Drosophila the role of insulators in 3D genome organization is, however, under current debate. Here, we addressed this question by combining bioinformatics analysis and multiplexed chromatin imaging. We describe a class of Drosophila insulators enriched at regions forming preferential chromatin interactions genome-wide. Notably, most of these 3D interactions do not involve TAD borders. Multiplexed imaging shows that these interactions occur infrequently, and only rarely involve multiple genomic regions coalescing together in space in single cells. Finally, we show that non-border preferential 3D interactions enriched in this class of insulators are present before TADs and transcription during Drosophila development. Our results are inconsistent with insulators forming stable hubs in single cells, and instead suggest that they fine-tune existing 3D chromatin interactions, providing an additional regulatory layer for transcriptional regulation.In mammals, insulators contribute to the regulation of loop extrusion to organize chromatin into topologically associating domains. In  Drosophila genome by combining bioinformatics analysis and Hi-M, an imaging-based methods developed to detect the 3D positions of multiple genomic loci in single cells.In Drosophila, insulators may be involved in the organization of Topological Associated Domains, but the mechanism of action is a still a matter of investigation. Here the authors investigate the role of insulators in the 3D organization of the  Notably, this multi-scale organization of the genome is conserved from Drosophila to mammals6. However, the mechanisms responsible for the acquisition and maintenance of these structures seem to differ between species.Eukaryotic chromosomes are organized in a multi-layered structure comprising chromosome territories, compartments, topologically associating domains (TADs) and nano-domains7. These specific looping interactions may facilitate the communication between genes and their cis-regulatory elements  most often localized within TADs9. In mammals, the formation of TADs is thought to involve loop extrusion, a mechanism by which Structure Maintenance of Chromosome (SMC) proteins (e.g. cohesin) bind chromatin and reel it in until they encounter properly-oriented CTCF sites10. In contrast, the Drosophila homolog of CTCF (dCTCF) binds preferentially within TADs and is only mildly enriched at TAD borders6 with no preferential convergent orientation as in mammals11. These results suggest that other mechanisms may instead be responsible for the establishment of TADs in Drosophila.In vertebrates, TADs are often separated from each other by convergent CCCTC-binding factor (CTCF) sites localized at TAD boundaries. TAD borders bound by CTCF/cohesin form “focal chromatin loops” in contact matricesDrosophila contains tens of non-evolutionary conserved insulator binding proteins (hereafter IBPs)12. Since before the genomic era, Drosophila insulators were shown to be involved in the regulation of long-range chromatin interactions: either by blocking enhancer-promoter interactions or by establishing barriers between chromatin states15. Early genome-wide studies showed that insulators preferentially bind to genomic regions containing housekeeping genes and highly transcribed regions16. In addition, IBPs frequently bind to TADs borders19 that can often interact in 3D20. Taken together, these data suggest that insulators may be involved in the organization of Drosophila TADs.While CTCF is the main chromatin insulator in mammals, 23. On the other hand, insulators may not form CTCF-like focal chromatin loops, but rather restrict interactions between domains24.Recent studies suggest different modes of action. On one hand, insulators may promote TAD border interactions by forming contacts between insulator factorsDrosophila genome by combining advanced bioinformatics analysis and Hi-M, an imaging-based method we recently developed to detect the 3D positions of multiple genomic loci in single cells25. First, we show that genomic regions occupied by insulators display preferential interactions genome-wide. These preferred interactions occur inside TADs and can also span TAD borders. Second, we show that chromatin regions displaying the most prominent 3D interactions are preferentially bound by insulators. We detect TAD border preferential interactions, but these represent the minority of the interactions detected. Interestingly, non-border interactions quantitatively increased with the occupancy of IBPs. By visualizing 3D chromatin structure at the dpp locus, we observed, however, that spatial colocalization between insulators is infrequent and similar to neighboring regions not bound by insulators. Finally, by mapping preferential interactions during development, we found that non-border regions harboring insulators display a tendency to preferentially interact before the emergence of TADs and transcription.Here, we investigated the role of insulators in the 3D organization of the Drosophila insulators in 3D genome organization during early embryogenesis, we deployed Chromatin Assortativity analysis (ChAs)27. Assortativity measures the preference for the nodes of a network to interact with other nodes that have the same characteristics. In ChAs analysis, a chromatin interaction network is built from a genome-wide contact map28. This network represents the genomic loci (nodes) displaying high frequency interactions amongst each other 20, a developmental stage coinciding with the zygotic genome activation (ZGA) and with the emergence of TADs20. For this, we obtained chromatin interaction networks by mapping preferentially interacting chromatin regions using Chromosight29 on Hi-C data  are calculated to estimate if ChAs for a feature is higher than expected for regions separated by similar genomic distances, indicating the importance of 3D interactions for establishing preferential contacts. Regions enriched in Zelda, Polycomb group proteins (Pc and Ph), and RNAPII CTD phospho-Ser5 (S5P) displayed positive ChAs Z-scores Fig. S, consistBPs Fig. , indicatBPs Fig. . NotablyBPs Fig.  and S1e.BPs Fig. . For som33. These focal loops, however, represent a small proportion of preferential interactions in our network  k) Fig. S. The stapeak 2 in Fig. SNotably, the positive correlation between ChAs and log2(O/E) Fig.  indicate20 , is positively correlated to the binding level of insulator proteinsgth Fig. , providiNext, we wondered whether preferential chromatin contacts may be detected in locations other than TAD boundaries and what their determinants may be. To this aim, we divided the interactions in our chromatin network into three categories: border/border (blue), border/non-border (red), and non-border/non-border (black) Fig. . Then, wTo better understand the role of insulators in each of these interaction categories , for each category we calculated the proportion of interactions displaying binding of Class I IBPs in two, one or none of the anchors. The vast majority of the anchors for all categories were bound by at least one Class I IBPs . We found that ~38% of borders take part in loops in our Chromosight network Fig. S, with thTo further support this conclusion, we performed aggregation Hi-C analysis on non-border/non-border regions occupied by Class I IBPs. Notably, this analysis displays a clear peak Fig. , suggest38. As conventional DNA-FISH can only measure proximity between a limited number of genomic targets, we used Hi-M, a multiplexed imaging method that enables the detection of tens of genomic loci at once39  in intact nc14 Drosophila embryos at ~12 kb resolution. The dpp locus contains three TADs and several regions displaying high levels of class I insulator binding, named barcode I1 to I10 , with similar barcode detection efficiencies  or/and with colocalization being highly dynamic.The proximity and PWD distance maps revealed multiple regions displaying preferential 3D spatial proximity Fig. . These mdpp locus, barcodes co-localize at similar frequencies irrespective of whether they contain insulators. Proximity frequencies dropped with genomic distance, as expected, but the difference between insulator and non-insulator barcodes remained small for all genomic distances. We note that use of larger cutoff distances increases the proximity frequency, but this would happen for both insulator and non-insulator barcodes. Overall, these results show that insulators coalesce in space infrequently, and only at slightly higher frequencies than non-insulator regions.Next, we investigated the specificity of insulator barcode co-localizations by calculating the proximity frequency versus cutoff distance curve for non-insulator (control) barcodes located at similar genomic distances Fig. S. This an41. On the other hand, different combinations of insulator barcodes may co-localize at low frequencies in a pairwise manner in single cells. In this case, the multiplicity of peaks in the Hi-M matrix would arise from ensemble averaging. To discern between these two models, we calculated how often insulator barcodes were proximal (i.e. at a distance ≤ 200 nm) to any other insulator barcode in single cells. This frequency was comparable for all the insulator barcodes investigated, and on average lower than 12%  or multiple insulator barcodes (multiway cluster). Clusters containing only two insulator targets were the most common in all cases (>65%) Fig. . Next, wcis-regulatory