > 1 are reported. Additionally, the corresponding summit-centered 500 bp windows used for motif enrichment analysis are listed for each cell population. Download Figure 3-2, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f3-3Figure 3-3Genes with differential promoter accessibility between PV+and PV–populations in each brain region. Standardized 10 kb windows around the TSS of each gene were assessed for differential chromatin accessibility. Differential TSS regions with padj < 0.1 are reported. Download Figure 3-3, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f3-4Figure 3-4Functional enrichment of PV+and PV–enriched genes. All g:Profiler results with padj < 0.05 for either gene set are shown. Download Figure 3-4, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f3-5Figure 3-5Motif enrichment among PV+and PV–ATAC-seq peaks. All AME results with E value < 10 are reported for each analysis. Download Figure 3-5, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f4-1Figure 4-1Quantification of dopamine depletion per hemisphere. A. Levels of striatal Tyrosine Hydroxylase for each hemisphere of the four 6-OHDA lesioned animals and 2 sham animals. The p value reflects a significant difference in populations by the standard t test. B. Examples of the quantified images, representing the two red points in A. Download Figure 4-1, TIF file.10.1523/JNEUROSCI.1634-20.2020.f4-2Figure 4-2RNA-seq sample similarities. A. The first two principal components separate samples by brain region and cell type. B. In hierarchical clustering, samples mainly cluster by brain region and then cell type. Download Figure 4-2, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f4-3Figure 4-3Differentially expressed genes with dopamine depletion. Information for all genes with DESeq2 padj < 0.2 are shown for each cell population. Download Figure 4-3, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f4-4Figure 4-4Example images of Hif2a staining in healthy and DD GPe tissue. Some instances of PV+Hif2a- cells are highlighted by white arrows. The scale bars measure 100 µm. Download Figure 4-4, TIF file.10.1523/JNEUROSCI.1634-20.2020.f4-5Figure 4-5Functional enrichment of DD-affected genes in GPe PV+neurons. Enrichments were conducted on the differential gene sets at the relaxed threshold of DESeq2 padj < 0.2 using g:Profiler. All results with padj < 0.05 for either gene set are shown. Download Figure 4-5, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f5-1Figure 5-1ATAC-seq data are high quality. A & B. ATAC-seq samples tend to cluster with other samples of the same tissue and cell type by PCA and hierarchical clustering of genome-wide open chromatin profiling. C. The data exhibit the characteristic periodicity in fragment length distributions of high quality ATAC-seq data, reflecting nucleosome positioning. The plot shown is a representative example from one GPe PV+ sample. D. ATAC-seq signal is enriched at transcription start sites (TSS), indicative of high signal-to-noise. The displayed data are a representative example from one GPe PV+ sample. Download Figure 5-1, TIF file.10.1523/JNEUROSCI.1634-20.2020.f5-2Figure 5-2Differential ATAC-seq regions with dopamine depletion. For PV+ cell populations, differential peaks that met the lenient significance of padj < 0.2 are reported. For PV– cell populations, differential peaks that met a significance of padj < 0.05 and |log2FoldChange| > 0.5 are reported. The corresponding summit-centered 500 bp regions for differential motif discovery are also listed. Download Figure 5-2, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f5-3Figure 5-3Overlap between DD-affected ATAC-seq peaks of different cell types. The width of each outer segment of the circle shows how many DD-affected peaks were recovered in that cell type. Bidirectional arcs between two cell types signify shared DD-affected peaks where the width of the connection represents the number of shared peaks. Plotted with circlize  or DD-decreasing peaks (down arrow) and the color indicates the cell population. Download Figure 5-4, TIF file.10.1523/JNEUROSCI.1634-20.2020.f5-5Figure 5-5Enriched annotations in DD-affected ATAC-seq peak sets. All results from the GREAT analysis with FDR Q < 0.05 and minimum region-based fold enrichment of 2. Download Figure 5-5, XLSX file.10.1523/JNEUROSCI.1634-20.2020.f5-6Figure 5-6TSS accessibility of DD-affected genes in GPe PV+neurons. The mean of each population is marked with the red star and the reported p