Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).The online version contains supplementary material available at (10.1186/s13059-022-02690-2). Each TF can harmonize expression of many genes by binding to genomic regions that regulate transcription. Alteration in sequence or quantity of a given TF can be the primary cause of hereditary disorders, complex disease, autoimmune defects, and cancer .TFs bind to accessible chromatin based on weak non-covalent interactions between amino acid residues and nucleic acids. DNA’s primary structure (sequence) , secondaChromatin immunoprecipitation-sequencing (ChIP-seq)  can map We propose that using more accurate tools to predict TF binding will allow understanding the role of TF binding in more contexts.Several methods use unsupervised approaches such as hierarchical mixture models  or hiddeMost studies use different benchmarking approaches. For example, some methods , 12 onlyHere, we introduce Virtual ChIP-seq, a novel method for more accurate prediction of TF binding in new cell types. Virtual ChIP-seq predicts TF binding by learning from publicly available ChIP-seq experiments, genomic conservation, and the association of expression of all genes with TF binding. It does so by learning a novel representation of the effect of transcriptome on TF binding and integrating various epigenomic and genomic features using a supervised multi-layer perceptron.chromatin factors [Virtual ChIP-seq also accurately predicts the locations of some DNA-binding proteins without known sequence preference. This would be impossible for most existing methods, which rely on sequence preference. Strictly speaking, only some of these proteins are TFs. We use the term  factors  in this https://virchip.hoffmanlab.org).Virtual ChIP-seq predicted binding of 36 chromatin factors in new cell types with a minimum MCC of 0.3. Eight of these chromatin factors  do not have DNA-binding domains and therefore are not TFs according to Lambert et al. . We predFor training, our method requires ChIP-seq data of each chromatin factor in as many cell types as possible, with matched RNA-seq data from the same cell types. We used ChIP-seq data .For each chromatin factor, we created an ets Fig. a–c. In texpression score for a chromatin factor in a new cell type using the association matrix and RNA-seq data for the new cell type, but no ChIP-seq data. The expression score is the Spearman correlation between the non-NA values for that genomic bin in the association matrix and the expression levels of those genes in the new cell type .The expression score can only provide predictions for genomic regions bound to the chromatin factor in training cell types. Across these genomic regions, expression score has a mean auPR of 0.16 (range 0–0.57). The expression score relies on the number of cell types used in calculating the correlation scores. Decreasing the number of training cell types resulted in decrease in auPR of the expression score in 12/17 TFs we tested. Only CTCF and EP300 had more than 8 training cell types. For both these factors, using fewer training cell types decreased prediction performance, a decrease only statistically significant in EP300  in our model  which we did not use their ChIP-seq data in calculating the expression score, training the multi-layer perceptron, or optimizing hyperparameters   of less We trained and benchmarked Virtual ChIP-seq both on DREAM Challenge data, and on the Cistrome database, which provides a higher number of ChIP-seq datasets. The DREAM Challenge assessed participant entries by measuring performance on three validation chromosomes , combined.To assess performance of Virtual ChIP-seq on DREAM Challenge data, we did the same. To assess performance on Cistrome data, however, we measured performance on each chromosome independently. This allowed us to examine the variance in performance among these chromosomes.Although Virtual ChIP-seq used features not allowed in the DREAM Challenge, comparing with DREAM Challenge participants is the only sound way to show how any method including these features compares to the state of the art. Leading DREAM Challenge methods potentially could improve their performance by including the features used by Virtual ChIP-seq. We compared Virtual ChIP-seq with DREAM Challenge results when we trained and validated on either Cistrome DB data or DREAM Challenge data.The final submission round of the DREAM Challenge evaluates predictions for 12 chromatin factors in held-out cell types. The datasets we used, however, allow us to predict binding of 63 chromatin factors in new cell types. Of these chromatin factors, 41 are unique to our dataset and do not overlap any of the DREAM Challenge chromatin factors  cells, Virtual ChIP-seq had a higher auPR than 8/14 Challenge participants. The Virtual ChIP-seq auPR for binding of REST in liver was also higher than that of 9/14 DREAM Challenge participants  than Avocado  to a lesser extent than recall .Using the cutoff which maximized MCC in H1-hESC only slightly decreased performance measurements from what one could achieve with the optimal cutoff for each cell type Fig. b. For exhttps://virchip.hoffmanlab.org) with predictions for 33 Roadmap cell types . Except where otherwise specified, we used the Genome Reference Consortium GRCh38/hg38 assembly [To generate the input matrix for training and validation, we used 200 bp genomic bins with sliding 50 bp windows. We excluded any genomic bin which overlaps with ENCODE blacklist regions . We assigned each bin the mean PhastCons score of the nucleotides within.We used GRCh38 primate and placental mammal 7-way PhastCons genomic conservation , 29 scorp-value threshold of 0.001 and did not adjust for multiple testing. If the motif for the TF did not exist in JASPAR, we used other motifs with same initial 3 letters and counted any TF binding site which had overlap with any of those motifs. For example, SIX5 does not have a sequence motif in JASPAR. In that case, we used the sequence motif of SIX3 instead  [p=10−6). The DREAM Challenge, however, had processed RNA-seq of all cell types uniformly, allowing us to directly use transcripts per million reads (TPM) in analysis of DREAM Challenge datasets.We downloaded an ENCODE expression matrix (.txt.gz)  with RNA.txt.gz) . Since t.txt.gz)  of rank N≥5 training cell types with the following procedure: M 100 bp non-overlapping genomic bins.We divided the genome into M bins and N cell types and quantile-normalized this matrix.We created a non-negative ChIP-seq matrix mentclass2pt{minimC to C′, scaling the values of each row between 0 and 1.We row-normalized G=5000 genes with the highest variance among the N cell types.We identified the G=5000 genes in N cell types , we set Ai,g to NA. These coefficients constitute an association matrix For each bin i∈  ,M and eaWe created an expression matrix for each chromatin factor with matched ChIP-seq and RNA-seq data in We performed power analysis of the Pearson correlation test using the R pwr package .p>0.1 cutoff would exclude depending on the number of available cell types with matched ChIP-seq and RNA-seq data. For CTCF, which had the largest number of cell types available—21 cell types with matched ChIP-seq and RNA-seq—this cutoff provided 80% power to detect an absolute value of Pearson correlation |r|≥0.52. Many chromatin factors had only 5 cell types with matched data and the cutoff provided 80% power to detect only larger correlations, |r|≥0.92.Power analysis identified which correlations the ρ for expression of the same G=5000 genes in the new cell type with every row of the association matrix A. Each of these rows represents a single genomic bin. An expression score close to 1 indicates that genes with high expression have high values in the association matrix, and genes with low expression genes have low values. An expression score close to −1 indicates that genes with high or low expression have opposite values in the association matrix  in any of the training cell types. To speed the process of training and evaluation, we further limited training input data to four chromosomes . For validation, however, we used data from these same four chromosomes in completely different cell types held out from training. We evaluated the performance on all of the 9,635,407 bins in these four chromosomes  or rectifier—proved optimal for different chromatin factors  also haFor the purpose of training and validating the model on Cistrome datasets, we only used chromosomes 5, 10, 15, and 20. These 4 chromosomes constitute 481.78 Mbp (15.6% of the genome). For training only, we excluded any genomic region without chromatin accessibility signal and previous evidence of chromatin factor binding. For validation and reporting performance, we included these regions, using the totality of the 4 chromosomes. We concatenated data from training cell types  in validation cell types. For CTCF, we used MCF-7, PC-3, and iPSC for validation and trained on all other cell types . For MAX, we used all cell types except liver and K562 for training. For GABPA, REST, and JUND, we used all cell types except liver for training. We compared these metrics to those of DREAM Challenge participants in the final round of cross–cell-type competition.We used the same set of sequence motifs as described in Keilwagen, Posch, and Grau . Some ofaccess using GRCh38-aligned BAM files of each cell type to obtain the chromatin accessibility profile.We used the Catchitt software module For Virtual ChIP-seq, we had generated B (bound) and U (unbound) labels. Catchitt, however, requires at least three labels: S (summit), B (non-summit but bound), and U (unbound). For comparison of Virtual ChIP-seq and Catchitt on GRCh38 datasets, we tried two different strategies. Once, we kept the B labels from our datasets at the edge of a peak region unchanged and swapped other B labels to S.labels to derive the four Catchitt labels A (ambiguous), S, B, and U from optimal and stringent IDR-thresholded narrowPeak files. When stringent IDR labels were not available for some ENCODE experiments, we used the optimal IDR labels for both the c and the r parameters as recommended by the authors.We also used the Catchitt software module For both set of labels, we trained Catchitt on the same training cell types and chromosomes as Virtual ChIP-seq. For evaluating the binary classification performance, we excluded ambiguous labels from the prediction space and considered either of S or B as TF binding.We compared the performances of Virtual ChIP-seq and Catchitt using the auPR curve of predictions on chromosome 10 using identical labels in validation cell types.To identify groups of chromatin factors involved in similar biological processes, we examined the biological pathways that the targets of each chromatin factor regulate. We ranked genes according to the number of genomic regions where the expression of each gene correlated positively or negatively with chromatin factor signal. We calculated a correlation matrix for enrichment of targets of each chromatin factor in various biological processes. We performed hierarchical clustering on the correlation matrix. We sought to identify clusters of chromatin factors, and the best number of clusters between 2 and 10, inclusive. For use in this process, we created a Gaussian random matrix of 1681 rows and 113 columns as a control and calculated its correlation matrix. Then, we compared cluster stability between the original correlation matrix and the control for each potential number of clusters. To do this, we subsampled 75% of each correlation matrix rows twice without replacement. Then, we clustered chromatin factors in each matrix into the specified number clusters. For both of these clusterings, we constructed the set of every pair of chromatin factors present in the same cluster. We then calculated the Jaccard index between the first clustering’s constructed set and that of the second . We repehttps://virchip.hoffmanlab.org).We downloaded Roadmap DNase-seq and RNA-seq data aligned to GRCh38 from the ENCODE Data Coordination Center (DCC) . For eachttp://mkweb.bcgsc.ca/colorblind) and chose colors also distinguishable by those with protanopia and tritanopia.For plots with three categories, we used a color palette optimized for viewers with deuteranopia . Our datasets should also accelerate the development of future machine learning methods by many groups.Virtual ChIP-seq uses a fully connected neural network to integrate data from the transcriptome, chromatin accessibility, genomic context, and predict TF binding. Although Virtual ChIP-seq uses direct evidence of chromatin factor binding at each genomic region as one of the input features, it is able to correctly predict new peaks which do not exist in training cell types. Our datasets, using a combination of Cistrome DB and ENCODE, allow training and validating models for predicting binding in a more extensive 63 chromatin factors compared to DREAM Challenge datasets. Our provided predictions of binding of 36 high-confidence chromatin factors in 33 different Roadmap tissue types will allow the research community to better investigate epigenomics of disease affecting those tissues : Supplemental text and figures [ figures –110.Additional file 2 Office Open XML Workbook (XLSX): Supplemental tables.Additional file 3 Review history.