elements (e.g. enhancers and promoters) can occur before nc1432, the nuclear cycle at which most zygotic genes get activated and when TADs first emerge20. We reasoned that insulators may display similar features. To test this hypothesis, we first analyzed the changes in accessibility of class I IBP sites at different time points within nc12–1342. Surprisingly, we found that many of these sites are accessible as early as nc12, with a progressive acquisition of accessibility with time within this restricted time window , nc14 (ZGA) and 3–4 h post fertilization (hpf) (post-ZGA). As expected, preferential interactions between TAD borders first appear at nc14 and are sustained thereafter Fig. , consistNext, we quantified the timing at which preferential interactions between non-border, insulator-bound regions emerged. For this, we performed APA analysis for non-borders for different developmental timings. Notably, we found that preferential interactions between non-border regions bound by insulators were already present in nc12 embryos for most Class I IBPs Figs. . We notedpp locus displayed relatively minor changes between these two nuclear cycles . This finding suggests that class I insulators are likely also involved in modulating interactions within TADs and across TAD boundaries. Members of the class I insulator group (e.g. BEAF-32) tend to co-localize with promoter regions48 and tend to demarcate differentially-expressed genes49, suggesting that class I insulators may play a role in modulating contacts between cis-regulatory modules within and between TADs. Direct promoter regulation and reduction in TAD insulation can only account for a minority (20%) of the genes downregulated upon depletion of BEAF-3245. Non-border chromatin interactions by Class I IBPs appear before TADs and the onset of the zygotic genome activation , suggesting that they may contribute to defining pre-established topologies to demarcate cis-regulatory networks. Drosophila homologous chromosomes are often paired, and several factors, including insulators, play a role in this process50, therefore contacts between insulators bound to different homologous chromosomes could also contribute to cis-regulation51.55. However, we found that interactions between Class I IBPs at non-border regions are not affected by the depletion of Zelda. This surprising result may be explained by our finding that a significant portion of class I IBPs peaks (~90%) are open at early developmental cycles (e.g. nc12) but do not co-localize with Zelda, suggesting that other unidentified pioneering factors may be required to provide access to most Class I IBPs.Zelda plays a central role in rendering the zygotic genome accessible37. The low proximity frequencies between insulator-enriched regions are consistent with a recent study showing that depletion of insulators only partially weakens the strength of TAD borders45, and with the overall absence of “focal loops” involving class I insulators in Hi-C contact maps56. Finally, our genome-wide analysis shows that interactions between insulator-bound regions are on average preferential, but highly variable and often weak.Despite the genome-wide enrichment of IBPs at regions displaying 3D preferential interactions, the quantification of absolute proximity frequencies using Hi-M shows that insulator-bound regions (borders and non-borders) physically co-localize in space infrequently (~12%), and marginally more frequently than neighboring genomic regions (10.8%). This observation is consistent with low proximity frequencies between TAD borders measured in S2 cells (~10%)60. More recently, it was shown that CP190 and Su(HW) insulator bodies formed in cultured-cells under stress conditions exhibit liquid-liquid phase separation properties61. This model predicts that genomically close insulators should interact in space often, nucleating interactions between multiple partners. In contrast, we observed low frequencies of pairwise proximities that rapidly decrease with the number of interacting partners (<5% for 3-way interactions and <1 % for 4-way interaction). Therefore, these results do not provide support for a widespread role of stable insulator hubs or LLPS-mediated insulator bodies in the 3D organization of the Drosophila genome, at least in normal physiological conditions at the dpp locus.The early discovery of insulator bodies led to the proposal that insulators mediate the formation of stable, rosette-like hubs involving multiple insulator-bound genomic regionsDrosophila IBPs in mediating distant interactions62. Our genome-wide analysis and imaging data are inconsistent with stable interactions between class I IBPs, and suggest that these insulators may play a role at stabilizing 3D distant chromatin conformations arising from other processes, including polymer dynamics64. It is well established that binding peaks from multiple insulators often cluster together49. In this scenario, combinatorial binding of multiple insulator binding sites at single genomic locations45 would provide a means to modulate the strength of the stabilization, to regulate its specificity, and to enable a locus to time-share 3D interactions with multiple genomic locations in an asynchronous manner. Consistent with this concept, analyzing binding of RNAPII and polycomb members in mouse embryonic stem cell promoter-centered chromatin interactions using network measures such as bridgeness and betweenness centrality, it was suggested that RNAPII-bound chromatin fragments would belong to multiple communities at once, whereas polycomb bound fragments appeared to participate in multiple interactions at once26.Previous studies proposed a role for Drosophila insulators. However, recent studies showed that GAF and mammalian CTCF can remain bound to their cognate chromatin sites for minutes66, and that CTCF loops are dynamic68. These data are consistent with a model whereby insulators help modulate the dynamics of specific interactions between distant cis-regulatory regions, but do not form stable scaffolds. These transient structures, however, may be more stable than the typical residence time of transcription factors (~10 s)69. In this picture, insulators could help promote transcription by stabilizing transient cis-regulatory interactions to allow for the rapid binding and unbinding of transcription factors, or rather contribute to transcriptional repression by promoting 3D conformations that prevent functional interactions. This said, the lack of clear focal peaks, the high variability in interaction strength genome-wide, and the low proximity frequencies between class I insulator-bound regions, argue for the involvement of additional molecular actors in the 3D regulation of transcription.Direct measurements of residence times have, unfortunately, not been reported for class I Drosophila insulators only moderately increase the frequency of border and non-border chromatin interactions may be used to investigate insulator mechanisms in other organisms.Finally, the methods used in this manuscript to show that yw fly stocks were maintained either in a 21 °C room or in a 25 °C incubator with a natural light–dark circadian cycle. Following a pre-laying period of 16–18 h in cages with yeasted apple juice agar plates, flies were allowed to lay eggs during 1.5 h on new plates. Layed embryos were then incubated at 25 °C for an extra 2.5 h to reach the desired developmental stage. Embryos were collected and fixed as previously described39. Briefly, embryos were dechorionated with 2.6% freshly opened bleach for 5 min and thoroughly rinsed with water. Then, embryos were fixed in 10 mL of a 1:1 mixture of fixation buffer . They were then agitating for 25 min at RT. The bottom formaldehyde layer was replaced by 5 mL of methanol and embryos were vortexed for at least 30 s. Embryos that sank to the bottom of the tube, devitellinized, were rinsed three times with methanol. Embryos were then stored in methanol at −20 °C until further use.The 39. Briefly, each oligo had an homology region of 35–41 nt followed by a flap encoding a sequence complementary to the readout probes. We selected 138 genomic regions of interest (barcodes) in the dpp locus (2L:2343645..2758688 BDGP Release 6 + ISO1 MT/dm6). For each barcode we used ~50 probes, covering ~3 kb. The coordinates of the targeted genomic regions are listed in Supplementary Data 39. Briefly, the seven-step strategy consist of (i) emulsion PCR (emPCR) to extract the desired library from the pool using specific couple of primer; (ii) limited-cycle PCR from the emPCR product to determine the optimal amplification cycle; (iii) large-scale PCR with T7 promoter on the reverse primer; (iv) in-vitro transcription using T7 RNA polymerase; (v) reverse transcription; (vi) alkaline hydrolysis; and (vii) purification and concentration of the ssDNA. The sequences of the primers used for amplification of the library are listed in Supplementary Data Oligopaint libraries were constructed as in previous studies39. For fiducial, we used an adapter complementary to the reverse primer that can be bound by an unique Atto 550 labeled oligonucleotide. The sequences of the adapters and labeled barcodes purchased from Integrated DNA Technology (IDT) are listed in Supplementary Data For imaging, we used a combination of 4 barcodes to cover ~12 kb, the list of positions of the barcodes are listed in Supplementary Data 39. Briefly, embryos were rehydrated and permeabilized