Drosophila central brain produce transit-amplifying intermediate neural progenitors (INPs) that exhibit temporal patterning. However, the known temporal factors cannot account for the neuronal diversity in the adult brain. To search for missing factors, we developed NanoDam, which enables rapid genome-wide profiling of endogenously tagged proteins in vivo with a single genetic cross. Mapping the targets of known temporal transcription factors with NanoDam revealed that Homeobrain and Scarecrow (ARX and NKX2.1 orthologs) are also temporal factors. We show that Homeobrain and Scarecrow define middle-aged and late INP temporal windows and play a role in cellular longevity. Strikingly, Homeobrain and Scarecrow have conserved functions as temporal factors in the developing visual system. NanoDam enables rapid cell-type-specific genome-wide profiling with temporal resolution and is easily adapted for use in higher organisms.Temporal patterning of neural progenitors is an evolutionarily conserved strategy for generating neuronal diversity. Type II neural stem cells in the   •NanoDam: rapid genome-wide profiling of endogenously tagged chromatin-binding proteins•in vivo without cell isolationNanoDam allows precise spatial and temporal profiling •Scro and Hbn are temporal factors in both the CB and the visual system•Regulatory interactions of Scro (NKX2.1) and Hbn (ARX) are also conserved in vivo. By combining results from NanoDam and scRNA-seq, Tang et al. identify conserved temporal transcription factors acting in neural progenitors to regulate neuronal patterning in the Drosophila central brain and optic lobe.In NanoDam, a nanobody targets Dam methylase to endogenously tagged DNA-binding proteins for genome-wide profiling  Drosophila embryonic central nervous system (CNS), the larval central brain (CB), and optic lobe (OL)  and progenitors that are patterned both spatially and temporally . Spatialobe (OL) . In the obe (OL) . GMCs inobe (OL) . The seqobe (OL) . The tigThe INPs produced by the 6 dorsal-medial type II lineages (DM1–6) sequentially express the temporal TFs Dichaete , Grainyhead  and Eyeless  . These tThree further factors contribute to INP temporal progression, but they are expressed broadly rather than in discrete temporal windows: Osa, a SWI/SNF chromatin remodeling complex subunit; and two further TFs, Odd-paired (Opa)  and Hamlin vivo without cell isolation, cross-linking, or immunoprecipitation. Temporal factors are expressed transiently in a small pool of rapidly dividing progenitor cells. NanoDam provides a simple, streamlined approach to obtain genome-wide binding profiles in a cell-type-specific and temporally restricted manner.Given the feedforward and feedback transcriptional regulation previously observed in temporal transcription cascades, we surmised that previously unidentified temporal factors would be among the transcriptional targets of D, Grh, or Ey. Therefore, we devised NanoDam to identify the genome-wide targets of TFs within their normal expression windows Using NanoDam, we identified the transcriptional targets of D, Grh, and Ey in INPs and, by performing single-cell RNA sequencing (scRNA-seq), determined which of the directly bound loci were activated or repressed. Next, we assessed which of the target loci encoded TFs and whether these were expressed in restricted temporal windows within INPs. We surveyed where in the INP transcriptional cascade these factors acted and ascertained whether they cross-regulate the expression of other temporal TF genes, as expected for temporal factors. Finally, we showed that Hbn and Scro act as temporal factors, playing similar roles and exhibiting the same cross-regulatory interactions, in the temporal cascade of the developing visual system. This is particularly striking as the INPs and the NSCs of the developing OL have different cells of origin, and yet the mechanism they use to generate neuronal diversity is conserved.E. coli Dam methylase , in which the DNA- or chromatin-binding protein of interest is fused to an ethylase . As in Dethylase . Endogenethylase , such thethylase . TaDa en4 system , while a4 system . For eac4 system .In designing NanoDam, we sought to benefit from all of the advantages of TaDa while, first, bypassing the need to generate recombinant transgenes and, second, assessing genome-wide binding only when and where the DNA- or chromatin-binding protein is normally expressed. NanoDam recruits the Dam methylase to endogenously tagged proteins, using nanobodies as targeting agents. Nanobodies are recombinant antibody fragments derived from the variable region of heavy-chain antibodies present in camelid species . We fuseWe fused nanobody vhhGFP4—which recognizes GFP and a number of its variants, including eGFP, YFP, CFP, BFP, and Venus —to the CDrosophila homolog of CTIP1/2/Bcl11a/b, which we call Chronophage (Cph). The cph locus had been tagged by insertion of a YFP protein trap (Cph::YFP), resulting in Cph-YFP expression from its own promoter in the embryonic CNS (worniu-GAL4 and compared our results from NanoDam with those we obtained using TaDa. NanoDam for Cph::YFP and TaDa for Cph (2 replicates of each), were performed under the same conditions -like curves to measure peak recovery between individual replicates and references  and the endogenously tagged protein (under the control of its own regulatory elements) D. This iD-GAL4  (nSyb and high pros); and clusters 9–10 corresponding as glial cells (repo)  C and S3Cs (repo) D. Clusteand pros C and 3D.D, grh, and ey were expressed in the INP cluster  and those shown previously to regulate temporal identity , we found several candidate temporal TFs. We focused our attention on two factors that were expressed in a subset of INPs: the paired-like homeobox TF homeobrain (hbn) (scarecrow (scro) (We determined which TFs in each NanoDam cluster were expressed in INPs by comparison with our scRNA-seq dataset . In addiin (hbn)  and the w (scro) F.homeobrain clusters in ND4   , 2018. sung INPs G. scro wd with D I and S4Gest INPs K and S4Il window L.hbn locus and is expressed in INPs after Hbn. Given that temporal factors are thought to repress transcription of the factor that precedes them, we tested whether Ey repressed Hbn expression. We found that ectopic expression of Ey in all INPs resulted in a significant reduction in Hbn expression    . By contned Ey+) D and 6E. cascade F.scro were also expressed in subpopulations of medulla NSCs , hbn (Arx), and scro (Nkx2.1) are restricted to distinct progenitor populations in the developing mouse forebrain  neurons but no change in the number of lower layer (early-born) neurons . The minimum number of cells needed for NanoDam remains to be determined, though factors intrinsic to the protein, such as binding affinity or nuclear concentration, may influence this.ahb1000@cam.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Andrea Brand , insc-GAL4 (GAL4MZ1407) , wor-GAL4 (tub-GAL80ts (BDSC 7019), Ay-GAL4, UAS-GFP (BDSC 4411), 10xUAS-IVS-mCD8-RFP (BDSC 32219), UAS-mCD8-GFP (BDSC 5130), UAS-Dicer2 (VDRC 60008) and hsFLP122. The fusion proteins used were: cph::YFP , D-GFP (BDSC 66758), Grh-GFP (BDSC 42272), Ey-GFP (BDSC 42271). The misexpression lines used were: UAS-D (BDSC 8861), UAS-ey (BDSC 6294), UAS-grh (BDSC 42227), UAS-hbn (this study), UAS-scro (this study). The RNAi lines used were: UAS-ey-RNAi (VDRC 106628), UAS-grh-RNAi (VDRC 101428), UAS-hbn-RNAiCDS2 (this study), UAS-hbn-RNAi3UTR (this study), UAS-mCherry-RNAi (BDSC 35785), UAS-scro-RNAi (BDSC 33890) and UAS-scro-RNAiCDS (this study). The generation of UAS-NanoDam is described below. w1118 was used for NanoDam control experiments and as a reference stock for functional experiments.The following lines were used to drive transgenes under the control of UAS in a spatially and temporally restricted manner: 4MZ1407) , ogre-GAwor-GAL4 , tub-GALpUAST-mCherry-Dam-vhhGFP4 (pUAST-NanoDam) was generated by PCR amplifying vhhGFP4 from genomic DNA isolated from deGradFP flies  using thhbn coding sequence from an embryonic cDNA library using the primers Forward: AGATGAATTCATGATGACCACGACGACCTCG and Reverse: ATGACTCGAGTCAGTCCTCGCCCTTGGTG. The resulting PCR product was cloned into the pUAST-attB vector  and pWALIUM20-scro-shRNACDS (targets scro CDS), were generated by annealing 10 μM concentration of the following oligonucleotide pairs at 98 °C for 5 minutes in annealing buffer  and then leaving the mixture to cool at room temperature. The resulting products were cloned into the pWALIUM20 vector .aattcgcGCCCAGGTGTACAGACCTATTtatgcttgaatataactaAATAGGTCTGTACACCTGGGCactg. For all expression and RNAi constructs, transgenic flies were generated by germline injection into embryos carrying NanoDam line was crossed to Cph::YFP; wor-GAL4. As a control, UAS-NanoDam was crossed to wor-GAL4. wor-GAL4 is expressed in neuroblasts from approximately stage 10. Embryos were collected at 25 °C and harvested at stage 16. 10-15 μl of embryos were harvested for DNA purification.To perform Cph NanoDam, a Cph::YFP; UAS-NanoDam, tub-GAL80ts and D-GAL4 were crossed to w1118 (control), D-GFP, Grh-GFP or Ey-GFP. For optic lobe NanoDam, flies carrying UAS-NanoDam, tub-GAL80ts and ogre-GAL4 were crossed to w1118, D-GFP, or Ey-GFP. Temporal restriction of NanoDam expression was achieved using GAL80ts, a temperature-sensitive negative regulator of GAL4  at 4 °C. Single cells were sorted into PBS with 0.03 % Bovine Serum Albumin to prevent clumping. Live, single cells were sorted based on size, and fluorescence intensities of RFP and SYTOX Blue Dead Cell Stain. Gates for FACS were established using a negative control to adjust for autofluorescence and a positive control for SYTOX Blue Dead Cell Stain using dissociated cells incubated at 65 °C for 15 minutes.Single cell RNA sequencing libraries were generated using 10X Chromium  and sequenced on an Illumina HiSeq 4000 at the Genomics Core Facility, Cancer Research UK Cambridge Institute.Larval brains were dissected in PBS and fixed on a shaker for 20 minutes in 4 % formaldehyde/PBS. Fixed brains were washed well with PBS containing 0.3 % Triton-X (PBTx) before immunostaining and then for at least 15 minutes in 10 % normal goat serum/PBS. Samples were incubated overnight at 4 °C with primary antibodies diluted in 0.3 % PBTx, washed well with 0.3 % PBTx, then incubated overnight at 4 °C with secondary antibodies diluted in 0.3 % PBTx. Samples were washed well with 0.3 % PBTx then mounted in Vectashield (Vector laboratories) for imaging.The following primary antisera were used: guinea pig anti-D 1:200 (a gift from Alex Gould), guinea pig anti-Dpn 1:5,000 , rat antscro mRNA, custom probes designed against the scro coding sequence, buffers and fluorophore-labelled amplification hairpins were sourced from Molecular Instruments . Data processing was performed with a wrapper script to automate and parallelize the application of the damidseq_pipeline (∗.fastq.gz-files were mapped with bowtie2 (v2.3.4.1) to the Drosophila dm6 genome assembly and all reads were assigned to bins defined by consecutive GATC sites throughout the genome. For NanoDam of each transcription factor, all replicates  were normalised individually to all control replicates (8 w1118 replicates) followed by quantile normalisation of all pairwise comparisons to each other. Binding profiles for each transcription factor were generated by averaging the binding intensities across all normalised comparisons per GATC-bin. The averaged logarithmic binding intensities were subsequently backtransformed and bedGraphToBigWig (v4) was used to generate ∗.bw-files for visualisation of the binding profiles in IGV  into consensus peaks. Consensus peaks were filtered by false discovery rate  and by occurrence in more