Keratinocyte senescence contributes to skin ageing and epidermal dysfunction. According to the existing knowledge, the transcription factor ΔNp63α plays pivotal roles in differentiation and proliferation of keratinocytes. It is traditionally accepted that ΔNp63α exerts its functions via binding to promoter regions to activate or repress gene transcription. However, accumulating evidence demonstrates that ΔNp63α can bind to elements away from promoter regions of its target genes, mediating epigenetic regulation. On the other hand, several epigenetic alterations, including DNA methylation, histone modification and variation, chromatin remodelling, as well as enhancer-promoter looping, are found to be related to cell senescence. To systematically elucidate how ΔNp63α affects keratinocyte senescence via epigenetic regulation, we comprehensively compiled the literatures on the roles of ΔNp63α in keratinocyte senescence, epigenetics in cellular senescence, and the relation between ΔNp63α-mediated epigenetic regulation and keratinocyte senescence. Based on the published data, we conclude that ΔNp63α mediates epigenetic regulation via multiple mechanisms: recruiting epigenetic enzymes to modify DNA or histones, coordinating chromatin remodelling complexes (CRCs) or regulating their expression, and mediating enhancer-promoter looping. Consequently, the expression of genes related to cell cycle is modulated, and proliferation of keratinocytes and renewal of stem cells are maintained, by ΔNp63α. During skin inflammaging, the decline of ΔNp63α may lead to epigenetic dysregulation, resultantly deteriorating keratinocyte senescence. INK4a and p21WAF1/CIP1) and consequently downregulating their expression [The largest organ of the human body, skin, acts as a protective barrier on our surface. It is also responsible for maintaining homoeostasis of various substances as well as sensory perception and temperature regulation ,2. Keratpression . Recentlpression ,11. In tThe skin consists of three layers, the epidermis, dermis, and hypodermis, each of which is separate but functionally interdependent. The outermost layer, epidermis, is predominantly composed of keratinocytes, which provide hardness and moisture-lock properties to the skin . KeratinSkin undergoes intrinsic or chronological ageing, as a consequence of internal and external changes due to passage of time . Owing tWAF1/CIP1, p16INK4a, miR-130, MM1, Noxa and Puma, maintaining cell proliferation and survival , i.e., DNMT1, DNMT3A and DNMT3B. 5-methyl cytosine (5mC) is enriched in CpG dinucleotides and directly affects the ability of transcription factors and other DNA binding proteins to access DNA. Therefore, hypermethylation of CpGs in gene promoters generally results in silencing or low expression of downstream genes, while hydroxymethylation upregulates gene transcription ,59.INK4a and p21WAF1/CIP1 [It has been reported that senescence-associated DNA methylation alterations accumulate during cell senescence and consequently cause cell cycle arrest . MislocaAF1/CIP1 , downregAF1/CIP1 , as wellAF1/CIP1 .In eukaryotic chromatin, DNA strands are packaged into nucleosomes by histones including H2A, H2B, H3, and H4 . DiverseARF [INK4b [INK4a [WAF1/CIP1 [It is well documented that histone-associated epigenetic processes occur during senescence. These processes include modification, variation and depletion of histones . It was ARF ,76, p15IF [INK4b ,76, p16Ib [INK4a , p21WAF1AF1/CIP1 ,79, and AF1/CIP1 ,80, as wAF1/CIP1 ,79. In tAF1/CIP1 ,82, prolAF1/CIP1 ,84, and AF1/CIP1 ,79,85.INK4a, p21WAF1/CIP1, and p53 [Chromatin remodelling complexes (CRCs), also named as remodellers, are needed to fully package the genome, to specialize chromatin regions, and to provide regulated DNA accessibility in packaged regions. Four classes of CRCs are found in eukaryotes ,87: swit and p53 .Chromatin structure in mammals can also regulate transcription by modulating three-dimensional interactions between enhancers and promoters, which can be facilitated by chromatin loops . CCCTC-bAs a traditional transcription factor, ΔNp63α is well established to bind to canonical p53 DNA binding sites in promoter regions of its target genes and thus compete with TAp63/p73/p53, resulting in transinhibition . ΔNp63α-Using epigenome profiling of differentiating human primary epidermal keratinocytes, Kouwenhoven EN et al. found that ΔNp63α binds to a batch of enhancers . RinaldiRecent investigations suggest that ΔNp63α affects histone acetylation and nucleosome repositioning and changes DNA accessibility to regulate gene expression , but also repressive ones (H3K27me3 and H3K9me3) . ConsistThese modifications may be due to ΔNp63α-mediated recruitment of epigenetic enzymes. Lin-Shiao E et al found that the histone methyltransferase KMT2D interacts with ΔNp63α on chromatin and at a broad array of enhancers, resulting in histone modifications, including H3 lysine 4 monomethylation (H3K4me1) and H3 lysine 27 acetylation (H3K27ac), in keratinocytes. This may maintain transcription of hundreds of genes , which are essential for p63 to keep proliferative capacity of keratinocytes through positively regulating genes critical for epithelial development, differentiation, and adherence . Ramsey ΔNp63α can also coordinate chromatin remodelling complexes (CRCs) to orchestrate the remodelling , which is a subunit of SWI/SNF CRC and co-amplified with ΔNp63α in head and neck squamous cell carcinoma (HNSCC); this collaboration leads to a downregulation of tumour suppressors such as WWC1 and GPRC5A . In addiApart from the direct interactions with CRCs, ΔNp63α can also modulate chromatin remodelling via regulating gene expression of multiple remodellers or related proteins  their expression , as well as upregulates proliferative genes . Hence, ΔNp63α promotes proliferation of keratinocytes and prevents them from senescence (depicted in INK4a and p21WAF1/CIP1) are commonly regulated by the abovementioned mechanisms, indicating that these ΔNp63α-mediated epigenetic regulation mechanisms may be intertwined. During skin inflammaging, the decline of ΔNp63α induced by various factors may lead to dysregulation of chromatin remodelling, consequently deteriorating senescence of keratinocytes. It is worthful to study how these epigenetic changes differentially regulate proliferative or anti-proliferative genes. The genes regulated by ΔNp63α during keratinocyte senescence are also remained to be systematically investigated.Since accumulating data demonstrate that ΔNp63α binds to regions away from promoters  ,11,120, It has been reported that some chemicals  exert anti-senescence effects in multiple cell types via regulating epigenetic alterations ,121–125.


We show that nob-1 and php-3 are co-expressed in gastrulation-stage embryos in cells that previously expressed the anterior Hox gene ceh-13. This expression is controlled by several partially redundant transcriptional enhancers. These enhancers act in a ceh-13-dependant manner, providing a striking example of an anterior Hox gene positively regulating a posterior Hox gene. Several other regulators also act positively through nob-1/php-3 enhancers, including elt-1/GATA, ceh-20/ceh-40/Pbx, unc-62/Meis, pop-1/TCF, ceh-36/Otx, and unc-30/Pitx. We identified defects in both cell position and cell division patterns in ceh-13 and nob-1;php-3 mutants, suggesting that these factors regulate lineage identity in addition to positional identity. Together, our results highlight the complexity and flexibility of Hox gene regulation and function and the ability of developmental transcription factors to regulate different targets in different stages of development.Hox transcription factors play a conserved role in specifying positional identity during animal development, with posterior Hox genes typically repressing the expression of more anterior Hox genes. Here, we dissect the regulation of the posterior Hox genes  nob-1 and php-3, in the nematode worm, C. elegans. We identified six new transcription factors and three enhancer regions of DNA that can activate expression of nob-1/php-3. Unexpectedly, these activating transcription factor genes included ceh-13, an anterior Hox gene, and elt-1, a regulator of skin development that is briefly expressed in many cells that do not adopt skin fates, including the cells that express nob-1. Furthermore, the cellular defects we observed in ceh-13 and nob-1;php-3 mutant embryos indicate that the early embryonic functions of these Hox genes help determine the identity of cells as well as their position within the embryo. Our findings identify new roles for Hox genes in C. elegans and emphasize the ability of transcription factors to contribute to the diversification of cell types and the adoption of specific cell types at different phases of embryonic development.Hox genes are critical for head-to-tail patterning during embryonic development in all animals. Here we examine the factors that are necessary to turn on two posterior Hox genes,  Hox genes encode conserved transcription factors famously expressed in specific positions along the anterior-posterior axis during animal development that specify axial position. Mutations in Hox genes cause a wide variety of developmental defects in both model organisms and humans. Hox gene regulation is complex and includes both transcriptional and post-transcriptional control. In most animals, Hox genes are found in genomic clusters, and their expression along the A-P axis is collinear with their genomic position, and often show “posterior dominance,” where posterior Hox genes repress the expression of more anterior Hox genes (Reviewed in –3).Caenorhabditis elegans encodes a single set of six Hox genes on Chromosome III, loosely organized into three degenerate “clusters” that each contain two adjacent genes . A t. A tnob-nob-1 transcriptional reporter includes both the -3.4kb and -4.3kb enhancers, we deleted each enhancer from this construct to test their necessity for nob-1 expression  ) and nob-1(syb2679[nob-1::GFP]) were created under contract by SUNY Biotech .Worm strains  from either pJIM20::nob-1 or N2 genomic DNA, with overhangs for stitching, which were then either gel or PCR purified (Qiagen). Putative enhancers were attached to a  plasmid  . Embryos from self-fertilized hermaphrodites were mounted in egg buffer/methyl cellulose with 20μm beads as spacers  and imags7h44j86 .Cell averages of nuclear fluorescence were computed for each cell based on all  measurements across its lifetime  Early (B) and late (C) overlap of a CEH-13::GFP fosmid translational reporter transgene with a -9kb NOB-1::GFP rescuing translational reporter transgene. The expression patterns are nearly identical to the endogenously tagged alleles except the 9kb NOB-1::GFP reporter has reduced expression in the C lineage (see A) Overlap in the expression of endogenously tagged NOB-1::GFP (blue) and PHP-3::GFP (green). Expression is nearly identical (cyan overlap) except