by sequential dilution of methanol with 0.1% Tween-20 PBS (PBT): 90%MeOH; 70%MeOH; 50%MeOH; 30%MeOH; 100%PBT (5 min each). Embryos were RNase A treated during 2 h, permeabilized 1 h with 0.5% Triton in PBS and rinsed with increased concentration of Triton/pHM buffer. pHM ): 20%pHM; 50%pHM; 80%pHM; 100%pHM (20 min each). Then, 225 pmols of ssDNA were diluted in 25 µL of Fish Hybridization Buffer . The ssDNA and embryos were preheated at 80 °C during 15 min in separated tubes. The supernatant of the embryo’s tube (pHM) is removed and the 25 µL of FHB containing the ssDNA is added. Next the mixture is transferred in a PCR-tube and deposited in the thermomixer set at 80 °C. Immediately, the thermomixer is set to decrease to 0.1 °C/min until it reaches 37 °C for an overnight incubation. The next day, the embryos were transferred to a new 1.5 mL eppendorf tube and washed two times at 37 °C during 20 min with 50% formamide, 2X SSC. Next, embryos were sequentially washed at 37 °C for 20 min with serial dilutions of formamide/PBT: 50% formamide/2× SSC; 40% formamide/2× SSC; 30% formamide/70% PBT; 20% formamide/80% PBT; 20% formamide / 80% PBT; 10% formamide/90% PBT; 100% PBT. An additional crosslink step with PFA 4% was performed and labeled embryos were washed, resuspended in PBS and stored at −20 °C for months until further use.The ssDNA library is hybridized to the DNA as previously described39. Software-controlled microscope components, including camera, stages, lasers, needles, pump and valves, were run using Qudi-HiM, an homemade software developed in python70 . Embryos were imaged using an ×60 Plan-Achromat water-immersion objective  mounted on a closed-loop piezoelectric stage . The Illumination was provided by three lasers (OBIS-405 nm and Sapphire-LP-561 nm from Coherent and VFL-0-1000-642-OEM1 from MPB communications Inc.) and the images were acquired using an sCMOS camera . A homemade autofocus system was used to correct for axial drift in real time using a 785 nm laser (OBIS-785nm from Coherent).Experiments were performed on a homemade imaging setup built on a RAMM modular microscope system (Applied Scientific Instrumentation) coupled to an improved microfluidic device, as the one described previouslyl-lysine):water coated coverslip and mounted into a FCS2® flow chamber . ~20–30 embryos were selected and imaged using two regions of interest (ROI 200 × 200 µm²). Then, a mixture containing the fiducial adapter  was injected in the chamber and let incubate for 15 min to allow complete hybridization on the primary FISH library. Embryos were washed for 10 min with a washing buffer solution  and for 5 min with 2× SSC before injecting 0.5 µg ml−1 of DAPI in PBS to stain nuclei. Prior to imaging, the imaging buffer  was injected to reduce photobleaching of the fiducial barcode. A stack of images was acquired for DAPI and the fiducial tagged with Atto 550  using 405 nm and 561 nm sequential illumination. Next, the sample was sequentially hybridized as follows. A solution containing the barcode and the imager oligo was injected  and incubated for 15 min. Then, the embryos were washed with 1.5 mL of washing buffer and with 1.5 mL of 2× SSC before injecting the imaging buffer. In each cycle, fiducials and readout probes were sequentially imaged with 561 nm and 647 nm excitation lasers. After imaging, the fluorescent tag of the readout probes was cleaved and discarded using 1 mL of chemical bleaching buffer . Finally, samples were washed with 1 mL of 2× SSC for 5 min before a new hybridization cycle started. Further details can be found on our previously published protocol39.Embryos were aligned on a 2% agar:PBS pad, attached to a 1:10 poly(https://svi.nl). The analysis was performed using our pyHiM analysis pipeline (https://pyhim.readthedocs.io/en/latest/). Briefly, images were first z-projected using either sum (DAPI channel) or maximum intensity projections . Fiducial images from each hybridization cycle were used to register barcode images using global and local registration methods. Next, barcode images were segmented in 3D using stardist71 and the positions of the centers of barcodes were detected with subpixel resolution using Big-FISH (https://github.com/fish-quant/big-fish)72. DAPI images were segmented in 3D using stardist. Barcodes were then attributed to each single nucleus mask by using their XY coordinates. Finally, pairwise distance matrices were calculated for each single nucleus. From the list of pairwise distance maps, we calculated the proximity frequencies as the number of nuclei in which pairwise distances were within 200 nm normalized by the number of nuclei containing both barcodes. Hi-M maps of nc14 embryos were generated from a total of 23531 traces from 22 embryos from 2 separate experiments. The maps for nc12 embryos are constructed from 1792 traces from 4 embryos from 2 separate experiments.DCIMG files were converted to TIFF using proprietary software from Hamamatsu. TIFF images were then deconvolved using Huygens Professional 21.04  over the Hi-M matrix.73. Briefly, we counted the number of multiway contacts where the selected anchor barcode was interacting with other partners within a 200 nm radius. These values were normalized by the number of pairwise interactions for each anchor. The expected proximity frequency is derived by considering all events as independent. For this, we computed the mean of the product of all possible barcode combinations for various numbers of interacting partners.The proportion of multiway contacts is calculated from single nucleus proximity frequency nc14 matricesGSE62904 and GSE54337 primary accession numbers. The quality of the reads was estimated with FastQC74 (0.11.7). Sequencing reads were aligned to the reference Drosophila melanogaster genome assembly (dm6) using Burrows–Wheeler Aligner75 (0.7.17-r1188) with default parameters. Finally, peak calling was performed using MACS276 (2.2.7.1) with default parameters. BEAF-32, ZELDA, Zw5, PolIISer5 and Pc / Ph raw data were downloaded from GEO under series accession code GSE62904, GSE30757, GSE76997, GSE62925, GSE60428 respectively, and processed as previously described. ChIP-on-Chip insulator proteins data from GSE26905 GEO series have been downloaded as bed files and peak coordinates have been converted from dm3 to dm6 by using FlyBase’s sequence coordinates converter . The accession numbers of the data used in this study are listed in Supplementary Data Insulator proteins ChIP-Seq fastq files were downloaded from Gene Expression Omnibus (GEO) with GSE8385142. Wig files were converted to BigWig using wigToBigWig from UCSC. Heat maps of ATAC-seq profiles were then plotted over ±1 kb window centered on Class I IBPs sites using computeMatrix followed by plotProfiles from deepTools77. Average ATAC-seq profiles derived from the heat maps for individual IBPs were constructed using a custom Matlab script. Venn diagrams between Class I IBPs and Zelda peaks were generated from bed files using Intervene78 and plotted using a custom python script. Different bed files coming from each group were generated using intersect and subtractBed from bedtools v.2.3.ATAC-seq data were downloaded from 20 Briefly, boundaries were called using the insulation score metric defined by Crane et al.79 using a 5 kb balanced contact matrix with a window size of 8 bins.We used the previously annotated list of TAD boundaries from Hug et al.29 (1.3.3) was used with different sets of parameters to create different networks. Network 1 was built with the following parameter set: --pearson 0.3, --min-dist 20 kb, --max-dist 2 Mb, --min-sep 5 kb, --max_perc_0 10. Network 2 was built using: --pearson 0.3, --min-dist 10 kb, --max-dist 200 Mb, --min-sep 5 kb, --max_perc_0 50. Network 3 was built using: --pearson 0.2, --min-dist 10 kb, --max-dist 200 Mb, --min-sep 5 kb, --max_perc_0 50. In all cases, Chromosight was used with the “--norm” parameter set to “auto” to instruct Chromosight to use matrices normalized using the Knight–Ruiz balancing algorithm80. As a pre-processing step, Chromosight normalizes Hi-C matrices by genomic distance using observed/expected values. Significant chromatin loops are called on these normalized matrices. A specific genome scale chromatin network was built where nodes are genomic fragments and edges are significant interactions between two fragments. Then, chromatin networks were loaded on R and ChIP-seq peaks were used as features assigned to nodes using the ChAseR R package81 (0.0.0.9) to calculate chromatin assortativity. For each feature, 1000 randomized networks preserving genomic distances and corresponding chromatin assortativity values were computed.In order to build networks needed for Chromatin Assortativity, Hi-C contact matrices were used with a 5 kb resolution. ChromosightX is the feature chromatin assortativity value, µ is the randomizations ChAs average and σ is the standard deviation from the randomization distribution.The chromatin assortativity Z-Scores calculation is given by the following formula:The calculation of the Z-Score allows estimating the significance of the assortativity values with respect to the assortativity expected based purely on correlation of feature values along the linear genome. For example, domains of a feature that span multiple bins in the Hi-C matrix are more likely to produce high ChAs values, but this does not imply the importance of the 3D contacts.27.Cytoscape (3.8.0) was used for