than 50 % across all pairwise combinations.The quality of all pipeline  with thes in IGV . Broad p∗.bam-files of individual samples after extension to 150 bp across consecutive 500 bp-bins. Pearson correlation coefficients were calculated with the R stats (v3.6.1) package between these NanoDam, TaDa, NanoDam only and TaDa only libraries or for comparison of normalized, pairwise replicates. Complexity and fingerprint analysis were conducted with preseq (v2.0.0) and custom R scripts adapting plotFingerprint’s algorithm (v3.0.2). Enrichment of binding intensities on peaksets and ROC-like curves were calculated with custom R scripts employing the R rtracklayer package (v1.46.0).For genome-wide correlation analysis of individual libraries, bamCoverage (v3.0.2) was used to aggregate reads from Binding intensities for all GATC-bins overlapping with individual peaks were averaged and normalised for the length of the peak. Intensities for GATC-bins intersecting with the peak borders were weighted depending on the overlap of the respective bin with the peak. The resulting intensities per peak and genotype were converted into z-scores and clustered with the ‘kmeans’ function of the R stats package (v3.6.1). The optimal number of clusters was determined by using the factoextra (v1.0.5), clValid (v0.6-6) and mclust (v5.4.5) packages in R. All considered clustering approaches were evaluated by calculating silhouettes with the cluster (v2.0.7-1) package.Drosophila transcription factors from FlyTF.org (https://www.mrc-lmb.cam.ac.uk/genomes/FlyTF/old_index.html).Peaks were assigned to the closest transcriptional start site of protein-coding genes according to ensembl annotations with bedtools . To identify genes encoding transcription factors the resulting genes were intersected with the curated list of supported https://github.com/AHBrand-Lab/NanoDam_analysis. Raw and processed data can be acquired at NCBI GEO under the accession number GSE190210.R markdowns outlining all analytical workflows as well as the suite of Python3 scripts for automated DamIDseq analysis are available at ∗.gtf- and ∗.fa-files for the Ensembl dm6 genome assembly. The Dichaete-BAC used to generate the D-GFP (BL66758) was in silico cloned and custom made ∗.gtf- and ∗.fa-files of the resulting plasmid were combined with the Drosophila transcriptome to generate a separate reference for validation of D-GFP expression as a means to quality check the sample preparation. Replicates (with and without D-GFP) were found to be very similar when the two samples were integrated. Data for both single cell data sets were separately normalized and scaled with the Seurat R package (v2.3.4) prior to their integration via canonical correlation analysis (Drosophila transcription factors from FlyTF.org.The acquired single cell RNA sequencing files were mapped and count matrices derived from Cell Ranger (v2.2.1). The required reference transcriptome was build from analysis . The numhttps://github.com/AHBrand-Lab/NanoDam_analysis). Raw ∗.fastq.gz files and data processed by Cell Ranger (v2.2.1) are deposited at NCBI GEO under the accession number GSE190210.The entire analysis is outlined in the ‘scRNAseq_analysis.Rmd’ R markdown (www.graphpad.com) was used for statistical analyses. Following normality tests, Mann-Whitney U tests were used to assess the statistical significance between two genotypes and Kruskal-Wallis tests were used when experiments contained more than two genotypes. N numbers for each experiment are found in the figure legends. Error bars indicate the standard deviation (SD) and is also noted in the figure legends.GraphPad Prism 8 for Mac OS X (


We identified over 140,000 cis-regulatory elements throughout development, including classes with distinct features dependent on their activity in time and space. We delineated the distinct distance topology and chromatin features between regulatory elements active during zygotic genome activation and those active during organogenesis. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predicted functional relationships between them beyond sequence similarity, thus extending the utility of zebrafish developmental genomics to mammals.Zebrafish, a popular organism for studying embryonic development and for modeling human diseases, has so far lacked a systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created a central repository to store and process zebrafish developmental functional genomic data. Our data coordination center ( cis-regulatory elements throughout development and perform a comparison with the mouse regulatory landscape.The DANIO-CODE consortium leverages a large-scale multiomic dataset to improve zebrafish genome annotation. They identify ~140,000  Use of zebrafish in genomic studies has discovered chromatin signatures6, DNA codes of promoter usage7, regulatory patterns of DNA methylation and post-transcriptional messenger RNA regulation13, while zebrafish single-cell genomics pioneered applications for spatially resolving lineage-specific transcriptomes during development14, and comparative genomics has predicted conserved regulatory elements and their long-range target genes15. Exploiting the ease of zebrafish transgenesis, automated in vivo imaging and image processing, which can be upscaled to high throughput16, provided validation of predicted disease-associated human enhancers18. However, despite these many landmark studies, zebrafish has lacked systematic functional annotation programs at a scale seen in other models, including ENCODE19, Roadmap Epigenome21 and modENCODE23. Thus, disparate zebrafish genomic datasets remain largely inaccessible to thousands of laboratories. Furthermore, while promoters and enhancers from some adult zebrafish tissues have been annotated24, embryonic and larval stages lack functional annotation despite representing the bulk of zebrafish-based research. Recognizing these needs, DANIO-CODE was established as a multinational bottom-up effort25.Zebrafish is used as a model vertebrate in over 1,200 laboratories worldwide for studies of organismal, cell and gene function in development, regeneration, behavior, toxicology and disease modeling. Its popularity is due to its experimental advantages26; (3) developing standardized analysis pipelines and remapping all sequencing datasets; and (4) generating an integrated track hub that allows visualization with common genome browsers. Additionally, DANIO-CODE aimed to conduct an integrated analysis of these datasets to promote discovery, functional element classification and determination of features of developmental dynamics. Finally, in this study, we applied new approaches for comparative analysis of zebrafish and mammalian genomic datasets to uncover conservation of the genomic landscape and to expand the utility of zebrafish developmental genomics resources.DANIO-CODE aimed to functionally annotate the developing zebrafish genome by (1) collecting all published and producing new genomic data from 38 laboratories worldwide and standardizing metadata annotation; (2) creating and maintaining a single data coordination center (DCC) for continued accumulation and user download of zebrafish genomic datasets26, which we populated with zebrafish developmental genomic data, including standardized annotation of metadata of diverse, often inconsistently annotated, published datasets . The DCC is accessible from ZFIN and includes datasets, their underlying samples and sequencing protocols using ZFIN and ENCODE nomenclature (www.danio-code.zfin.org). To identify and analyze the developmental dynamics of genomic features, direct comparison across datasets produced by different laboratories and different protocols is required. To this end, we carried out consistent reprocessing starting from the raw sequencing data . The DCC data include 1,438 published datasets contributed by data producers directly or collected by DANIO-CODE data annotators, together with strategically selected datasets for developmental stages from the public domain. In addition, 366 datasets were generated by consortium members to fill gaps and to aid functional annotation and functional element characterization, including 15 CAGE-seq, 18 ChIP–seq, 11 ATAC-seq, 2 Hi-C and 320 4C-seq datasets  annotated by ChromHMM categories. The bulk sample-based predictions for regulatory elements are complemented with annotations of cell-type specificity of candidate regulatory elements provided by single-cell ATAC-seq30  precisely, we used DANIO-CODE expression data to improve current Ensembl models of developmentally active genes. We utilized 139 developmental RNA-seq samples to identify 31,458 genes comprising 55,596 transcripts , and predicted binding sites for these motifs across all promoters  to be expressed in similar stages and highly likely  to have multiple promoters in mouse. Multipromoter genes were enriched in KEGG signaling pathways in zebrafish  to allow this activity analysis on any RNA-seq data.Precision promoter annotation and expression dynamics allow exploitation of this resource to predict TF activity regulating the promoters. We implemented Motif Activity Response Analysis (MARA)42 peaks as PADREs in four pre-zygotic genome activation (ZGA) and seven post-ZGA stages, which we further classified on the basis of the presence of four histone marks using ChromHMM44 in five post-ZGA stages  a manually curated catalog of published enhancers functionally validated in transgenic reporter assays  and calculating the phastCons score for each PADRE Fig. . Early-a30 data , containing ~140,000 regions open in at least two neighboring stages  promoters. First, we characterized these promoters at Dome and Prim-5 stages on the basis of their chromatin accessibility at nucleosome resolution, revealing eight clusters Fig. . We detense Fig. . Notablynse Fig. , suggestnse Fig. , and wernse Fig. . Howevernse Fig.  with notnse Fig. . Overall15, which also often contain other ‘bystander’ genes that do not respond to those enhancers. The extent of GRBs coincides with those of topologically associating domains (TADs) around developmental genes54  within genomic regulatory blocks (GRBs)s54 Fig. . We expl56  embryo TADs, chromatin started opening at the boundaries as early as the Dome stage and remains open thereafter Fig. . GRB TAD57, of which ~1,300 fell in TADs and were enriched in GRB TADs , we distinguished them from super-enhancers and called them ‘H3K27ac ensembles’. We hypothesized that they might be associated with the lack of fully formed TADs in the early stages, when enhancers are used proximally to early active promoters. To test this, we investigated the relationship between the chromatin interactions and activity of H3K27ac ensemble-associated genes during early versus late embryogenesis.When we estimated the activity of enhancer candidates by H3K27ac in TADs, we observed that such elements in late stages are numerous, short and distributed throughout the entire TAD length. In contrast, many fewer PADREs were active early at Dome stage, and they often occurred in clusters with uninterrupted H3K27ac signal connecting them  among vertebrates. Existing comparative methods rely on direct alignments between species of interest60. However, the large evolutionary distance between fish and mammals limits the power of comparison, due to loss of noncoding sequence similarity. We developed a method to predict functional conservation across large evolutionary distances and genomic scales independent of direct sequence alignment, exploiting the fact that functional elements often maintain collinear syntenic positions, while their spacing scales with genome size, particularly in GRB TADs62. We selected 13 high-quality bridging species reference genomes and