Transposable elements (TEs) encode regulatory elements that impact gene expression in multiple species, yet a comprehensive analysis of zebrafish TEs in the context of gene regulation is lacking. Here, we systematically investigate the epigenomic and transcriptomic landscape of TEs across 11 adult zebrafish tissues using multidimensional sequencing data. We find that TEs contribute substantially to a diverse array of regulatory elements in the zebrafish genome and that 37% of TEs are positioned in active regulatory states in adult zebrafish tissues. We identify TE subfamilies enriched in highly specific regulatory elements among different tissues. We use transcript assembly to discover TE-derived transcriptional units expressed across tissues. Finally, we show that novel TE-derived promoters can initiate tissue-specific transcription of alternate gene isoforms. This work provides a comprehensive profile of TE activity across normal zebrafish tissues, shedding light on mechanisms underlying the regulation of gene expression in this widely used model organism. Transposable elements (TEs) are highly repetitive DNA sequences comprising approximately half of mammalian genomes . TEs canMounting evidence has revealed that TEs serve as a rich source of functional regulatory elements in host genomes . TEs conSupplemental Fig. S1). The most abundant TE class in zebrafish is the DNA transposon, comprising 34% of the genome (cis-regulatory element (CRE) contribution of TEs in zebrafish.Zebrafish is an important model organism for various research areas including development , human de genome . In humae genome . Only a e genome . This ise genome . Additioe genome . Howevere genome . Comprehe genome . Recentle genome . TogetheHere, we intercalate these multidimensional transcriptome, epigenome, and three-dimensional genome structure data to create a comprehensive epigenetic and transcriptional landscape of zebrafish TEs across adult tissues. We characterize the TE landscape in zebrafish and their contribution to regulatory networks in both shared and tissue-specific fashions. Specifically, we explore TEs’ potential in forming regulatory elements and/or alternative transcripts and in interacting with surrounding genomic regions. We provide insights into the evolutionarily conserved phenomenon of TEs as a powerful source of regulatory function in host genomes.Supplemental Fig. S2A). Whereas TEs are depleted in the active regulatory states, different classes of TEs show different epigenetic profiles . For example, SINEs are positioned in more active enhancer states, distal ATAC-seq peaks, and TAD boundaries and loop anchors than other TE classes, whereas LTRs are positioned in more quiescent states and CpGs with missing methylation data. Overall, all TE classes are highly methylated, and only 1%–4% of CpGs in TEs are lowly methylated.To profile the epigenetic landscape of TEs in zebrafish, we used the epigenetic states recently defined in 11 adult zebrafish tissues . These eSupplemental Fig. S2C,D). Taken together, these results show that TEs contribute substantially to active regulatory regions in zebrafish, despite being depleted in these regions. This implies that TEs play a vital role in shaping the regulatory machinery of the zebrafish genome, a result that is consistent with observations in mammals  . These data suggest that different classes of TEs contribute to individual epigenetic states differently, with DNA transposons being the major contributor.To understand how the epigenetic states of TEs change across adult zebrafish tissues, we investigated the proportion of all 2,532,468 zebrafish TE fragments’ epigenetic annotation within each tissue. A small fraction (<10%) of individual TE fragments are positioned in active regulatory regions, including active promoters, weak promoters, active enhancers, ATAC-seq peaks, UMRs, and LMRs in a given tissue A. Howeve× 10−16) B. LTR elWe next examined the dynamics of TEs’ epigenetic profiles across zebrafish tissues. Each TE is annotated with a specific epigenetic state across a number of different tissues. The majority of TEs annotated with active enhancers are found only in a single tissue, suggesting that active enhancer TEs are highly tissue-specific C,D. ConvSupplemental Fig. S3). Taken together, these results indicate that TEs vary in epigenetic state across different tissues and that the tissue specificity of TE annotation varies across different epigenetic states.We further investigated the extent to which TEs are annotated with different epigenetic states over all tissues E–H. For Supplemental Fig. S4). Among embryonic TE enhancers, 63.9% are also detected in adult tissues. This suggests that, in zebrafish, a large proportion of embryonic TE enhancers remain active and acquire tissue specificity in development. This result echoes recent findings in humans comparing hESC- and iPSC-derived neurons  enrichment of each TE subfamily in each tissue-specific or universal active regulatory element relative to the genomic background and found 152 enrichments with LOR > 2  A–G. The Supplemental Fig. S5A). Among the zebrafish transcription factors corresponding to these motifs, we found nkx2.1b expression is significantly higher in the testis sample compared to other adult tissues  . Gene Ontology enrichment analysis with Nkx2.1 motif-containing TE fragments from DNA-2-2_DR subfamily suggests a functional association with response to hormone and estrogen . Moreover, previous work in mice has shown that homologs of nkx2.1b and hoxd13a are associated with urogenital development, pointing to the possibility that these factors are involved in similar processes in zebrafish through TE-derived regulatory elements  and found many TE subfamilies are enriched for CTCF-bound sites . For example, SINE3-1a and HATN9_DR contributed 1016 and 647 CTCF-bound sites in total, respectively.We also analyzed TE subfamilies enriched for CTCF binding sites, which are critical for establishing 3D genome structure. CTCF binds chromatin at TAD boundaries and loop anchors . TherefoSupplemental Fig. S7A). We interrogated tissue-specific expression of TE subfamilies and identified 99, 96, and 103 TE subfamilies that show tissue-specific expression by equal fraction, TEtranscripts, and SQuIRE, respectively . Among these, 37 TE subfamilies were consistently identified by all three methods. However, close examination of individual loci shows allocated reads spreading across intronic regions, reducing the confidence of representing the actual TE transcript structures . This indicates that expression quantification through read allocation is limited at the subfamily level, resulting in high levels of noise at individual genomic sites.To investigate TE expression levels across different zebrafish tissues, we first used three classical approaches to allocate multimapped reads to genomic loci: equal distribution of multimapped reads, TEtranscripts , and SQuSupplemental Fig. S8). We highlight two examples we identified: a novel noncoding transcript and an intact full-length endogenous retrovirus.To address this issue, we used a transcript assembly approach that we recently developed . Brieflyerv , encompasses the internal element and two LTRs in the spleen . Testis comprises 73% of these tissue-specific transcripts followed by kidney and liver which each comprise 5% . This testis specificity is likely due in part to epigenetic reprogramming occurring during gametogenesis . To confirm TE promoter activity with an orthogonal approach, we generated nanoCAGE data from both testis and brain tissues. Of the 241 testis-specific TE-TSSs we identified, 85% are supported by reads covering the TSS, with 59% also supported by peaks . The testis-specific TE-TSSs not supported by nanoCAGE peaks have lower RNA-seq expression levels compared to those with peaks, suggesting the lack of nanoCAGE peak is likely due to lower expression  . Out of the six brain-specific TE-TSSs, three are supported by nanoCAGE data but there is not a significant correlation with RNA-seq expression level, likely due to small sample size . We also examined epigenetic signatures indicative of active transcription. We found that testis-specific TE-TSSs have enriched signals of ATAC-seq, H3K4me3 and H3K27ac ChIP-seq signals only in the testis sample . DNA demethylation signals are also observed in nontestis tissue samples , suggesting testis-specific TE-TSSs may be primed in other tissues by DNA hypomethylation but not by chromatin regulation. Other tissue-specific TE-TSSs showed similar tissue-specific epigenetic landscape patterns .We focused on the testis-specific TE-TSS transcripts. The TEs contributing to TSSs are mainly comprised of DNA classes . Among these transcription factors, homologs of sox4 and pbx1 are known regulators of gonadal differentiation, suggesting TEs’ involvement in testis-specific gene regulation might be mediated by these transcription factors . In heart and muscle, the gene srl is transcribed from a previously annotated TSS that is not active in the testis. We found that in testis, srl is transcribed from the TE located in its upstream intergenic region, which contains a previously unannotated TSS . The epigenetic signatures and the RNA-seq reads further support the promoter activity and the tissue-specific expression of the transcript . Similarly, the DNA transposons hAT-N38_DR, DNA-2-20_DR, and DNA8-9_DR contribute to novel testis-specific usage of TSSs in the introns of the genes ank3b, cyp2j20, and fez1, respectively .The tissue-specific TE-derived alternative promoters are not limited to intergenic TEs. We observed many intronic TEs that also serve as tissue-specific alternative promoters. For example, the DNA transposon DNA-X-6_DR located in intron 11 of the citb, LTR-10_DR element serves as a kidney-specific TSS for the gene add1, and Polinton-1N1_DR serves as a blood-specific TSS for the gene dnase1l4.1 . Whereas these TSSs were not exclusively used over the canonical TSSs in the kidney or in the blood , usage of these alternative promoters is supported by the epigenetic signatures and the RNA-seq reads.Whereas most TE-derived alternative promoters were found in the testis and in DNA transposons, we also observed similar instances in other tissues and other classes of TE. For example, the DNA2-5_DR element serves as a brain-specific TSS for the gene Supplemental Fig. S20A), similar to observations made in human tissues , suggesting that the activity-age relationship is more complex than the current analysis resolution can detect.Finally, we investigated TEs’ activities as a function of their evolutionary age. Sequence divergence-based age estimation shows that TE elements contributing to testis TE-TSSs are younger compared to other elements . The shared usages of canonical TSS and TE-derived TSS may be due to the different cell types present in tissues. A finer resolution of different cell types and single-cell analysis would further identify the specific cell types using TE-derived promoters.We also show that these TE-derived testis-specific isoforms are present in sperm and early developmental stages. Fertilized zygotes are known to use maternal transcripts from the oocyte until the zygote genome is activated . In zebrIn summary, our work represents an important synthesis of epigenomic and transcriptomic data in the context of TEs in the zebrafish genome. We showed that TEs contribute substantially to diverse tissue-specific regulatory elements and transcriptomes in zebrafish. Rapidly evolving technologies such as single-cell and genome/epigenome editing tools will further advance our knowledge on the biological function of TEs in