chromatin network visualization. Methods for Cross-ChAs and AND-ChAs are provided elsewhere20 (E-MTAB-4918). We then selected the interaction between the regions bound by the set of factors on the distance-normalized Hi-C dataset. The distribution of log2(O/E) between all pairwise combinations of regions bound by the investigated factors was then displayed in a violin plot or divided into equal-size groups depending either on protein occupancy or insulation score and displayed in Hi-C aggregate plots.ChIP-seq peaks were used to extract a list of regions bound by a set of putative factors. Next, for each autosomal arm, we computed the log2(Observed/Expected) Hi-C contact value by calculating the average contact frequency for all the combinations of regions separated by a certain genomic distance on a 5-kb-Hi-C datasetN BEAF-32 anchors from the full list of anchors . We then calculated the mean log2(O/E) for this set of N anchors and repeated this process 10,000 times for each value of N.Hi-C aggregate plots were performed using a homemade analysis pipeline developed in MATLAB Release R2019b . The distance-normalized sub-matrices over a window of 100 kb surrounding the intersection between two anchored peaks were extracted. Finally, the aggregate plots were then created by averaging all of the sub-matrices together. For the bootstrapping method Fig. S, we perfFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting SummarySource Data


X-chromosome inactivation (XCI) has evolved in mammals to compensate for the difference in X-chromosomal dosage between the sexes. In placental mammals, XCI is initiated during early embryonic development through upregulation of the long noncoding RNA Xist from one randomly chosen X chromosome in each female cell. The Xist locus must thus integrate both X-linked and developmental trans-regulatory factors in a dosage-dependent manner. Furthermore, the two alleles must coordinate to ensure inactivation of exactly one X chromosome per cell. In this review, we summarize the regulatory principles that govern the onset of XCI. We go on to provide an overview over the factors that have been implicated in Xist regulation and discuss recent advances in our understanding of how Xist’s cis-regulatory landscape integrates information in a precise fashion. •Complex regulatory interactions ensure female-specific monoallelic Xist expression.•Stochastic upregulation combined with a negative feedback can explain the Xist pattern.•A series of transcription factors activate Xist in different cellular context.•Differentiation cues control distal enhancers, X-dosage is sensed promoter-proximally. Current Opinion in Genetics & Development 2023, 81:102063Genome Architecture and ExpressionThis review comes from a themed issue on Giacomo Cavalli and Job DekkerEdited by Genome Architecture and Expression (2023)”For complete overview of the section, please refer to the article collection, “https://doi.org/10.1016/j.gde.2023.102063http://creativecommons.org/licenses/by/4.0/)0959–437X/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license  as a strategy for dosage compensation between the sexes  Xist, which is encoded on the X chromosome. Between 50 and 200 Xist molecules spread over the entire chromosome in vivo dynamics of Xist upregulation, reaching 50–80% of Xist-positive cells after 2 days with a transient occurrence of biallelic Xist expression in a subset of cells In mice, XCI occurs in two waves : Xist isWhile biallelic Xist expression is transient in mice, in primates, including humans, XIST is expressed from both X chromosomes for an extended period of time before it transitions to a monoallelic state after implantation in vitro and in vivoInitially, it was assumed that the processes of sensing the number of X chromosomes in the nucleus and choosing one for inactivation were distinct phenomena Xist upregulation in an X-dosage-dependent manner, it contributes to female specificity providing an explanation why Xist is upregulated only in cells with two X chromosomes. For cells to be able to reliably distinguish between a single and a double dose of X-linked activators, an activation threshold has been proposed that can only be reached, if two or more X chromosomes are present in cis, causing their levels to drop below the activation threshold, and thereby preventing Xist upregulation from the second X chromosome. If all X chromosomes in a cell initiate XCI , complete silencing of essential X-linked activators would lead to Xist downregulation from one or both alleles  can however still upregulate Xist In vivo, a complete absence of RNF12 prevents Xist expression during iXCI, but not during rXCI In vitro, it appears to depend on the differentiation system whether RNF12 is essential for the onset of XCI 2 concentrations, which generally enhance Xist expression, reduce the dependency of XCI on RNF12 Jpx, which is located just upstream of the Xist gene , which is encoded close to the ist gene a and silist gene a and escXist locus  does not In female mice, Xist is almost universally expressed in developmental and somatic tissues, with the exception of pluripotent cells in the late blastocyst and the germline. Therefore, Xist expression was initially thought to be primarily governed through repression in those cells where it is absent. Accordingly, several pluripotency factors, such as OCT4, NANOG, REX1 , and PRDM14 It is becoming evident that Xist expression is activated and maintained by a diverse set of autosomal transcription factors in a tissue-specific manner. For example, P53 has been suggested to activate Xist during early brain development TsixIn recent years, a series of additional proteins have been implicated in Xist regulation. The ubiquitous transcription factor YY1 has been shown to function as a Xist activator Xist locus?As summarized above, a variety of trans-acting factors involved in Xist regulation have been identified. But how are those signals sensed and interpreted by the Xist gene is located in the X-inactivation center (Xic), which contains a series of activating and repressive elements, including numerous regulatory lncRNA genes , TAD-E and TAD-D Xist and its repressive antisense gene Tsix are transcribed across the TAD-D/-E boundary with the Xist promoter being located in TAD-E and the Tsix promoter in TAD-D. Tsix is transcribed through the entire Xist gene, including the promoter, and represses Xist in cis through depositing repressive histone modifications, such as H3K36me3 The NA genes a. The XiDistal regulatory regions sense the developmental state of the cell, while promoter-proximal elements b–d integLinx, which also represses Xist in cis but in a Tsix-independent manner, through a yet-unknown mechanism The repressive effects of elements in TAD-D are thought to be largely mediated via regulation of Tsix, which allows communication across the TAD boundary via antisense transcription Jpx, Ftx, and Xert, and an enhancer cluster within Xert, called XertE. They all activate Xist mostly in cisXic is also supported by aberrant Xist upregulation upon inversions of the Xist/Tsix transcription unit or within TAD-D In TAD-E, numerous regulatory regions have been identified, including the three lncRNA genes Xist, which is bound by various pluripotency factors, has been suggested to repress Xist in undifferentiated cells Developmental control of distal regions in TAD-E appears to involve repression of the region in undifferentiated cells through a large H3K27me3 domain Xist (Tsix transcription in conjunction with lack of distal enhancer and lncRNA activity. Upon differentiation, the proximal region will maintain its active state on the Xi, where Xist is upregulated, and undergo heterochromatization through recruitment of H3K9me3 on the Xa Tsix transcription, TRIM28, and the G9A methyltransferase While distal elements are activated in both male and female cells, the promoter-proximal region appears to respond to X-dosage cues Xist b–c. It cXist b–c. The In recent years, our understanding of developmental and tissue-specific Xist regulation has progressed significantly. As for other developmental genes, tissue specificity is largely controlled by long-range regulation. Near-ubiquitous expression in female cells is likely achieved via a diverse set of enhancers that are controlled by various lineage-specific transcription factors. By contrast, X-dosage sensing, choice of the X to inactivate, and establishment of epigenetic memory seem to be governed by a promoter-proximal regulatory element. This region acts as a binary switch that can assume a repressed or an active state, where the switching probability is determined by a complex interplay of different factors. How this bifunctional element is controlled, and whether it can indeed maintain an epigenetic memory, remains to be uncovered. Also, the search for trans-acting factors involved in X-dosage sensing will continue to eventually test whether the predicted activation threshold indeed exists and how it is implemented on the molecular level. Although much work has focussed on transcriptional control of Xist, the picture appears to be more complex. Feedback loops acting at the level of RNA stability fine-tune Xist levels, potentially to make sure that XCI stays restricted to the X chromosome. The mechanisms at play for post-transcriptional control of Xist will be an interesting topic for future studies.The authors declare no conflicts of interest.