using stepped pairwise sequence alignment  and mouse E10.5; . H3K27mecus Fig.  shows H3cus Fig. , suggestent Fig. .irx3a in Fig. cis and trans in Fig. We next looked at conservation of functional elements marked by open chromatin. We classified zebrafish ATAC-seq peaks in the GRB TADs as directly conserved (DC) if they fall in a region of direct sequence alignment with mouse , indirectly conserved (IC) if they do not directly align  but were alignable through bridging species and nonconserved (NC) for all other peaks  and syntenic enhancer TFBS content, highlighting the predictive value and functional relevance of epigenetic subdomains within syntenic TADs. This zebrafish resource thus expands on and complements the existing functional genome mapping efforts in mammals and modENCODE species.https://www.birmingham.ac.uk/generic/danio-code/index.aspx)Overview of the DANIO-CODE consortium and contributors (danio-code.zfin.org)DANIO-CODE Data Coordination Center (DCC) (http://genome.ucsc.edu/cgi-bin/hgTracks?db=danRer10&hubUrl=https://danio-code.zfin.org/trackhub/DANIO-CODE.hub.txt danRer11: http://genome.ucsc.edu/cgi-bin/hgTracks?db=danRer11&hubUrl=https://danio-code.zfin.org/trackhub/DANIO-CODE.hub.txt)DANIO-CODE track hub for UCSC browser Session for WashU EpiGenome Browser Motif Activity Response Analysis (MARA) (https://swissregulon.unibas.ch/sr/downloads)Regulatory motifs and regulatory site annotations (gitlab.com/danio-code)Code repository for DANIO-CODE processing pipelines Code repository for data analysis in this paper 26 (https://danio-code.zfin.org). The DCC facilitated data collection as well as data annotation and subsequent data distribution.Members of the zebrafish community were invited to provide their published as well as unpublished data to the DANIO-CODE consortium. Benefiting from experiences of consolidating data in the decentralized data production of the modENCODE consortium72 and employs controlled vocabularies based on ZFIN nomenclature73. In addition to the community-provided data, DANIO-CODE annotators strategically selected additional published datasets to complement developmental stages or assays so far under-represented in the DCC. These datasets were annotated by the DANIO-CODE curators on the basis of the respective publications.Demultiplexed FASTQ files were provided by community members to the DCC file system. Using the DCC web frontend, the community members were guided through an annotation process to annotate the data they provided. The DCC data model is derived from Sequence Read Archive (SRA) data structureshttps://www.dnanexus.com). Data and annotation quality control measures were established for all data in the DCC.Consistent data and annotation formats allowed the consistent processing of all data in the DCC. For this, we developed computational workflows for all the data types and implemented these workflows for the DNAnexus system . StringTie v.1.33b (ref. 79) was used to call transcripts, which were then all assembled using TACO80, generating a total of 194,508 transcripts in canonical chromosomes. Transcript quantification was done using Salmon v.0.11.2 (ref. 81). We removed read through, mono-exonic and transcripts overlapping three or more Ensembl genes. All protein-coding transcripts above 200 kb and long noncoding RNAs above 100 kb were excluded (permissive set). We defined transcripts that are expressed in a minimum of two closest stages as the robust set. To get high confidence transcripts, we added those that have consensus CAGE transcription start site clusters (TCs) in the proximity (±500 bp), yielding 70,354 transcripts (permissive set) and 55,596 transcripts (robust set). Out of 35,117 Ensembl genes (v.91), 22,065 and 23,568 genes were covered in our robust and permissive sets, respectively.Wild-type embryonic paired-end and stranded RNA-seq samples 82 regions were excluded from the set of CAGE-supported TSSs (cTSSs). After an initial application of CAGEr83 we discovered systematic differences between nAnTi and tagging CAGE samples both at the number of transcription start site clusters number and summed promoter/gene expression across samples produced with the two CAGE protocols. In particular, the fraction of CAGE signal coming from annotated exons was elevated in nAnTi samples, skewing the statistics. To a varying extent this phenomenon (known as exon painting/carpeting) has been previously observed and attributed to recapping of degraded mRNAs. Since the majority of true TSSs are initiated at either YC or YR dinucleotides9, we analyzed dinucleotide frequencies at initiation sites and confirmed an increased proportion of other (non-YC and non-YR) dinucleotides in nAnTi compared to tagging samples. We therefore decided to remove all CAGE tags not initiated at YC or YR dinucleotides.First, all reads mapping to poorly assembled genomic regions or otherwise blacklisted84 to a common exponent alpha = 1.1 and 5 to 1,000 tags fit range, and the TCs were produced using the following parameters of CAGEr: threshold = 0.7, thresholdIsTpm = TRUE, nrPassThreshold = 1, method = ‘distclu’, maxDist = 20, removeSingletons = TRUE, keepSingletonsAbove = 5. This yielded a comparable number of TCs across samples without an obvious bias towards high numbers in nAnTi samples. The number of TCs moderately increased in post-ZGA samples.The remaining set of TSSs was power-law normalizedTo compare expression levels across samples we called consensus clusters  with settings tpmThreshold = 1.0, qLow = NULL, qUp = NULL, maxDist = 20. To further filter weak or spurious tag clusters we kept consensus clusters that were expressed in at least two consecutive developmental stages. Specifically, we required that there exist a TC within a consensus cluster in both consecutive stages with at least 1.0 tags per million (tpm) expression. This yielded 27,781 consensus clusters. We calculated expression of each consensus cluster by summing all YC- and YR-initiated CAGE tags from within the cluster across stages. This approach differs from CAGEr implementation, which includes expression only from TCs within a consensus cluster and is subject to generating noise at lowly expressed regions due to the tpmThreshold parameter.To visualize obtained expression levels we made a two-dimensional principal components analysis plot, which correctly grouped nAnTi CAGE and tagging CAGE samples from the same stage.A gene can have multiple transcripts/isoforms that differ in their TSSs by few nucleotides, to tens of kilobases. When a gene has multiple transcripts, Ensembl assigns the longest transcript as a reference transcript and its promoter as a reference promoter. To comprehensively assign CAGE peaks to transcripts, we analyzed transcript models from Ensembl, RefSeq and novel RNA-seq transcripts from DANIO-CODE. Thus, we focused only on the transcripts that are supported by CAGE peaks. Similar to the Ensembl method, we annotated the longest transcript as the reference transcript and its promoter as the reference promoter. Remaining transcripts whose TSSs were proximal (<300 nucleotides) to the assigned reference transcripts were excluded. On the remaining distal transcripts, the longest transcript was assigned as an alternative transcript and excluded other transcripts with proximal (<300 nucleotides) transcription start sites. Some genes have more than one alternative transcript, thus we iterated this process to annotate additional alternative promoters that are distal from assigned reference or alternative transcripts.28 from four embryonic stages , which are similar to the zebrafish stages analyzed. We analyzed Ensembl, RefSeq and RNA-seq transcripts models downloaded from UCSC table browser. We used a similar method to that described above to annotate alternative promoters in mouse. To identify orthologs of alternative transcripts/promoters, we downloaded mouse/zebrafish ortholog tables from Ensembl85.To annotate alternative promoters utilized during mouse embryonic developmental stages, we analyzed FANTOM5 CAGE-seq data36, we extracted the DBD sequences of the human and mouse TFs associated with these regulatory motifs. Using BLAT86, we then aligned all zebrafish DBD sequences to the human/mouse DBD sequences and associated zebrafish TFs with the human or mouse TF (and regulatory motif) that best matched their DBD sequences. Note that multiple zebrafish TFs can thus end up being associated with the same regulatory motif. These procedures led to 814 zebrafish TF genes being assigned to 581 unique regulatory motifs.To curate a set of regulatory motifs for zebrafish we first collected all Pfam models that corresponded to DNA-binding domains (DBDs). To define a set of zebrafish TFs we extracted a representative protein sequence for each zebrafish gene, ran HMMER with these Pfam models and extracted the DBD sequences for each protein with substantial hits. Starting from a previously curated collection of regulatory motifs for human and mouse87 and multiply aligned the orthologous regions using T-coffee88. We also obtained a phylogenetic tree of the four species from the observed fractions of conserved nucleotides in the promoter alignments of each pair of species. For each regulatory motif, we then ran MotEvo89 on these multiple alignments to obtain TFBS predictions genome wide. Using these TFBS predictions, we constructed a site count matrix N for the MARA analysis, where each component Npm corresponds to the sum of the posterior probabilities of all binding sites for motif m in promoter p. Motifs whose site counts across promoters genome wide had a higher Pearson correlation than r = 0.6 were grouped into motif groups, leading to 489 motif groups. ISMARA analysis was then performed on the CAGE expression data across the developmental time course89.For each promoter, we defined the proximal promoter region as the region from 500 base pairs (bp) upstream to 500 base pairs downstream of the CAGE transcription start region (TSR). For each proximal promoter region, we obtained the orthologous regions from the goldfish, common carp and grass carp genomes using LASTcis-regulatory elements genome wide using their characteristic ChIP–seq signal. For example, acetylation of lysine residue 27 and monomethylation of lysine residue 4 on the histone H3 (H3K27ac and H3K4me1) are features of active chromatin. The modification H3K4me3 is characteristically found on promoters, while H3K27me3 represents Polycomb-repressed chromatin. Those modifications were localized ChIP–seq. We used ChromHMM44 to segment the genome into regions containing specific chromatin marks. We captured the epigenetic state of the genome in five different development stages. We used published data for the Dome, 75% epiboly, 5–9 somites, Prim-5 and Long-pec stages93, as well as newly produced data for the 5–9 somites and Long-pec stages. After optimization, we found ten optimal latent states on basis of the emission parameters of chromatin marks. The states were matched between stages and manually assigned a function using The Roadmap Epigenome Project21 annotation as a reference. The identified functional elements were annotated as follows:We identified 1. Active TSS 1 (1_TssA1), 2. Active TSS 2 (2_TssA2), 3. TSS Flanking region 1 (3_TssFlank1), 4. TSS Flanking region 2 (4_TssFlank2), 5. Active enhancer 1 (5_EnhA1), 6. Enhancers flanking region (6_EnhFlank), 7. Primed enhancer (7_EnhWk1), 8. Poised elements (8_Poised), 9. Polycomb-repressed regions (9_PcRep), 10. Quiescent state (10_Quies).The active promoters and promoter flanking regions, in addition to active chromatin marks, show strong emission of H3K4me3. Moreover, the promoter-associated states are mostly found on and around the annotated TSS. The states missing H3K4me3 