zebrafish.https://www.ncbi.nlm.nih.gov/geo/] accession number GSE134055) were used for transcriptome analysis. ATAC-seq raw sequence  was used for the footprint analysis of CTCF.All zebrafish epigenome data used in this study were previously generated . All anaSupplemental Fig. S1C), hampering the intersection analysis. To overcome this, we took advantage of the fact that TAD boundaries and loop anchors are enriched for the CTCF binding motifs. The TAD boundary CTCF sites were defined as the ATAC-seq peaks with CTCF motifs residing in the TAD boundaries. The loop anchor CTCF sites were defined as active regulatory elements  with CTCF motifs residing in loop anchors.We used four different categories of epigenetic states previously defined . In brieTEs, genome, and Ensembl genic features were intersected with each epigenetic state using BEDTools , and oveAll 2,532,468 individual TE fragments were annotated by epigenetic state per tissue. Each TE fragment was assigned to one state per each category. When a TE fragment overlaps more than one epigenetic state, the state with the highest number of base pairs sharing that TE fragment was chosen. To calculate DNA methylation levels of TE fragments, 1,941,161 TE fragments that have CpGs were used. A mean DNA methylation level of CpGs with a read coverage of five or more reads was calculated per TE fragment.Genomic coordinates and class/family labels of all TEs were used as input for bedtools shuffle from BEDTools  with defTE subfamily enrichment was calculated as the log odds ratio as previously described . Only suFor motif analysis focusing on TE fragments in testis-specific enhancer regions, we performed HOMER known motif analysis  using thFor motif analysis focusing on TEs contributing to testis-specific TE-TSSs, we performed both known and de novo motif analyses using HOMER . We usedTo benchmark the TE expression quantification tools, we used the raw RNA-seq reads of 11 adult tissues and two embryonic tissues. First, adapter sequences were trimmed from the reads by using Trim Galore! (The Babraham Institute) version 0.6.1. The trimmed reads were directly used to run SQuIRE . For theTranscript assembly of each RNA-seq sample was performed as described previously. Briefly, StringTie2  was usedWe first assembled and annotated all the transcripts using the modified version of the TEProF pipeline for zebrafish study, similar to that used previously for human cancer data . In briehttps://www.ncbi.nlm.nih.gov/bioproject/) accession number PRJEB12982 + RNAs for each replicate . RNA was extracted from the aqueous phase using RNA Clean & Concentrator-5 (Zymo). Poly(A)eplicate .To estimate zebrafish TE age, we obtained alignment and output files of GRCz10 genome from RepeatMasker-4.0.6 and calculated Jukes–Cantor distance from substitutions in the alignments between each TE and its consensus sequence as described previously . TEs witRaw nanoCAGE sequencing data were processed as follows: Template switching oligos and UMIs were trimmed by Tagdust (version 2.33) with parameters “-1 O:N -2 F:NNNNNNNNN -3 S:TATAGGG -4 R:N -d 10000 –show_finger_seq”. Trimmed reads were aligned to the reference genome (GRCz10) using STAR (version 2.5.4b) with parameters “‐‐runMode alignReads ‐‐twopassMode Basic ‐‐chimOutType WithinBAM SoftClip” and GENCODE gene annotation (version 91). Only uniquely mapped and deduplicated reads were retained and converted into CTSS files using an in-house script. CTSS files were run through CAGEr (version 1.28) for peak calling as previously described . We adjuTo find tissue-specific TE-TSSs supported by nanoCAGE data, the closest distance from tissue-specific TE-TSSs and the nanoCAGE signal was calculated using “bedtools closest” (parameters: “-s -d -t first -a < tissue specific TE-TSSs > -b”). A tolerance window of 100 bp was used to determine TE-TSSs supported by nanoCAGE peaks. The same analysis was repeated at the level of reads using unencoded G reads.https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA799647. All data generated in this study can be visualized in the WashU Epigenome Browser (https://epigenome.wustl.edu/zebrafishENCODE/). All custom scripts used to perform the analysis in this study are available at GitHub (http://github.com/twlab/zebrafish_TE_epignome) and as Supplemental Code.All raw data generated in this study have been submitted to the NCBI BioProject database (


We find that genetic variants located within the central region of enhancers are most likely to alter TF binding and enhancer activity. We observe that the AP-1 family of TFs (Fos/Jun) are frequently required for binding of TEAD TFs and for enhancer function. However, many sequence variants outside of core motifs for AP-1 and TEAD also impact enhancer function, including sequences flanking core TF motifs and AP-1 half sites. Taken together, these data represent one of the most comprehensive assessments of allele-specific TF binding and enhancer function to date and reveal how sequence changes at enhancers alter their function across evolutionary timescales.Sequence variation in enhancers that control cell-type-specific gene transcription contributes significantly to phenotypic variation within human populations. However, it remains difficult to predict precisely the effect of any given sequence variant on enhancer function due to the complexity of DNA sequence motifs that determine transcription factor (TF) binding to enhancers in their native genomic context. Using F There are hundreds of different types of cells in the body. Each one performs a unique role, but they all share the same genes. Sequences of the genetic code called enhancers decide which genes each cell uses. Enhancers work like genetic switches: to turn a gene on, proteins called transcription factors assemble on an enhancer. Each transcription factor recognises a short sequence on the enhancer, and several distinct transcription factors work together to promote the activatation of a gene.The relationship between transcription factors, enhancers, and gene activation is complex. The specific genetic sequences of enhancers differ between species, changing the way these genetic switches work. But scientists are not yet able to reliably predict the effects of small changes in the DNA sequence of an enhancer. One way to tackle this problem is to look at different versions of the same enhancers side by side to see how small mutations change their behaviour.Mammalian cells generally carry two copies of each chromosome (the molecules that contain the genetic code), one inherited from each parent. Each of the two copies carries the same genes and enhancers, but there are many small differences in the DNA sequences of enhancers between the chromosomes inherited from each parent, which can potentially alter their functionYang, Ling et al. generated cells from mice that come from different inbred strains, which are similar to purebred dogs. By breeding two distinct inbred mouse strains together that are very different from one another, they generated a panel of hybrid mouse cell lines that have a relatively large number of differences in their DNA sequence between the maternal and paternal chromosomes.Looking at the different versions of each enhancer side-by-side revealed thousands of single letter changes in the DNA sequence of enhancers that changed how they work. Mutations affecting the binding site of one transcription factor within an enhancer can indirectly affect the binding of other types of transcription factors. Yang, Ling et al. found that if a transcription factor could no longer find its place on an enhancer, it stopped others from binding even if their own places had not changed. Sometimes, mutations on either side of the binding sequences also affected transcription factor binding. This suggests a more complex relationship than previously thought may exist between the DNA sequence of an enhancer and the transcription factors that bind to it.Spotting the differences caused by mutations could help further the efforts of scientists to read and write the genetic code. This could have many benefits. It would allow scientists to control natural or artificial genes, and to predict the effects of genetic changes that are identified in humans with genetic diseases. This might improve genetic experiments, medical screening, gene therapy, and our understanding of evolution. H3K4me1/2 and H3K27ac), and bi-directional transcription of short enhancer RNAs . These c1 hybrids, it is possible to directly compare the activity of two alleles of each enhancer locus within the same cellular environment.Sequence variants that disrupt TF binding can be highly informative for identifying sequences critical for the control of enhancer function in specific cell types . Our labUsing mouse embryonic fibroblasts (MEFs) derived from two distinct inbred strains, we found that the binding of AP-1 TFs is required for chromatin accessibility and activity at many active enhancers in fibroblasts . However1-hybrid MEFs derived from crosses between C57BL/6 J mice and a panel of nine inbred mouse lines, including several wild-derived inbred strains from distinct sub-species of mice that contain a high frequency of SNPs/indels (1 in every ~85–170 bp) compared to C57BL/6 J mice. Using these genetically divergent strains, we examined the frequency and distribution of SNPs/indels at thousands of enhancers with allele-specific chromatin features and/or TF binding patterns. We found that sequence variants within the central ~50 bp of enhancer sequences were most likely to lead to an allele-specific change in enhancer activity. These data also revealed that AP-1 binding is often required for TEAD TF binding to enhancers, whereas TEAD is generally dispensable for AP-1 TF binding. This result is consistent with a model in which AP-1 TFs function as pioneer factors and facilitate binding of additional TFs during enhancer selection in fibroblasts. An analysis of our allele-specific data revealed that sequence features, such as partial AP-1 motifs and nucleotide sequences flanking core AP-1 binding motifs, also contribute to enhancer function. These findings provide new insight into how AP-1 TFs function at enhancers in fibroblasts, and suggest that across other cell types, AP-1 TFs may employ similar collaborative binding mechanisms at enhancers. In addition, our data provide new insight into the crosstalk between Ras/MAPK and Hippo/YAP/TAZ/TEAD signal-dependent gene expression and suggest that Ras/MAPK-induced AP-1 can play an instructive role in determining the output of Hippo/YAP/TAZ/TEAD-dependent transcriptional programs.In the present study, we carried out an extensive allele-specific analysis of chromatin state  and TF binding  in F1-hybrid embryos derived from crosses between C57BL/6 J females and males from nine distinct inbred mouse strains, including four wild-derived inbred strains  that have a high frequency of SNPs/indels compared to C57BL/6 J mice , occupancy of TFs that bind many CREs in fibroblasts (Fos and Tead1), putative insulator elements (CTCF), and gene transcription levels , we only performed CUT&RUN for Fos and H3K27ac (0 by serum starvation and (2) serum-starved MEFs that were re-stimulated with serum for 90 min. These defined conditions reduce technical variability between samples by synchronizing cells in the population at a specific stage of the cell cycle and allow us to measure the binding of TFs that are induced by serum stimulation, such as AP-1 TFs, at the peak of their activity  and active CREs (ATAC-seq summits overlapping H3K27ac peaks)  within +/-60 bp of the ATAC-seq summit used to define each enhancer locus.From each of the two