eage see . D) Expr(TIF)Click here for additional data file.S2 Fignob-1/php-3 mutants. Nearby bead is 20μm in diameter.A) Representative examples showing the expression variability for each of the enhancer reporters tested. Colored lines indicate the lineages of interest. Some lineages with no expression are shown as partial trees. B) Brightfield image of a hatched L1 larva carrying the -4.3kb enhancer reporter and showing the “no backend” phenotype characteristic of (TIF)Click here for additional data file.S3 Figceh-13(sw1) mutation on endogenously tagged NOB-1::GFP, as well as the control values for the nob-1 enhancer reporters  mutants  in the lineages that generate the specified cells, ABp(l/r)appppp and Abp(l/r)pppaap. C) Graphs showing the effect of ceh-13(sw1) mutation on endogenously tagged PHP-3::GFP expression in the specified lineages. Early cells same as in (A). Red * indicates p<0.05 in Wilcoxon Ranked sum test. Trends in ABp(l/r)ap and ABp(l/r)ppp are similar to those for NOB-1::GFP, but do not reach significance likely due to smaller number of mutant embryos analyzed . D) Lineage trees showing expression of the wild-type -3.4kb enhancer reporter and a version with mutated HOX sites.A) Graphs showing the effect of (TIF)Click here for additional data file.S4 Figunc-62 RNAi on nob-1 promoter transcriptional reporter expression in the ABpl lineage. Lineages with loss of expression are underlined. The ABp(l/r)apap lineage showed a loss of reporter fluorescence in 13/14 unc-62 RNAi lineages examined. B) Trees showing the effect of unc-62 RNAi on NOB-1::GFP translational reporter expression, with two examples shown. Lineages affected are underlined. C) Graphs for all embryos tested (n = 8) for the same lineages shown in (B). Red * indicates p<0.05 in Wilcoxon Ranked sum test. D) Effect of unc-62 RNAi on CEH-13::GFP fosmid reporter in lineages where the two genes are both expressed (n = 4 for control and RNAi). No significant changes were detected.A) Tree showing effects of (TIF)Click here for additional data file.S5 FigExample full lineages for CEH-20::GFP, CEH-40::GFP, UNC-62::GFP, and ELT-1::GFP, all fosmid translational reporters. Full lineages for endogenously tagged CEH-13::GFP, NOB-1::GFP and PHP-3::GFP are also shown. All lineages are shown to at least the 350 cell stage—selected lineages are shown later to identify additional expression or dynamics.(TIF)Click here for additional data file.S6 Fignob-1 enhancer reporters in control and elt-1 mutant conditions in the ABp(l/r)ap and Cpap lineages (if expressed in control). Mutant enhancer reporter values are the same as reported in elt-1 mutant conditions in the specified lineages. Underlined lineages show significant loss of expression (quantified in A). In the C lineage, only underlined cells express ELT-1::GFP fosmid reporter in control embryos. C) Fold change values of endogenously tagged NOB-1::GFP and nob-1 enhancer reporters in control and elt-1 mutant conditions in the ABp(l/r)ppp and Ep lineages (if expressed in control) where ELT-1::GFP is not expressed. Red * indicates p<0.05 in Wilcoxon Ranked sum test. D) Expression of the -3.4kb enhancer reporter and a version from which all GATA sites have been mutagenized in the ABp, E and C lineages. Note: the mutagenesis also disrupted one ceh-20/40 predicted site, two nob-1 predicted sites, and one pop-1 predicted site, as these were fully overlapping with GATA sites.A) Fold change values of endogenously tagged NOB-1::GFP and (TIF)Click here for additional data file.S7 Figceh-36(ok795);unc-30(ok613) mutant embryos expressing the NOB-1::GFP reporter transgene. Expression is shown in the ABp lineage at the ~200 cell stage. Note: ceh-36 and unc-30 are not expressed in the ABp(l/r)ap lineages (pink underline). B) Boxplot showing the ratio of expression in the ABp(l/r)ap lineage to the ABp(l/r)ppp lineage for wild-type and ceh-36(ok795);unc-30(ok613) mutant at the 350 cell stage for at least 4 embryos. Red * indicates p<0.05 in Wilcoxon Ranked sum test.A) Wild-type and (TIF)Click here for additional data file.S8 Fignob-1 are highlighted in the context of the whole embryo at ~320 minutes . As compared to the wild-type average (left), the nob-1(ct223) mutant (right) cells are disorganized and displaced anteriorly, particularly ABp(l/r)ppp and some ABp(l/r)app cells (ABp(l/r)appp). B) The positions of the SAB neuron and neuroblasts (red) and DA motorneurons (blue) are shown relative to the intestine (green) in wild-type (left) and ceh-13(sw1) mutant (right), showing the anterior displacement of these cells. C) The positions of the neuroblasts that fail to divide the nob-1(ct223) mutant as compared to control (left), showing that these are dramatically mispositioned. D) Cell division defects in nob-1(ct351) and ceh-13(ok737) mutants. Defects highlighted are the same as shown in A) The position of cells that normally express (TIF)Click here for additional data file.S9 Fignob-1/php-3 expression in the specific lineages noted. Asterisk indicates indirect regulation. Note: although nob-1/php-3 are expressed in other lineages in C, only Cpap was possible to analyze with the transgenic reporters examined. Regulation by pop-1/sys-1 from Zacharias et al., 2015 [nob-1/php-3 by the indicated factors. Specific enhancers regulated by ceh-20/ceh-40, ceh-36/unc-30, pop-1/sys-1 and unc-62 were not defined, but their activity can be localized to the regions marked by brackets. Based on our results, additional cis-regulatory elements likely exist within the blue, green and yellow regions (yellow encompasses the rest of the genome).A) Diagram summarizing the transcriptional regulation of l., 2015 . B) Diag(TIF)Click here for additional data file.S1 Tablenob-1 enhancers -3.4kb, -4.3kb, -8.3kb. Table F: Cell averaged reporter values for all embryos analyzed. Table G: Number of embryos analyzed for each genotype/condition. Table H-J: Cell division times for wild-type, ceh-13(sw1), and nob-1(ct223) embryos.Table A: Strain list. Table B: Tested enhancer sequences. Tables C-E: Motifs found in (XLSX)Click here for additional data file.S2 Table(ZIP)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 Table(CSV)Click here for additional data file.


How DNA is folded and packaged in nucleosomes is an essential regulator of gene expression. Abnormal patterns of chromatin folding are implicated in a wide range of diseases and disorders, including epilepsy and autism spectrum disorder (ASD). These disorders are thought to have a shared pathogenesis involving an imbalance in the number of excitatory-inhibitory neurons formed during neurodevelopment; however, the underlying pathological mechanism behind this imbalance is poorly understood. Studies are increasingly implicating abnormal chromatin folding in neural stem cells as one of the candidate pathological mechanisms, but no review has yet attempted to summarise the knowledge in this field. This meta-synthesis is a systematic search of all the articles on epilepsy, ASD, and chromatin folding. Its two main objectives were to determine to what extent abnormal chromatin folding is implicated in the pathogenesis of epilepsy and ASD, and secondly how abnormal chromatin folding leads to pathological disease processes. This search produced 22 relevant articles, which together strongly implicate abnormal chromatin folding in the pathogenesis of epilepsy and ASD. A range of mutations and chromosomal structural abnormalities lead to this effect, including single nucleotide polymorphisms, copy number variants, translocations and mutations in chromatin modifying. However, knowledge is much more limited into how abnormal chromatin organisation subsequently causes pathological disease processes, not yet showing, for example, whether it leads to abnormal excitation-inhibitory neuron imbalance in human brain organoids.The online version contains supplementary material available at 10.1007/s12035-022-03106-9. Atypical neurodevelopment is increasingly being shown to play a role in the pathogenesis of neurodevelopmental and neurological disorders. Epilepsy and autism spectrum disorder (ASD) provide prototypical examples of this; studies suggest they have a shared pathological aetiology in the form of atypical specification of neural progenitor cells (NPC), which leads to an imbalance in the number of excitatory and inhibitory neurons to form in the adult cortex . HoweverChromatin folding is controlled at multiple scales Fig. , each ofAbnormal patterns of chromatin topology are implicated in the pathogenesis of several diseases. Deletions, duplications and translocations can disrupt normal TAD architecture, causing fusions, shuffling, loss or new TADs to form. This subsequently re-organises regulatory elements into TADs they would not normally be within, which can cause abnormal enhancer-promoter interactions and subsequent ectopic gene expression patterns, a process known as “enhancer hijacking” . Single Question 1: Is abnormal chromatin topology implicated in the pathogenesis of epilepsy or ASD?Question 2: If implicated, how does abnormal chromatin topology contribute to epilepsy or ASD pathogenesis?Abnormalities in chromosome topology are increasingly implicated in ASD and epilepsy; however, no review has yet attempted to synthesise the findings of the literature. Here, we present a meta-synthesis with systematic searching of studies on the role of abnormal chromatin topology in the aetiology of epilepsy and ASD. We seek to answer two primary questions:Where relevant, this review has followed PRISMA guidelines whilst systematically searching for articles, which was performed using the following databases: Web of Science, Medline, Scopus and Embase. The same search criteria (below) were used for each database: AND ("epilepsy" OR "epileptic" OR "seizure" OR "convulsion" OR "autism").Inclusion: primary biological studies (lab-based), peer-reviewed, specific techniques used in study [chromatin confirmation capture (CCC) OR assay for transposable accessibility sequencing (ATAC-seq) OR bulk/single cell RNA sequencing OR super-resolution microscopy], specific results Exclusion: non peer-reviewed, reviews, non-English language, unpublished, no focus on chromatin folding , not open accessThe results were screened by one reviewer using the following inclusion and exclusion criteria:The search identified 274 studies  and large-scale (translocations and copy number variation) mutations of the structural elements themselves. The second concerns mutations of genes that encode proteins with roles in setting up or maintaining the architecture of chromatin topology, or its regulation, such as by epigenetic modifications and action potential cell–cell signalling.Common genetic variants in ASD, identified by genome-wide association studies (GWAS) of cases and controls, have been computationally studied in several papers. In one study, the SNP were mapped either directly to genes or, in those arising in non-coding DNA, their partner genes, which