We also identify a second subset of c-miR that targets flipons essential for retrotransposon replication, exploiting that vulnerability to limit their spread. We propose that miR can act in a combinatorial manner to regulate the readout of genetic information by specifying when and where flipons form non-B DNA (NoB) conformations, providing the interactions of the conserved hsa-miR-324-3p with RELA and the conserved hsa-miR-744 with ARHGAP5 genes as examples.The classical view of gene regulation draws from prokaryotic models, where responses to environmental changes involve operons regulated by sequence-specific protein interactions with DNA, although it is now known that operons are also modulated by small RNAs. In eukaryotes, pathways based on microRNAs (miR) regulate the readout of genomic information from transcripts, while alternative nucleic acid structures encoded by flipons influence the readout of genetic programs from DNA. Here, we provide evidence that miR- and flipon-based mechanisms are deeply connected. We analyze the connection between flipon conformation and the 211 highly conserved human miR that are shared with other placental and other bilateral species. The direct interaction between conserved miR (c-miR) and flipons is supported by sequence alignments and the engagement of argonaute proteins by experimentally validated flipons as well as their enrichment in promoters of coding transcripts important in multicellular development, cell surface glycosylation and glutamatergic synapse specification with significant enrichments at false discovery rates as low as 10 I. In) conn) are sites of stress-induced duplex destabilization (SIDD)  to t to t6] tFor flipons present in distal intergenic regions, mRS overlapped mainly with the AT-rich SIDDs (mean length = 170 bp). The most frequent overlap was for miR-203a (~20%) . Other mIn contrast, proximal promoters (PP) within 1 kb of a TSS were enriched for cCRE marks and cCRE marks associated with CCCTC binding factor (CTCF). Z and G flipons A, but noThere was no apparent strand preference for miR binding to flipons, either for TRE or for PP, with respect to the direction of transcription C. When aThe number of interactions with flipons for individual c-miR varied greatly . The numTo examine the role of G and Z flipons in PP, we mapped their location in 100 base pair windows either side of the TSS . We foun−20 in many cases (−63), while scores for genes specifying GABAergic synapses were less significant (FDR < 10−18). We also analyzed genes with G and Z flipon mRS matches separately , junction binding (J), or as both motif and junction binding (MJ). The J sites were defined by a one base overlap with an experimentally determined flipon site, while MJ-sites required a 2-6-base overlap with the flipon sequence . We founWe also found that flipons were enriched in proximal promoters of coding genes compared to non-coding genes (24.71% c.f. 8.53%). In contrast, the count of flipons over all gene bodies was highest for non-coding genes C,D 23.1.To confirm the potential for interaction of miR with flipons, we examined the binding of AGO1 and AGO2 proteins to DNA using the ENCODE ChIP-seq (chromatin immunoprecipitation and sequencing) datasets that are available for the human HepG2 liver cell line, as no paired datasets were available from murine studies . The binWe performed GO annotation of those c-miR most frequently found to interact with flipons . Each c-We noted that the potential sites at which c-miR dock to flipons are associated with cCRE marks . These fThe localization of both G and Z flipons to promoters opens up the possibility of combinatorial control of gene expression by miR, as illustrated in Il11) gene that encodes a protein with a role in female fertility and inflammation-induced pulmonary fibrosis [Il11 PP, the flipon and c-mRS matches favoring G4 formation are associated with histone H3 lysine 4 trimethylation marks (H3K4me3), marks that are characteristic of active promoters (The interleukin 11 (fibrosis  has a laromoters . Sites wJag2 gene promoter (n repeat but rather by inhibiting formation of the junction between B- and Z-DNA helices (BZJ). The variable nature of BZJ forming sequences allows miR to specifically target a particular Z flipon. Instead of flipping B-DNA to Z-DNA, the miR likely promotes the opening up of the helix to expose single-stranded DNA. In the case of miR-744, the inhibition of Z-DNA will favor G4 formation. Otherwise, formation of Z-DNA will reduce the available free energy for G4 formation. The Jag2 PP also contains two SIDDs with mRS matches to multiple c-miR. They lack cCRE marks and are not associated with TRE.We also analyzed the promoter . Jag2 enThe activin A receptor type 2B (Acvr2b) is a high-affinity ligand for activin, a member of the transforming growth factor superfamily that signals through the SMAD pathway and plays key roles during neural development ,53. The Adgrb2), which has roles in neurogenesis [The adhesion G-protein-coupled receptor B2 . The NFAcvr2b promoter. For the growth factor receptor-bound protein 10 (Grb10) gene, there is a series of Z flipons on the coding strand, and they are self-complementary and that could potentially fold into hairpin structures (Nol4l) gene where one Z flipon strand is GT rich (Nol4l coding strand could instead form a left-handed quadruplex similar to the recently described fold formed by r(GT)n repeats longer than 11 bases that is likely to be a more stable structure than Z-RNA [Grb2 and Nol4l transcripts would not be disrupted by c-miR described above and could fold as Z-RNA or potentially as a Z-quadruplex.In other situations, Z flipons have different roles to play. Their interaction with miR may prevent Z formation as described above for the ructures A–D. We e GT rich E,F. If w GT rich G, wherea GT rich H. Bindin GT rich E,F. The an Z-RNA . We noteWe observe a high frequency of matches between c-miR and flipons and evidence that these interactions are under selection. There is enrichment of flipons in proximal promoters , most notably for G and Z flipons. The PP flipons are associated with cCRE marks and their distribution differs by flipon type. A different subset of flipons, particularly SIDDs, localize to TRE in distal intergenic regions A,B that The function of genes with PP flipons is highly enriched for multicellular development, cell-surface N-linked glycosylation, glutamatergic and GABAergic signaling, all of which play an important role in neural development  and in rRegulation of flipon conformation by c-miR is one way to control expression during embryogenesis that does not rely on the production of sequence-specific transcription protein factors. The high degree of transcription observed in the early embryo delivers the energy needed to power these flipon-dependent transactions . The contrans-RNA [There are 40–1500 miR copies of each expressed miR per cell , a numberans-RNA . In addirans-RNA , that trrans-RNA ,67. We aAdgrb2 ); c-mRS , mouse basic gene annotation vM25 from GENCODE (https://www.gencodegenes.org/ accessed on 4 March 2022), mouse genome assembly GRCm38, candidate cis-Regulatory Elements and RepeatMasker tracks from UCSC , miR sequences from miRbase , AGO1 ChIP-seq (GSE174905) and AGO2 ChIP-seq (GSE136467).KMnO4 mapping of non-B-DNA structures in murine lipopolysaccharide activated spleen B Cells ( (Bartel ); human https://github.com/theopavlove/article_2023_conserved_microRNAs (accessed on 12 February 2023).To uplift mouse data from an mm9 assembly to mm10, we used the liftOver executable from the UCSC binary utilities library . The bighttps://david.ncifcrf.gov/) (accessed on 24 December 2022) [Gene lists were mapped to Gene Ontology terms using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Resource (er 2022) . The DAVhttps://doi.org/10.1101/2023.01.12.523822 (accessed on 12 February 2023).The propensity of sequences to form Z-DNA was analyzed using the transformer algorithm implemented in the Z-DNABERT program that is described in a preprint available at The c-mRS binding was computed as three separate categories. The first one is a direct motif overlap (M) between mRS and a whole flipon region. The second is a junction overlap (J) of mRS with a single nucleotide in a flipon’s motif sequence, meaning that for each flipon, there are only two possible places for such overlaps: 5′-end minus 6 bp and 3′-end plus 6 bp. The last one is a motif-junction overlap (MJ), which considers mRS binding to, at most, 6 nucleotides of a motif from both 5′ and 3′ ends, with the rest of the sequence being outside of a flipon frame.Each flipon was associated with multiple genomic data items. The genomic feature mapping was computed via ChIPpeakAnno algorithm, which resulted in the following peak categorization: Promoter , Exon/Intron, 5′/3′ UTR, Downstream and Distal Intergenic associations. In order to calculate overlaps between flipons and both cCREs and LINE/LTR repeats, each flipon was extended by 200 bp on either side and then matched to each region of interest. Each flipon was then assigned to one of the following categories: cCRE only, LINE/LTR only, cCRE and LINE/LTR, CTCF only, cCRE and CTCF, LINE/LTR and CTCF, cCRE and LINE/LTR and CTCF.For each of the two flipon categories—cCRE only and LINE/LTR only—the number of mRS was counted with strand orientation taken into account. For each miR family, the number of bound sites was calculated with respect to the RNA transcription direction.We counted flipons present in PP (±1 kb from TSS) after splitting the regions into 100 bp windows. Flipons that overlapped two windows were counted as present in both. The counts were then summed across every gene promoter.For the analysis of distance to TSS, we used the ENCODE annotation. Only the distance to the nearest region of interest (AGO1 or AGO2 ChIP peak) is shown on the plot G. A rollhttps://github.com/Madund3ad/article-mi-RNA/ (accessed on 17 February 2023).For the analysis of AGO1 and AGO2, we used custom R 4.1.3 and Python 3.7.13 scripts. Overlaps were calculated using GenomicRanges R library, and values for smooth curves were calculated using the zoo R library. The distances to TSS were drawn using the ggplot2 R library. The scripts are available in the repository For the analysis of overlaps between ENCODE AGO narrow peaks, experimental Kouzine SIDD peaks and promoters, we took 1 kb upstream regions from TSS as a promoter. TSS coordinates were obtained from the ENCODE annotation. Two regions were considered to be overlapping if they had at least one base pair in common. Significance of the overlap was calculated using Monte-Carlo simulation (n = 1000).