alleles in Fc peaks) . For allc peaks) . In tota1-hybrid lines, 24.4% of pairs of active enhancer alleles on autosomes show a statistically significant difference in H3K27ac levels between maternal (C57BL/6 J) and paternal alleles  and gene-distal binding of CTCF (2.6%) are less likely to exhibit significant differences between alleles . These d% of enhancers in the top decile of allele-specific enhancers have a significant and >2-fold concordant loss of H3K4me1 on the inactive allele, compared to 0.6% of the bottom decile of allele-specific enhancers . Chromatin accessibility and AP-1 binding exhibit similar changes to H3K4me1 at enhancers with strongly allele-specific H3K27ac . However, each enhancer allele is also located within a cis-regulatory unit or topologically associated domain (TAD), which contains additional genetic changes outside of the enhancer itself that could potentially impact TF binding or chromatin state at the enhancer in an allele-specific manner (cis-acting mechanisms could include: (1) sequence variants in other CREs at the same locus that interact with an enhancer in 3D, (2) gains or losses in CTCF-binding sites that influence 3D interactions between CREs within the cis-regulatory unit associated with that enhancer, (3) structural variants that disrupt the organization of the locus such that the enhancer is subject to different 3D interactions, and (4) variation in repeat elements  within the locus that are not generally well annotated in genomic datasets  in the C57BL/6 J genome using the KMAC algorithm  generateKMAC also identified an enriched k-mer  that matches the known core binding motif for the TEAD family of TFs GGAAT;  and inclWe observed a similar enrichment of AP-1 and TEAD k-mers at both constitutively active enhancers and enhancers that control transcription of late-response genes activated by serum stimulation (identified in VTGACTCAB) can affect AP-1 binding at active Fos-bound enhancers. More broadly, there is an enrichment of SNPs in the three nucleotides flanking each side of the core AP-1 binding site when considering all allele-specific Fos-bound sites from our data , we next sought to determine the impact of SNPs within these motifs on AP-1 and TEAD binding at active enhancers using allele-specific TF-binding data . We perfK), and SNPs were enriched at all positions within this motif at allele-specific Tead1-bound enhancers  was restricted to G/T in the KMAC output  and have a SNP/indel that alters this binding motif into a sequence not predicted to bind AP-1 or TEAD based on in vitro binding experiments, respectively. For both classes of TFs, motif-disrupting SNPs are correlated with a marked loss of binding of their cognate TFs, as expected, but this incomplete loss (in aggregate) also suggests that SNPs within core motifs alone are not completely predictive of changes in AP-1 or TEAD binding . Loss ofAlthough SNPs that disrupt core TF-binding motifs  are enriched at enhancers with allele-specific TF binding, our data also indicate that SNPs in these motifs are not sufficient to explain all instances of functional variation between enhancer alleles. For example, among all enhancers in the top decile of allele-specific H3K27ac signal, only 13.3% had a SNP/indel overlapping a core AP-1, TEAD, and/or ETS motif in their central region. In contrast, we found that 21.5% of allele-specific CTCF binding sites  that contain a CTCF motif (identified using position weight matrix match) had at least one SNP/indel overlapping the CTCF binding site. These data favor a model in which other types of SNPs outside core TF-binding motifs can collectively modulate enhancer activity.1-hybrid lines, we could more systematically examine a larger number of loci to define the functional relationship between AP-1 and TEAD.In previous work, we found that strain-specific instances of AP-1 TF binding in MEFs (in a comparison of two inbred mouse strains) that lack a mutation in a core AP-1 site were enriched for SNPs in TEAD motifs, suggesting a model in which AP-1 binding was dependent, at least in part, on the presence of TEAD binding sites . HoweverWe first quantified how often consensus TF motifs are mutated at allele-specific versus shared AP-1 and TEAD peaks. If the binding of a given TF was entirely dependent on the occupancy of another TF, we would expect to observe a similar loss in binding of the dependent TF, regardless of which TF motif was mutated. For these analyses, we included all distal Fos and Tead1 peaks in our dataset, including those that do not co-occur with H3K27ac. We observed that AP-1 motif mutations are frequently associated with a loss of TEAD binding, whereas AP-1 binding is more weakly affected by TEAD motif mutations . StrikinData from in vitro studies examining TF-binding specificity have shown that TFs that bind to composite motifs often prefer sequences that are distinct from their consensus individual motifs . This leThus far, our data suggest that many instances of AP-1 and TEAD binding cannot be explained solely by mutations in consensus, core motifs for these TFs. This lack of enriched TF motif mutations has been observed for other classes of TFs and in a variety of model systems, suggesting that this is a general, unresolved problem in genetic studies of TF binding . Our datFirst, we identified allele-specific gene-distal binding sites for Fos, Tead1, and CTCF, and then examined the frequency of SNPs/indels (relative to the ATAC-seq peak center) at these sites compared to sites with shared binding on both alleles. In Next, given that AP-1 motif SNPs likely contribute to the distribution observed in , when we plotted the distribution of SNPs/indels at allele-specific and shared CTCF peaks, we observed a narrow enrichment of SNPs (within +/- 10 bp) relative to the CTCF motif at allele-specific compared to shared peaks  to our datasets that has been optimized to detect k-mers of similar length to typical TF-binding motifs . SupportWe first compared 60 bp of DNA sequences from AP-1 peaks in C57BL/6 J MEFs (positive set) to GC- and length-matched, randomly sampled background DNA sequences from the C57BL/6 J genome (negative set). The area under the receiver operating characteristic curve (AUROC = 0.872) from this gkm-SVM analysis is highly similar to the corresponding value obtained from a control analysis of CTCF peaks from human cells, suggesting that the gkm-SVM is able to classify Fos-bound and unbound regions with a low rate of detecting false positives while correctly assigning true negatives. Similarly, a relatively high value for the area under the precision-recall curve (AUPRC = 0.881) indicates that the gkm-SVM is able to reliably distinguish true and false positives. Together, these results suggest that the information within the central 60 bp sequences (+/-30 bp relative to the ATAC-seq summit) at Fos-bound peaks is sufficient to train a model to reliably distinguish Fos-bound sites from control non-coding regions of the same genome . InclusiInterestingly, k-mers containing AP-1 sites also contributed the most to model performance when gkm-SVM was applied to Tead1-bound sites , consistWe next sought to apply this gkm-SVM approach to attempt to identify k-mers that distinguish between AP-1 binding sites with and without H3K27ac. Our data indicate that the AP-1 binding is required for the function of many of the active enhancers at which they bind in MEFs. However, AP-1 binding alone is clearly not sufficient for enhancer activity. For example, 34.9% of gene-distal Fos peaks do not overlap H3K27ac peaks. This suggests that the sequence features that are permissive for AP-1 binding in MEFs might be separable from those that confer activity. For this gkm-SVM analysis, we selected a curated set of Fos-bound allele pairs  that (1) have equivalent levels of Fos binding, (2) contain a consensus AP-1 site on both alleles, but (3) exhibit allele-specific H3K27ac levels. We input 60 bp DNA sequences (centered on the shared AP-1 consensus motif) from the active (positive set) and inactive (negative set) alleles at Fos-bound enhancers . In contHaving defined some features of sequences that determine AP-1 binding to CREs in MEFs, we next extend our analyses to data derived from a larger number of other cell types. To do this, we used DNase-seq footprinting data generated from a large panel of human tissues and cell types . These d6 total footprinted regions) that contain VTGACTCAB motifs. These AP-1 footprints were centrally enriched within CREs  within CREs, which we interpret as individual instances of AP-1 binding. We found a total of 164,705 TF footprints  and multiple chromatin features  in up to ten distinct alleles for each CRE locus. By assessing the frequency and distribution of genetic variants at large numbers of CREs with shared or allele-specific TF binding and/or cis-regulatory features compared to enhancers that have H3K27ac enrichment because they represent enhancers that were active in a previous developmental stage characterized by a distinct complement of TFs expressed.We find from our analysis of enhancer alleles with different H3K27ac levels that loss of the active enhancer histone modification H3K27ac is generally not genetically separable from loss of H3K4me1/2. This is interesting to consider given a number of previous observations about the relative contribution of these histone modifications to enhancer function: (1) we previously found that enhancers that regulate late-response genes exhibit H3K4me1 enrichment in serum-starved MEFs, but have low chromatin accessibility and lack H3K27ac, and upon serum-stimulation, gain H3K27 acetylation and inducibly bind AP-1 TFs , (2) reccis-regulatory activity, such that loss of any individual single enhancer is often insufficient to cause a large change in gene expression and/or result in an organismal phenotype  (Stock No. 000664) for all breeding pairs. Four- to 8-week-old male mice from the following strains were also obtained from Jackson Labs: CAST/EiJ (Stock No. 000928), MOLF/EiJ (Stock No. 000550), PWK/PhJ (Stock No. 003715), SPRET/EiJ (Stock No. 001146), 129S1/SvImJ (Stock No. 002448), A/J (Stock No. 000646), BALB/cJ (Stock No. 000651), DBA/2 J (Stock No. 000671), NOD/ShiLtJ (Stock No. 001976). No new mouse strains were generated in this study.The study of inbred mice that are more genetically divergent from C57BL/6 J in combination with the use of longer sequencing reads increases the proportion of informative allele-specific reads. However, higher frequencies of SNPs/indels per strain results in a greater percentage of CREs with multiple genetic variants, making it difficult to assign which specific SNP/indel is likely responsible for observed changes in TF binding or chromatin state. Therefore, to balance these considerations, we included wild-derived inbred strains with a relatively high frequency of SNPs/indels compared to C57BL/6 J mice (1 SNP/indel per ~85–170 bp), as well as more commonly used inbred strains that are less genetically divergent from C57BL/6 J mice  12430062) with 10% CCS  SH3008704), Penicillin-Streptomycin (Thermo Fisher 15140148), MEM non-essential amino acids (Thermo Fisher 11140050), and 1 mM sodium pyruvate (Thermo Fisher 11360070). Trypsin was quenched with 10 mL complete media, and cells were rapidly triturated up/down 10 times with a 10 mL serological pipette to generate a single-cell suspension. An additional 10 mL complete media was added per plate, and cells were grown at 37 °C in 5% CO2.Embryos were harvested on embryonic day 13.5–14.5 and washed in room-temperature PBS. The heads and internal organs were removed, and the dissected tissue was re-washed in PBS. Individual