were identified based on chromatin interaction profiles and expression quantitative trait loci . Mapped SLC6A1 due to two de novo point mutations at an intronic enhancer in a neuroblastoma cell line. Interestingly, SLC6A1 is a high-risk gene for both ASD and epilepsy, suggesting its dysregulation by perturbed enhancer function could contribute to epilepsy pathogenesis as well as ASD [DRD2) upon monoallelic, CRISPR-mediated deletion of a downstream, intergenic, promoter-interacting region in iPSC-derived excitatory neurons [Evidence of an association between ASD pathogenesis and perturbed enhancer-promoter interactions has been provided from studies of de novo mutations. In one study, de novo mutations were enriched at distal promoter sites, where transcription factors bind and interactions with enhancers are mediated . More rel as ASD , 3. Thes neurons .Having identified that enhancer dysfunction is a plausible candidate for understanding ASD pathogenesis, a question arises about whether and how commonly non-coding DNA mutations arise at these sites. However, computational analyses indicate they are not very common at these sites. In one study, only two ASD-associated SNP mapped out of 86) to active enhancer regions present in cells of the adult brain . Further6 to actiTogether, these studies show a clear association between ASD-associated single nucleotide polymorphisms and pathological enhancer-promoter interactions. Initial work has helped to provide a pathological link, but further research is needed to show the effect of these mutations and variations on the cellular context, such as whether dysregulated gene expression from perturbed enhancer-promoter dynamics leads to abnormal cell type specification. Further work is also needed to understand if there is a strong association between epilepsy-associated SNP and perturbed enhancer function.cis and trans regulatory elements are a consequence of CNV and a driver of the widespread transcriptomic change. Similar findings come from studies of people with duplication or deletion of the 16p11.2 BP2-BP3 interval, which is associated with an ASD diagnosis, and reduced or increased head circumference size [Duplications and deletions of genomic areas can lead to changes in a gene copy number with local and widespread effects on gene expression and regulation. Several studies of syndromes in which ASD is common have shown this. For example, duplications and deletions of 16p11.2, a common cause of ASD, have been shown to cause widespread transcriptomic changes in lymphoblastoid cells . These tnce size .AUTS2 expression, a high-risk gene for ASD [KCTD7 gene, which is high risk for epilepsy [Although evidence has shown CNV has widespread effects on genome folding, studies suggest the largest effects can occur in the local genomic area of the mutation. Research on 22q11 deletion, which causes DiGeorge syndrome (in which ASD is very common), show prominent changes in the regions flanking the deletion site, affecting CTCF binding patterns and topological domain organisation, as well as the pattern of histone modifications, including for H3K27 acetylation . One con for ASD . Interesepilepsy . It alsoSome of the effects described above, of local and widespread changes in chromosome topology, have been ascribed to the DNA regions targeted by CNV. A computational analysis by Fudenberg showed that structural deletions associated with ASD were enriched at strongly active transcriptional start sites and CTCF binding sites . DeletioIn conclusion, these studies show a clear association between CNV and dysregulated gene expression through altered chromatin topology. However, these were conducted on tissues not pathologically implicated in ASD (lymphoblastoid cells) and do not provide mechanistic proof. Further work is required to show the associations in human neurological tissues, more strongly demonstrate an association for patients with epilepsy, and provide molecular proof of an aetiological mechanism.FOXG1 expression [Translocations leading to changes in chromatin topology have been implicated in ASD and epilepsy pathogenesis. Their presence can cause a loss of normal regulatory elements, which data suggests is one cause of dysregulated pression . This gepression , and abnpression .MEF2C gene are predicted to disrupt TAD boundaries and the activity of its enhancers. This is predicted to cause reduced expression of MEF2C in lymphoblastoid cells of patients with this aberration, as suggested by a change in chromosomal contact data [MEF2C expression is caused in and a main driver of 5q14.3 microdeletion syndrome, in whom epilepsy and ASD-like features are characteristic. In another study of a patient with epilepsy and neurodevelopmental delay, a translocation of chromosomes 15 and 16 juxtaposed heterochromatic and euchromatic areas, which led to a change in the expression of the genes flanking the deletion sites [Reorganisation of local chromatin topology is another effect of translocations that can cause dysregulated gene expression. Balanced translocations of 5q14.3 in regulatory regions of the act data . Decreason sites ; the norLRFN5 gene locus that was not present in the female siblings [LRFN5 is a high risk-of-pathology ASD gene that is located in the middle of a gene desert and large TAD [Sex-specific regulation of chromosome topology is a recent mechanism implicated in ASD pathogenesis. A study of a family with high-functioning ASD males identified a maternally inherited topological haplotype at the siblings . LRFN5 iarge TAD . It was arge TAD .The combined findings of these studies show abnormal chromatin topology due to translocations and sex-specific gene expression regulation are associated with ASD and epilepsy pathogenesis. However, no mechanistic investigation of these associations has been conducted and so the association have not yet been shown to be causal.The previous studies have focused on studying the impact of mutations and alterations of structural elements. However, other mechanisms have also been identified that alter chromatin folding and may contribute to the pathogenesis of epilepsy or ASD.Folding topology of the genome is strongly influenced and controlled by the actions of chromatin regulators. These regulators encompass a wide range of proteins, including transcription factors and chromatin regulators . StudiesPOGZ, a high-risk gene for ASD, showed important roles during mouse neurodevelopment in controlling gene expression patterns, including an enrichment for high-risk ASD genes [POGZ homozygous-deletion mutants showed low levels of ADNP and HP1γ binding in wild-type neurons (compared to upregulated or unchanged genes). Interestingly, POGZ expression-regulated target genes were also enriched for binding by the chromatin regulator CHD8, another high-risk ASD gene [Transcription factors have important roles in regulating chromatin topology, for example, through their effects on chromatin accessibility and enhancer activity . In a stSD genes , 37. TheASD gene . These rMeCP2, which when mutated causes Rett syndrome , plays an essential role in this process [MeCP2 mutations.Another way in which transcription factors control chromosome topology is by mediating the formation of liquid-like phase condensates, for example, in structures like heterochromatin.  process . In mous process . This suResearch has shown that altered epigenetic modifications occur in ASD over genomic areas important for the structural organisation of chromatin. In a post-mortem study of ASD patient brain tissue, it was shown that the DNA 5-hydroxymethylcytosine mark, which controls gene expression patterns, differs across the genome . Most diCHD8 and KDM6B [Many genes mapped as high risk for epilepsy have broad enhancer-like domains (BELD), for which most of their gene body exhibits open chromatin and bears the H3K27 acetylation and H3K4 mono-methylation histone modifications . These gnd KDM6B .Recent data has shown that action potentials received by cortical neurons can rapidly alter the chromosome topology of rapidly transcribed genes as well as induce longer-lasting changes in chromatin loop formation . In a stNPTX2.Neuronal activation in a model of status epilepticus in mice has also shown that synaptic activity leads to acute and chronic topological changes in chromatin folding. In a study by Fernandez-Albert et al., changes were seen over hours and days in neuronal transcriptomes and chromatin accessibility at genes and regulatory elements (including enhancers) . These cThese results show that chromatin topology is altered following seizure-induced activity. It would be interesting to see how the changes in chromosome topology due to seizure activity influence the future patterns of electrical activity in affected brains. Similarly, it would be interesting to see how the chromosome topology of a brain with excitatory-inhibitory imbalance responds to electrical activity as in these experiments, as well as whether mutations in high-risk epilepsy genes alter the normal response in topological re-arrangement following seizure activity.Beyond the focus of this review, the chromatin-based mechanisms discussed in this paper provide numerous therapeutic targets to investigate. This is especially relevant given current anti-epileptic drugs (AED) are known to have wide-spread epigenetic effects . For exaIn addition to therapeutics, further consideration needs be given to environmental influences that are implicated in pathologies with a neurodevelopmental basis and have been shown to alter gene expression patterns . For exaThis review provides strong evidence implicating abnormal chromatin folding in the pathogenesis of epilepsy and ASD. This can be due to small or large scale genetic alterations, or less direct mechanisms like mutations of transcription factors. These pathological processes converge on general mechanisms whose common output is to perturb normal enhancer-promoter contact formation and lead to abnormal gene expression patterns Fig. .Fig. 3AbMuch less mechanistic research has been done to study the pathological consequences and causality of abnormal promoter-enhancer contacts. Initial work has shown that disease-associated SNPs at enhancers can reduce transcription at target genes, whilst other studies have identified that action potentials regulate epigenetic modifications at enhancers in pre-formed chromatin loops . Much more work is required to go beyond these initial insights. For example, what pathological cellular processes arise from the abnormal enhancer-promoter contacts and the aberrant transcription patterns they cause? Answers to these and other questions, using patient-derived brain organoids, are needed to provide new insights into the pathogenesis of epilepsy and ASD. This, in turn, will lead to new insights into how to diagnose and treat these conditions.Supplementary file1 (XLSX 61 KB)Supplementary file2 (DOCX 34 KB)Below is the link to the electronic supplementary material.