and not found around TSSs were annotated as enhancer related. Depending on whether both H3K27ac and H3K4me1 are present, as well as the strength of the emission, the enhancer states were divided into active enhancers , enhancers flanking  and primed enhancers (H3K4me1 emission only). States emitting H3K27me3 were annotated as Polycomb related. In addition to H3K27me3, when active marks were present, the state was assigned as poised; otherwise, it was assigned as Polycomb repressed. When no marks were present, the region was assigned as quiescent. Most of the genome shows no marks at all.94 less than 0.1, in seven developmental stages . We termed those regions as predicted ATAC-supported developmental regulatory elements. The reason for naming them in this way was to distinguish them from ENCODE cCREs in two segments as: (1) they contain open regions even without the support of functional marks, and (2) we wanted to emphasize the developmental aspect of the defined set of elements. All stage-specific PADREs were merged to form a set of regions called consensus PADREs (cPADREs). Two different sets of cPADREs were defined. The permissive cPADREs consist of all PADREs merged and number around ~240, 000 elements. The strict set considers regions that are open in at least two neighboring stages. This set counts ~140,000 elements. All cPADRE analyses in this paper were done on strict cPADREs. We assigned each ATAC-seq peak a functional annotation on the basis of overlaps with the ChromHMM state in available stages.We constrained our subsequent analyses to the regions in the genomes that are open, that is, depleted in nucleosomes as identified by ATAC-seq. We identified stage-specific open chromatin regions consistent between replicates, with the irreproducibility discovery rate95  phastCons scores from Chen et al.24.Constitutive elements were defined as the intersection of distal PADREs at every developmental stage. COPEs were defined as constitutive, annotated as quiescent at every developmental stage. DOPEs were defined as cPADREs, annotated as quiescent at every developmental stage. DOPEs were further classified as adults-marked DOPEs if they overlapped H3K27ac marked regions in any of the adult tissuesk-means clustering .For each TSR we defined a reference point as the TSS with the highest mean post-MBT expression as ‘dominant TSS’  and required that it amounts to at least 0.2 tpm. This further reduced the set of consensus clusters to 21,914 elements. We then merged ATAC samples from the Prim-5 stage and extracted Tn5 cut sites from both ends of ATAC reads while correcting for Tn5 overhang, smoothed with a Gaussian kernel with standard deviation 3 bp and log-transformed. These ATAC cut-site profiles served as input to 57 with the distance from TSS to exclude adjusted to 500 bp.Enhancer ensembles were detected using H3K27ac peaks and mapped reads from the Dome stage (DCC data identities: DCD006167DT and DCD008973DT) as input for the ROSE algorithmx overlaps an anchor point a. The score decreases exponentially as the distance |x − a| increases. For a single species comparison, the function is defined as follows:g denoting the genome size of the respective species and s a scaling factor that can be determined by defining a distance half-life dh, as the distance |x − a| at which the scoring function returns a value of 0.5:Genomic coordinates of GRB loci were projected between zebrafish and mouse using multiple pairwise sequence alignments between a set of 15 species. The basic concept of our approach is that, under the assumption of conserved synteny, a nonalignable genomic region can be projected from one species to another by interpolating its relative position between two alignable anchor points. The accuracy of such interpolations correlates with the distance to an anchor point. Therefore, projections between species with large evolutionary distances, such as zebrafish and mouse, tend to be inaccurate due to a low anchor point density. Including so-called bridging species may increase the anchor point density and thus improve projection accuracy. Extended Data Figure The length of a path through the graph is then given by subtracting the product of the distance scoring function for every node in the path from 1:lp:P denoting the set of all paths through the graph. The optimization problem presents a classic shortest path problem and is solved using Dijkstra’s shortest path aAlgorithm98.The shortest path 99, when their transcriptomes are most similar100. To match the whole-embryo zebrafish data, we created a virtual embryo dataset for mouse by merging data for six different tissues . The mouse H3K27me3 profile was projected onto zebrafish genomic coordinates using the multispecies approach by splitting the zebrafish GRB into 1-kb windows, projecting their center coordinates onto mouse and retrieving the signal from the respective 1-kb bin in mouse. ‘Signal’ stands for H3K27me3 coverage represented as quantiles after quantile normalization of the two distributions in zebrafish and mouse. Signal overlap is represented by the log signal ratio and capped to values in . Signal amplitude represents the maximum signal of zebrafish and mouse to the power of 10 to increase the variance of signal amplitude. For mouse and zebrafish comparison of H3K27ac ensembles, previously published data were used101.We compared H3K27me3 ChIP–seq data from phylotypic stages in zebrafish (Prim-5 stage) and mouse (E10.5 stage)Zebrafish ATAC-seq peaks were classified into three conservation classes on the basis of the projection using the multispecies approach. Directly conserved ATAC-seq peaks overlap a direct alignment between zebrafish and mouse. Indirectly conserved ATAC-seq peaks do not overlap a direct alignment, but are projected with a score >0.99, that is, either overlapping or very close to a multispecies anchor. The remaining peaks are classified as nonconserved. A score of 0.99 means that the sum of the distances from peak to anchor points is <150 bp considering all intermediate species in the optimal species path.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01089-w.Supplementary InformationSupplementary Figs. 1–6, Methods and references.Reporting SummarySupplementary TablesSupplementary Tables 1–15.Supplementary Video 1DANIO-CODE DCC upload tutorial.Supplementary Video 2DANIO-CODE DCC upload data with a .csv file.Supplementary Video 3Add a track from the DANIO-CODE track hub to the UCSC genome browser.Supplementary Video 4How to use the DANIO-CODE DCC to study stage-specific gene expression.Supplementary Video 5How to use the DANIO-CODE DCC to explore regulatory elements and their cell-type specificity.


This study aimed to clarify the regulation and mechanism of meiotic initiation in FGSC development.FGSCs were induced to differentiate into meiosis in differentiation medium. RNA sequencing was performed to analysis the difference of transcription level. High‐through chromosome conformation capture sequencing (Hi‐C) was performed to analysis changes of three‐dimensional chromatin structure. Chromosome conformation capture further confirmed a spatial chromatin loop. ChIP‐qPCR and dual luciferase reporter were used to test the interaction between Stimulated by retinoic acid gene 8 (STRA8) protein and Trip13 promoter.Compared with FGSCs, the average diameter of STRA8‐positive germ cells increased from 13 μm to 16.8 μm. Furthermore, there were 4788 differentially expressed genes between the two cell stages; Meiosis and chromatin structure‐associated terms were significantly enriched. Additionally, Hi‐C results showed that FGSCs underwent A/B compartment switching (switch rate was 29.81%), the number of topologically associating domains (TADs) increasing, the average size of TADs decreasing, and chromatin loop changes at genome region of Trip13 from undifferentiated stage to meiosis‐initiation stage. Furthermore, we validated that Trip13 promoter contacted distal enhancer to form spatial chromatin loop and STRA8 could bind Trip13 promoter to promote gene expression.FGSCs underwent chromatin structure remodelling from undifferentiated stage to meiosis‐initiation stage, which facilitated STRA8 binding to Trip13 promoter and promoting its expression. FGSCs were induced into meiosis initiation in differentiation medium . Meiosis‐associated genes were upregulated in Stra8‐germ cells. FGSCs underwent chromatin structure remodelling from undifferentiated stage to meiosis‐initiation stage, which facilitated STRA8 binding to the promoter of Trip13 and promoting its expression. Using Hi‐C technology, studies have revealed the dynamics of 3‐D chromatin organization during mammalian embryogenesis and spermatogenesis.In this study, we explored the regulation mechanism at the initiation stage of meiosis in FGSCs induced by RA in vitro. The results showed that the 3‐D chromatin structure of FGSCs was remodelled during inducing FGSC differentiation into meiosis‐initiation stage with RA. The higher‐order chromatin loop also changed at the genome region of Trip13 between FGSCs and Stra8‐positive germ cells. The STRA8 bound to the promoter of Trip13 in Stra8‐positive germ cells, which contacted distal enhancer to form spatial chromatin loop and promote its expression. These findings deepen our understanding of early in vitro differentiation in FGSCs and provided a theoretical basis for the clinical treatment of female infertility.22.1Induction of FGSC differentiation was performed as described in a previous report.2.2 T3 cells into 24‐well plates and cultured cells until they reached 70%–80% confluence. Lipo8000  was used to transfect equal amounts of pGL3‐mTrip13, pRL‐TKPCDNA3.1‐Stra8 vector for each well, together with the pGL3‐basic/pRL‐TK vector as the negative control (Sigma‐Aldrich). After 48 h, cells were harvested and analysed by the dual luciferase assay following the specific protocols . Each independent experiment was performed three times.We plated 32.3ChIP assays were performed as previously described.2.46 cells were cross‐linked with 1% formaldehyde for 10 min at room temperature. Cross‐linked samples were digested overnight with 500 U of AluI  at 37°C. After inactivation, the diluted samples were ligated using T4 DNA ligase (NEB) at 16°C overnight. RNase A was then added to remove RNA for 1 h at 37°C. Proteinase K (20 mg/ml) was added to the samples at 65°C overnight. DNA was purified by phenol–chloroform extraction. The final qRT‐PCR reactions were performed in triplicate using SYBR Green. Primers for 3C‐qPCR are listed in Table The 3C assay was performed as described previously.2.57 cells were harvested and re‐suspended in 45 ml of DMEM medium, followed by cross‐linking with 125 μl of 37% formaldehyde (Sigma‐Aldrich) to obtain a final concentration of 1% at room temperature for 10 min. Next, 2.5 ml of glycine was added to obtain a final concentration of 0.2 M to quench the formaldehyde. The cells were incubated for 5 min at room temperature and subsequently transferred on ice for 20 min. Fixed cells were centrifuged at 1500 rpm at 4°C for 10 min, washed with cold PBS once, and stored at −80°C. For experimental evaluation, the fixed cells were lysed with cold lysis buffer using dounce homogenizer and the subsequent steps of chromatin digestion, labelling and ligation were performed according to the protocol. Hi‐C libraries were purified using AMPure XP beads . The high‐quality libraries were sequenced using an Illumina sequencing platform.Hi‐C libraries were generated according to the previously published protocol, with minor modifications.2.6FGSCs were collected and total RNA was extracted by using Trizol reagent (Life Technologies) according to manufacturer's instructions. A Bioanalyzer 2100  was used to assess the RNA integrity, and NanoDrop 2000 spectrophotometer  was used to evaluate RNA quantity and concentration. Sequencing libraries were created with an Illumina Hiseq2500 platform  in accordance with the manufacturer's instructions. The quality of RNA‐seq data was evaluated using FastQC. RNA‐seq reads were aligned to the reference genome (mm9) using Hisat2 (version 4.8.2).2.7p < 0.05 was considered statistically significant.The experiments were performed at least three times and data are shown as the mean ± SEM. Student's t‐test was used to calculate differences among groups. GraphPad Prism 6.0 software was used for performing statistical analysis. 