values denote the significance of the difference in means by the two-tailed t test. Download Figure 5-6, TIF file.10.1523/JNEUROSCI.1634-20.2020.f5-7Figure 5-7Motif enrichments among DD-affected peak sets. All AME results with E value < 10 are reported for each analysis. Download Figure 5-7, XLSX file.Sun1GFP fusion sequence fragment was synthesized by Integrated DNA Technologies and contained flanking restriction sites for SgsI and BspoI. The Sun1GFP sequence was inserted into an AAV DIO backbone vector with an Ef1a promoter using standard restriction cloning. pAAV-Ef1a-DIO-EGFP-WPRE-pA was a gift from Bernardo Sabatini  (Addgene_160141.The modified e_37084) . The lighttp://addgene.org/; 103005; RRID:Addgene_103005)  293T cells . pUCmini-iCAP-PHP.eB was a gift from Viviana Gradinaru (_103005) . After cPvalb-2A-Cre mice Aibs/J; Jackson stock #012358) 26Sortm14(CAG-tdTomato)Hze/J; Jackson stock #007914) (D) were conducted on WT mice .All molecular experiments were performed on 9- to 12-week-old heterozygous #012358)  on a C57#012358)  were per#007914) . Experim#007914) D were co11 vg of AAV into the retro-orbital cavity. The mice received 0.5% proparacaine hydrochloride ophthalmic solution for comfort and were monitored for physical and behavioral abnormalities after procedure, but none were observed. The virus incubated for 3–5 weeks to reach peak expression before downstream experiments.Mice were anesthetized with isoflurane for 2-5 min until breathing slowed and the animal had no pedal reflex. We injected 4 × 10The molecular experiments were designed to reproduce previous conditions and maximize cell yield. Therefore, we induced bilateral acute 6-OHDA or saline injections to the medial forebrain bundle as described by D), tissues were stained with primary anti-NeuN  and secondary AlexaFluor-405  to label neurons. For images related to Figure 4D and Extended Data Wherever possible, tissue for imaging was preserved by 4% PFA perfusion. The brains were dissected and incubated overnight at 4°C in 4% PFA. Shortly after, they were vibratome sectioned coronally at 100 µm for antibody staining and slide mounting. To visualize cSNAIL specificity see D, tissueD and 4D. To quantify the levels of striatal TH as a proxy for remaining dopamine (see C), we measured mean pixel intensity with Fiji . After this process, the bead-bound nuclei (Sun1GFP+ fraction) were purified and washed on a magnet, while the unbound nuclei were spun down and resuspended in water to preserve the Sun1GFP– fraction.The nuclei suspension was precleared for 10-15 min with Protein G Dynabeads  to remove nuclei or debris that had native affinity for the beads. Free nuclei were incubated with anti-GFP antibody  for 30 min before fresh beads were added to the reaction and incubated for another 20 min. All incubations took place at 4°C with 40 rpm end-to-end rotation in wash buffer ; 10 pg to 10 ng of total RNA per sample was processed using the Ovation SoLo RNA-seq System with mouse AnyDeplete to remove ribosomal RNA . Because many nuclear transcripts are immature, no poly A selection was performed. Libraries were paired-end sequenced at 3-9 million reads per sample on the Nextseq 500 system.https://github.com/tecangenomics/nudup). The quality of the filtered data was assessed using Picard CollectRnaSeqMetrics version 2.8.1 (http://broadinstitute.github.io/picard). A counts per gene per sample matrix was constructed using Subread featureCounts version 1.6.4 (rlog function (GSE157359.The sequencing output files were mapped to the mm10 genome (downloaded from UCSC in November 2015) using Hisat2 with default parameters. Duplicate UMIs were removed using NuDup  gene expression signatures of Sun1GFPions see  and (2) ions see . Toward n 1.14.1 : read coA small subset of nuclei was stained with DAPI, and fluorescent nuclei were manually counted using a hemocytometer to determine their concentration; ∼50,000 nuclei were transferred to a new tube in 22.5 µl water and mixed with 2.5 µl Tagment DNA Enzyme I and 25 µl Tagment DNA Buffer . The tn5 transposition reaction was conducted at 37°C for 30 min with 300 rpm mixing. After tagmentation, the samples were taken through column purification, amplification, and double-sided size selection (100-1000 bp) with AmpureXP beads . To reduce amplification bias, we evaluated a side qPCR reaction for each sample to determine the optimal number of PCR cycles for each sample to reach one-third maximum intensity. To get initial sample quality estimates