The expression of genetic information is tightly controlled by chromatin regulatory proteins, including those in the heterochromatin gene repression family. Many of these regulatory proteins work together on the chromatin substrate to precisely regulate gene expression during mammalian development, giving rise to many different tissues in higher organisms from a fixed genomic template. Here we identify and characterize the interactions of two related heterochromatin regulatory proteins, heterochromatin protein 1 alpha (HP1α) and M-phase phosphoprotein 8 (MPP8), with hepatoma-derived growth factor-related protein 2 (HRP2). We find in biochemical experiments that HRP2 copurifies and co-sediments with heterochromatin-associated proteins, including HP1α and MPP8. Using the Chromatin in vivo Assay in multiple cell types, we demonstrate that HP1α-mediated gene repression dynamics are altered by the presence of HRP2. Furthermore, the knockout of HRP2 in MDA-MB-231 cells results in significant changes to chromatin structure and stability, which alter gene expression patterns. Here, we detail a mechanism by which HRP2 contributes to epigenetic transcriptional regulation through engagement with heterochromatin-associated proteins to stabilize the chromatin landscape and influence gene expression. The human genome catalogues a dynamic array of information. Extensive cooperation between protein macromolecules is required to precisely express pertinent genetic information in response to developmental and physiological stimuli. Careful maintenance of gene transcriptional activity—governed in part by unique patterns of DNA modifications and post-translational modifications (PTM)s on chromatin-associated histone proteins—is critical for proper cell growth and development . DysreguEpigenetic proteins facilitate gene regulation by modulating chromatin structure, allowing for changes in gene expression without altering the genetic code. Chromatin dynamically exists between two structural states, euchromatin and heterochromatin, which respectively enhance or occlude access to DNA by transcriptional machinery. The specific mechanisms by which facultatively silenced genes are activated upon differentiation or disease progression are not fully understood.Until the last decade, mammalian epigenetic silencing through histone H3 lysine 9 (H3K9) methylation was thought to be directed solely by the heterochromatin protein 1 (HP1) pathway. HP1 contains several isoforms, α, β, and γ, with HP1α being the most well-characterized at heterochromatin and HP1γ often localizing to euchromatin . Our stuIn 2015, using forward genetic screening, Tchasovnikarov and Timms et al. identified the human silencing hub (HUSH) as a second mediator of H3K9me3-mediated gene repression in humans . DependeSupt6H), and transcription elongation factor LAP2 (Tmpo), as well as lesser characterized hepatoma-derived growth factor-related protein 2  , where min = percentage of GFP+ scramble cells with CIP-HP1α recruitment, max = percentage of GFP+ scramble cells without CIP-HP1α recruitment, and x = percentage of GFP+ target cells with CIP-HP1α recruitment.For shRNA experiments, cell sorting was performed on a BD FACSAria II cell sorter. Upon doxycycline induction, turboRFP+ cells were sorted to enrich populations that were expressing the inducible pTRIPZ vector used for shRNA knockdown experiments. Flow cytometry was performed using an Intellicyt iQue Screener and analyzed with FlowJo software. Cell populations were gated based on forward and side scatter areas. Single-cell populations were isolated using forward scatter area by forward scatter height gating. Dead or dying cells that equally fluoresced in the GFP and APC channels upon excitation with 488 nm laser were omitted as autofluorescent. Cells were again sorted for RFP+ to include only those highly expressing shRNA constructs, and the remaining cells were gated into GFP- and GFP+ populations. Dot plots and histograms demonstrate representative samples while bar graphs and charts contain all biological replicates. Percent inhibition was calculated using the following formula: % Inhibition = . We. We25]. KO) line in 293Ts, cells were transfected with both CRISPR/Cas9 and an Hdgfrp2-targeted CRISPR dual-cut guide RNAs (gRNA)s designed to create two double-stranded breaks in the Hdgfrp2 genomic sequence (KO) was confirmed via immunoblot analysis, mRNA extraction and qRT-PCR at gene regions both upstream and downstream of the double-stranded breaks, as well as immunofluorescence microscopy (WT) and account for the presence of CRISPR/Cas9.To generate an HRP2 knockout  co-migrates with HP1α (22 kDa) at lower density fractions in agreement with molecular standards ranging from 100–150 kDa in size and with MPP8 (110 kDa) at higher density fractions  dimerization is required for HUSH-driven epigenetic silencing, these higher molecular weight complexes may reflect HRP2 association with HUSH-bound MPP8 . UnfortuWe also observed instances of HRP2 co-migration with both HP1α and MPP8 within the same fractions, consistent with previously reported associations of HUSH with all HP1 isoforms . InteresGiven that HRP2 associates strongly with HP1α and other H3K9me3-depositing molecular players, we were interested in investigating the impact of HRP2 on heterochromatin dynamics. We used a previously described variation of our CiA system in 293T cells (CiA:293T), in which a GFP reporter is located downstream of Gal4- and Zinc Finger-binding arrays . By fusiKO) and non-targeting (HRP2WT) variants of our CiA:293T reporter line , mid-HP1α recruitment (D-3), initial HP1α release (D0), and post-HP1α release (D + 3 and D + 6)  A. By comd D + 6) . Notablysampling B,D. We aE-cadherin, in a triple-negative breast cancer cell (TNBC) line, MDA-MB-231. E-cadherin is epigenetically silenced by H3K9me3 installation and DNA methylation to facilitate the epithelial-to-mesenchymal transition (EMT) in metastatic TNBC cells [KO−1 and HRP2KO−2), we used ChIP to examine whether HRP2 influences H3K9me3 enrichment at a non-engineered, heterochromatinized locus (E-cadherin promoter region (TSS -127), resulting in decreased mRNA expression , we observed significantly decreased enrichment of H3K9me3 in both HRP2KO lines (N-cadherin mRNA expression (E-cadherin has been previously characterized as an H3K9me3-targeted locus in MDA-MB-231 cells, changes to chromatin structure and gene expression at N-cadherin could be a subsidiary effect of HRP2 loss. Even so, these data suggest that HRP2 is critical for maintaining established transcriptional patterns, such as those involved in EMT. Interestingly, HRP2 knockout also induced consistent changes to MDA-MB-231 cell morphology, including increased size, loss of membrane boundaries, enlarged nuclei, and increased granularity (To determine whether HRP2 knockout results in decreased transcription, we wanted to examine another gene. We next investigated H3K9me3 occupancy at 31 cells . At the KO lines B. Changepression C. It is nularity . TogetheTaken together, our work suggests that HRP2 influences heterochromatin assembly and disassembly to effectively regulate transcription. Others have demonstrated that HRP2 is required for the activation of precise gene expression patterns during cellular differentiation and disease ,24,27. WGiven that HRP2 interacted with our HP1 pathway inhibitor in previous work, we became interested in understanding how HRP2 engages with heterochromatin-associated proteins to influence transcriptional outcomes . Here wede novo heterochromatin formation, its interactions with repressive epigenetic complexes predominantly support the stabilization of H3K9me3-marked heterochromatin domains within the existing chromatin landscape (Together with previous studies, our data suggest that HRP2 is important for maintaining a balance between gene transcription and repressive elements. We found that while HRP2 may contribute to andscape . It will


Metallothioneins (MTs) are cysteine-rich low-molecular-weight proteins that protect cells from heavy metal toxicity. MT1 and MT2 are considered ubiquitously expressed among the MT isoforms ranging from 1 to 4. These MT1 and MT2 transcriptions are regulated by metal regulatory transcription factor 1 (MTF1) binding to the metal response element (MRE) of the promoter, which is upregulated in response to zinc. The functional MT isoforms are MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M, MT1X, and MT2A in humans, but these expressions were differently regulated. Here, MT1A was shown to be significantly less upregulated by zinc than MT1E, MT1G, MT1X, and MT2A. The poor responsiveness of the MT1A zinc was suggested to be due to the MRE sequence in the MT1A promoter region having a lower MTF1 binding affinity compared to the other isoforms. MT1A may be induced via pathways other than the MTF1–MRE binding pathway. These findings may help elucidate the differential regulation of MT isoform expression. For more than one and a half centuries, metallothionein (MT) has been a metal-binding protein with a molecular weight of approximately 6000–7000 found in the kidney cortex of horses . It is fO tetradecanoylphorbol-13-acetate responsive element, antioxidant responsive element, glucocorticoid responsive element, and IL-6 responsive element type II [Regarding the MT induction mechanism, the metal regulatory transcription factor 1 (MTF1) is believed to play a central role in sensing heavy metal signals and transacting MT genes via the metal response element (MRE) . Althoug type II . The mye type II . Epigene type II . It is pIn this study, each MT isoform expression induction by zinc was examined, i.e., differential expression regulation by MTF1. Regulation by MREs was found to be nonuniform among the numerous MT isoforms and that MT1A is particularly poorly zinc-responsive. Electrophoresis mobility shift assay revealed that MRE sequences in the MT1A promoter region have a low binding affinity to MTF1.The MT expression level and MT promoter activity upon the addition of zinc were examined to elucidate the difference in induction by zinc for each isoform. HeLa cells were treated with 20–200-µM zinc for 6 h, and then the expression levels of the MT isoforms highly expressed in many tissues, MT1A, MT1E, MT1G, MT1X, and MT2A, were quantified A. Zinc iNext, whether the same difference in responsiveness was observed when zinc treatment was performed after 2 days of culture in a zinc-low medium was investigated, which was prepared using Chelex-treatment FBS. In MT1E, MT1G, MT1X, and MT2A, 100 μM zinc treatment significantly increased the mRNA levels and promoter activity compared with normal medium, whereas MT1A showed only a slight increase in mRNA expression levels and no change in promoter activity . The folChanges in the MT1A promoter activity after zinc treatment when the MTF1 expression vector was co-transfected and investigated to determine whether transcriptional MTF1 regulation also involves MT1A expression. MTF1 co-expression increased the MT1A promoter activity, which was further increased by zinc treatment. Furthermore, the MT1A promoter activity was increased in cells co-transfected with the MTF1 expression vector compared with non-transfected cells and was further increased by zinc . Thus, lMT1A or MREMT2A to the binding reaction solution of Cy5-MREs and MTF1 to compare the MTF1 binding affinity to MREs in the MT1A and