33.1To trace the early differentiation of FGSCs induced by RA in vitro, a FGSC line was identified by immunofluorescence Figure  and then3.2To investigate the changes in gene expression between FGSCs and Stra8‐positive germ cells, we isolated Stra8‐positive germ cells at day 3 using flow sorting and performed RNA‐seq. The rate of Stra8‐positive germ cells was 34.13% Figure . A totalTo determine the functional categories of the differentially expressed genes, we performed GO analysis and identified 2506 enriched terms. Germ cell‐associated biological processes were significantly enriched, such as meiosis‐specific processes , cell cycle processes  and hormone‐associated processes . Additionally, chromatin‐structure associated processes (e.g. ‘chromatin organization’ and ‘chromatin remodelling’) were significantly enriched Figure . Several3.3cis contacts while 0.26 billion read pairs belong to inter‐chromosomal trans contacts. Heat maps showed an overview of the intra‐chromosomal contact maps (chromosome 17) randomly selected and suggested that the FGSCs exhibited a distinct chromatin organization from the undifferentiated stage to meiosis‐initiation stage  and 0.34 billion read pairs of short‐range (<10 kb) belong to intra‐chromosomal cis/trans ratio of FGSCs and Stra8‐positive germ cells, and we found a markedly smaller ratio for Stra8‐positive germ cells compared with that in FGSCs   value. TADs of 1396 for FGSCs and 1560 TADs for Stra8‐positive germ cells were identified at a resolution of 40 kb; 1606 TADs for FGSCs and 1850 TADs for Stra8‐positive germ cells were identified at a resolution of 20 kb Figure . The Str) Figure . We then) Figure . Next, w) Figure . Violin ) Figure . In addi) Figure . These r3.6To further investigate whether chromatin loops participate in the meiosis‐initiation processes through bringing distal regulatory elements into spatial proximity, we analysed the chromatin loop changes between FGSCs and Stra8‐positive germ cells. GO enrichment analysis of chromatin loop showed that genes were related to “cell differentiation,” “chromatin organization” and “development of female sexual characteristics” Figure . AccordiTo determine whether Stra8‐positive germ cells had contacts between enhancers and promoters to activate Trip13 transcription, we performed chromosome conformation capture (3C) to evaluate the relative frequencies of the Trip13 promoter interacting with the flanking enhancers or nearby control sites, according to the previously published high‐throughput sequencing data of mice testis (the promoter marker (H3K4me3) and enhancer markers (H3K27ac and DNase)) Figure .37 In To confirm that chromatin loop changes at the genome region of Trip13 play a pivotal role in meiosis initiation and further explore whether STRA8 regulated Trip13 transcription, we performed STRA8 ChIP‐qPCR. As shown in Figure Taken together, our results indicated that the chromatin loop at the genome region of Trip13 was established at early differentiation of FGSCs in vitro. The Trip13 promoter interacted with the distal enhancer to facilitate the access of STRA8 protein, further promoting Trip13 gene expression to participate the meiosis processes.4In our previous study,cis/trans ratio was observed for Stra8‐positive germ cells compared with that for FGSCs.In mammals, chromatin conformation is highly organized, and chromatin undergoes drastic reorganization during cell processes. Recent years, with the development of chromosome conformation capture‐based technologies, studies have demonstrated the importance of chromatin structure in cell development.The genome is divided into A and B compartments.n = 1502, median size 1.34 Mb in FGSCs; n = 1705, median size 1.16 Mb in Stra8‐positive germ cells), especially for the chromosome 13. In mammals, TAD‐TAD are demarcated by boundaries and flanked by CTCF sites.Because the analysis of compartment was at the megabase scale, we further investigated TADs, which are large self‐interacting domains and fundamental structural units.Within the TADs, regulatory interactions between promoter and enhancers are mediated through chromatin loops. Structural proteins (e.g. CTCF) play central roles in chromatin loop organization.We analysed the 3‐D chromatin structure and found that chromatin loop at the genome region of Trip13 played a pivotal role in meiosis initiation. However, much work need to be done in future studies. First, during the differentiation process, whether spatial chromatin structure is altered by knocking out Trip13 in FGSCs and the target gene of Trip13 should be further explored. Second, our RNA‐seq data indicated that hormone‐associated processes were important for meiosis initiation, and future studies should examine the hormone‐associated genes that may be involved in regulation of 3‐D chromatin structure. Finally, our results showed that over 30% of TADs were specified in Stra8‐positive germ cells compared with those in FGSCs. How they are involved in meiosis? The alterations during development from the preleptotene stage to later stages should be further investigated.In conclusion, our study revealed that when FGSCs were induced to differentiate into meiosis‐initiation stage, the cells underwent extensive transcriptome changes and chromatin structure remodelling. At the stage of Stra8‐positive germ cells, STRA8 was expressed. Furthermore, STRA8 bound to the Trip13 promoter which contacted distal enhancer to form spatial chromatin loop and promote its expression. Our findings on the regulation of chromatin structure deepen our understanding of early in vitro differentiation in FGSCs, which was important for the clinical treatment of female infertility.Yabin Zhang performed most of the experiments. Geng G Tian collected data and performed the RNA‐seq and Hi‐C analysis. Xiang Wang performed the RNA‐seq control of FGSCs. Changliang Hou conducted the pLVX‐mcherry‐Stra8‐EGFP dual luciferase reporter vector. Ji Wu designed the experiments conceptualization and supervized the project. Ji Wu and Xiaopeng Hu designed the experiments. Ji Wu and Xiaopeng Hu revised the final manuscript. All authors reviewed and approved the final paper.The authors declare that there is no conflict of interest regarding the publication of this article.FIGURE S1 Immunofluorescence analysis of FGSCs with antibodies against MVH and OCT4. Scale bars: 20 μmClick here for additional data file.FIGURE S2 Observed/expected number of contacts between the two stages of cells. The upper panel showed the observed/expected number of contacts between any pair of 19 euchromosomes in FGSCs or Stra8‐positive germ cells; the lower panel showed the chromosome length (Mb) of each chromosomeClick here for additional data file.TABLE S1 Sequences of the primers used in this paperClick here for additional data file.SupinfoClick here for additional data file.


Myog and Myh3, we identified eight activating enhancers. Furthermore, dCas9-KRAB epigenome editing and RNA-Seq revealed a role for Myog enhancers in the regulation of Myog expression and myogenic differentiation in the native genomic context. Taken together, this study lays the groundwork for understanding 3D chromatin interaction changes of myogenic genes during myogenesis and provides insights that contribute to our understanding of the role of enhancers in regulating myogenesis.Skeletal muscle differentiation (myogenesis) is a complex and highly coordinated biological process regulated by a series of myogenic marker genes. Chromatin interactions between gene’s promoters and their enhancers have an important role in transcriptional control. However, the high-resolution chromatin interactions of myogenic genes and their functional enhancers during myogenesis remain largely unclear. Here, we used circularized chromosome conformation capture coupled with next generation sequencing (4C-seq) to investigate eight myogenic marker genes in C2C12 myoblasts (C2C12-MBs) and C2C12 myotubes (C2C12-MTs). We revealed dynamic chromatin interactions of these marker genes during differentiation and identified 163 and 314 significant interaction sites (SISs) in C2C12-MBs and C2C12-MTs, respectively. The interacting genes of SISs in C2C12-MTs were mainly involved in muscle development, and histone modifications of the SISs changed during differentiation. Through functional genomic screening, we also identified 25 and 41 putative active enhancers in C2C12-MBs and C2C12-MTs, respectively. Using luciferase reporter assays for putative enhancers of  The terminal differentiation of MBs, including the proliferation, differentiation, and fusion of myocytes into multinucleated myotubes (MTs), is essential for the growth and development of skeletal muscle  genes involved in skeletal muscle differentiation (MHC) gene families (Mymk)  . The mol3D chromatin interactions in the nucleus are thought to have a fundamental role in gene regulation by facilitating or restricting regulatory element interactions such as promoter and enhancer interactions. Gene activation and inactivation are linked to dramatic changes in chromatin interactions. The development of chromosome conformation capture (3C) technology and its variations, including circularized chromosome conformation capture sequencing (4C-seq), high-throughput chromatin conformation capture (Hi-C), and promoter capture Hi-C (PCHi-C) have uncovered the principles of higher-order chromatin structures and identified chromatin contacts at different genome structural levels. Compared with Hi-C and PCHi-C, 4C-seq enables the detection of single promoter interactions at a higher resolution (1–2 kb) and lower sequencing depth . Previoucis-regulatory elements that control spatial and temporal gene expression by recruiting specific TFs in a sequence-specific manner  gene is regulated by an enhancer located 1 Mb distant in an intron of the unrelated Lmbr1 gene and point mutations of the enhancer segregated with polydactyly in humans and mice  that are important for the terminal differentiation of MBs to MTs. We found a change in the dynamic chromatin interactions of the myogenic marker genes during C2C12-MB differentiation and identified the significant interaction sites (SISs) of C2C12-MBs and C2C12-MTs. We used dCas9-Krüppel-associated box (KRAB) and RNA-Seq to study the role of Myog enhancers in target gene regulation and myogenic differentiation. These findings enhance our understanding of the molecular regulation of myogenesis associated with the promoter–enhancer chromatin interactions.Here, we used 4C-seq to explore the chromatin interactions of eight myogenic marker genes (in vitro by culture with 2% horse serum for 5 days (A–C). Giemsa staining showed the presence of dark purple-stained MT cytoplasm and pink multinuclei in differentiated C2C12-MBs (A). Immunofluorescence staining revealed the high expression of myosin heavy chain (MHC) protein (markers of MB differentiation) in differentiated C2C12-MBs (A). We observed significant changes in cell morphology and visible MT formation in differentiated C2C12-MBs, with a high fusion index (FI) of 70% (A and B). The mRNA expressions of eight well-characterized myogenic marker genes  were evaluated in C2C12-MBs and differentiated C2C12-MBs by quantitative RT-PCR (qRT-PCR). Compared with C2C12-MBs, the expression levels of the myogenic marker genes after differentiation were upregulated significantly (p < 0.005) (C). These results suggested C2C12-MBs were successfully induced to differentiate into MTs in vitro and highlighted the dynamic mRNA expression of the myogenic marker genes during differentiation.We induced C2C12-MBs to differentiate into C2C12-MTs r 5 days , A–C. Gi2C12-MBs A. Immuno2C12-MBs A. We obs) of 70% , A and B< 0.005) C. These cis-chromosome (A). This conforms to the “cis/overall ratio of > 40%” criteria proposed by Van De Werken et al. (B). All 4C datasets had cov1Mb > 50%, and 87.5% (28/32) of the 4C datasets had cov1Mb > 60%, indicating that most reads were located within 1 Mb of the viewpoint (C). Detailed quality metrics are provided in To reveal genome-wide chromatin interactions of the myogenic marker genes during myogenic differentiation, we performed 4C-seq experiments with the promoters of eight genes. Overall, we generated 32 libraries for the eight genes in C2C12-MBs and C2C12-MTs with two replicates each. We obtained ∼201.37 M filtered reads with an average of 6.29 M reads for each 4C data, and 57.55% to 85.95% of the total reads in 32 datasets were distributed on the romosome A. This cn et al. , indicatn et al. B. All 4Ciewpoint C. Detailcis- and trans-interaction sites  (B), which may be related to the higher heterogeneity and complexity of C2C12 MB differentiation  B, which ntiation , 26.FiguC), which is comparable with other 4C-seq studies , Myog , and Mef2a . These results indicated the interaction divergence and high complexity of the chromatin interaction model of the myogenic marker genes during C2C12 MB differentiation.To identify the reliable interaction sites, we overlapped interaction sites between replicates using r3Cseq. We identified 353 to 1641 interaction sites after overlapping replicates in C2C12-MBs and C2C12-MTs C, which  studies , 28, 29.Myod1, Myog, Mef2d, Myh2, and Myh3 genes but a weak division for the Mef2a, Mef2b, and Mymk genes (A and S6A). Principal component analysis suggested an interaction discrepancy of the myogenic marker genes in C2C12-MBs and C2C12-MTs. (B and S6B). In addition, we compared the interaction profiles of the myogenic marker genes within ±500 kb of the viewpoint in C2C12-MBs and C2C12-MTs. The interaction sites of the myogenic marker genes were changed , with the proportion of SDISs versus total interaction sites close to or greater than 40% for genes . Of note, the Myh2 gene had the most SDISs, with more than 70% of interaction sites significantly changed. In comparison, 27.54% and 33.28% of interaction sites were changed for the Mef2b and Mymk genes, which had the least SDISs. In addition, the mean |Log2FC| of SDISs ranged from 7.48 to 12.42 for each gene (E), indicating a marked alteration in the interaction frequency of SDISs during differentiation. For example, Myod1 had the largest average |Log2FC| (12.42) of SDISs. We also analyzed the mean fold changes for SDISs within ±500 kb of myogenic marker genes and found that the mean |Log2FC| of SDISs ranged from 5.49 to 10.73 (B). These results indicated a marked change in the interaction profiles of myogenic marker genes during myogenesis. Next, we examined the fold change distribution of the upregulated and downregulated SDISs. The results showed that the |Log2FC| values of upregulated and downregulated SDISs had different distributions (F and S9A). Except for Myog, most upregulated SDISs of other myogenic genes had larger fold changes than those of most downregulated SDISs, such as Myod1 and Mef2a (F and S9A). We also examined the fold change of cis- and trans-SDISs and created density plot of the fold change distribution. We found that most trans-SDISs of seven myogenic genes had larger fold change than most cis-SDISs, such as Myod1 and Mef2b (G and S9B). However, most cis-SDISs of Myog had higher fold change than most trans-SDISs (B). This suggests that the trans-interactions of most myogenic marker genes have a stronger change in interaction frequency than the cis-interactions between C2C12-MBs and C2C12-MTs. These results indicate that the interaction sites and interaction intensity of myogenic marker genes changed markedly during differentiation.To clarify the detailed interaction changes during differentiation, we identified significant differential interaction sites (SDISs) between C2C12-MBs and C2C12-MTs using DESeq2 analysis with a ach gene E, indicato 10.73 B. These ibutions F and S9And Mef2a F and S9And Mef2b G and S9Bns-SDISs B. This sq-value ≤ 0.05) of the myogenic marker genes in C2C12-MBs and C2C12-MTs. Overall, 477 SISs were detected, with 163 and 314 in C2C12-MBs and C2C12-MTs, respectively (A). For example, the Mef2b gene had more SISs within ±500 kb around the viewpoint in C2C12-MTs (n = 15) than C2C12-MBs (n = 11) (B). We also identified peaks using PeakC (35) and used on monotonic regression to confirm this result. When the wSize set at 5, the peak number of each myogenic marker gene in C2C12-MTs was higher than in C2C12-MBs (A). Although the numbers of SISs and peaks identified by the two software packages were different, both methods revealed more SISs or peaks in C2C12-MTs, which had the higher expression levels of the myogenic marker genes. This result is consistent with a previous study , suggesting dynamic chromatin interactions during differentiation. In summary, these results indicate that the interaction profile of the myogenic marker genes changes during C2C12-MB differentiation.The high confidence of the SISs suggests that they are potential candidate regulatory elements that regulate gene expression , 30, 31.ectively . Of noteectively A. For ex(n = 11) B. We als2C12-MBs A. Althouus study  reportin of SISs B, suggesMyh genes  are located adjacently across the genome and are coexpressed to promote myogenesis  genes during differentiation. These results demonstrated the coregulated expression of the fMyh genes and their potential coordinated roles in myogenic differentiation. To further investigate the biological function of the interacting genes, functional enrichment analysis was performed. The interacting genes in C2C12-MTs that showed significant enrichment were related to muscle development and function, including the terms “myosin complex” , “cytoskeletal motor activity” , “glutamatergic synapse” , “long-term potentiation ”, “actin filament binding” , “contractile fiber” , and “regulation of system process”  . These results indicated dynamic histone modifications of the SISs occur during differentiation and that the chromatin state of the SISs in C2C12-MTs tends to be open.Next, we integrated our 4C-seq data with published epigenome datasets, including five histone modifications  to calculate enrichment factors for each histone mark in the SISs to assess the regulatory potential of the SISs , 35. GivFig. 3D, . In contA), we identified 41 and 25 putative active enhancers in C2C12-MTs and C2C12-MBs, respectively .We used two 4C-seq software programs to identify putative active enhancers, based on a nonoverlapping window and number of fragment ends. The two software programs have advantages in identifying the precision and recall of interaction regions . After iFig. 4ectively . As expeectively . For exaMyod1, Myh2, Myh3, and Mymk, we observed that the binding sites of MYOD1 TF were significantly enriched (p < 0.05). MYOD1 is a transcriptional activator that activates its own transcription and promotes the transcription of muscle-specific target genes during muscle differentiation (Myog (C), suggesting the TFs might cobind to functional enhancers of Myog to promote the transcription of Myog. Next, we downloaded the chromatin immunoprecipitation sequencing (ChIP-seq) data of MYOD1 and MYOG to determine whether MYOD1 and MYOG TFs occupied the enhancer of Myog. The ChIP-seq results confirmed that the enhancer regions of Myog were occupied by MYOD1 and MYOG TFs (D), which is consistent with the predicted results of TF-binding sites.The enhancer serves as a platform to supply binding sites of TFs to regulate gene expression , 38. Furd p < 0.0. MYOD1 intiation , 42. Of on  to perform conserved element analysis in 60 vertebrates. Overall, 58 out of 66 (87.88%) putative active enhancers contained at least one conserved element (E). These results suggested the high confidence of the putative active enhancers. To validate the transcriptional activity of putative active enhancers, we performed luciferase reporter assays for 13 candidate enhancers of two genes , which are important for the differentiation of myoblasts. Eight enhancers had activity that increased gene expression. For enhancers of Myog, we found that Myog-En1, Myog-En3, Myog-En4, and Myog-En5 had a significant increase in luciferase activity compared with the pGL3-promoter vector (F), and Myog-En4 had the strongest activity (∼1.5-fold higher). Myh3 enhancers other than Myh3-En1 and Myh3-En2 significantly increased the luciferase activity (G). To test whether the enhancement was associated with its conservation, we conducted Pearson correlation coefficient analysis between Lod score/length and the fold change of relative fluorescent intensity across 19 putative active enhancers. There was a significant positive Pearson correlation coefficient  (H), suggesting a weak relationship, but a significant correlation, between the activity of enhancers and their conservation. In summary, we comprehensively analyzed putative active enhancers and evaluated their transcriptional activity.Previous studies have shown that enhancers are usually evolutionarily conserved, which allows them to be used to identify functional elements within genomes , 48, 49. element , includi element E. These r vector F, and Myactivity G. To tes < 0.05) H, suggesMyog, which is critical for myoblast differentiation in functional studies. First, we visualized published Hi-C data (http://3dgenome.fsm.northwestern.edu/index.html) and analyzed published SMC3 (a subunit of the cohesin complex) and CTCF ChIP-seq data from mouse muscle tissues or cells. These results showed that these Myog-Ens and Myog promoter are located in an interaction domain, and they are present in the cohesin- and CTCF-enriched DNA region with convergent CTCF motif orientation  for each enhancer (A). We transduced C2C12 cells with lentiviruses expressing dCas9-KRAB or dCas9-KRAB-sgRNAs (B and Myog expression compared with cells expressing dCas9-KRAB on days 4 and 5 of differentiation (C). Of note, Myog expression was significantly reduced by dCas9-KRAB-En4-gRNAs  and dCas9-KRAB-En5-gRNAs  on day 5 of differentiation. The immunofluorescence analysis of Myogenin (MYOG) was performed using C2C12 cells expressing dCas9-KRAB or dCas9-KRAB-sgRNAs. The repression of Myog-En1, En3, En4, and En5 reduced the proportion of Myogenin-positive cells and fluorescence intensity compared with dCas9-KRAB at day 5 of differentiation, indicating the repression of enhancers impaired MYOG expression (D). For C2C12 cells expressing dCas9-KRAB-En4-sgRNAs or dCas9-KRAB-En5-sgRNAs, the proportion of Myogenin-positive cells was 23.81% and 7.86%, respectively. In addition, qRT-PCR analysis showed that myogenic genes including Myod1, Myh1, Myh2, Myh3, Myh4, Myh7, and Mymk of dCas9-KRAB-En4 cells and dCas9-KRAB-En5 cells were significantly downregulated compared with dCas9-KRAB cells (E). To investigate whether the repression of Myog-En4 and En5 affected myoblast differentiation, we performed immunofluorescence staining of MHC on dCas9-KRAB cells and dCas9-KRAB-sgRNAs cells. We observed few MHC-positive cells on day 5 of differentiation, indicating the repression of Myog-En4 and En5 markedly attenuated the ability of C2C12 to differentiate and form multinucleated MTs (F).Understanding the complex mechanism involved in the transcriptional regulation of myogenic genes remains a major challenge. Classical luciferase reporter assay studies have already enabled the discovery of active enhancers; however, the influence of genomic context is still largely unknown. To test the transcription regulating potential of active enhancers for the target gene, we selected the active enhancers of the transcription factor entation , suggestentation , 51. Theenhancer A. We traB-sgRNAs B and S14ntiation C. Of notpression D. For C2AB cells E. To invated MTs F.Figure A). RNA-Seq analysis revealed Myog expression was reduced significantly (p < 0.01) in dCas9-KRAB-En5 cells than in dCas9-KRAB cells (B), which is in line with the qRT-PCR results (C). Differential expression analysis indicated that 2342 genes were differentially expressed, including 1379 upregulated and 963 downregulated genes in dCas9-KRAB cells (C). Known MYOG targets  , and late myogenic differentiation genes  (p < 0.01) in dCas9-KRAB-En5 cells (D). Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of downregulated differentially expressed genes (DEGs) in dCas9-KRAB-En5 cells revealed significant enrichment for terms implicated in muscle structure and differentiation, such as contractile fiber, sarcomere, myofibril, muscle structure development, and muscle cell differentiation.  , the myod Mylpf) , 54 weren5 cells D. Gene OMyog enhancers in the regulation of Myog expression and myogenic differentiation by epigenome editing and RNA-seq.The formation of skeletal muscles involves 3D chromatin interactions and epigenetic modifications, which ultimately lead to alterations in myogenic gene expression , 56, 57.in vitro, leading to the production of nuclei of different MT types  and Myog (chr1) loci is reduced during C2C12-MB differentiation, suggesting that Myod1 and Myog have increased trans-interactions after muscle cell differentiation . To induce differentiation, we cultured C2C12 cells in the differentiation medium consisting of 2% horse serum (HyClone) when grown to 50% to 60% confluence. The differentiation medium was changed every 48 h. Differentiated C2C12 cells are treated until day 5.C2C12 mouse MB cells and human embryonic kidney cells (HEK293T) were kindly provided by the Cell Bank of the Chinese Academy of Sciences. HEK293FT cells were purchased from Thermo Fisher Scientific. Cells were maintained in Dulbecco's modified Eagle's medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin–streptomycin (Gibco) at 37 °C under 5% COCells on the dishes were fixed with 100% methanol for 2 to 3 min at room temperature (RT) and then washed with PBS. Giemsa stock solution (Solarbio) was diluted 1:9 in 0.01 M sodium phosphate buffer (pH 6.8), then cells were incubated with 1 ml of Giemsa working solution for 15 to 30 min at RT. Cells were finally visualized and photographed using an Olympus IX73 inverted microscope (Olympus). Each dish was photographed in three randomly selected regions. Protein-rich MTs are stained darker purple color, and nuclei are stained pink.Cells were fixed in 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4) for 10 min. Cells were washed three times with PBS and permeabilized by treatment with 0.25% Triton X-100 for 10 min. Cells were washed three times with PBS and blocked in 10% goat serum  for 40 min at RT. Cells were incubated with myosin 4 monoclonal antibody  overnight at 4 °C. Then, cells were washed three times with PBS and incubated with Alexa Fluor 488–labeled goat antimouse IgG H&L secondary antibody  at RT protected from light for 1 h. Finally, the nuclei were stained with 1 μg/ml 4′,6-diamidino-2-phenylindole (Beyotime) for 3 min at RT. Cells were washed three times with PBS and photographed with Olympus IX73 inverted microscope (Olympus). The images were processed using ImageJ 1.52a software (NIH).The FI was calculated as has been described . MTs wer-ΔΔCt method  following the manufacturer’s instructions. Complementary DNA synthesis was performed with the HiScript III RT SuperMix (Vazyme). qRT-PCR amplification was performed on the CFX Connect Real-Time System (Bio-Rad) using the ChamQ Universal SYBR qPCR master mix (Vazyme). The reaction conditions were 95 °C for 5 min, followed by 40 cycles at 95 °C for 10 s and 60 °C for 30 s. After PCR amplification, a melting curve was obtained by the following process: 95 °C for 15 s, 60 °C for 1 min, followed by 95 °C for 15 s to verify primer specificity. Relative expression of genes was calculated using the 2t method  after noA and B. In brief, ∼1 × 107 million cells were harvested and crosslinked with 2% formaldehyde for 10 min at RT. The nuclei were digested using primary enzyme Dpn II (RE1) (New England Biolabs) overnight. Following proximity ligation, DNA was reverse crosslinked and purified using phenol/chloroform extraction, and the ligated circular DNA was precipitated with ethanol. The ligated circular DNA was digested with secondary enzyme Csp6 I (RE2) (New England Biolabs) overnight, followed by proximity ligation and purification to obtain the 4C library. The 4C-seq library was generated by performing a two-step PCR using Phusion DNA polymerase . The primers of the 4C-seq library are listed in 4C templates were prepared as previously described , 62 withDemultiplexing, trimming, mapping, and quality control of the 4C-seq data were performed using the pipe4C pipeline . r3Cseq q-value ≤ 0.05 were considered interaction sites. Interactions within ±500 kb around the viewpoint were visualized. The differential analysis of the interaction sites was performed using DESeq2 (version 1.35.0) with the “ashr” algorithm . Interactions sites with a lgorithm , 89. In lgorithm , 90. Ovehttp://metascape.org/) is an online gene functional annotation tool to provide a comprehensive set of biological information of genes and proteins , CC (cellular components), and MF (molecular functions). The KEGG is a database resource for understanding high-level functions and utilities of the biological system from molecular-level information. Metascape (proteins . To undehttps://www.ebi.ac.uk/ena/browser/home). The detailed information of ChIP-seq data is listed in Publicly available ChIP-seq sequencing data were downloaded from the EBI ENA database  and visualized in Integrative Genomics Viewer (v 2.10.0).For ChIP-seq data, raw sequence reads were aligned to the mouse reference genome (mm10) using BWA (v0.7.17), and BAM files were created by Samtools (v1.11). PCR duplicates of ChIP-seq were removed with Samtools. Prior to downstream analysis, mitochondrial reads were removed. Peak calling for individual replicates was carried out by MACS2 (v2.2.7.1). Peaks were called at a The active enhancers were identified through two 4C-seq software combined with H3K27ac and H3K4me1 signal. We used two stringent criteria to identify active enhancers of myogenic marker genes. First, SISs identified by r3Cseq  show simhttps://www.ncbi.nlm.nih.gov/). TF motif enrichment analysis was performed using AnimalTFDB 3.0  (https://jaspar.genereg.net/)   and JASPeg.net/)  by inputhttp://genome-asia.ucsc.edu/). Each element is assigned a log-odds score (LOD) equal to its log probability under the conserved model minus its log probability under the nonconserved model. The “score” contains transformed log-odds scores, taking values between 0 and 1000. The raw log-odds scores are retained in the “name.” The sequence conservation is evaluated as the sum of the LOD of all elements divided by the length of the sequence, which is defined as the LOD score/length.The conserved elements of the predicted enhancers were analyzed by using phastCons method in the UCSC genome browser (Putative active enhancers were amplified from C2C12 genomic DNA and cloned upstream of the luciferase gene in the pGL3-promoter vector (Promega) at the Kpn I site. The constructs were validated by Sanger sequencing. HEK293T cells were cotransfected with 95 ng of the tested construct and 5 ng of Renilla vector per well of 96-well plate using the Lipofectamine 3000 (Thermo Fisher Scientific). Thirty-six hours after transfection, luciferase activity was measured using the Dual-Glo luciferase assay kit  on a GloMax 96 microplate luminometer (Promega) according to the manufacturer’s instructions. Luciferase activity of firefly was normalized by renilla, and relative luciferase activity was obtained as compared with empty pGL3-promoter. The experiments were in triplicate and repeated at least once. The data are presented as means with their SD (mean ± SD). The PCR primers of constructed vectors are shown in http://crispor.tefor.net/) (http://crispr-era.stanford.edu/) (https://wge.stemcell.sanger.ac.uk/) (http://www.rgenome.net/cas-offinder/) (Two sgRNAs were designed for each enhancer using CRISPOR (or.net/)  and CRISrd.edu/) . All sgR.ac.uk/)  and Cas-finder/) . SynthesUnder sterile BSL-2 conditions, lentiviral particles were generated in HEK293FT cells using the pVSV-G  and psPAX2  packaging plasmids alongside the Lenti-dCas9-KRAB-enhancer-sgRNA plasmid . The virus supernatant was harvested at 24, 48, and 72 h post-transfection and filtered with a 0.45 μm polyvinylidene difluoride filter . Viruses were purified and concentrated using Amicon Ultra  and titer was determined using a colloidal gold kit (BioDragon). Concentrated viruses were stocked in a final volume of 1 ml and stored at −80 °C.For transduction, C2C12 cells were seeded in a 12-well plate. When confluence reaches about 50%, the cells were infected with 1 ml lentiviruses medium containing dCas9-KRAB-sgRNA or dCas9-KRAB at a multiplicity of infection of 100. The day after transduction 24 h, the medium was exchanged to remove the virus. At 48 h postinfection, 2 μg/ml puromycin was used to select transduced cells. C2C12 cells were kept at ∼30% confluence during antibiotic selection. C2C12 cells stably expressing dCas9-KRAB-sgRNAs or dCas9-KRAB were induced to differentiation with the differentiation medium consisting of 2% horse serum for 5 days. C2C12 cells expressing dCas9-KRAB served as controls. Cells were examined by qRT-PCR and immunofluorescence staining.p-value ≤ 0.01 and |Log2fold-change| ≥ 1. Gene expression levels for each sample were calculated as transcripts per million values. Functional enrichment analysis of the DEGs was performed using Metascape. Terms with p-values ≤ 0.01 were considered significantly enriched for DEGs. GSEA was performed using GSEA version 4.2.3 software (https://www.gsea-msigdb.org/gsea/index.jsp) with MSigDBv7.5.1.Total RNA (RIN ≥ 9) was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB). mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. The library preparations were sequenced on an Illumina Novaseq 6000 platform and 150 bp paired-end reads were generated. Sequence reads were aligned to the mouse reference genome (GRCm38/mm10) by HISAT2 (v2.2.1) and quantified by featureCounts from the Rsubread package (v2.8.1). Differential analysis was performed using DESeq2. DEGs were defined as genes with adjusted t test using SPSS Statistics 21. Data are presented as mean ± SD. p ≤ 0.05 was considered statistically significant.Statistical analysis was performed for unpaired or paired two-tailed Student’s https://www.ncbi.nlm.nih.gov/sra/) under BioProject PRJNA795427. RNA-seq data were deposited into the Gene Expression Omnibus (GEO) under accession code GSE201138.All 4C-seq data were deposited in the NCBI Sequence Read Archive (SRA; This article contains The authors declare that they have no conflicts of interest with the contents of this article.