Cartilage development plays a crucial role in human health and animal agriculture, and fetal development takes on particular importance in this process. In this study, we identified open chromatin regions in bovine fetal chondrocytes and peaks enriched in the promoter region. By integrating chromatin accessibility with transcriptome data and Genome-Wide Association Studies results of bovine stature traits, we further described the regulatory functions of these regulatory elements defined by open chromatin accessibility. Overall, these findings provide valuable information for understanding the regulatory mechanisms in cartilage development and conducting beef cattle genetic improvement programs.Despite significant advances of the bovine epigenome investigation, new evidence for the epigenetic basis of fetal cartilage development remains lacking. In this study, the chondrocytes were isolated from long bone tissues of bovine fetuses at 90 days. The Assay for Transposase-Accessible Chromatin with high throughput sequencing (ATAC-seq) and transcriptome sequencing (RNA-seq) were used to characterize gene expression and chromatin accessibility profile in bovine chondrocytes. A total of 9686 open chromatin regions in bovine fetal chondrocytes were identified and 45% of the peaks were enriched in the promoter regions. Then, all peaks were annotated to the nearest gene for Gene Ontology (GO) and Kyoto Encylopaedia of Genes and Genomes (KEGG) analysis. Growth and development-related processes such as amide biosynthesis process (GO: 0043604) and translation regulation (GO: 006417) were enriched in the GO analysis. The KEGG analysis enriched endoplasmic reticulum protein processing signal pathway, TGF-β signaling pathway and cell cycle pathway, which are closely related to protein synthesis and processing during cell proliferation. Active transcription factors (TFs) were enriched by ATAC-seq, and were fully verified with gene expression levels obtained by RNA-seq. Among the top50 TFs from footprint analysis, known or potential cartilage development-related transcription factors FOS, FOSL2 and NFY were found. Overall, our data provide a theoretical basis for further determining the regulatory mechanism of cartilage development in bovine. Stature, body weight, meat quality, reproductive traits, etc., are important economic traits for cattle . Body shSOX9 and RUNX2 genes were highly expressed in chondrocyte chondrocytes. Transcriptional process of Sox9 regulation Class I sites were mainly concentrated near the transcription start sites of highly expressed genes with non-chondrocyte-specific markers [COL2A1 gene was initiated by the activation of the Sox family transcription factors to drive the expression of COL2A1 [ACAN gene is involved are highly conserved in various chondrocytes [ACAN abundantly expressed in the initial stage of chondrocyte proliferation [The process of chondrocyte proliferation involves complex regulatory networks such as paracrine, autocrine, and endocrine, and it is accompanied by the transcription and translation of a large amount of genetic information. TFs specifically bind cis-regulatory elements, such as enhancers and promoters, to regulate gene transcription. The majority of TFs bind to open chromatin surveyed from the ENCODE project and chromatin accessibility status reflects TF binding information . It has f COL2A1 . The prodrocytes . Sox famferation . These tThe developing mammalian cartilage is mainly divided into resting zone, proliferative zone and hypertrophic zone. Chondrogenesis is the result of aggregation of mesenchymal cells. When mesenchymal cells aggregate and develop into chondrocytes, collagen types I, III, and V are expressed, while the differentiation of chondrogenic progenitor cells is accompanied by the specific expression of collagen types II, IX, and XI in cartilage . After pIn recent years, transposase accessible high-throughput sequencing (ATAC-seq) technology has made it possible to explore the epigenetic characteristics of various biological process in cattle. For example, dynamic changes in chromatin accessibility were characterized among four stages of bovine myoblast proliferation and differentiation . Gao et Cows were electrocuted and bled out. Three 90-day bovine fetuses were removed from the uterus of slaughtered pregnant cows.The long bone cartilage tissues were extracted from the ends of a long bone. The perichondrium was peeled off, and then the cartilage was taken out with forceps. The cells were trypsinized with a 0.25% trypsin and washed with PBS twice. Then, a 0.1% type IV collagenase was added into tubes to digest.4 chondrocytes were collected after di-gestion and resuspended in precool lysis buffer for 10 min. The lysed nucleus was re-suspended with a Tn5 transposase mixture and incubated at 37 °C for 30 min. The fragments were purified using the VAHTS DNA Clean Beads  and then amplificated for 16 cycles. Magnetic beads were used again to screen suitable fragments and the final libraries were quantitated using Qubit 4 Fluorometer  and then sequenced on an Illumina HiSeq2500 platform following a PE150 strategy.ATAC-seq libraries were constructed with the TruePrep DNA Library Prep Kit V2 for Il-lumina . Briefly, 5 × 106 chondrocytes) was purified from the total RNA with oligo-dT magnetic beads for three biological replicates, and then the RNA was fragmented with Mg2+. The RNA-seq library was constructed according to the following steps: reverse transcription, cDNA end repair, adding poly (A) tail. All libraries were sequenced on the HiSeq2500 platform according to the PE150 strategy.The mRNA  . Trimminhttps://github.com/WentaoCai/GWAS_enrichment), URL (accessed on 20 April 2022) was used for the GWAS enrichment analysis to check whether SNP effects were more enriched in ATAC-seq peaks than in background area [As described previously, a total of 27,214 Holstein bulls with 2,048,052 imputed SNPs and available stature traits were included . The oriund area .Open chromatin regions were annotated to their nearest genes. GO enrichment analysis and KEGG pathway enrichment were carried out by the R package clusterProfiler . The parAccording to the position of the open chromatin region, the enrichment of the motifs corresponding to known TFs was performed using the findMotifsGenome.pl function of HOMER . The sigRaw sequencing fastq files were assessed for quality, adapter content and duplication rates with FastQC (v.0.11.7). The sequence reads for the removal of Illumina adapters and low-quality sequences were trimmed by Trim Galore (v.0.6.3). Trimmed reads were aligned to the ARS-UCD1.2 version of bovine reference genome using Tophat2 software (v.2.1.1) with the default setting. Quantification of gene expression counts were conducted by Hisat2 (v2.2.1)  and thenBigWig files for the coverage tracks of ATAC-seq and RNA-seq were generated using the bamCoverag function of the deeptools (v.3.3.0) software. Visualization of the ATAC-seq and RNA-seq coverage track was conducted by Integrated Genomic Viewer (IGV 2.6.2).After the paired-end sequencing data was filtered, 9.75 Gb of high-quality data was obtained. A preliminary analysis on the quality of the data was conducted. Raw sequences accounting for less than 0.5% of the total data volume were filtered in the quality control. We found that above 95% of clean reads were mapped to the bovine reference genome (bosTau9 version) , which aA total of 6.05 Gb of high-quality RNA-seq data was obtained after quality control. The mapping rates of three RNA-seq biological replicates were higher than 85% and the data quality satisfied the downstream analysis standard. In addition, Pearson’s correlation coefficient was 0.8 between biological replicates. Our RNA-seq data retained 10,962 genes with the expression levels greater than two (TPM > 2) in the three replicates.SOX9 and RUNX2. In addtion, we found that XBP1 and REEP6 with intense signals may be the potential chondrocyte proliferation-related genes that were rarely reported to be directly associated with cartilage development.We performed ATAC-seq to map the open chromatin accessible regions in fetal bovine chondrocytes. Three biological replicates and their combined data were named rep1, rep2, rep3, and merged peak, respectively. To establish a common peak set, the strict strategy for candidate peaks were adopted. Finally, a total of 9860 peaks were identified as shown in Chromatin opening usually indicates that DNA with transcriptional regulatory elements is bound by transcriptional factors to regulate gene expressionand replication or transcription occurs . To explp ≤ 0.05 were considered significantly enriched. Our results of GO terms demonstrated a highly significant enrichment of the chondrocyte-related processes such as peptide synthesis, amide synthesis, translation and regulation. The results of KEGG pathway enrichment showed a highly significant enrichment in the endoplasmic reticulum protein processing, ribosome, nucleocytoplasmic transport and other pathways  enriched motifs are shown in p-value generated by TFs enrichment is sorted out, and the corresponding significance is listed in descending order as follows: NF-Y (1 × 10−616), ATF3 (1 × 10−317), FOSL2 (1 × 10−300), ATF2 (1 × 10−260) and Fos (1 × 10−106).TFs combine specific DNA sequences to activate or inhibit the transcription of DNA, and modulate the gene expression to control a series of key cellular processes . In our Footprint visually displays that the ATAC-seq signal near the TF motif in the 200 bp genome range, read coverage suddenly drops within peak regions of high coverage  Figure b. The prp value was calculated by one-tailed test . From a different viewpoint, the reliability of peak regions could be certificated.To check whether SNPs effect was more abundant in the ATAC-seq peaks than those in the background area, we obtained the result