embryos were placed at the center of 15 cm plates and incubated for 45 min in 1 mL trypsin-EDTA 0.25% (Life Technologies 25200072). Excess trypsin was carefully aspirated, and the dissected tissue was manually dissociated with scissors for ~1 min. Dissociated cells were then incubated in ~1 mL trypsin-EDTA at 37 °C in 5% CO2 for long-term storage.When cells became fully confluent in ~2–3 days, MEFs were washed in PBS and trypsinized in 3 mL trypsin-EDTA. A small aliquot of cells from each embryo were frozen for genotyping (see below). Cells were pelleted by spinning at 300 g and expanded onto five 15 cm plates with 20 mL complete media per plate. When fully confluent once again, MEFs were trypsinized and frozen down in freeze media  in aliquots of 1 plate per cryogenic vial. Cells were placed at –80 °C for ~24 hr in a cell freezing container and then transferred to liquid NCTTCWTCGACTTYCAGACCCAAGGCAT-3’ (Myco2(cb)) with 5’-ACACCATGGGAGYTGGTAAT-3’ (Myco11(cb)) and 5’-GGTGTGGGTGAGTTATTACAAARTCAATT-3’ (Myco5(cb)) with 5’-GGAGTGAGTGGATCCATAAATTGTGA-3’ (Myco6(cb)). Genotyping for the sex of each MEF line was performed with the following primer pair: 5’-CTGAAGCTTTTGGCTTTGAG-3’ with 5’-CCACTGCCAAATTCTTTG-3’. A single 340 bp product was expected for female cells, and an additional 310 bp product was present in male cells.For genotyping, cells were processed with the DNeasy Blood and Tissue kit  69506). All MEF lines were tested for mycoplasm contamination with the following primer pairs: 5’-We generated an in-house antibody against the full-length mouse protein for c-Fos (NCBI Reference Sequence: NP_034364.1). Briefly, we purified GST-c-Fos-His as detailed in Cells were thawed onto one 15 cm plate per MEF line and grown in complete media until fully confluent. For ChIP-seq and Hi-ChIP experiments, MEFs were split onto five 15 cm plates and grown in complete media until ~70–80% confluent. Cells were washed in 10 mL room-temperature PBS and switched into 20 mL warmed starve media . After 26+hours in starve media, samples to be serum stimulated were incubated with 20 mL warmed stimulation media  for 0, 10, or 90 min.5 cells per well in 2 mL warmed starve media. Cells were grown for 26+hours, and appropriate wells were serum stimulated with 2 mL warmed stimulation media.For ATAC-seq, RNA-seq, and CUT&RUN experiments, MEFs were thawed as above and were split into 6-well dishes at a concentration of 5x10Media was aspirated from MEFs, and 2 mL or 15 mL crosslinking buffer  with 1% formaldehyde was added for six-well or 15 cm dishes, respectively. Cells were crosslinked by shaking gently for 10 min at room temperature. Crosslinking was quenched by adding glycine to a final concentration of 125 mM and incubating for 5 min at room temperature while shaking. Cells were washed once in 2 mL or 15 mL PBS for six-well or 15 cm dishes, respectively. Cells were scraped and collected in 1 mL or 5 mL cold PBS for six-well or 15 cm dishes, respectively, and pelleted by spinning at 1000 g for 5 min at 4 °C.2, 0.1% NP-40 0.1% Tween 20, 0.01% digitonin) and incubated for 3 min on ice. Lysed cells were washed once in 1 mL ATAC wash buffer  by gently inverting the tube 3 times and pelleted by spinning at 500 g for 10 min at 4 °C. Pelleted nuclei were resuspended in 50 μL transposition mix  20034197), 2.5 μL TDE1 transposase (Illumina 20034197), 0.5 μL 10% Tween 20, 0.5% 1% digitonin, 16.5 μL PBS, 5 μL NF-H2O) and incubated for 30 min at 37 °C with a Thermomixer set to 1,000 rpm. Samples were purified with MinElute PCR Purification Kit (QIAGEN 28004) per manufacturer’s instructions and eluted in 13 μL NF-H2O. Libraries were amplified by adding the following to 10 μL purified DNA: 2.5 μL 25 μM Ad1 universal primer, 2.5 μL 25 μM Ad2.* indexing primer, 25 μL NEBNext Hi-Fi 2 x PCR Master Mix  M0541S), 10 μL NF-H2O. After an initial 5 PCR cycles, libraries were quantified by qPCR by adding the following to 5 μL partially amplified DNA: 0.5 μL 25 uM Ad1 universal primer, 0.5 μL 25 μM Ad2.* indexing primer, 5 μL NEBNext Hi-Fi 2 x PCR Master Mix, 0.15 μL 1 x SYBR Green I (Thermo Fisher S7563), 3.85 μL NF-H2O. All primer sequences referenced are described in MEFs from a 6-well dish were washed twice in 1 mL cold PBS and pelleted each time by spinning at 300 g for 5 min at 4 °C. A total of 50,000 MEFs were resuspended in 50 μL cold ATAC lysis buffer  per 15 cm dish starting material and rotated for 10 min at 4 °C to lyse cells. Nuclei were pelleted by spinning at 1350 g for 5 min at 4 °C and resuspended in 1 mL L2 buffer  per 15 cm dish starting material and rotated for 10 min at room temperature. Nuclei were pelleted by spinning at 1350 g for 5 min at 4 °C and resuspended in 300 μL LB3 buffer  per 15 cm dish starting material. Chromatin was sonicated with a Bioruptor Plus ) on ‘high’ power setting with an ‘on’ interval of 30 s and ‘off’ interval of 45 s for 36 cycles). DNA concentration was determined by taking 100 μL aliquot of sonicated chromatin, decrosslinking at 95 °C for 15 min, and purifying with QIAquick PCR Purification Kit (QIAGEN 28104) and quantifying by Nanodrop. One μg of purified chromatin in 10% glycerol was run on a 2% agarose gel and stained with ethidium bromide for 30 min to validate fragment size . The remainder of the sonicated chromatin was transferred to 1.5 mL tubes and centrifuged at 16,000 g for 10 min at 4 °C to pellet insoluble debris. Triton X-100 was added to soluble chromatin to a final 1% concentration. Protein A Dynabeads (Thermo Fisher 10008D) were washed twice in 1 mL cold block solution (0.5% BSA (w/v), 1% Triton X-100, diluted in LB3 buffer). For coupling antibodies to beads, 15 μL bead slurry per IP were resuspended in 1.5 mL cold block solution, and the appropriate amount of antibody  ab4729), 0.5 μg for anti-H3K4me1 (Abcam ab8895), 0.5 μg for anti-H3K4me2 (Abcam ab7766), 0.5 μg for anti-H3K4me3 (Abcam ab8580), 2 μg for anti-Fos  sc-7202X), 2 μg for anti-Tead1 (Abcam ab133533), 2 μg for anti-CTCF  61312), and 2 μg for anti-JunD (Santa Cruz Biotechnology sc74)) was added before rotating beads for >2 hr at 4 °C. For pre-clearing chromatin, 15 μL bead slurry was added to appropriate amount of chromatin , and additional cold LB3 buffer with 1% Triton X-100 was added such that all samples had a final volume of 1.5 mL before rotating samples for >2 hr at 4 °C. Pre-cleared chromatin was added to antibody-coupled beads, and additional cold LB3 buffer with 1% Triton X-100 was added such that all samples had a final volume of 1.8 mL before rotating samples overnight at 4 °C. 50 μL of pre-cleared chromatin was stored at –20 °C for making input libraries. For all wash steps listed below, samples were rinsed with 1 mL cold wash buffer and rotated for 5 min at 4 °C before separating beads with a magnet and discarding supernatant. Samples were washed sequentially twice in low salt buffer , twice in high salt buffer , twice in LiCl buffer , and once in TE buffer . Samples were eluted from beads by addition of 200 μL TE buffer with 1% SDS and incubating at 65 °C for 30 min, with brief vortexing every 10 min to mix. IP samples were placed on magnet, and supernatant was transferred to new tubes. Input samples were also thawed, and 150 μL TE buffer with 1% SDS was added. Both IP and input samples were decrosslinked by incubating at 65 °C overnight. 10 μg RNase A  (R6513) was added and samples were incubated at 37 °C for 1 hr to digest RNA. 7 μL 20 μg/μL proteinase K (New England Biolabs P8107) was added, and samples were incubated at 55 °C for 2 hr to digest protein. DNA was extracted with 1 volume of 25:24:1 phenol-chloroform-isoamyl alcohol and purified with QIAquick PCR Purification Kit (QIAGEN 28104). Libraries were prepared with the Ovation Ultralow V2 DNA-Seq Library Preparation Kit  0344NB-32) per manufacturer’s instructions. Libraries were sequenced on an Illumina NextSeq 500 with 150 bp paired-end reads.2, 1 mM DTT, 0.1% Triton X-100, Roche Protease Inhibitor Cocktail  11873580001)). Cells were permeabilized to isolate nuclei by rotating for 10 min at 4 °C. Nuclei were pelleted by spinning at 500 g for 5 min at 4 °C and resuspended in 1 mL cold CUT&RUN wash buffer . Ten μL concanavalin-coated bead slurry  BP531) per sample was washed twice in 1.5 mL CUT&RUN binding buffer  and resuspended in a final volume of 10 μL CUT&RUN binding buffer per sample. After adding 10 μL bead slurry to each sample, tubes were inverted 10 times and incubated for 10 min at room temperature to bind nuclei. Beads were separated from wash buffer by placing on magnet for >30 s and were resuspended in 50 μL antibody buffer . Antibodies , or rabbit IgG  2729 S)) were added at 1:50 dilution, and samples were incubated overnight at 4 °C. Beads were washed once in 1 mL Triton-wash buffer  and resuspended in 50 μL antibody buffer. Protein-A MNase , 2 pg/mL yeast spike-in DNA (provided by S. Henikoff)) was added, and samples were incubated for 20 min at 37 °C to release CUT&RUN fragments from nuclei. Samples were placed on magnet, and supernatant was transferred to a new tube and added to 2 μL 10% SDS and 2 μL 20 mg/mL proteinase K (New England Biolabs P8107). Samples were incubated overnight at 65 °C to reverse crosslinks. DNA was extracted with 1 volume of 25:24:1 phenol-chloroform-isoamyl alcohol and precipitated in 2.5 volumes of 100% ethanol with 2 μL glycogen (Sigma-Aldrich 10901393001). Pellet was washed once in 1 mL 100% ethanol and dissolved in 40 μL 10 mM Tris-HCl pH 8.5. Libraries were prepared as described in Crosslinked MEFs per protocol above from six-well dish were washed once in 2 mL PBS and collected in 1 mL cold NE1 buffer . Tubes were gently vortexed twice for 2 sec, incubated for 1 min on ice, and centrifuged at 18,000 g for 2 min at 4 °C. These steps were repeated once more to achieve higher purity in the chromatin fraction. The remaining chromatin pellet was resuspended in 50 μL cold PBS and vortexed briefly. 500 μL of TRIzol (Thermo Fisher 15596026) was added to the pellet and vortexed for several minutes until fully resuspended. Chromatin-associated RNA was isolated with RNeasy Mini Kit (QIAGEN 74104) per manufacturer’s instructions, and libraries were generated from 250 ng starting material with NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs E7765). Libraries were sequenced on an Illumina NextSeq 500 with 150 bp paired-end reads.MEFs from 15 cm dish were washed once in 15 mL cold PBS and pelleted by spinning at 300 g for 5 min at 4 °C. Cell pellet was resuspended in 200 μL cold cytoplasmic lysis buffer  and rotated for 5 min at 4 °C. Lysate was layered on top of 500 μL cold sucrose buffer ) and centrifuged at 16,000 g for 10 min at 4 °C. These steps were repeated once more to achieve higher purity in the nucleoplasmic fraction. Pelleted nuclei were resuspended in 200 μL glycerol buffer , and an equal volume of cold nuclear lysis buffer was added  and extended by 150 bp with bedtools slop -l 0 r 150. All samples were normalized to a depth of 10 million reads, and read coverage was calculated by bedtools genomeCoverageBed. The output.bedgraph file was then converted with UCSC’s bedGraphtobigWig utility, and all tracks displayed were visualized with the UCSC Genome Browser (GRCm38/mm10).--nomodel--extsize 200. Peaks were also called using spp  of total reads were generated by samtools view -h -b -s 1.5 and samtools view -h -b -s 2.5. Peaks were called from pooled reads and from two psuedoreplicates using macs2  with thesing spp  with -npsing spp  by runnising spp . We then1-hybrid line were inputted separately into macs2 and spp. CUT&RUN peaks were then intersected with all ATAC summits detected across all genotypes and were recentered on the summit of ATAC-seq signal. This was important to do because peak calling algorithms that we used would often identify multiple histone modification peaks for individual CREs due to the non-continuous enrichment in signal. This also enabled us to generate uniform windows centered around ATAC-seq summits to consistently quantify signal for CUT&RUN data across different ATAC-seq summits. Peaks from both the C57BL/6 J and pseudogenome were concatenated, and only peaks with at least one SNP/indel within +/-60 bp of the ATAC summit were retained for allele-specific analysis (as highly “mappable” sites). To determine whether the CUT&RUN signal is significantly skewed towards one allele, we used HOMER  from MNase by chromatin-associated protein binding were identified from Fos CUT&RUN experiments performed in serum-stimulated C57BL/6 J MEFs. Peak calling, motif identification, and footprinting analysis were performed using CUT&RUNtools  with defPeak calling was performed as detailed above for CUT&RUN data, except we considered all ChIP-seq peaks that overlapped ATAC-seq summits. All experiments  were performed in serum-starved and restimulated MEFs (90 min) and peaks from these datasets were analyzed separately and subsequently combined for downstream analyses, with the exception of experiments that directly queried enriched motifs at stimulus-responsive enhancers . We mergH3K27ac Hi-ChIP reads were aligned with HiC-Pro  using an--outSAMattributes NH HI AS nM vG vA --waspOutputMode SAMtag. Genome-specific reads were extracted and converted into.bam format with samtools view -h -b -F 3844 -q 10. The featureCounts command from Subread  on the active allele matched that of the allele-specific CREs. Bigwig files with phastCons scores for 60 vertebrate species for each mouse mm10 chromosome were obtained from UCSC. For each CRE, we computed a phastCons score for a 150 bp window centered on the ATAC-seq summit using the bigWigAverageOverBed script from UCSC Tools (Version 3.6.3).--k_seqs [n]) and enriched k-mer clusters are determined relative to random GC-matched control regions of equal length from the C57BL/6 J genome (using --gc –1) with the following additional options: --k_min 5 k_max10 --k_top 10.Nucleotide sequences present at the central 60 bp of enhancers were extracted using bedtools getfasta . These.f1-hybrid lines. For performing the active versus inactive Fos-bound site comparison, we used the gkm-SVM package developed by Dr. Michael Beer’s lab  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Detlef Weigel as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Characterization of sequence determinants of enhancer function using natural genetic variation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:We all thought the data are quite strong. We have included the full review from both reviewers below and have summarized the essential three items for revision here:1) The authors claim "Our findings provide new insight into how enhancer function is encoded within DNA sequences…" but it was hard to see the new insights that were provided. We suggest hardening up the abstract to clarify precisely what the authors have discovered, and considering reorganizing the text to consolidate and focus the introductory material on what is relevant to these key claims.2) The overall data set is strong, but they are not utilized to the fullest extent for analyses. For example, no population genetics data were considered. Can the authors examine allele frequency and effect size relationships? Basic population genetics tells us that rare SNPs are more likely to be deleterious. So, in situations where C57BL/6J has the rare allele -- is accessibility and TF binding more likely to be reduced? There are several analyses that could address this general question.3) Data sharing through GEO must be complete.Reviewer #1 (Recommendations for the authors):The authors claim "Our findings provide new insight into how enhancer function is encoded within DNA sequences…" but I had trouble seeing the new insights that were provided here.Several claims were made about enhancer priming, selection, and other chromatin kinetics. But to really understand this, I think one needs time series data, which is not part of the present study.In the Allele-specific CUT&RUN peak calling section, what does it mean to recenter a CUT&RUN peak onto an ATAC-seq summit? Are you shifting the coordinates of the CUT&RUN peak so the middle occurs where the ATAC summit is located? Does this make sense to do? Given that a modified histone should not occur at the same place as an open chromatin summit, I'm not sure why this was done. Perhaps there are missing details that should be here.It looks like there are issues with the GEO upload and these should be resolved before the manuscript is finalized.The abstract claim of "impact of sequence variation on enhancer function" seems a little broad.Sometimes, the figures should be labeled more clearly. For example, Figure 6A-B has two different line colors but these are not explained with a color key in the figure.Figure 2H-J and every panel in Figure 5 have typos on the x-axis. -100 is incorrectly plotted as -10.The acronym CRE is used extensively throughout the paper, beginning in the second sentence of the introduction, but is never defined.Reviewer #2 (Recommendations for the authors):1) The abstract is pretty general and did not help me figure out what the main claims of the paper were. I had difficulty triangulating the claim I found most interesting with its description in the narrative and the data in the figures. For example, the abstract claims "our data…reveal a hierarchical relationship between AP-1 and TEAD TF binding at enhancers" (lines 38-39), which is too vague. The relevant section begins on page 21, but includes two paragraphs of introduction before arriving at the new claim. I would suggest hardening up the abstract to clarify precisely what the authors have discovered, and considering reorganizing the text to consolidate and focus the introductory material on what is relevant to these key claims.2) I found the use of the term "k-mer" confusing. It appears that the authors use this as shorthand for "TF recognition sequence" or "motif match", but sometimes as meaning TF consensus/motif (e.g. the AP1 consensus in line 666). The gk-SVM uses another definition, where k-mers are used as SVM features. I eventually gathered that the first use follows from the KMAC  tool. I would suggest clarifying this terminology to refer more generally to TF recognition sequences where appropriate.3) The manuscript would benefit from a supplemental table describing the details of the sequencing data generated, including basic experimental metadata, sequencing QC statistics , and identification of replicates.4) The authors say that the data have been submitted to GEO but the accession was not available upon submission. The GEO record would need to be verified as providing the sequencing data underlying the manuscript in a well-organized fashion.5) Several figures include data and trend lines which are not clearly described in the legends .6) The authors mention that experiments were performed in both serum-starved and stimulated cells. There are occasional references to differences between these conditions , but it appears the authors generally decided to lump data from both conditions together. The authors should provide an assessment of the similarity of the two conditions, and clearly describe and justify in the narrative how they handled this variable. Essential revisions:We all thought the data are quite strong. We have included the full review from both reviewers below and have summarized the essential three items for revision here:1) The authors claim "Our findings provide new insight into how enhancer function is encoded within DNA sequences…" but it was hard to see the new insights that were provided. We suggest hardening up the abstract to clarify precisely what the authors have discovered, and considering reorganizing the text to consolidate and focus the introductory material on what is relevant to these key claims.Based on the feedback from the reviewers, we have modified the abstract to better highlight the new findings in the manuscript. We have also extensively edited the introduction of the revised manuscript so that it is more focused.2) The overall data set is strong, but they are not utilized to the fullest extent for analyses. For example, no population genetics data were considered. Can the authors examine allele frequency and effect size relationships? Basic population genetics tells us that rare SNPs are more likely to be deleterious. So, in situations where C57BL/6J has the rare allele -- is accessibility and TF binding more likely to be reduced? There are several analyses that could address this general question.1-hybrid cells were generated by crossing inbred mouse lines to C57BL/6J mice. Each inbred strain was originally derived from a relatively small number of mice from wild populations that were subsequently inbred for many generations. Thus, each inbred strain is homozygous for most alleles across the genome, and as a result is only representative of a small fraction of the diversity of alleles present in the natural population from which these mice were first caught. The allele frequency across each of these inbred strains is thus not indicative of their frequency in natural populations. In the future, it would be interesting to examine this question in a more appropriate context, such as commercially available outbred mouse strains. This is mentioned in the revised discussion (Lines 740-745).It would be interesting to examine the relationship between allele frequency and effect size relationships, however, we do not think that our experimental design is appropriate for this analysis because all of our F3) Data sharing through GEO must be complete.We thank the reviewers for reminding us about this critical issue. All of the data is now uploaded and publicly available via the following accession number: GSE193728. The GEO accession number has also been added to the revised manuscript (Lines 1313-1314).Reviewer #1 (Recommendations for the authors):The authors claim "Our findings provide new insight into how enhancer function is encoded within DNA sequences…" but I had trouble seeing the new insights that were provided here.Several claims were made about enhancer priming, selection, and other chromatin kinetics. But to really understand this, I think one needs time series data, which is not part of the present study.We have removed the discussion of chromatin kinetics at enhancers from the revised manuscript.In the Allele-specific CUT&RUN peak calling section, what does it mean to recenter a CUT&RUN peak onto an ATAC-seq summit? Are you shifting the coordinates of the CUT&RUN peak so the middle occurs where the ATAC summit is located? Does this make sense to do? Given that a modified histone should not occur at the same place as an open chromatin summit, I'm not sure why this was done. Perhaps there are missing details that should be here.We apologize for not explaining this in sufficient detail. In the Methods section of the revised manuscript, we have included a more detailed description of how we uniformly called ATAC-seq peaks across different genotypes and our rationale for doing so, reproduced here (Lines 1126-1131):et al., 2010). We then used the HOMER function getPeakTags with the -center option to generate single bp coordinates with maximal ATAC-seq signal (which we refer to in our manuscript as ATAC-seq summits).”“Since peaks called across samples from different genotypes can vary somewhat in their specific coordinates, we generated a total universe of possible ATAC-seq peaks by combining all sequencing reads into a single tag directory in HOMER :1-hybrid line were inputted separately into macs2 and spp. CUT&RUN peaks were then intersected with all ATAC summits detected across all genotypes and were recentered on the summit of ATAC-seq signal. This was important to do because peak calling algorithms that we used would often identify multiple histone modification peaks for individual CREs due to the non-continuous enrichment in signal. This also enabled us to generate uniform windows centered around ATAC-seq summits to consistently quantify signal for CUT&RUN data across different ATAC-seq summits.”“Peak calling was performed as detailed above for ATAC-seq data, except reads mapping to the C57BL/6J and corresponding pseudogenome for each FIt looks like there are issues with the GEO upload and these should be resolved before the manuscript is finalized.Since the initial submission, we have uploaded all of our raw and processed data, and these data are now publicly available via the following accession number: GSE193728. This accession number has been added to the revised manuscript as well (Lines 1313-1314).The abstract claim of "impact of sequence variation on enhancer function" seems a little broad.We have replaced that sentence with a more specific statement:“Taken together, these data represent one of the most comprehensive assessments of allele-specific TF binding and enhancer function to date and reveal how sequence changes at enhancers alter their function across evolutionary timescales.”Sometimes, the figures should be labeled more clearly. For example, Figure 6A-B has two different line colors but these are not explained with a color key in the figure.We have modified the figures to include further description of these important details, as suggested. We added a color key in Figure 6A-B now to indicate that the black and green traces represent the area under curve values for Fos peaks in the C57BL/6J genome with and without masking core AP-1 sites, respectively.Figure 2H-J and every panel in Figure 5 have typos on the x-axis. -100 is incorrectly plotted as -10.We have corrected this mistake in the revised manuscript.The acronym CRE is used extensively throughout the paper, beginning in the second sentence of the introduction, but is never defined.In the revised manuscript, we now define cis-regulatory elements (CREs) in Lines 49-54 and 176-178 of the manuscript.Reviewer #2 (Recommendations for the authors):1) The abstract is pretty general and did not help me figure out what the main claims of the paper were. I had difficulty triangulating the claim I found most interesting with its description in the narrative and the data in the figures. For example, the abstract claims "our data…reveal a hierarchical relationship between AP-1 and TEAD TF binding at enhancers" (lines 38-39), which is too vague. The relevant section begins on page 21, but includes two paragraphs of introduction before arriving at the new claim. I would suggest hardening up the abstract to clarify precisely what the authors have discovered, and considering reorganizing the text to consolidate and focus the introductory material on what is relevant to these key claims.See response to Essential revisions.2) I found the use of the term "k-mer" confusing. It appears that the authors use this as shorthand for "TF recognition sequence" or "motif match", but sometimes as meaning TF consensus/motif (e.g. the AP1 consensus in line 666). The gk-SVM uses another definition, where k-mers are used as SVM features. I eventually gathered that the first use follows from the KMAC  tool. I would suggest clarifying this terminology to refer more generally to TF recognition sequences where appropriate.We have clarified our definition of k-mers in the revised manuscript (Lines 366-370). When referring to specific TF binding motifs, we have replaced k-mer with “motif”.et al., 2018). This is distinct from the more commonly used position weight matrices (PWM) representation, because k-mers explicitly account for the occurrence of particular nucleotide identities at each position, whereas PWM representations of motifs indicate the probability that a given nucleotide will be observed at a given position. This is indicated now in the text in Lines 378-383.The output of the KMAC algorithm is also “k-mer based motif representation”, which are nucleotide sequences of variable length (k) that are enriched within CREs. k-mer based motif representation is an alternative method for representing TF binding motifs, which preserves positional dependencies between nucleotides  identified as predictive by the gkmSVM model. We have modified the text in this section to clarify this point (Lines 618-620).3) The manuscript would benefit from a supplemental table describing the details of the sequencing data generated, including basic experimental metadata, sequencing QC statistics , and identification of replicates.In the revised manuscript, we now include a supplementary table (Table 6) that includes the details of all of the sequencing data generated for this study (cited on Line 370 of the revised manuscript).4) The authors say that the data have been submitted to GEO but the accession was not available upon submission. The GEO record would need to be verified as providing the sequencing data underlying the manuscript in a well-organized fashion.Since the initial submission, we have uploaded all of our raw and processed data, and this should be viewable with the following accession number: GSE193728. This accession number has been added to the revised manuscript as well (Lines 13131314).5) Several figures include data and trend lines which are not clearly described in the legends .We have modified the figure legends to include complete description of these features (Lines 1253-1258 and 1445-1447).6) The authors mention that experiments were performed in both serum-starved and stimulated cells. There are occasional references to differences between these conditions , but it appears the authors generally decided to lump data from both conditions together. The authors should provide an assessment of the similarity of the two conditions, and clearly describe and justify in the narrative how they handled this variable.We have added a subsection in our Methods  to detail how we handled data from different timepoints and our reasoning for when we would compile data between serum-starved and stimulated MEFs (Lines 1194-1206).et al., 2017), in which we noted that the vast majority of active MEF enhancers (~83%) exhibited similar levels of H3K27ac enrichment at 0, 10, or 90 min of serum stimulation.In general, all experiments were performed in both serum-starved (0 min) and restimulated MEFs (90 min), with the exception of our CTCF binding dataset that was generated in asynchronous, proliferating MEFs. For all analyses, we defined active enhancers and promoters based on the presence of an ATAC-seq peak with H3K27ac enrichment at either the 0 min or 90 min timepoint. This decision was informed by data from a previous publication . The only analysis where we separately considered the set of enhancers that inducibly gained H3K27ac upon 90 min of serum stimulation was the KMAC search we performed to identify enriched binding motifs at late-response gene enhancers .Moreover, for the TF-binding data, we only considered FOS peaks present after 90 min of serum stimulation because Fos expression is induced by serum stimulation, and Fos protein is undetectable in serum-starved MEFs (0m). On the other hand, TEAD1 peaks in our dataset were defined based on an enrichment of signal at either 0 or 90 min, since it expressed robustly at both timepoints.


The promoter activities of the A5 strain were 1.98-fold and 2.58-fold less active than that of the A17 and A23 strains, and the promoter activities of A12 strains were 1.955 and 1.5 times lower than the promoter activity of A23 and A17 strains, respectively. Furthermore, the promoter activity of A17 strains was 1.3 lower than the promoter activity of A23 strains. Our findings suggest that subtype-specific genetic diversity, mainly in the transcription factor’s binding sites, has an impact on their transcriptional activity, producing a distinct activity pattern for the subtypes. This study provides new information that is important for better understanding the function of the SRLV LTR. However, further research including more strains and subtypes as well as other cell lines is needed to confirm these findings.A long terminal repeat (LTR) plays an indispensable role in small ruminant lentivirus (SRLV) gene expression. In this study, we present the LTR sequence of Polish SRLVs representing different subtypes, and analyzed their impact on SRLV promoter activity, as measured in transient transfection assays. Although certain nucleotide motifs (AML(vis), TATA box and the polyadenylation site (AATAAA)) were conserved across sequences, numerous mutations within the LTR sequences have been identified. Single nucleotide polymorphisms (SNPs) were detected in both regulatory  and non-regulatory sequences, and subtype-specific genetic diversity in the LTR region of Polish SRLVs was observed. In vitro assays demonstrated subtype-specific functional differences between the LTR regions of distinct SRLV subtypes. Our results revealed that the promoter activity of Polish strains was lower (1.64–10.8-fold) than that noted for the K1514 reference strain; however, the differences in most cases were not statistically significant. The lowest promoter activity was observed for strains representing subtype A5 (mean 69.067) while the highest promoter activity was observed for strain K1514 representing subtype A1 (mean 373.48). The mean LTR activities of strains representing subtypes A12, A17, A23, A18 and A24 were 91.22, 137.21, 178.41, 187.05 and 236.836, respectively. The results of the inter-subtype difference analysis showed that the promoter activity of strains belonging to subtype A5 was significantly lower than that for subtype A12 strains (1.32-fold;  Lentivirus genus of the Retroviridae family, which infect sheep and goats globally. SRLVs are monocyte/macrophage tropic viruses, and viral replication is restricted until the maturation of monocytes to macrophages [Small ruminant lentiviruses (SRLVs), which include the maedi-visna virus (MVV) and caprine arthritis encephalitis virus (CAEV), are a heterogeneous group of viruses belonging to the rophages . Transmirophages ,3. SRLVsrophages ,5,6,7.gag), envelope proteins (env) and enzymes (pol), as well as accessory proteins. The cells may either remain latently infected with little or no expression of the virus or be productively infected. In the latter cells, cellular transcription factors activate the transcription of the virus, which leads to viral gene expressions by interacting with specific DNA sequences. LTR is known to play a key role in transcriptional activation, virus integration and RNA polyadenylation. LTR consists of three regions, U3, R and U5, wherein U3 contains viral promoters and enhancer elements that interact with host proteins to form a complex network to regulate viral gene expression. In this region, different transcription-factor-binding sites (TFBS) have been described, such as AP-1, AP-4, E-box, AML (vis), gamma-activated sites (GAS), TNF-activated site (TAS) and IRF-1 [The SRLV genome comprises two identical copies of single-stranded RNA (ss-RNA). This viral RNA is reverse transcribed into a double-stranded DNA, which is integrated into the host cell genome in the form of a provirus. The provirus is flanked by long terminal repeats (LTR) and contains a gene-encoding capsid , which include different subtypes ,12. The The 22 blood samples analyzed in this study originated from sheep and goats that were naturally infected with an SRLVs from 5 herds within different regions in Poland. All samples were tested previously and were PCR-positive for an SRLVs ,30. SampCTCGAGACTGTCAGGRCAGAGAACARATGCC3′; XhoI site underlined) and LTRERW , as described by Ryan et al. [XhoI and HindIII restriction sites. PCR was performed in reaction mix volumes of 25 µL. The mixture contained 500 ng of genomic DNA, 2.5 µL of buffer (10×), 0.5 µL of dNTP solution (10 µM), 1 µ Mof gCl2 (25 mM), 0.125 µL of DreamTaq polymerase (200 U) and 0.75 µL of each primer (10 µM). The thermal conditions were as follows: 1 min at 94 °C, 35 cycles  and the final extension step at 72 °C for 10 min. PCR products were separated and analyzed by electrophoresis on 1.5% agarose gel containing SimplySafe  and diluted at 1:10,000 in 1× TAE buffer. The amplicons representing LTR sequences were purified on NucleoSpin Gel and PCR Clean-up columns  and cloned into a pDrive vector using a QIAGEN PCR Cloning Kit, according to the manufacturer’s protocol. Ligation products were used to transform EZ Competent Cells (Qiagen), and plasmid DNA was extracted using a NucleoSpin Plasmid kit . Next, the LTR inserts were sub-cloned in the XhoI and HindIII restriction sites of pGL4.10 vector  after XhoI/HindIII digestion. Chemically competent Escherichia coli One Shot TOP10 cells were used for the transformations and maintenance of the plasmid DNA. Finally, bacterial cultures for the preparation of the LTR plasmids were grown for 14 h in a Luria Bertani (LB) medium supplemented with ampicillin (100 µg/mL) and purified using a NucleoSpin Plasmid Kit according to the manufacturer’s protocol. The obtained recombinant plasmids containing different LTR inserts were confirmed by sequencing using a Big Dye Terminator v3.1 Cycle Sequencing kit on a 3730xl DNA Analyzer . The direct sequencing of PCR products was also performed to check if recombinant plasmids constitute the most abundantly represented sequences. The generated plasmids were used to transfect immortalized caprine fibroblast cells (TIGEFs). The cells were maintained in an RPMI 1640 medium  supplemented with 5% heat-inactivated fetal calf serum  and 1% penicillin–streptomycin mixture , and they were cultured at 37 °C in an incubator with 5% CO2.To perform a transcriptional analysis comparing the LTRs from different SRLVs variants, the LTR fragment was amplified using the standard PCR protocol with primers LTREFW . The cells were transfected with 200 ng of test plasmids using 1.6 µL of the FuGENE® HD Transfection Reagent (Roche). The pGL4.13 plasmid , expressing the luc2 reporter gene under the control of the SV40 early enhancer/promoter, was used as a positive control, and the empty vector pGL4.10 as a negative control. At the same time, 20 ng of pGL4.73 (Rluc/SV40) Vector  was co-transfected, so firefly activities were standardized according to Renilla luciferase activities. Then, 24 h after transfection, the cells were washed with phosphate-buffered saline (PBS), and cell extracts were prepared with a Passive Lysis Buffer . The firefly and Renilla luciferase activities were measured using a Dual-Luciferase® Reporter Assay System  following the manufacturer’s instructions and a Centro XS3 LB 960 Microplate Luminometer . All experiments were performed in triplicate. The firefly luciferase activity was normalized to Renilla luciferase activity (Firefly Luciferase/Renilla Luciferase) and presented as relative luciferase activity.One day before transfection, the T-immortalized goat embryonic fibroblast (TIGEF) cells were seeded in 6-well plates at a density of 1 × 10The LTR sequences were analyzed using MEGA 6 software . Multiplp value of <0.05 was considered significant. Differences in the luciferase activities between the two groups were analyzed by the Mann–Whitney U Test.All statistical analyses were performed using STATISTICA version 10 , where a The LTR sequences obtained from the PCR products and recombinant plasmids were identical, indicating that the cloned LTRs reflect the most abundantly represented viral sequences. Based on the multiple sequence alignment of 22 sequences of Polish strains analyzed in this study and reference sequences, a phylogenetic tree was constructed using the neighbor-joining method . The meaMultiple motifs were identified within the LTR sequences, including AP-1 sites, AML, AP-4, TATA box, poly A, CAAAT, GAS, TAS, Stat-1, NF-κB and IRF-1. Numerous mutations within the viral promoter have been identified in field isolates. AML (vis), TATA box and the polyadenylation site (AATAAA) were the most conserved elements between all analyzed strains. Our results revealed that strains representing subtype A17 had a unique T to A substitution in the fifth position of TATA box. AP-4 was also well conserved, showing only minor variabilities between sequences belonging to different subtypes. Six variably degenerate AP-1 sites were observed in sequences analyzed in this study. The AP-1 sites revealed rather subtype-specific conservation. Three Stat-1 sites and one GAS region were identified. Some point mutations were observed in both regions. Our results revealed that only the A5 strains had three Stat-1 sites, while in the other analyzed strains, only two Stat-1 sites were identified. Furthermore, sequences belonging to subtype A5 had two NF-κB sites that were not present in the sequences of the other subtypes. The IRF-1 site was also identified in 8 out of 23 sequences, which belonged to subtypes A12 (7 strains) and A23 (1 strain). Furthermore, a sequence from subtype A1 had two unique insertions in the U3 region: one (10 nt GGTCATGTCA) located near the 5′ end of the U3 region, and the second (21 nt GGATGACACAGCAAATGTAAC) located in the central region of the U3. The latter contains additional putative AP-1 and AML (vis) binding sites and the CAAAT sequence. No transactivation response elements were identified in the R region. However, strains representing subtypes A1 and A17 had a specific 11 nt (CGAAGGAAAGA/G) insertion in the R region. Our results showed that the LTR sequences reflected subtype-specific patterns of sequence diversity . The numTo evaluate the effect of the basal transcriptional activity of Polish field SRLV strains representing different subtypes, the LTR sequences of these strains were subcloned into the pGL4.10 vector, a luciferase-based reporter. At 24 h post-transfection, the transcriptional activity of the LTR promoter of tested strains was examined by the luciferase gene activity in TIGEF cells. The promoter activity measured by luciferase expression is shown in p > 0.035). The lowest promoter activity was observed for strains representing subtype A5 (mean 69.067), while the highest promoter activity was observed for reference strain K1514, representing subtype A1 (mean 373.48). The mean LTR activities of strains representing subtypes A12, A17, A23, A18 and A24 were 91.22, 137.21, 178.41, 187.05 and 236.836, respectively  than that noted for reference strain K1514; however, the differences in most cases were not statistically significant. Only the promoter activity of A5 strain 6038 was significantly lower than that of strain K1514 (ectively .p < 0.00)   strains.The expression of proviral DNA is favored by transcriptional activation in the LTR, which is regulated by transcription factor’s binding sites within the U3 region. Several putative TFBSs, including the AML (vis), TATA box and polyA sites, were highly conserved among the SRLV LTR sequences, as shown in the sequences obtained in our study. Although certain nucleotide motifs were conserved across the sequences of the Polish strains, numerous mutations within the LTR sequences were identified. Single nucleotide polymorphisms (SNPs) were detected in both the regulatory  and non-regulatory sequences of the U3, R and U5 subregions. The detection of SNPs in regulatory elements suggests that there may be differential SRLV expressions in hosts infected with a particular subtype. Sequence variations were also observed between different HIV-1 and HIV-2 subtypes, and it was revealed that the subtype-specific LTRs dictate different promoter activity and replication rates, which resulted in biological differences between the subtypes ,27,28. TAll subtype LTRs were found to be functional promoters. Different levels of promoter activity were observed for strains representing different subtypes. The lowest promoter activity was observed for strains representing subtype A5, while the highest promoter activity was observed for the K1514 reference strain representing subtype A1. Our results revealed that the LTR from the K1514 strain contained 10 nt and 21 nt insertions in the U3 region, which were not present in the sequences of the other analyzed strains. While no putative transcription factor binding domains were identified within the 10 nt insertion, a 21 nt repeat contains potential transcription factor binding sites for AP-1, AML(vis) and the CAAAT sequence. The AML (vis) motif is a recognition site for the members of the AML/PEBP2/CBF transcription factor family. Polyomavirus enhancer-binding protein 2 (PEBP2) and core-binding factor (CBF) were identified as proteins that regulate the transcription of viral and cellular genes . The AMLThe detailed characterization of the single binding sites performed by Juganaru et al.  revealedOur results also revealed that the sequence derived from the K1514 strain and two strains representing subtype A17 had an 11 nt deletion in the R region. This deletion was previously described by Angelopoulou et al. , who sugSRLV transcription is thought to be partially regulated and dependent on the presence of pro-inflammatory cytokines. It was demonstrated that interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α) activate viral transcriptions via the GAS and TAS sites, respectively . The menOur study revealed that subtype A5 strains that had the lowest promoter activity had the most distinct promoter architecture. Only the LTR sequences of the A5 subtype strains contained two binding sites for nuclear factor kappa B (NF-κB). The number of NF-κB sites differed among the HIV-1 subtypes. While subtype B had two adjacent NF-κB sites, three NF-κB sites are present in subtype C, and the CRF01_AE HIV-1 LTR contains a single NF-κB site . FurtherLTR-mediated gene expression may be modulated by different transcription factors in a cell-type-dependent manner. The cell type and its differentiation state may lead to variations in the transcriptional activity of an LTR. Here, we reported measurements of LTR-driven gene expression upon the transfection of TIGEF cells. Therefore, the presence of a cell-type specific combination of transcription factors in TIGEF cells may generate distinct transcriptional activity in comparison to other cell lines.In conclusion, our results provide further insight into subtype-specific differences in the SRLV LTR. Our findings suggest that subtype-specific genetic diversity, mainly in the transcription factor binding sites, impacts their transcriptional activities, producing a distinct activity pattern for the subtypes. This study provides new information that is important for better understanding the function of the SRLV LTR. However, further research, including more strains and subtypes as well as other cell lines, is needed to confirm these findings. It is also important to perform assays with mutant strains to discern the functionality of the identified subtype-specific sites.


In this study, we identify the promoters, typical enhancers (TEs), and super-enhancers (SEs) in the livers of Chinese local and Western commercial pig breeds. Western breeds included fewer SEs in number, while more QTLs of growth-related economic traits were associated with these SEs. Comparison among different porcine tissues and liver tissues from pigs, humans, and mice suggested a high tissue specificity of SE. We concluded that intense selection could concentrate functional SEs; thus, SEs could be applied as effective detection regions in genomic selection breeding.cis-regulatory elements, including promoters, typical enhancers (TEs), and recently-identified super-enhancers (SEs). In this study, we identified these cis-regulatory elements in the livers of two Chinese (Meishan and Enshi Black) and two Western (Duroc and Large White) pig breeds using ChIP-seq data, then explored their similarities and differences. In addition, we analyzed the conservation of SEs among different tissues and species . We observed that SEs were more significantly enriched by transcriptional initiation regions, TF binding sites, and SNPs than other cis-elements. Western breeds included fewer SEs in number, while more growth-related QTLs were associated with these SEs. Additionally, the SEs were highly tissue-specific, and were conserved in the liver among humans, pigs, and mice. We concluded that intense selection could concentrate functional SEs; thus, SEs could be applied as effective detection regions in genomic selection breeding.The transcriptional initiation of genes is closely bound to the functions of  SE comprises a cluster of typical enhancers (TEs) densely occupied by master regulators and mediators. SE is speculated to act as a switch to determine cell identity and fate [cis-regulatory elements [Cis-regulatory elements are DNA sequences with transcriptional functions, such as promoters and enhancers. They can be recognized by sequence-specific transcription factors (TFs), which cooperatively recruit other co-factors to activate or resist transcription activity ,2. Lateland fate . With thand fate , have beelements . Generalelements , while eelements ,8. SEs celements ,10,11. Felements .The pig is undoubtedly one of the most important livestock species, serving as both a significant food source and an excellent animal model for biomedical research in humans thanks to similar physiological features and organ sizes ,14. Chincis-element, SE may play a more important role during the development and breeding process. However, the relationship between SE differences and phenotypes among breeds remains unknown. In this study, we hypothesize that cis-elements, especially SEs, change during breeding and are highly correlated with key genes of selected traits. We first identified the promoters, TEs, and SEs in the livers of two Chinese (Meishan and Enshi Black) and two Western (Duroc and Large White) pig breeds using ChIP-seq data and explored the effect of breeding on cis-element repertoires. Then, the SE profiles of different porcine tissues and different species  were generated and compared to reveal the tissue conservation and interspecific difference. The resulting study provides new insights into the regulatory features and practical breeding values of SEs.Cis-elements have essential roles in development, and their divergence is a common cause of evolutionary changes . Therefohttps://www.ebi.ac.uk/ena/browser/view/PRJNA597497?show=reads, accessed on May 2022). H3K27ac ChIP-seq data of human and mouse liver tissue were downloaded from PRJEB6906 . The individual accession IDs are listed in the H3K27ac and H3K4me3 ChIP-seq data for eleven tissues  of four pig breeds  were downloaded from PRJNA597497  with default parameters in paired-end mode. The reads were generated by removing low MAP(Q) reads < 25 and unmapped reads, and duplicated using SAMTools v1.8 [For ChIP-seq data, the raw data were trimmed by Trim galore  with default parameters. Briefly, the TFs were ranked by normalized peak counts and thresholds above a flex-point (slope > 1) and identified as super-enhancers. cis-Regulatory Elements were annotated using ChIpseeker (version 1.20.0) [The super-enhancers and typical enhancers were identified by H3K27ac peaks located away from TSS regions (outside 2.5 kb upstream and downstream of TSS) using the ROSE algorithm ( 1.20.0)  and ROSEDifferentially enriched regions (DERs) between Chinese breeds and Western breeds were identified from a consensus set of peaks using DiffBind  . This study required conserved SEs that overlapped more than 20% of the base pair unless otherwise stated. Specific and conserved SEs were identified according to the number of overlapping SEs across breeds, tissues, and species.p-values lower than 1 × 10−2 were considered enriched.The “findMotifsGenome.pl” script from the HOMER (version 4.9.1) package was used to search for enriched motifs . The whole genome was used as the background. Factors with https://genome.ucsc.edu/cgi-bin/hgLiftOver) with a minimum match of 0.5. The TEs and SEs that could be converted to human genomic locations were considered sequence-conserved TEs and SEs. Furthermore, TEs and SEs were considered usage conserved if the corresponding human homologous sequence was covered by TE and SE. To calculate the percentage of TE and SE sequences overlapping with genomic features, TE and SE annotations were compared to RefSeq Gene annotations using the BEDTools (version 2.26.0) intersect function.We converted SEs and TEs in susScr11 and mm10 hg38 and coordinates using UCSC LiftOver tools (https://david.ncifcrf.gov). Normalized ChIP-seq data were visualized using the Integrative Genomics Viewer (IGV). To predict the SE functions, we determined SE regions and QTLs on the genome with BEDTools v2.30.0, and the porcine QTL database was downloaded from the Animal QTLdb [https://www.animalgenome.org/cgi-bin/QTLdb/SS/index).The ChIpseeker v1.20.0 R package  was usedal QTLdb  , Enshihei (ES), Large White (LW), and Duroc (DR) pigs with two biological replicates. The Pearson correlation first confirmed satisfactory repeatability between individuals (correlation coefficient between 0.93 and 0.98) A. The dicis-elements   E. Notablelements F. The peelements G, in agrWe used the consensus peaks of the two groups  to investigate the enrichment of promoter or TE regions between Chinese and Western breeds. Both the heatmap clustering A and PCARegarding promoters, 380 and 94 genes were associated with H3K4me3 DERs of Chinese and Western breeds, and were enriched with “the response to progesterone, aging, positive regulation of the apoptotic process” and “positive regulation of transcription from RNA polymerase II promoter, response to the drug, insulin response, retinoid metabolic process”, respectively C. For TESTAT5A and STAT5B), the somatotropin/prolactin family of hormones (GH1 and PRL) and insulin-like growth factor binding protein (IGFBP) (IGFBP1)  . Furtherhlighted E.To investigate the relationship between breed-specific SEs and qualitative traits, we mapped the SEs to the QTL regions of five trait categories  using the pig QTL database. We observed that the QTLs associated with breed-specific SEs were quite different. First, Western breed-specific SEs had much higher global QTL overlapping rates, especially in meat quality-related traits F. SecondTo assess the genome-wide tissue-specific SE profiles, we identified SEs of eleven tissues  with H3K27ac ChIP-seq data of Large White , humans, and mice, respectively . The genWith respect to the potential functions, GO enrichment using SE-associated genes suggested that the top terms, including positive regulation of transcription from RNA polymerase II promoter and positive regulation of transcription, were similar among the three species . Howevercis-regulatory elements play a vital role in the selective expression of genes during development, environmental response, and diseases. Before the concept of ‘super enhancers’ was widely used, researchers noticed that cell identity and disease genes tend to be regulated by complex enhancer networks [cis-elements across different tissues, breeds, and species, and provides reliable evidence that SEs work as tissue-specific ‘network hubs’ of cis-elements. First, SEs overlap with other cis-elements with stronger transcription capacity. According to our identification strategy, SEs are generated based on H3K27ac ChIP-seq regions, which are not oriented to promoter regions. However, our results revealed that SEs were most enriched in the proximal promoter region, and contained the densest TF binding sites and QTL sites. This observation suggests that SEs are at the center of cis-regulatory networks. Second, the tissue specificity of SEs overpowered the difference between breeds and even species. The comparison among different breeds, species, and tissues hints that TEs are much more related to tissue specificity than species, while SEs are even more salient at this point. In other words, SEs are highly tissue-conserved based on their participation in tissue-specific functional genes and pathways. The fact that the similarities between humans and pigs are more significant than those between humans and mice is reasonable, due to the more similar organic physiological characteristics between humans and pigs [The networks . Our stuand pigs , particuand pigs . These rand pigs .The most significant difference between the Chinese and Western breeds we selected was in the intensity of selection based on the market demand. As mentioned above, the profiles of SEs are conserved based on cell/tissue function rather than breed or species; the different distribution of SEs between Chinese and Western pigs reflects the effect of selection based on the aspect of the transcriptional regulatory network. Interestingly, our study reveals three significant characteristic differences between Chinese and Western breeds.cis-elements regarding commercial traits at the single nucleotide level [GH1 and PRL and their downstream transcriptional activators STAT5A and STAT5B first attracted our attention, as both GH1 and PRL stimulate the growth of the liver and other tissues by promoting cell proliferation and angiogenesis [IGF1, which is mainly secreted in the liver and stimulated by GH1 [IGF1 promoter has recently been revealed to regulate porcine liver growth through long-distance regulatory action and to interact with regions around transcription start sites, CpG sites, and functional QTLs [cis-regulatory functions of SEs from another perspective. On the contrary, the genes associated with TEs and promoters of Western breeds are not distinct from the Chinese breeds, which suggests that, as a cis-element, SEs are more sensitive when reflecting the specific cell functions. (3) The breed-specific SEs were associated with breeding objectives. Artificial selection for desirable traits can yield rapid change in a relatively short period. Benefiting from traditional methods and recent genomic selection over time, Western breeds showed a significant advance in economic traits such as growth rate, muscle mass, feed efficiency, etc. [(1) Western breeds have more TEs and stronger TE conservation, while having fewer SEs compared to Chinese breeds. This observation hints that the SEs have been ‘concentrated’ during the decades of breeding; modern breeding strategies based on quantitative genetics may screen out less powerful de level . (2) Theogenesis . More ind by GH1 , was hignal QTLs , confirmcy, etc. , with recy, etc. .cis-regulatory elements, particularly SEs, in porcine livers of both Chinese and Western breeds, then explored the differences with regard to promoters, TEs, and SEs among breeds, tissues, and species. Our observations suggest that SEs are the key ‘network hubs’ of cis-elements, and are highly tissue-specific. The significant enrichment of transcriptional initiation regions, TF binding sites, and QTLs in SEs, along with the fact that intensely selected Western breeds include more concentrated and more functional SEs, hint at the promising application of SEs in genomic selection breeding.In summary, we identified and compared the genome-wide profiles of