MT2A promoter regions . The cells were seeded and cultured in DMEM for the zinc-deficient culture and supplemented with 10% Chelex®-100-treated FBS (zinc-deficient medium) prepared according to the manufacturer’s protocols.HeLa cells were sourced from RIKEN BioResource Center Cell Bank , and Dulbecco’s Modified Eagle’s Medium (DMEM)  was used for culture supplemented with 10% heat-inactivated fetal bovine serum  and a mixture of antibiotic–antimycotic solution  at 37 °C under a 5% CO density . The celRNA extraction reagent Isogen  was used to isolate the total RNA from cells according to the manufacturer’s protocol. The High-Capacity cDNA Reverse Transcription Kit  with a random primer was used to convert complementary DNA from the extracted total RNA. The Eco Real-Time PCR System  with PCR reagents TB Green Premix Ex Taq (Tli RNase H Plus)  or Premix Ex Taq (Probe qPCR)  was used to perform qPCR. The primer and probe sequences were as previously reported . All priflag was constructed in the same manner as previously described [https://jaspar.genereg.net/, Matrix ID: MA0863.1 (accessed on 30 November 2022)), an open-access database storing manually curated transcription factor-binding profiles, was used to find putative MTF1 binding sites. The reporter vectors that contain each MT isoform’s promoter regions were constructed using primers as presented in ® Cloning Kit (Takara Bio Inc.).Flag-tagged human MTF1 cDNA was generated, and the expression vector pcDNA3.1-hMTF1escribed . The firflag, was mixed at a 1:0.04:7.5 ratio in Opti-MEM medium . The plasmid mixture (0.5 µg per well) was mixed with FuGENE HD (1.5 µL per well) incubated for 15 min at room temperature, added to the cells cultured in a 24-well plate, and cultured for 24 h. The cells were then treated with zinc, incubated for 6 h, and then collected, and luciferase activity was measured. The cells were lysed in 1 × passive lysis buffer , whereas luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega) and GloMax 20/20n luminometer (Promega).After 24 h of cell seeding, various MT promoter reporter vectors were transfected. Transfection was performed according to the manufacturer’s protocol using transfection reagent FuGENE HD (Promega). In brief, the reporter vector, pRL-SV40  and pEGFP or pcDNA3.1-hMTF1® T7 Quick Coupled Transcription/Translation System were used to synthesize human MTF1 according to the manufacturer’s protocols (Promega). Cy5-labeled MREs (Cy5-MREs) were purchased from Integrated DNA Technologies, Inc., and a putative MRE within the MT1A promoter region (MREMT1A-c) and MT2A promoter region (MREMT2A-a), which has the highest relative score for MTF1 in each promoter, were purchased from Fasmac Co., Ltd. . These sequences are presented in 2, and 1-mM DTT) and 1 µL 1 mg/mL poly (dI-dC). Then, 1.0 µL of 0.1-µM Cy5-MREs and the indicated MREMT1A or MREMT2A amounts were added and incubated at 4 °C for 15 min. The mixture was separated from free Cy5-MREs and the MTF1–Cy5-MREs complex via electrophoresis on a 6% acrylamide gel. These Cy5-MREs were then visualized using the PharosFX System .The pcDNA3.1–MTF1flag and the TnTp < 0.05 considered significant) implemented in the PASW Statistics 18 package of SPSS .Data were analyzed for differences between multiple groups using Tukey’s test (with In the present study, the zinc responsiveness of MT1A was found to be significantly lower than that of other isoforms, and the MRE sequence in the MT1A promoter has a lower MTF1 affinity. The findings in this study suggest that the regulatory MT1A expression mechanism is poorly regulated by MTF1, unlike other isoforms. Further studies are warranted to determine the main pathway that induces MT1A expression, though it is possible that MT1A is mainly induced through pathways other than the MTF1–MRE binding pathway. Little is known about the differences in the roles between isoforms despite the fact that MTs are important proteins with many roles in biological defense. It is important to elucidate what environmental factors induce MT expression and through which pathways and whether the responsiveness to each pathway differs among isoforms. Further investigation is needed to explore the differences in expression regulation by isoforms and to understand the roles that they play.


Transposable elements (TEs) are major contributors of genetic material in mammalian genomes. These often include binding sites for architectural proteins, including the multifarious master protein, CTCF, which shapes the 3D genome by creating loops, domains, compartment borders, and RNA-DNA interactions. These play a role in the compact packaging of DNA and have the potential to facilitate regulatory function. In this study, we explore the widespread contribution of TEs to mammalian 3D genomes by quantifying the extent to which they give rise to loops and domain border differences across various cell types and species using several 3D genome mapping technologies. We show that specific families and subfamilies of TEs have contributed to lineage-specific 3D chromatin structures across mammalian species. In many cases, these loops may facilitate sustained interaction between distant cis-regulatory elements and target genes, and domains may segregate chromatin state to impact gene expression in a lineage-specific manner. An experimental validation of our analytical findings using CRISPR-Cas9 to delete a candidate TE resulted in disruption of species-specific 3D chromatin structure. Taken together, we comprehensively quantify and selectively validate our finding that TEs contribute to shaping 3D genome organization and may, in some cases, impact gene regulation during the course of mammalian evolution. Here the authors show transposable elements, formerly considered junk DNA, are a source of CTCF binding sites that contribute to species-specific 3D-genome structure, and may impact gene regulation during the course of mammalian evolution. TEs provide fodder to regulatory innovation by containing motifs that are very similar to transcription factor binding sites (TFBS) that can create functional TFBS upon mutations4. Consequently, these motifs have had a profound impact on remodeling gene regulatory networks13 in human and other mammalian genomes. When these TE-derived binding sites belong to architectural proteins such as CTCF, ZNF143, etc., they can lead to certain TE families dramatically shaping genome folding18. Deleting TEs that supply CTCF binding sites involved in conserved 3D genome folding, can lead to the falling apart of these preserved higher-order chromosomal structures, underscoring their functional importance15. Apart from carrying TF binding sites, elevated TE transcription can also contribute to dynamic 3D genome folding during development as has been shown in human pluripotent stem cells19 and in early mouse embryos20. Similarly, the MIR family of ancient repeats also plays a critical role in 3D genome folding independent of CTCF21. They show a distinct local chromatin environment poised to recruit PolII, various transcriptional complexes, chromatin modifying enzymes, and histone modifications consistent with transcription and enhancers, some of which in part may help establish domain boundaries22.Transposable elements (TEs) are major occupants of mammalian genetic real estate, including ~50% of the human genome24. Amongst its many functions includes anchoring chromatin loops and insulating TAD boundaries in close concert with cohesin and other insulator proteins26. Studies profiling multiple species have underpinned CTCF evolution as one of the robust mechanisms of 3D genome remodeling; with differential CTCF binding leading to altered 3D genome folding27. Specifically, CTCF binding has evolved under two distinct forces of evolution and selection at play: a selective constraint to maintain existing domain boundaries and loop anchors, and a stronger push to evolve novel local interactions like intra-domain chromatin loops. With the latter fostering regulatory crosstalk and potentially rewiring regulatory networks, thereby pushing regulatory innovation and permitting phenotypic evolution. Thus, one regime maintains tight conservation of 3D genome folding, and the second regime pushes for divergence28. Combined with the waves of TE expansion, we are served a perfect recipe for sequence evolution, altered CTCF binding, 3D genome reorganization, and large-scale genome evolution.CTCF is known to play an important role in setting up the 3D genome architecture inside the nucleus of the cellIn this study, we reconcile these two regimes of repeat-fueled CTCF evolution in the context of an evolving 3D genome. We hypothesize that TEs have been a rich source of sequence for the assembly and tinkering of higher-order chromosomal structures. To that end, we profile TE contributions at the class, family, and subfamily levels to higher-order chromosomal structures between humans and mice across multiple cell-types, as well as samples from dog and rhesus macaque, with the aim of understanding its impact on 3D genome evolution over deep time. We compare 3D genome folding patterns between species, with the aim to further our understanding of the role of TEs in the emergence and reshaping of genome topology throughout mammalian evolution.29. In general, we see that the proportion of the genome annotated as TEs increases from mouse to human, with dog having a slightly lower proportion than mice. The various classes of TEs also contribute differently . In studies where loop annotations and TAD boundary calls exist, we ran HiCCUPS (see Methods) to identify the underlying CTCF site that may be responsible for anchoring the loop or insulating the TAD boundary. For human cell types that lacked a corresponding CTCF ChIP data set, we used GM12878 ChIP data as a surrogate to call for CTCF occupancy. CH12-LX CTCF ChIP data was used as a surrogate for mouse cell types, which is supported by evidence of common CTCF binding across cell types in refs 31,32.In this study, we characterized 20 prior studies and collected 3D genome datasets across 52 cell types from 4 mammalian species Fig. . The protly Fig. . We starIn our data collection, 8–37% of loop anchors and TAD boundary CTCF sites were derived from REs across the four species Fig. . We find15. In mouse, SINEs contribute the most to CTCF sites involved in genome folding even though they occupy ~5% lesser genomic space when compared to the three other species. This underscores the profound impact SINEs have had on shaping 3D genome folding in the mouse lineage. On the other hand, the human lineage had consistent contribution from LINE, LTR and DNA class of TEs. SINEs, however, did not contribute as much to either loop anchor or TAD boundary CTCF sites in the human lineage. TE class distribution did not seem to depend on cell type and/ or assay performed on the sample. Interestingly, we observe that the dog and rhesus macaque samples have a distribution of TE classes that lies between mouse and humans in terms of the relative contribution of all the four classes of TEs, with SINEs contributing a significant fraction in these species—higher than in human, but lower than mouse. On average, LINEs contribute to 4.6–37% of TE-derived loop anchors and TAD borders, SINEs contribute to 6.7–81.1%, LTRs contribute 8.9%-41%, and DNA transposons contribute to 1.8%-28%  derived from TEs. Moreover, the class of origin of