of random sampling. The candidate SNPs were randomly sampled for 10,000 times and the number of samples each time was also random. The proportion of falling in the peak was summarized and counted, and the led test . p-valueSome studies have reported that epigenome data were used to identify the developmental regulatory mechanisms of bovine tissues . Chromatin open profiles have also been constructed in the study on growth traits in pig, chicken and other livestock and poultry, but chromatin openness during fetal bovine cartilage development has not yet been reported ,37,38.Chondrocyte differentiation is usually accompanied by the biological synthesis of various proteins. Generally, the genetic programming of cartilage undergoes a multistep process. Cell types experience a change consisting of mesenchymal condensation, highly enriched extracellular matrix (ECM) cells, and chondrocytes . The sigRUNX2 [XBP1 and REEP6 with highly effective signals were regarded as potential genes. Mice with XBP1 deficient caused a chondrodysplasia phenotype [REEP6 is related to the structure of the endoplasmic reticulum, which is consistent with the previously enriched KEGG pathway, so it is speculated that REEP6 is related to the synthesis of collagen on the endoplasmic reticulum.Amide biosynthesis and translation regulation were more prominent in GO enrichment. Glutamine has a versatile role in cell proliferation and metabolism, participating in biosynthetic precursors and tricarboxylic acid cycle . It is aRUNX2 . In the henotype . REEP6 iSOX9 gene, closely related to NF-Y, contains a CCAAT box, which is a cis-regulatory element responsible for responding to BMP-2 [SOX9 to activate the Sox9 gene expression [SOX9 is activated by NF-Y binding to the Sox9 promoter region, which explains the high footprint and mRNA expression of the transcription factor NF-Y significantly enriched by the motif.From the peak annotation result, we consider that these open chromatin regions near TSSs contain a large number of potential transcription factor binding sites. NF-Y, a heterotrimeric complex composed of NF-YA, NF-YB, and NF-YC, specifically binds to CCAAT sequences in eukaryotic promoters . Transcrto BMP-2 ,54. BMP-pression . In concFosl2 is necessary for cartilage development [Fosl2−/− mice impaired chondrocyte differentiation in embryos and newborns and decreased the deposition of extracellular matrix (ECM) [FOSL2 tg and control mice at two different time points (4 weeks and 3 months of age) showed that FOSL2 tg mice had increased bone volume and bone surface area [Transcription factors Fos, Fra2, Jun and other transcription factors together constitute the activator protein 1 (AP1) transcription factor family  which paelopment . Due to ix (ECM) . Histomoace area .SOX9 promotes cell division in the early stage of proliferation, and also inhibits chondrocyte hypertrophy [SOX9 activity, which may also explain that SOX9 is not at a high level of expression. In cytokine-induced MMP13-derived human chondrocytes, the expression of the transcription factor FOS was almost halved from 1 h to 1.25 h, further suggesting a transient nature expression of the cartilage-specific transcription factor [Both ATF2 and ATF3 transcription factors belong to the ATF family, and the members of the ATF-CREB family play an important role in cartilage development. Mice lacking ATF2 displayed achondroplasia, and ATF2 also regulated cell proliferation by targeting cyclin A . ATF3 haertrophy . ATF3 ovn factor . The development of chondrocytes in the fetal stage has a great influence on the bone morphology and bovine stature after birth. In this study, we characterized the chromatin accessibility of chondrocyte proliferation using fetal bovine long bone derived from chondrocytes. We identified the significant KEGG pathways and GO Terms related to cartilage development. Then, we identified several candidate TFs  that might participate in the biological process of chondrocytes using motif enrichment and foot printing analysis. The results provide a theoretical basis for explaining the regulatory mechanisms of bovine fetal chondrocyte development.


Hematopoiesis is an essential process for organismal development and homeostasis. Epigenetic regulation of gene expression is critical for stem cell self-renewal and differentiation in normal hematopoiesis. Increasing evidence shows that disrupting the balance between self-renewal and cell fate decisions can give rise to hematological diseases such as bone marrow failure and leukemia. Consequently, next-generation sequencing studies have identified various aberrations in histone modifications, DNA methylation, RNA splicing, and RNA modifications in hematologic diseases. Favorable outcomes after targeting epigenetic regulators during disease states have further emphasized their importance in hematological malignancy. However, these targeted therapies are only effective in some patients, suggesting that further research is needed to decipher the complexity of epigenetic regulation during hematopoiesis. In this review, an update on the impact of the epigenome on normal hematopoiesis, disease initiation and progression, and current therapeutic advancements will be discussed. Hematopoiesis is a highly regulated process that sustains the life-long production of blood cells. All mature lineages are derived from a pool of hematopoietic stem cells (HSCs) within the bone marrow. HSCs can self-renew or differentiate, leading to a balance between maintaining an HSC pool throughout life and producing mature cells needed for organism homeostasis. Hematopoiesis was initially thought to occur in a sequential stepwise hierarchy where HSCs contribute equally to each lineage . AdvanceHistone modifications are essential epigenome regulators that control chromatin structure and gene accessibility. Chromatin in an open configuration, termed euchromatin, has less condensed DNA and is readily available for gene expression. In contrast, heterochromatin is more condensed and results in transcriptional repression. Open and closed chromatin states are regulated by directly modifying the chromatin template. Two epigenetic mechanisms that are essential regulators include covalent histone modifications and DNA methylation. Early studies have documented that HSCs have a unique epigenetic footprint creating a highly coordinated progenitor differentiation process by activating and silencing genes leading to the preassembly of transcription factors at lineage-specifying promoters . There aThe establishment of regulatory element reference maps and correlating histone methylation and gene expression patterns in multiple cell types have provided a framework for the identification and activity of promoters and enhancers . HistoneHoxa9, Pbx1, and Mecom  and lymphoid primed progenitors after transplantation  and 5%–10% of acute myeloid leukemia (AML) (MLL1 translocations lead to the overexpression of HOX cluster genes and the HOX cofactor MEIS1 (HMTs may function as oncogenes or tumor suppressors in leukemogenesis. ia (AML) . MLL traor MEIS1 . MLL3 anor MEIS1 . MLL4 isor MEIS1 .Another commonly mutated epigenetic regulator in follicular lymphomas is the H3K27 methyltransferase, enhancer of zeste homolog 2 (EZH2) . EZH2 isGlobal analysis of human and murine HSPCs has shed light on the crucial players that help shape multilineage gene priming . For insLSD1 is specific for histone H3 lysine 4 and 9 methylation, which both activate and repress transcriptional programs. In hematopoietic cells, it was shown that Lsd1 is required for successful differentiation into mature blood cells. A knockout mouse model was utilized to show that conditional inactivation of Lsd1 led to defects in self-renewal, impairments in HSC differentiation into immature progenitors, and disrupts terminal erythroid and granulocytic maturation (Histone demethylases erase the existing methylation of histones and are classified into two families: amino oxidase homology lysine demethylase 1 (KDM1), and Jumonji-domain histone demethylase (JHDM). The KDM1 family members KDM1A, also known as lysine specific demethylase 1 (LSD1), and KDM1B demethylate mono- or dimethyllysine. turation . In erytturation . In manyKdm2b led to an impairment of Hoxa9/Meis1-induced leukemogenesis, which was partially mediated by decreased expression of p15Ink4b. In contrast, overexpression of Kdm2b led to cell growth advantages and suppression of differentiation in normal HSPCs  of the enzymes led to a significant reduction in myeloid, T, and B-cell 6 months after transplantation, hinting that KDM4 family members are essential for the maintenance of HSCs (Another demethylase subfamily shown to be required for the growth of MLL-AF9 translocated AML cells is the KDM4 (KDM4A-E) subfamily. Using transplantation assays, it was demonstrated that  of HSCs . KDM5 me of HSCs . Knockdo of HSCs . Thus, mFunctionally, transcription is enhanced by histone acetylation through the electrostatic interactions between DNA and histones, which in turn cause open active chromatin. Acetylated histones are enriched at promoters and enhancers of active genes and are modulated by two opposing groups of enzymes. Histone lysine acetyltransferases (KAT) acetylate histone proteins by adding an acetyl group to lysine residues resulting in an open chromatin structure accessible for transcription factors. In contrast, histone deacetylases (HDACs) erase acetyl groups from lysines leading to a condensed chromatin structure and gene repression. Opposing roles of KATs and HDACs keep the balance needed to regulate hematopoiesis.Histone lysine acetyltransferases are a diverse group of proteins that are divided into two classes, Type A and Type B, which are grouped based on being localized within the nucleus or the cytoplasm. Type A KATs function in transcription-related histone acetylation in chromatin and are grouped into five families based on their catalytic domain: P300/CBP, MYST, GNAT, transcriptional coactivators (KAT4 and KAT12), and steroid receptor coactivators (KAT13A-D) . Type B Moz, a member of the MYST family, lead to a failure of HSC development during embryogenesis. Deletion of Moz in adult murine bone marrow cells led to a loss of the long-term repopulating ability of HSCs; however, lineage-committed progenitors were unaffected, suggesting its role in the maintenance of HSCs , GATA2 and C/EBP represses HDAC1, while GATA1 activates it during erythroid megakaryocytic differentiation  function by maintaining long-term hematopoietic repopulation and protection from stress  catalyzes the monoubiquitination of lysine 119 on H2A, can condense nucleosomes, and exists in multiple canonical (PRC.1 and 1.4) and non-canonical variants  . The difult HSCs . In addigenitors . Multipld p19Arf . Studies factors .Mel18 was shown to promote self-renewal and increase quiescence acting as a negative regulator of differentiation (Cbx7) or differentiation and exhaustion of HSCs  when overexpressed , myelodysplastic syndrome (MDS), chronic myelomonocytic leukemia (CMML), and aplastic anemia (Pcgf1), USP7 and TRIM27 appear to have tumor suppressor functions in myelofibrosis and AML  to granulocyte-monocyte progenitor (GMP) transition   . TET1 isor IDAX) . Reductior IDAX) . Gene exncogenes . Tet2 nuDNMT3A, TET2, and ASXL1. IDH1 and IDH2 are metabolic enzymes that can affect TET2 function when mutated in CHIP or AML  is the expansion of a hematopoietic stem cell clone with an acquired driver mutation in individuals without cytopenias or overt malignant hematologic disease . CHIP stP or AML . Thus, eP or AML .DNMT3A are found in ∼10% of MDS and ∼30% of AML patients. In all instances, DNMT3A occurs early in transformation, making it an attractive target for new therapeutic approaches. DNMT3A mutations in CHIP and AML are primarily missense, with residue R882 being the most affected. In particular, the R882H variant leads to a dominant-negative effect over the wildtype protein, resulting in altered methyltransferase activity and genome-wide hypomethylation in patients  and Nras G12D double knock-in mice developed aggressive AML, mediated by an upregulation in Myc and its target genes (Dnmt3a deletion or mutations cooperate with Flt3ITD (Npm1c (cKIT (Npm1 mutations can lead to the development of myeloproliferative disorders (MPD), and additional transplantation leads to AML . Human and murine studies have shown that TET2 mutations occur in HSCs and are early events that skew differentiation toward the myeloid lineage , T-cell lymphoma (TET3) and chronic lymphocytic leukemia (TET1 and TET3)  .IDH1 and IDH2) were a new class of genes mutated in myeloid malignancies (+ and CO2. IDH1 (located in the cytoplasm and peroxisomes) and IDH2  are highly homologous to each other  or IDH2 (enasidenib) mutant AML  and RNA modifiers in the epigenetic regulation of HSC genome function.+ cells revealed differences in the isoform expression of transcription factors including HMGA2 and MEIS1, and the epigenetic regulator, DNMT1. However, these developmentally distinct CD34+ cell populations had little to no difference in the expression of these genes, highlighting the importance of alternative splicing in regulating stage-specific features of HSPCs during development (Messenger RNA (mRNA) splicing is the dynamic process whereby noncoding (intron) or coding (exon) nucleotide sequences are removed from a premature mRNA transcript to generate a mature transcript or isoform that will subsequently be translated into protein. Alternative mRNA splicing is a co-transcriptional process that increases the functional diversity of proteins through the generation of tissue-specific isoforms . Alternaelopment .+ HSPCs and downstream myeloid and lymphoid progenitor populations have documented the presence of lineage-specific alternative splicing events associated with differential isoform expression during myeloid and lymphoid lineage commitment  and long-non-coding RNAs (lncRNAs). miRNAs are small non-coding RNAs consisting of 19-24 nucleotides that regulate gene expression by binding to mature mRNAs and causing post-transcriptional silencing of their targets. Since the discovery of the first miRNA in 1993, recent reports estimate that the human genome encodes approximately 2,600 mature miRNAs .In normal hematopoiesis miRNAs help regulate stem cell self-renewal, differentiation, cell cycle and lineage specification. MiR-126, miR-125a/b and miR-129a regulate self-renewal of hematopoietic stem cells by repressing multiple targets . miR-126GATA-1 and are required for erythropoiesis  are also heterogenous and play vital roles in lineage specification by regulating gene expression by directly interacting with transcription factors, acting as a decoy for transcription factors, recruiting chromatin modifiers to target gene promoters, or by directly interacting with DNA to produce RNA-DNA hybrids . In normntiation , and funntiation . In NPM1R-152–3p .cis interactions, where newly transcribed RNA remains at the site of synthesis, and trans interactions, where RNA is released from their transcription sites to interact with DNA-binding proteins. Similar to epigenetic regulation, RNA modifications are carried out by enzymes that add methyl groups to RNA (writers), remove methyl groups from RNA (erasers), and proteins that recognize and bind to RNA (readers). Although RNA modifications were described over 50 years ago, recent studies have uncovered that RNA modifications and their binding partners are dysregulated in cancer, creating unique opportunities for new therapeutics. Over 100 different chemical marks can modify RNA, and N6-methyladenosine (m6A) is one of the most abundant modifications in mammalian cells. Some of the additional marks include 5-methylcytosine (m5C), Adenosine-to-inosine (A-I) RNA editing, and Cytosine-to-uracil RNA editing  in gene regulation and genome function. Chromatin-associated RNAs can occur via  editing .6A being the most prevalent internal modification, many studies have focused on uncovering its role during normal and pathological states. The critical writers, erasers, and readers for m6A include the methyltransferase complex (METTL3-METTL14-WTAP), demethylases ALKBH5 and FTO, and YTH-domain family RBPs   strategy was developed for rare HSCs. The highest level of expression of m6A was observed in LT-HSCs when compared to downstream progenitors. Epitranscriptomic maps of HSPCs identified 8,599 m6A-tagged mRNAs, with about half coming from LT and ST-HSCs. Further profiling revealed that high m6A levels were present in LT-HSCs and uncovered that m6A -IGF2BP expression controls the transcriptional state and maintenance of HSCs . Mettl16 is expressed at high levels in erythroid progenitors, and deletion in erythroid cells leads to impairments in erythroid differentiation; therefore, altered expression may lead to hematological malignancies (Due to m YTHDC1)  (Qing et YTHDC1) . Studieslineages . In adulf Mettl3 . To esta of HSCs . Mechanignancies .METTL16 was a strong dependency on AML cell lines. A small screen targeting DNA and RNA methylation genes demonstrated that METT16 is essential for AML cell survival  by adding m6A to their mRNA transcripts, further contributing to its oncogenic role in AML (A genome-wide CRISPR-Cas9 knockout screen of 800 cancer cell lines revealed that survival . Homozyge in AML .METTL3 is also associated with leukemogenesis and is upregulated in multiple AML cell lines (METTL3 in human cell lines increases cell differentiation and induces cell cycle arrest (c-MYC was identified as a transcriptional target of m6A-METTL3 oncogenesis either directly or through the transcription factors SP1 and SP2 (. Furthermore, it was also shown that METTL3 was recruited by binding protein CEBPZ to transcriptional start sites of active genes resulting in increased translation of SP1. Taken together, m6A modifications and their associated methyltransferases are important regulators with cell specific mechanisms that should be therapeutically explored.ll lines . Deletine arrest . Consist and SP2 . FurtherEpigenetic regulation is a dynamic phenomenon that is critical in cell fate decisions during hematopoiesis. Understanding chromatin regulation through both histone modifications and methylation has identified how key regulators of hematopoiesis are silenced and activated during lineage specification. Disruption in any of these mechanisms leads to hematological malignancies of both myeloid and lymphoid lineages. In many instances these epigenetic regulators can function as either tumor suppressors or oncogenes depending on the disease and stage of development. The heterogeneity seen in leukemogenesis presents a critical challenge in targeting epigenetic enzymes and the associated patterns of genes dysregulated in hematological diseases. Targeting vital regulators in hematopoiesis with a combination of therapies represents a great avenue in treating aggressive hematologic malignancies, however, some studies have led to less than favorable outcomes . Therefoin vivo. In many myeloid malignancies DNMT3A as well as TET2 are commutated with splicing factor mutations. Thus, further molecular insights into how epigenetic regulators and splicing factors cooperate during malignancies will not only lead to advancements in selective inhibition but will outline additional roles DNA methylation has in stem cell biology.Although, we understand the established functions of epigenetic regulation in hematopoiesis, less is known about the non-canonical functions that may be exploited during disease states. A recent study deciphering mechanisms needed to switch between stem and differentiated states, found that an increase in splicing regulated by DNMT3A was needed to govern the exit . The stu