NEPC is associated with poor survival rates as these tumors are aggressive and often metastasize to soft tissues such as liver, lung and central nervous system despite low serum PSA levels relative to disease burden. It has been recognized that therapy-induced NED involves a series of genetic and epigenetic alterations that act in a highly concerted manner in orchestrating lineage switching. In the recent years, we have seen a spurt in research in this area that has implicated a host of transcription factors and epigenetic modifiers that play a role in driving this lineage switching. In this article, we review the role of important transcription factors and chromatin modifiers that are instrumental in lineage reprogramming of prostate adenocarcinomas to NEPC under the selective pressure of various AR-targeted therapies. With an increased understanding of the temporal and spatial interplay of transcription factors and chromatin modifiers and their associated gene expression programs in NEPC, better therapeutic strategies are being tested for targeting NEPC effectively.Therapy-induced neuroendocrine prostate cancer (NEPC) is a highly lethal variant of prostate cancer that is increasing in incidence with the increased use of next-generation of androgen receptor (AR) pathway inhibitors. It arises  ADT results in cancer regression initially. However, in a significant fraction of patients, 2–3 years after ADT, the disease progresses to castration-resistant prostate cancer (CRPC) (Prostate cancer (PCa) is a leading cause of cancer incidence amongst men in US, with an estimated 268,490 new cases in 2022. It is one of the leading causes of cancer-related deaths among males, with an estimated 34,500 deaths in 2022 . Prostattor (AR) . Consider (CRPC) . The trer (CRPC) . Though r (CRPC) . A subser (CRPC) . These Pr (CRPC) . Therefor (CRPC) . Therapyr (CRPC) . Normal r (CRPC) . NEPC, lr (CRPC) . NEPC alr (CRPC) .via clonal evolution from CRPC-adenocarcinomas (RB1) and mutation or loss of tumor protein 53 (TP53) (TP53 and RB1 are important steps in the development of poorly differentiated NE tumors of the prostate (via expression of RB1 shRNA and dominant negative p53 (PTEN) loss (TMPRSS2-ERG gene rearrangements (NMYC and Aurora Kinase A (AURKA)  and chromatin modifiers that play a role in lineage reprogramming of prostate adenocarcinomas to NEPC under the selective pressure of various AR-targeted therapies  . Mouse mprostate . Normal tive p53 . In addiEN) loss  and freqngements  have bee (AURKA)  are card (AURKA) . The ove (AURKA) . The lac (AURKA)  Studies  (AURKA) . Recent herapies . As studIt has been recognized that AR activity is varied in metastatic CRPC and can stratify distinct phenotypes . Five diRB1 (via binding to its promoter region (Enhancer of zeste homolog 2 (EZH2), the catalytic subunit of Polycomb repressive complex 2 (PRC2), has been reported to be overexpressed in NEPC (11–13). EZH2 mediates the deposition of repressive histone modification, trimethylation of histone H3 at lysine 27 (H3K27m3) that represses lineage-specifying factors, thereby maintaining pluripotency  Figure . It has RB1 . An inter region . EZH2 isr region  and stemr region . Furtherr region . Activatr region . In agrer region .MYCN, encoding the transcription factor, N-Myc is frequently amplified in neuroblastomas . This recruitment of p-TEFb phosphorylates RNA polymerase II to activate gene expression  considered to be crucial for neurogenesis  upon NED alongwith BRN2. We showed that EV-mediated release of these TFs drive PCa NED  to further prove that ONECUT2 may be a direct repressor of AR signaling, thereby promoting NEPC  is known to be essential for the survival of normal prostate and prostate cancer tissue by maintaining AR signaling . The endting NED .RB1- and TP53-deficient cancer cells  family of protein comprises of 20 individual proteins that are essentially defined by a conserved DNA Binding element- HMG . SOX proer cells . SOX2 exstone H3 . This lestone H3 . Recent Ca cells .Hypoxia is often associated with later stage malignant prostate cancer. HIF proteins are crucial in the cellular responses to hypoxia. . An inteTranscription factors controlling early neurogenesis are critical for PCa NED . ASCL1 pNEUROD1 is a neuronal TF that is able to convert epithelial cells into neurons . TherapyRE-1 silencing transcription factor is a master regulator of differentiation that represses neuronal programs in non-neuronal cells. REST downregulation has been shown to be associated with NEPC . SRRM4 ivia epigenetic chromatin remodeling, the role of this protein family is speculated. It is established that all three isoforms show altered expression in PCa tissues as compared to normal prostate tissues , belongs to ATP-dependent chromatin remodeling complexes and is composed of 11–15 protein subunits . These aMUC1-C is an oncogenic protein that is predicted to have role in lineage plasticity. Studies show that MUC1-C directly binds to transcription factor E2F1. This axis elicits the expression of SWI/SNF complex components including BRG1, ARIDIA, BAF60a, BAF155 and BAF170 in CRPC and NEPC . Moreovevia histones H3 and H4. BRD4 is known to play versatile roles as transcription factor, nucelators of superenhancers and kinase involved in transcription  family of protein include BRD2, BRD3, BRD4 and BRDt. BRD4 consists of 2 bromo domains (BD1 and BD2) . These dcription . BRD4 recription . BRD4 alcription . BRD4 iscription . BRD4 hacription . AlthougCa cells . ProteolCa cells .The cellular heterogeneity of prostate tumors progressing from AR-positive adenocarcinoma to AR negative NEPC allows stratification of this disease into various subtypes. Recent studies show that evolution to NE states involve an inherent hierarchical TF network. Single cell studies on CRPC and NEPC has shown that NEPC is driven by constitutive regulation of pioneering transcription factors ASCL1 and FOXA1 alongwith selective regulation of NKX2-2 or POUF3F2 and SOX2. This expression profile could categorize NEPC into subtypes based on transcriptomic mechanisms with NE1 expressing NKX2-2 and NE2 subtype expressing POU3F2 and SOX2 .De novo motif analyses of these binding sites showed that these binding sites possess the respective consensus motifs for ASCL1 and NEUROD1. Further, ASCL1 binding motif was found to be enriched with motif for NKX2  and SRRM3  that conRB1/TP53 loss. EZH2 inhibitors PF-06821,497 and CPI-1205 are currently being tested in clinical trials in CRPC   . CPI-120ctively) . Considectively) . These ictively) .Targeting other epigenetic regulators such as BET family are also being investigated in advanced CRPC  and may Disruption of the molecular interaction between AURKA and N-Myc by Alisertib (MLN8237) has been examined as a therapeutic modality for NEPC . A phasevia siRNA or CRISPR/Cas9 or small molecule inhibitor was found to reduce proliferation in NEPC cell lines  in NEPC cases as compared to CRPC-adenocarcinomas, a humanized DLL3 antibody has been exploited for therapeutic targeting of NEPC 2019, . This thIn conclusion, transcription factors and chromatin modifiers play an integral role in driving neuroendocrine trans-differentiation in prostate cancer. Pioneer transcription factors such as ASCL1, FOXA1 and SOX2 can engage on their respective target sites on nucleosomal DNA and initiate transcriptional regulation at sites of closed chromatin. Further, interplay between transcription factors such as AR, FOXA1, BRN2 and BRN4 and epigenetic modifiers such as EZH2 drive chromatin states underlying lineage reprogramming in PCa. The complexities underlying emergence and maintenance of PCa NE states is being recognized and deciphered. With an increased understanding of the temporal and spatial interplay of transcription factors and their associated gene expression programs in PCa NED, better therapeutic strategies can be designed for targeting NEPC. Though considerable progress have been made in this direction in last few years, we are still far from understanding the complexy regulatory interplay. Further, it is important to unravel the interplay between these TFs, epigenetic modifiers and tumor microenvironment to gain a holistic understanding of the disease that would be required for devising effective therapeutic modalities.


HBG1/HBG2 promoters. Precise on-target editing and significantly increased γ-globin expression during erythroid differentiation were observed in both HUDEP-2 cells and primary HSPCs from β-thalassemia major patients. Moreover, edited HSPCs maintained the capacity for long-term hematopoietic reconstitution in B-NDG hTHPO mice. This study provides evidence of the effectiveness of introducing naturally occurring HPFH mutations as a genetic therapy for β-thalassemia.Reactivation of γ-globin expression is a promising therapeutic approach for β-hemoglobinopathies. Here, we propose a novel Cas9/AAV6-mediated genome editing strategy for the treatment of β-thalassemia: Natural HPFH mutations −113A > G, −114C > T, −117G>A, −175T > C, −195C > G, and −198T > C were introduced by homologous recombination following disruption of BCL11A binding sites in  HBB gene mutations that alter quantity or quality of the β-polypeptide of adult hemoglobin , with an annual incidence of one in 100,000 worldwide . Here, we mimicked natural HPFH mutations −113A > G, −114C > T, −117G>A, −175T > C, −195C > G, and −198T > C by delivery of an AAV6 homology repair template, followed by immediate electroporation of Cas9-ribonucleoprotein (RNP) complexes specific to the BCL11A binding site and then tested this system in both HUDEP-2 and HSPCs from β-thalassemia major patients. Our studies provide a novel and feasible genome-editing strategy for treating β-thalassemia.Hereditary persistence of fetal hemoglobin (HPFH) is a benign condition in which genetic variations attenuate γ-to β-globin switching, causing elevated and persistent production of fetal hemoglobin . Co-inheritance of HPFH with β-thalassemia ameliorates their clinical severity . Single-rt sites . Among trs GATA1 , TAL1  are provided in the Circulating G-CSF-mobilized human HSPCs and cord blood HSPCs were enriched by immunomagnetic bead selection . All samples were processed after approval by the local medical ethics committee. HSPCs and HUDEP-2 were edited with RNP using a Neon Transfection System . Approximately 5×10HBG1/HBG2 promoters with high on-target frequency (HBG1 AAV6 and HBG2 AAV6) to mimic HPFH mutations −113A > G, −114C > T, −117G>A, −175T > C, −195C > G, and−198T > C using approximately 800-bp homologous arms flanking the Cas9 RNP-induced cut site. In parallel, CtrAAV6 lacking homologous recombination elements was introduced as control. HBG1 AAV6 and CtrAAV6 were inserted with a GFP expression cassette and HBG2 AAV6 with mCherry to validate the effectiveness of transfection  and total HDR efficiency  after GFP-sorting in RNP-HBG1/HBG2 AAV6 group , B cells (CD19+), and erythroid cells (CD235a+) , similar to what has been reported previously , - 28 (A>G) and IVS-II-654 (C>T) which account for 86.0% of the cases studied  . In addieviously . It may  studied . Genome  studied . AdditioHBG2AAV6 . Howeverid cells . We deteid cells . These rHBG1/HBG2 promoters had no significant effect on erythrocyte maturation or terminal differentiation of CD34+ HSPCs derived from mobilized peripheral blood of two severe β-thalassemia patients by evaluating the markers CD235a and CD71 of erythroid lineage (Colony Forming Cell (CFC) Assayto evaluate the effect of the genome editing strategy on the proliferation and differentiation pattern of HSPCs. We found that introducing HPFH mutations in the HBG1/HBG2 promoters exerts no significant impact on frequency and proportion of colony forming unit (CFU)-erythroid (CFU-E) and burst forming unit-erythroid (BFU-E), CFU-granulocyte, macrophage (CFU-GM) and CFU-granulocyte, erythrocyte, macrophage, megakaryocyte (CFU-GEMM) in HSPCs cultured in a semisolid methylcellulose-based medium (HBG1/HBG2 promoters may be a promising therapeutic strategy to reactivate HbF without significantly altering erythrocyte differentiation.We examined the effect of the genome editing strategy on erythroid differentiation. We observed that introducing HPFH mutations in the  lineage . Further lineage . Finallyd medium . Taken tencouraging outcomes (Ongoing clinical gene therapy trial of autologous HSPCs genetically modified using lentiviral vectors expressing β-like globin transgenes are showing outcomes . Howeveroutcomes . HoweverHBG1/HBG2 promoters significantly induced fetal hemoglobin expression. However, Cas9-mediated disruption of BCL11A binding sites is accompanied by various NHEJ-related InDels, some of which might not lead to a significant increase in γ-globin expression as indicated by the monoclonal experiment (HBG1 and HBG2 promoters. As expected, the combination of HBG1/HBG2 AAV6 and Cas9-RNP created HDR-related genotypes that further reactivate γ-globin, which may translate to greater clinical therapeutic benefits. In addition, Traxler et al., and Métais et al., demonstrated that high efficiency of disruption of the BCL11A binding site can be achieved in SCD CD34+ HSPCs (in vitro and post-transplantation reconstruction of hematopoietic system in vivo enriched preclinical data for gene therapy of β-thalassemia.It was previously reported that Cas9-mediated disruption of the repressor BCL11A in the periment . Based o4+ HSPCs . However+ HSPCs. Moreover, in vivo studies indicated that the assays maintained long-term hematopoietic reconstitution in the edited cells. These findings may facilitate the clinical transformation of genome editing-based treatment of hemoglobinopathies.In summary, our studies demonstrate that introducing six specific HPFH mutations leads to significant re-activation of γ-globin and thus elevates HbF in both HUDEP-2 cells and HSPCs derived from β-thalassemia major patients. We found that the gene editing strategy does not lead to observable alteration in erythroid differentiation of CD34