these TEs  also showed a species-biased distribution Fig. A, B, as 17. In the SINEs, the ancient MIR family contributes ubiquitously (0.7–16.9%) to TE-derived loop anchor CTCF sites in all three species. Lastly, the prolific B2 family of SINEs contributes extensively to loop anchors in mouse, accounting for 63.3–76.9% of the TE-derived loop anchor CTCF sites , and MaLR (not present in humans) contributing modestly (0.2%-12.8%) to TE-derived loop anchors, as has been reported beforetes Fig. .Next, we studied the TE family contribution to TAD boundary CTCF sites and observed similar overall contributions as in the case of loop anchor CTCF sites. We see a widespread contribution of TE-derived TAD boundary sites from the hAT-Charlie family of DNA transposons, L1 and L2 families of LINEs, ERV1, ERVL, and ERVL-MaLR families of LTRs, and B2 and MIR families of SINEs (0.2–70.1%) in the same species-biased manner, as observed before. In dogs, a dog-specific lysine-transfer RNA (tRNA)-derived SINE family of TEs contributes 29.3% of the TE-derived TAD boundary CTCF sites Fig. .16. Specifically, we looked at TE subfamily contribution in rhesus macaque and dog as these species have not been profiled before in direct comparison to human and mouse datasets. We observed that like humans, rhesus macaques also showed enrichment for MIR, MIR3, MIRb (highly enriched in humans) family of SINEs, as well as L2a (highly enriched in humans), L2b, L2c. However, unlike humans certain DNA transposons like MER20 and MER91B subfamily of elements were not enriched in rhesus macaque, and while LTR41 and LTR13 subfamily of LTR elements were enriched in humans, they were not found to contribute significantly to 3D genome folding in rhesus macaque  and their orthology status (conserved or lineage-specific) in matched cell-types, including liver samples from dog and human, and lymphoblastoid lines from mouse and human. TAD boundary orthology between the dog liver data from ref. 27 and the human liver data from ref. 33 were directly assessed. In the human liver data, 29.4% of the CTCF TAD boundaries were orthologous to the canFam3 reference genome, 14.2% of which were RE-derived. In contrast, 16.2% of human TAD boundaries that were non-orthologous to dog were RE-derived  of these loops potentially brought a strong enhancer near an active promoter.For functional validation, we focused on the lymphoblastoid lines in mice and human. Using in situ Hi-C data from the lymphoblastoid lineage, we compared annotated loop calls in GM12878 (human) and CH12-LX (mouse). Comparing CH12-LX to GM12878, we see that 49.3% (1643/3331) of the loops are mouse-specific. 18% (296/1643) of these mouse-specific loops were anchored at TE-derived CTCF sites Fig. . From he15  to promoters of highly expressed genes in blood cells. We further characterized one such candidate loop derived from an L1MC1 element. While we see the clear presence of a CTCF ChIP-seq peak as well as a loop anchor  anchor lies close to the NCAPG2 promoter and the right (3′) anchor brings a ChromHMM-annotated  enhancer containing a regulatory SNP rs55752599 about 280 kb away in close proximity to each other  interactions within the region of interest (from the start of the affected domain to the end of the chromosome) take place upstream of the L1MC1 element, which is reduced to 24% in the KO contact map. This signifies the loss in domain-contained intra-TAD interactions upon deletion of the boundary element derived from L1MC1. Additionally, we see 28% of the long-range (>30 kb) inter-TAD interactions between the candidate domain (whose right 3′ boundary element is derived from the L1MC1 TE) and the downstream domain, increasing from the 19% observed in the WT contact map. This again signifies the increase in “interaction leakage” that takes place after deleting the domain boundary element, which in this case was derived from the L1MC1 TE.Thus, the L1MC1 element is necessary for anchoring a novel, human-specific loop and domain boundary in GM12878 cells.To identify any changes in gene expression, RNA-seq was conducted on GM12878 wildtype cells and two independent knockout lines. The expression of genes within the disrupted domain was consistently, but not significantly, lower in the knockout lines Fig. B, C. To 35. Indicating that these regions may play enhancer-like roles in either a subpopulation of GM12878 cells or other cell types. These newly accessible putative enhancers were cross-referenced using SCREEN35. A virtual 4C analysis was conducted anchoring on the two newly accessible peaks in the inactive domain, revealing no detectable increase in interaction between the newlyaccessible enhancers and the upstream genes when comparing the mutant GM12878 lines to the wild-type GM12878  and have actively contributed to chromatin organization of the mouse and dog genome. Once TEs disperse CTCF binding sites, selection can exploit the availability of these functional CTCF binding sites in the neighborhood to assemble novel regulatory interactions and form networks. Such a mechanism was recently speculated to play a role in the formation of super-interacting promoter groups by the ERVL-MaLR family of TEs that supply ZNF143 binding sites aiding corticogenesis in the human fetal brain45. Our observation of a very small change may be due to the deletion only affecting the genome conformation in a small fraction of the cells, making the overall effect size smaller at the level of an entire cell population.Deletion of a candidate L1MC1 TE, that anchored a regulatory SNP-containing enhancer  end of a human-specific enhancer-promoter loop, led to the collapse of the novel chromatin structure - showing the necessity of the TE-derived CTCF site, and consequently the TE. Hence, TEs have played a key role in establishing novel higher-order chromosomal structures and may be components of lineage-specific gene regulatory networks. The deletion of the TE-derived CTCF site coincided with a small, but consistent decrease in gene expression within the disrupted TAD. There were marked changes in chromatin accessibility at putative enhancers in the downstream, previously inactive region. These newly accessible putative enhancers may underlie the lack of ablation or even significant decrease in gene expression. The lack of significant change also demonstrates the plasticity and robustness that exists in gene regulation and consequent gene expression. The gain in accessibility of these downstream enhancers may also be due to increased interactions with regions within the disrupted active TAD. The overall effect on chromatin and gene expression we see is very small, and it is not uncommon to see no changes in expression after the deletion of a CTCF motifThus, we provide evidence of how bursts of lineage-specific TE activity can provide CTCF binding sites resulting in lineage-specific higher-order chromosomal structures that rewire local gene regulation, pushing regulatory innovation, ultimately permitting the emergence of novel evolutionary phenotypes. Taken together, our observations and results represent the regulatory innovation that TEs bring to host genomes.By analyzing publicly available high-resolution Hi-C datasets from four different species, we aimed to identify, quantify, and validate TE transposition as a widespread phenomenon that contributes to lineage-specific genome folding patterns and consequently lineage-specific gene regulation across multiple mammalian species. We found that 8–37% of loop anchor and TAD boundary CTCF sites were derived from repeats in the 37 cell types that were profiled. We outline lineage-specific contribution of TE classes, families, and subfamilies to loop anchor and TAD boundary CTCF sites, highlighting TE families like MIR (common), tRNA (dog), and B2 (mouse) SINEs, L1 (common) and L2  LINEs, ERV-family (common) of LTRs and hAT-Charlie (humans) family of DNA transposons that have contributed extensively to CTCF sites involved in 3D genome folding. Further, we characterize lineage-specific chromatin loops, showing that a significant fraction (14–18%) of these are anchored at TE-derived CTCF binding sites. A substantial fraction (27–28%) of these TE-derived, lineage-specific loops were predicted to be enhancer-promoter loops, representing the regulatory innovation TEs unintentionally bring along as they infect and invade the host genome. We also find that some of these lineage-specific TE-derived loops contribute to divergent gene expression due to lineage-specific transcriptional regulation. Finally, we used CRISPR-Cas9 to delete a candidate TE (L1MC1) that was predicted to anchor an enhancer-promoter loop as well as a TAD domain boundary in human lymphoblastoid cell lines. Upon deletion of the element, we see collapse of the novel loop as well as the domain boundary, underscoring the importance of TEs in the context of 3D genome remodeling and regulatory innovation. Taken together, our findings shed light on the extent to which TEs have contributed to loop anchor and TAD boundary CTCF sites in mammalian species, consequently rewiring the 3D genome and accelerating regulatory crosstalk and innovation.46. Loop calls from H3K27me ChIA-PET experiments on CD4 + T-cells were obtained from refs. 47. Loop calls from H3K27ac Hi-ChIP experiments on GM12878, HCASMC, K562, MyLa, Naive T-cells, TH17, TReg, and MESC samples were obtained from ref. 48. Loop calls from CTCF Hi-ChIP experiments on GM12878 and mESC samples were obtained from ref. 49. Loop calls from in-situ Hi-C experiments on THC-1 samples were obtained from ref. 50. Loop calls from in-situ Hi-C experiments on HAP1 cells were obtained from ref. 46. Loop and TAD calls from in-situ Hi-C experiments on HSC samples were obtained from ref. 51. Loop calls from additional Hi-C experiments on HSC samples were obtained from ref. 52. Loop and TAD calls from AML12 Hepatocytes were obtained from53. TAD calls from in-situ Hi-C experiments on hESCs were obtained from ref. 54. TAD calls from in-situ Hi-C experiments on Cortex, hESC, and IMR90 cells were obtained from ref. 25. TAD calls from in-situ Hi-C experiments on Lymphoma samples were obtained from ref. 55. TAD calls for LCL samples were obtained from ref. 56. TAD calls from adrenal gland, aorta, bladder, cortex, GM12878, hESC, hippocampus, IMR90, lung, liver, left ventricle, mesoderm, mesenchymal stem cell, NPC, ovary, pancreas, Psoas muscle, right ventricle, small bowel, spleen, and trophoblast-like cells were obtained from ref. 33. TAD calls on RPE1 cells were obtained from ref. 57. Links and cell-type abbreviations for the datasets are available in Supplementary Table Hi-C data for GM12878 and CH12-LX were obtained from ref. 26 from all of the above-mentioned cell and tissue types to generate a list of unique loop anchors. We used MotifFinder26 in conjunction with CTCF ChIP-seq data (GEO-accession: GSM935611)58 to identify loops that were anchored by CTCF sites. We used BEDTools59 to overlap loop anchor CTCF motifs with RepeatMasker60  with a required 10 bp overlap of the CTCF motif with a repetitive element (RE) to classify RE-derived loop anchor CTCF sites. Loops with at least one identified RE-derived anchor CTCF (RE-derived loop) or two anchors with no RE-derived CTCF sites (non-RE derived loop) were considered for analysis of RE-derived loop counts.We used published HiCCUPS outputSimilarly, we included published TAD boundaries for analysis. We considered the region 5 kb upstream and 5 kb downstream of the published TAD borders as the boundaries for analysis to standardize the resolution between studies. We used the same MotifFinder analysis and BEDTools intersections as the loop analysis to identify CTCF-derived TAD boundaries. TADs with at least one identified RE-derived CTCF site were classified as RE-derived TADs, and TADs with two anchors with no RE-derived CTCF site were classified as non-RE-derived TADs to be considered for summary analysis. For analysis of RE class contribution to CTCF TAD boundaries, samples with the same cell type, species, and assay were considered together with average class contributions (sum of unique RE-derived CTCF loop anchors/sum of unique CTCF loop anchors).60 with its slow search parameter to obtain a comprehensive list of REs as well as their class, family, and subfamily in the hg19, mm9, rheMac2, and canFam3 genome assemblies. We used the RE counts (described above) to quantify their contribution to loop anchor and TAD boundary CTCF sites.We used RepeatMasker v4.0.761 using a sequence match rate of 0.1. 