and balance sample representation for deep sequencing, each sample was sequenced at 1-3 million reads on the Illumina Miseq system. Then, samples were pooled and paired-end sequenced on a Novaseq 6000 to ∼25 million unique, nonmitochondrial reads per sample for analysis.https://github.com/kundajelab/atac_dnase_pipelines). Peaks were called on biological replicates of the same cell type, brain region, and treatment with an IDR threshold of 0.1 (https://github.com/nboley/idr). This design maximizes the potential to include peaks that may only be present in one condition while limiting potential false positives that may only be called in one sample. Count tables containing the number of reads per peak for each sample were constructed using Rsubread featureCounts version 1.28.1 , the regions tested were the union of IDR peaks from all sham animal samples. For the promoter analysis in Figure 3E, instead of ATAC-seq peaks, counts were computed within 10 kb windows around the transcription start sites (TSSs) for all genes assessed in the RNA-seq analysis. To assess DD-affected regions . Additionally, we ensured that each sample had TSS signal enrichment and exhibited the characteristic fragment length periodicity of high-quality ATAC-seq data, reflective of nucleosome positioning . The ATAC-seq data are available on GEO, accession number GSE157359.The fastq files from each sample were processed with the ENCODE ATAC-seq pipeline . For Figure 5C, enrichments were assessed relative to a background of all IDR peaks called in cells of the same brain region and Sun1GFP fraction as the foreground.Differential motif enrichment analyses . We defined peaks that marked each cluster as those with log2FoldDifference > 0.25 and p < 0.05 relative to all other clusters.To compare cSNAIL-isolated cell types with single nucleus sequencing cluster markers, we reprocessed a publicly available snATAC-seq dataset from adult mouse cortex, provided by 10× Genomics. The data were downloaded and processed following the Signac adult mouse brain vignette . Empty droplets were removed using the DropletUtils package , these metrics were quantified for four independent images and averaged. This approach is more appropriate than computing the specificity on the pool of all cells together because treating images as replicates allows us to also quantify variation in the data. To this end, the SEM across the four images is reported for each brain region. The same principles were used for the quantification of TH (Extended Data + PV+ neurons on DD (D). Images from tissue of the same treatment state (DD or no DD) were treated as replicates and the spread of the data are shown by the box plot. To test for a difference between the means of the two treatment groups, we used two-tailed t test statistics.To determine the specificity and efficiency of cSNAIL labeling in each brain region E, these ns on DD D. ImagesB), we used the one-sided hypergeometric enrichment test, sampling cell type-specific cSNAIL peaks in each brain region from an expected population frequency of cluster-specific snATAC-seq peaks. The hypergeometric distribution is commonly used to test for association of binary variables in genomics and is suitable to help us interrogate which clusters have the most in common with cSNAIL-isolated cells. Only peaks present in both the cSNAIL-isolated dataset and the snATAC-seq dataset  were evaluated. For simplicity, cluster-specific peaks from all excitatory neuron clusters were combined into one category. For visualization, we converted the hypergeometric test enrichments throughout each cSNAIL cell population into z scores and plotted them using gplots heatmap.2 in R .For comparing cSNAIL ATAC-seq markers to snATAC-seq cluster markers B, we useC), we computed a per-nucleus gene signature score that is the difference between the normalized expression of Sun1GFP+ specific genes and Sun1GFP– specific genes. Because the snRNA-seq came from the healthy cortical tissue, we focused on genes that were differentially expressed between sham cortical Sun1GFP+ and Sun1GFP– fractions. The Sun1GFP+ and Sun1GFP– scores were computed by averaging across the scaled  expression of signature genes across all cells.In order to visualize the extent to which each snRNA-seq nucleus had a similar expression profile to cSNAIL-isolated nuclei C, we comp value after multiple hypothesis correction with the Benjamini Hochberg method (padj). Significance cutoffs for specific analyses are described throughout Results, and the statistics for these tests can be found in Extended Data For differential gene expression and differential chromatin accessibility analyses see –5, genes+ neurons on DD . The resulting cSNAIL genome expresses a modified Sun1GFP protein in a Cre-dependent manner. We packaged the cSNAIL Sun1GFP vector into AAV variant PHP.eB, which is capable of crossing the blood-brain barrier to broadly transduce the CNS . This expression is sufficient to bind anti-GFP coated magnetic beads after tissue dissociation (C). To quantify the ability of cSNAIL to correctly target Cre+ cells, we compared the expression of cSNAIL to the expression of Cre reporter tdTomato of the Pvalb-2A-Cre/Ai14 mouse strain . For the purposes of this analysis, we defined virus specificity as the percent of Sun1GFP+ cells that also express tdTomato and virus efficiency as the percent of tdTomato+ cells that also express Sun1GFP. cSNAIL exhibited very strong (>94%) specificity in the cortex, striatum, and GPe. The efficiency ranged from 53% to 81%, which reflects the expected transduction of the viral capsid in these brain regions . When comparing our cell type-specific ATAC-seq markers to single nucleus (sn)ATAC-seq clusters from the adult mouse cortex (https://github.com/timoast/signac), the Sun1GFP+ peaks from each brain region were enriched for PV+ cluster markers . Aligning with the expected cell type proportions in each brain region, Sun1GFP– fractions were most enriched for snATAC-seq markers of excitatory neuron clusters (cortex only) and glial clusters.To confirm the molecular identity of cSNAIL-isolated nuclei from ulations    across all three tissues. Of these genes, a small number also had corresponding cell type-specific chromatin accessibility within a 10 kb window around the gene TSS , but Tcef3 or Hoxa2 were not. Consistent PV– motif enrichments across all brain regions included the motif for Sox10, which is necessary for the differentiation and survival of oligodendrocytes  , and three of its targets: Cp, Slc2a1, and Flt1  signaling and associated transcriptional responses.In the RNA-seq data from DD and sham animals, we recovered very few significant DD-affected genes in any cortical cell populations, striatal cell populations, or in the GPe PVand Flt1 . Additioactivity . These rEpas1 transcription in GPe PV+ neurons was accompanied by increased Hif2a protein levels, we performed double immunofluorescent staining for Pvalb and Hif2a in healthy and DD mouse tissue. Indeed, a higher proportion of GPe PV+ neurons expressed Hif2a in images from DD animals compared with healthy animals .To see whether increased + neurons of DD mice have also been implicated in processes of neurodegeneration and neuroprotection. For example, we observed overexpression of Timp3, a key inhibitor of matrix metalloproteinases which contribute to dopaminergic neuron apoptosis in PD , the ATAC-seq data were high quality for all samples, except one cortex PV+ population . Because there were too few examples of DD-affected PV+ peaks for statistical enrichment analysis at this threshold, we assessed PV+ enrichments within peaks that met a more lenient threshold of padj < 0.2  (C). Moreover, the motifs were not enriched in DD-increasing peak sets from other cell types with the exception of some weaker enrichment in striatal PV+ neurons. Finally, sets of ATAC-seq peaks with decreased accessibility in DD (DD-decreasing) were not enriched for HIF motifs in any cell type, including GPe PV+. These results indicate a cell type-specific induction of HIF signaling in GPe PV+ neurons on DD.Because of the increased expression of HIF-related transcripts in GPe PV8 motifs  for (1) 8 motifs , (2) Arn8 motifs B. Becausn on DD) C. Moreov+ peaks and, to a lesser extent, also DD-increasing striatal PV+ peaks, we observed an enrichment for Ets-related transcription factor motifs, including Gabpa . Gabpa is an essential regulator of many cellular respiration genes, including multiple cytochrome c oxidase subunits . Glucocorticoid receptors are emerging as key regulators of neuroinflammation, and disruption of their proper regulation is thought to play a role in the onset of PD by allowing the infiltration of cytotoxins in the SNpc . Changes in accessibility at distal regulatory elements may or may not confer an appreciable change in gene regulation.Unlike the specific nature of the transcriptional response to DD, the open chromatin data suggest more global epigenetic effects. These open chromatin observations could be a signature of an epigenetic “scar,” where DD temporarily disrupted gene expression and an epigenomic response restored gene expression to near normal levels, but the open chromatin state remains altered . Under sas1 gene . This pr+ neurons corroborate and strengthen the HIF upregulation that we observed at the transcript level. Namely, HIF family motifs are selectively enriched within DD-increasing ATAC-seq peaks in GPe PV+ neurons. Finally, a higher proportion of GPe PV+ neurons appear to have high Hif2a protein levels in DD tissue compared with healthy tissue. Together, the evidence strongly supports an increase in HIF transcription factor activity in GPe PV+ neurons on DD. In the future, cSNAIL could be paired with protein assays to determine the precise HIF protein expression levels, cellular localizations, and DNA binding events in GPe PV+ neurons in DD.Nonetheless, the changes in open chromatin within GPe PV+ neurons in DD mice, cSNAIL enabled targeted transcriptomic and epigenetic interrogation and yielded new cell type-specific results. Most notably, we recovered evidence of HIF oxidative stress signaling that was specific to GPe PV+ neurons in this model of DD. These results may help explain the pathogenic progression of DD throughout the basal ganglia and provide hypotheses toward new cell type-specific interventions in PD. We believe a neuron subtype aware approach, such as cSNAIL, will be necessary to tease out molecular correlates of many neurologic and psychiatric disorders, especially where rare neuron populations are heavily implicated.In conclusion, cSNAIL is a reliable new strategy for labeling and isolating nuclei of a specific subset of cells within cell type-specific Cre animals. In the case of GPe PV


Ten-eleven translocation-2 (TET2) is a crucial driver of cell fate outcomes in a myriad of biological processes, including embryonic development and tissue homeostasis. TET2 catalyzes the demethylation of 5-methylcytosine on DNA, affecting transcriptional regulation. New exciting research has provided evidence for TET2 catalytic activity in post-transcriptional regulation through RNA hydroxymethylation. Here we review the current understanding of TET2 functions on both DNA and RNA, and the influence of these chemical modifications in normal development and pluripotency contexts, highlighting TET2 versatility in influencing genome regulation and cellular phenotypes. The expression of the three TET enzymes differs during early embryonic development, with TET3 mostly restricted to oocytes and zygotes, while TET1 and TET2 are highly expressed later in preimplantation embryos  with a large low-complexity insert . Contrarin vitro and in vivo  embryo-specific genes and endogenous retroviral elements up to the preimplantation blastocyst stage in mouse , ESCs are a heterogeneous population of cells with different developmental potential, including cells with totipotency-like features and the duet of naïve and primed pluripotent subtypes , with Oct4 silencing causing a reduction of Tet2 expression  of mouse origin are derived from the ICM of the preimplantation blastocyst  is sufficient to restore naïve pluripotency  of cells with an expanded potency  leads to a delay in the induction of differentiation genes, molecularly explained by a gain in 5mC at enhancers in absence of TET2 activity  called OSKM or Yamanaka factors   to regain pluripotency from somatic cells was first achieved by overexpressing four transcription factors  responsible for depositing m5C on RNA for TET2 oxidation, nor the hm5C readers in charge of destabilizing TET2-targeted RNAs are currently known. Furthermore, the observation that a fraction of its interactome is comprised by unmodified RNAs raises the possibility that TET2 could also have catalytic-independent functions on RNA, similar to what has been previously described on DNA (Chen et al., 5C from 5hmC/hm5C at a single base resolution on DNA and RNA, respectively, opens up exciting possibilities to explore TET2 function as an epigenetic and epitranscriptomic regulator during early development, in ESC subpopulations and in tissue heterogeneity in the near future.TET2 function as an epigenetic modifier through 5mC to 5hmC, 5fC and 5caC oxidation is currently fully accepted. The relevance of TET2-mediated catalytic-dependent functions, has been implicated xidation . The detxidation , 2015 maAll authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.