3245 out of 3331 mouse peaks were successfully lifted over from mm9 to hg19 to enable us to compare peak annotations across species. Mouse loop anchor pairs that fell within a minimum threshold  of an existing GM12878 loop anchor pair were considered to be conserved in humans. The 50 kb threshold is intended to account for errors in cross-species liftOver.We converted CH12-LX loop calls in the mm9 assembly to hg19 coordinates using liftOver14. We converted the human liver CTCF TAD boundaries in the hg19 assembly to canFam3 using liftOver61 with a sequence match rate of 0.1. 1146 out of 1216 CTCF TAD boundaries were successfully lifted from hg19 to canFam3. Lifted TAD boundary CTCF motifs that fell within 50 kb of an existing dog liver CTCF TAD boundary were considered to be conserved.We identified CTCF TAD boundaries in human and dog livers by identifying all CTCF motifs within the 40 kb TAD boundary, and intersecting those with human and dog liver CTCF ChIP peaks from ref. 62 with “unmarked” indicating genomic sequences without evidence of regulatory information.To annotate loops with putative regulatory function, we used ChromHMM state annotations in the respective cell type. We considered if there was a ChromHMM-annotated promoter  within 10 kb of one loop anchor and either a ChromHMM-annotated enhancer ) or polycomb repression  at the opposite loop anchor. ChromHMM chromatin states are as described in ref. 26. To be considered for CRISPR validation, we required that a GM12878 loop had at least one anchor at a RE-derived CTCF that is not orthologous in CH12-LX cells. Then we looked for loops that had a promoter within 10 kb of one anchor and an enhancer or repressor within 10 kb the opposite anchor, as annotated by ChromHMM58. We also required that the TE-derived CTCF was not nearby another active CTCF binding site. CTCF ChIP data was downloaded58, and MACS2 was used to call peaks to determine locations with active CTCF binding63. Loop anchors with more than one active CTCF site within 15 kb were excluded. Furthermore, the RE-derived CTCF could not overlap a p300 binding peak58 to avoid interrupting enhancer function. Enhancer-promoter interactions that had been validated in the GeneHancer database were preferred, but not required.Candidate selection and filtering were conducted using loop calls on GM12878 and CH12-LX cells from ref. 2.GM12878 cell lines were cultured according to ENCODE standards. Cells were cultured in 10 mL of RPMI1640 media  with 100 U/ml penicillin–streptomycin  and 15% fetal bovine serum . They were grown at a density of 200K–800K cells/mL in T-25 flasks and incubated at 37 °C with 95% CO64 and CRISPRScan65 to design several pairs of sgRNAs with minimal off-target effects and high cutting efficiency. We used three sgRNA oligos; one upstream of the L1MC1 CTCF (sequence: ggtgataatgacgttactgtggg), and two downstream of the L1MC1 CTCF (sequences: ctctggggggtcaggtatgt and gacaggcagaaggtcacgcctgg).These sgRNA oligos were designed with compatible overhangs to be cloned into BbSI digested BFP and mCherry CRISPR vectors. We constructed BFP-CRISPR vectors that expressed the sgRNA upstream of the candidate TE and mCherry-CRISPR vectors that express the downstream sgRNA. The BFP and mCherry-CRISPR vectors were co-transfected into GM12878 cells. 24 h post-transfection, we used flow cytometry (Sony SH800 Cell Sorter) to single-cell sort BFP and mCherry double-positive cells into 96-well plates for clonal expansion for 28 days. The targeted region of nine clones was amplified through PCR and Sanger-sequenced to verify the CRISPR deletion. four had heterozygous deletions and five had homozygous deletions.We used a CRISPR workflow to delete the L1MC1-derived CTCF loop anchor (identified through the outlined steps above) from cultured GM12878 cells. The pU6-(BbsI)_CBh-Cas9-T2A-BFP plasmid  and pU6-(BbsI)_CBh-Cas9-T2A-mCherry plasmid  were used as CRISPR delivery vectors. We referenced CRISPOR66. Specifically, 5 million GM12878 WT and L1MC1-KO cells were pelleted at 300 × g. They were cross-linked using 1% formaldehyde for 10 min and quenched with 2.5 M glycine  and incubated on a rocker at room temperature for 5 min. The cells were centrifuged at 300 × g at 4 °C, and pellets were flash-frozen in dry ice and ethanol. Cells were lysed using 250 ul of ice-cold Hi-C lysis buffer  and incubated on ice for 15 min. The cells were pelleted at 2500 × g and washed with ice-cold lysis buffer. The pellets were resuspended in 0.5% sodium dodecyl sulfate (SDS) ad incubated at 62 °C for 10 min. 145 µl of water and 25 µl of 10% Triton X-100  were added, mixed, and incubated at 37 °C for 15 min. The DNA was digested using 100 U of MboI in 10X NEBuffer 2 overnight at 37 °C with rotation. MboI was inactivated at 62 °C for 20 min. Then at room temperature, the DNA overhangs are filled with dNTPs and biotinylated using biotin-14-dATP  and 40U of DNA Polymerase I, Large (Klenow) Fragment . The mixture is incubated at 37 °C with rotation for 45 min. The ends were then ligated using 120 µl of 10X NEB T4 DNA ligase buffer , 100 µl of 10% Triton X-100, 12 µl of 10 mg/ml Bovine Serum Albumin (100X BSA), and 5 µl of 400 U/µl T4 DNA Ligase  and incubated at room temperature for 4 h. Nuclei were pelleted at 2500 × g and resuspended in 10 mM Tris-HCl, pH8; 0.5 M NaCl; 1% SDS solution, and treated with 20 mg/ml proteinase K  to be incubated for 30 min at 55 °C, then overnight at 68 °C. The pellet is cleaned using 2× volumes of pure ethanol and incubating at −80 °C for 2 h. Next, the blunt-end fragments were resuspended in 130 µl of 1X Tris buffer . And sheared to 400–700 bp fragments using six cycles  on Bioruptor® Pico sonication device (Diagenode Cat# B01060010). 75 µl of 10 mg/ml Dynabeads MyOne Streptavidin T1 beads  were used to pull down the biotinylated fragments, from which an Illumina library was built and amplified through 10–12 rounds of PCR. These fragments were finally analyzed using shallow paired-end sequencing (1.4M-4M reads) to ensure library quality  before Hi-C2. Libraries that passed the quality check were used as pools for Hi-C2 experiments.The Hi-C datasets used to validate the L1MC1-derived loop were generated using the standardized protocol from the 4DN consortium26. Hi-C data were visualized using the web version of JuiceBox67.We generated two in situ Hi-C libraries of GM12878 cells with verified L1MC1 deletions and 1 Hi-C library of wildtype GM12878 cells. Details about all in-situ Hi-C libraries are available in Supplementary Tables 46 to design probes for Hybrid Capture Hi-C (Hi-C2). Briefly, we designed probe sequences to a target region (chr7:157.1-159.1MB), which surrounded the L1MC1-derived CTCF loop anchor we had removed with CRISPR. These sequences were required to be near MboI restriction sites. We executed a three-pass probe design strategy, incrementally increasing several parameters, including the distance of the probe from the MboI restriction site, GC content, probe density in gaps, and the number of repetitive bases. Probes with identical sequences and overlapping probes were removed from the pool. Using this strategy, we identified 3195 probes for the region surrounding the L1MC1-derived CTCF loop anchor. We appended primer sequences (15 bp) to both ends of the 120 bp probe sequence. Similarly to ref. 46, we conducted probe construction and hybrid selection with our study-specific sequences. Hi-C2 libraries were sequenced to a depth of 100 M reads.We followed the approach used in ref. 2 data were aligned and processed using Juicer68. Matrices for analysis were generated using juicer dump . Percentages of long-range (>30 kb) interactions were calculated within the region of interest (chr7:158400000-159000000). The intra-TAD interactions upstream of the L1MC1 element were within chr7: 158490000-158770000, and the inter-TAD interactions were between chr7:158490000-158770000 and chr7:158770000-159100000.Hi-C69. Gene-wise read counts were extracted using HTSeq270 and CPM was calculated for GAPDH, ACTB, NCAPG2, ESYT2, WDR60, and VIPR2 using edgeR71.500k cells from each of the genetically edited clones and GM12878 WT cells were treated with Trizol for lysing. RNA was extracted using a Zymo RNA Clean & Concentrator-5 kit. 200 ng of RNA from each sample was used to generate RNA-seq libraries using the NuGEN Universal Plus mRNA-Seq + UDI kit. The libraries underwent 2 × 75 paired end sequencing and the resulting data were aligned to hg19 using STAR72. 100K–200K cells from the WT GM12878 and homozygous knockout lines were collected on day 6 of repriming. Cells were resuspended and centrifuged at 500 × g for 5 min at 4 °C. The supernatant was aspirated and cells were washed once with cold PBS (0.04% BSA). The cells were resuspended in 300 μl DNaseI solution  on ice for 5 min. Subsequently, 1 ml of PBS (0.04% BSA) was added, and the mixture was centrifuged at 500 × g for 5 min at 4 °C. The cells were washed twice in PBS + BSA and resuspended in 100 μl ATAC-seq RSB  with 0.1% NP40, 0.1% Tween-20, and 0.01% Digitonin and incubated on ice for 3 min to lyse. After lysis, 1 mL of ATAC-seq RSB with 0.1% Tween-20 was added and mixed by inversion. Next, the nuclei were collected by centrifugation at 500 × g for 5 min at 4 °C. The nuclei were resuspended in 20 µL 2 × TD buffer , and 50,000 nuclei were transferred to a tube with 2 × TD buffer filled up to 25 µL. 25 µL of transposition mix  was added. The transposition reactions were mixed and incubated for 30 min at 37 °C with gentle tapping every 10 min. Reactions were purified with the Zymo DNA Clean and Concentrator-5 kit . The ATAC-seq library was amplified for nine cycles on a PCR machine. The PCR reaction was purified with Sera-Mag Select beads  using double size selection with 27.5 µL beads (0.55 × sample volume) and 50 µL beads (1.55 × sample volume). The ATAC-seq libraries were quantitated by Qubit assays and sequenced by an Illumina NextSeq platform. QC and analysis on ATAC-seq libraries were performed using AIAP73. Peaks in the ATAC data were called using MACS263 and visualized on the WashU Epigenome Browser38. Differentially accessible peaks between the wildtype and knockout cells were identified using DiffBind74.ATAC-seq was performed as described in ref. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary