Using cellular fractionation and imaging, we found a fraction of ADAM9 was present in the nucleus and was uniquely associated with gene loci known to be linked to the angiogenesis pathway demonstrated by genome-wide ChIP-seq. Mechanistically, nuclear ADAM9, triggered by hypoxia-induced translocation, functions as a transcriptional repressor by binding to promoters of genes involved in the negative regulation of angiogenesis, and thereby promotes tumor angiogenesis in plasminogen/plasmin pathway. Moreover, ADAM9 suppresses plasminogen activator inhibitor-1 gene transcription by interacting with its transcription factors at the promoter. Our findings uncover a novel regulatory mechanism of ADAM9 as a transcriptional regulator in angiogenesis and highlight ADAM9 as a promising therapeutic target for ESCC treatment.Hypoxia and angiogenesis play key roles in the pathogenesis of esophageal squamous cell carcinoma (ESCC), but regulators linking these two pathways to drive tumor progression remain elusive. Here we provide evidence of ADAM9's novel function in ESCC progression. Increasing expression of ADAM9 was correlated with poor clinical outcomes in ESCC patients. Suppression of ADAM9 function diminished ESCC cell migration and  Esophageal cancer is one of the most fatal types of malignancies, with rapid tumor mass growth and frequently a poor prognosis Hypoxia-associated biomarkers are associated with poor outcomes in esophageal cancer Tumor metastasis involves degradation of the extracellular matrix and requires several proteolytic enzymes, such as matrix metalloproteinases, and a disintegrin and metalloproteases (ADAMs) either from tumor cells or surrounding stromal cells in ESCC th edition.Formalin-fixed paraffin-embedded ESCC samples were obtained from 213 patients who received curative esophagectomy between August 2000 and September 2009 at China Medical University Hospital in compliance with protocols approved by the hospital's institutional review board (DMR98-IRB-059). The informed consent was obtained for experimentation with human subjects. Clinical staging and clinicopathological TNM classification were determined according to criteria proposed by the American Joint Committee on Cancer, 6ADAM9 knockdown CE146T or KYSE170 cells (5×104). All experimental procedures were approved by the Institutional Animal Care and Use Committee of China Medical University and Hospital. After two months, the mice were sacrificed and tumors were excised and weighed.SCID mice (8 weeks of age) were used as models of ESCC distal metastasis by intracardial injection with control and For immunogold EM analysis of ADAM9, samples were performed as previously described In situ Red Starter Kit  according to the recommendations. The primary antibodies against ADAM9 , TP53 , HIF1α , and USF1  were used.The experiments were conducted as previously described Comparisons between the analyzed parameters were performed using Student's t-test or two-way ANOVA for continuous variables. All statistical tests were two-sided. Survival curves were obtained by the Kaplan-Meier method. Statistical significance was set for all tests at P < 0.05.We first investigated the relationship of ADAM9 with the clinicopathological parameters of ESCC patients, and found significantly higher levels of ADAM9 mRNA in ESCC tumors than in normal esophageal tissues from the Gene Expression Omnibus (GEO) databases (GSE20347) Figure A. Next, ADAM9 knockdown cells were generated using lentiviral shRNAs targeting different regions of ADAM9. No obvious change in cell proliferation rate was detected in control versus ADAM9 knockdown cells by MTT assays over a period of 3 days  developed several distal metastatic tumors within 64 days after cancer cell injection, whereas only one mouse (20%) had one small distal metastatic tumor in the ADAM9 knockdown group  in the top three functional enrichment analysis  70 cells A. The res Figure A. Given ADAM9 KO KYSE170 cells compared to control cells  but increased PAI-1 (encoded by SERPINE1) in ADAM9 knockdown cells compared with control cells  dataset. In patients with high ADAM9 expression (above the mean), a positive correlation of ADAM9 and PLAT RNA levels was detected (r = 0.34), whereas a negative correlation of ADAM9 and SERPINE1 levels was detected (r = - 0.26) , suggesting that the correlation of ADAM9 and PLAT or SERPINE1 was not caused by a negative correlation between PLAT and SERPINE1  is known to contain three to five basic residues that are required for nuclear translocation and interaction with importin β1. Using two independent prediction programs, cNLS Mapper program 2 treatment in a time-dependent manner (2), as well as under stressed conditions of low serum with hypoxia  is listed in all these functions , which showed enrichment over an extended region (from site 2 to 6) in control compared to ADAM9 KO KYSE170 cells  using an antibody that can pull down ADAM9 in ESCC cells A. A genoentified . Gene onentified A. Notabls Figure A. By dees Figure B. ADAM9's Figure C. SimilaTE cells B.SERPINE1 and PGK1 (encoding phosphoglycerate kinase 1), identified for their roles in regulating plasminogen activation , an inhibitor of intracellular protein transport. The levels of nuclear ADAM9 were decreased and the total PAI-1 and PGK1 proteins were increased in the control cells in a time-dependent manner Figure F. In ADAADAM9 knockdown KYSE170 tumors whereas IHC staining of PGK1 was lower in control tumors than ADAM9 knockdown KYSE170 tumors  have been previously identified in the SERPINE1 promoter, but the identity of potential repressors remains unknown SERPINE1 promoter at positions -1200, -600, and -300, respectively, with the luciferase gene to measure the promoter activity in control and ADAM9 KO cells  showed greater promoter activity than the longest construct (-1200) , these transcription factors may increase the transactivation of SERPINE1 promoter and this would be reversed upon further knockdown of transcription factors  of SERPINE1 promoter. Taken together, these results suggest that ADAM9 proteins interact with selective transcription factors to dampen transcription of SERPINE1 gene in ESCC.Because ADAM9 has no DNA binding domain, we speculate that it may be recruited by known transcription factors and then suppress their transactivation. To screen for the potential ADAM9-interacting transcription factors, we evaluated the promoter activity in control and ADAM9 KO cells by transiently knocking down known transcription factors involved in s Figure C, top. Cd Figure C, bottoms Figure C, bottoms Figure C. Similas Figure C, as wels Figure C. Re-ChIr Figure G. In re-r Figure G. Similain vitro and in vivo, such as invasive and metastatic ability, and distant metastasis. We propose that ADAM9 coordinates plasminogen activator-based network for ESCC progression through two major functions of ADAM9: protease activity for increasing tPA expression/activity and nuclear translocation of ADAM9 for suppressing PAI-1 and PGK1 expression (PAI-1 for reducing plasmin and PGK1 for increasing angiostatin), two inhibitory factors involved in plasminogen activator-mediated angiogenesis. Thus, we revealed a novel role of ADAM9 in promoting angiogenesis through a previously unappreciated mechanism of transcriptional repression of genes participating in the negative regulation of angiogenesis (Figure Here we have demonstrated that a high level of ADAM9 is correlated with poor outcomes in ESCC patients. Silencing of ADAM9 reduced tumorigenic characteristics of ESCC s Figure .SERPINE1 promoter over nearly 1 kb and suppress SERPINE1 transcription. We provide evidence suggesting that ADAM9 is likely to interact with several transcription factors previously known for regulation of SERPINE1 expression, such as p53, HIF1α, and USF1. These transcription factors play important roles in tumorigenesis and interplay with each other to regulate target gene expression. They serve as positive regulators by binding to SERPINE1 promoter to activate PAI-1 gene expression under various contexts. Beyond the role as a TF, the tumor suppressor p53 binds to PAI-1 mRNA 3'-UTR and stabilizes PAI-1 mRNA, leading to enhanced PAI-1 expression The sheddase function of ADAM9 has been considered critical for promoting cell proliferation and migration in ESCC through yielding soluble EGFR ligands, thereby activating the EGFR-AKT pathway Previous studies showed positive nuclear staining of ADAM9 and its association with lethal phenotypic progression of prostate cancer Angiogenesis contributes to the aggressive characteristics of ESCC Supplementary methods, figures and tables.Click here for additional data file.


NOTCH1 gene, which is required for CRPC and TNBC cell self-renewal, we demonstrate that MUC1-C is necessary for (i) occupancy of JUN and ARID1A/BAF, (ii) increases in H3K27ac and H3K4me3 signals, and (iii) opening of chromatin accessibility on a proximal enhancer–like signature. Studies of the EGR1 and LY6E stemness–associated genes further demonstrate that MUC1-C–induced JUN/ARID1A complexes regulate chromatin accessibility on proximal and distal enhancer–like signatures. These findings uncover a role for MUC1-C in chromatin remodeling that is mediated at least in part by JUN/AP-1 and ARID1A/BAF in association with driving the CSC state.The oncogenic MUC1-C protein promotes dedifferentiation of castrate-resistant prostate cancer (CRPC) and triple-negative breast cancer (TNBC) cells. Chromatin remodeling is critical for the cancer stem cell (CSC) state; however, there is no definitive evidence that MUC1-C regulates chromatin accessibility and thereby expression of stemness-associated genes. We demonstrate that MUC1-C drives global changes in chromatin architecture in the dedifferentiation of CRPC and TNBC cells. Our results show that MUC1-C induces differentially accessible regions (DAR) across their genomes, which are significantly associated with differentially expressed genes (DEG). Motif and cistrome analysis further demonstrated MUC1-C–induced DARs align with genes regulated by the JUN/AP-1 family of transcription factors. MUC1-C activates the BAF chromatin remodeling complex, which is recruited by JUN in enhancer selection. In studies of the These findings show that MUC1-C, which is necessary for the CRPC and TNBC CSC state, activates a novel pathway involving JUN/AP-1 and ARID1A/BAF that regulates chromatin accessibility of stemness-associated gene enhancers.  MUC1 gene evolved in mammals to protect epithelia from exposure to the external environment (The ironment . In assoironment . MUC1-C ironment . In addiironment . The effironment  and (ii)ironment . WhereasThe importance of MUC1-C–induced epigenetic reprogramming and gene regulation has been further supported by the involvement of MUC1-C in driving dedifferentiation in cancer progression . MUC1-C Mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit (Lonza).Human DU-145 CRPC cells (ATCC) were cultured in RPMI1640 medium (Thermo Fisher Scientific) containing 10% heat-inactivated FBS (GEMINI Bio-Products). Human BT-549 TNBC cells (ATCC) were cultured in RPMI1640 medium (Thermo Fisher Scientific) containing 10% heat-inactivated FBS (GEMINI Bio-Products), 100 μg/mL streptomycin, 100 U/mL penicillin, and 10 μg/mL insulin. Authentication of the cells was performed by short tandem repeat (STR) analysis. Cells were monitored for MUC1shRNA  or a control scrambled shRNA  was inserted into the pLKO-tet-puro vector (Addgene). JUNshRNA , ARID1AshRNA (MISSION shRNA TRCN0000059090), NOTCH1shRNA (MISSION shRNA TRCN0000003362), or a control scrambled shRNA  was inserted in the pLKO-puro vector (Addgene RRID:Addgene_32017). The viral vectors were produced in 293T cells . cDNAs were synthesized and amplified as described previously . PrimersTotal lysates prepared from subconfluent cells were immunoblotted with anti-MUC1-C , anti-JUN , anti-ARID1A , anti-NOTCH1 , anti-β-actin , and anti-GAPDH .Assay for transposase-accessible chromatin using sequencing (ATAC-seq) libraries were generated from three biologically independent replicates per condition. Library preparation and quality control were performed as described previously . Brieflyhttps://github.com/macs3-project/genomics-analysis-pipelines). The data from each replicate were aligned to the human genome (version GRCh38) with bwa-mem (https://arxiv.org/abs/1303.3997v2), evaluated by customized quality control scripts, and cleaned by Picard  tools to mark and remove the duplicated reads. Detection of the accessible regions for each replicate was performed using MACS2 . qPCR results were analyzed according to the formula 100/2Ct(DNase I) −Ct(no DNase I). The data were normalized to input DNA without DNase I treatment.DNase1 chromatin accessibility assays were performed on chromatin isolated as described previously . AliquotThe count-table containing fragment counts in each 100 bps bin within consensus accessible regions for each replicate was loaded into DESeq2  was used to represent the significance of the overlap in Supplementary Fig. S2.The ChIPseeker package  was usedtrome DB . In addiP value cutoff of 0.05 in the Kolmogorov–Smirnov test was applied to find the list of DEGs close to the DARs. The enrichGO function in clusterProfiler . TruSeq Stranded mRNA (Illumina) was used for library preparation. RNA-seq analysis was performed as described previously . The AP-Chromatin immunoprecipitation (ChIP) was performed on cells crosslinked with 1% formaldehyde for 5 minutes at 37°C, quenched with 2 mol/L glycine and washed with PBS, and then sonicated in a Covaris E220 sonicator to generate 300–600 bp DNA fragments. Immunoprecipitation was performed using a control IgG (Santa Cruz Biotechnology) and antibodies against MUC1-C , JUN , ARID1A , PBRM1 , EP300 , H3K27ac , H3K4me1 , and H3K4me3 . Precipitated DNAs were detected by PCR using primers listed in Supplementary Table S3. Quantitation was performed on immunoprecipitated DNA using SYBR-green and the CFX384 Real-Time PCR Machine (Bio-Rad). Data are reported as fold enrichment relative to IgG (pMIEG-c-Jun (Addgene plasmid #40348 RRID: Addgene_21915-plko tet puro) was used to construct and purify full-length (FL) GST-JUN (aa 1–331) and to generate the (i) GST-JUN (aa 1–194) and (ii) GST-JUN (aa 195–331) fragments. GST-MUC1-CD FL; aa 1–72), GST-MUC1-CD (aa 1–45), GST-MUC1-CD (aa 46–72), and GST-MUC1-CD (CQC→AQA) were prepared as described previously  at a density of 5,000 cells per well of a 6-well ultralow attachment culture plate (Corning) for 10 days. Mammospheres with a diameter >50 μm were counted under an inverted microscope in triplicate wells.U test or Student t test to determine differences between means of groups. A P value of < 0.05 denoted by an asterisk (*) was considered statistically significant.Each experiment was performed at least three times. Data are expressed as the mean ± SD. All statistical analyses for qRT-PCR and ChIP studies were performed by the unpaired Mann–Whitney The RNA-seq and ATAC-seq data have been deposited in the GEO database under accession codes GSE139335, GSE164141, and GSE180599.MUC1-C drives dedifferentiation and self-renewal of DU-145 CRPC cells  and BT-5In analyzing the DU-145 cell ATAC-seq samples by DESeq2, we found that silencing MUC1-C results in 12,752 DARs. Of the MUC1-C–regulated DARs, 6,584 and 6,168 were identified with increases and decreases, respectively, in accessibility . In BT-5Comparison of MUC1-C–induced closing and opening DARs identified 44 and 132, respectively, that are common in DU-145 and BT-549 cells . AnalysiRNA-seq demonstrated that MUC1-C silencing in DU-145 cells is associated with 882 upregulated and 1,159 downregulated genes . We therefore probed for an interaction between the MUC1-C and JUN proteins. MUC1-C consists of a 58 aa extracellular domain, a 28 aa transmembrane domain, and a 72 aa cytoplasmic domain . The MUCNOTCH1 gene in a region (-1575 to -1590 bp upstream to the TSS) that contains an AP-1–binding motif (NOTCH1 pELS that is dependent on the presence of each component. Consistent with a pELS (NOTCH1 chromatin accessibility and expression contributes to the CSC state in different types of carcinoma cells.MUC1-C drives NE dedifferentiation of CRPC/NEPC cells by activating the ARID1A/BAF complex and inducing NOTCH1 expression . MUC1-C ng motif . Signifing motif . In adding motif  and (ii)ng motif . These fh a pELS , we deteh a pELS . In addih a pELS  and by dh a pELS , silenciEGR1, which is associated with promoting CRPC and TNBC CSC states  potential pELS (-1985 to -1974 bp) within 2 kb from the TSS, and (ii) dELS (+6350 to +6361) at over 6 kb from the TSS  EP300 occupancy, and (ii) H3K27ac and H3K4me3 signals on the EGR1 pELS and dELS , suggesting that the EGR1 dELS may be an inactive site. Nonetheless, chromatin accessibility of the EGR1 pELS and dELS was decreased by silencing MUC1-C in DU-145  and is linked to CRPC and TNBC progression . These results indicate that MUC1-C–mediated activation the LY6E pELS, PLS, and dELS is differentially regulated depending on cell context.To further investigate involvement of MUC1-C in integrating activation of enhancer regions, we studied the human gression . Putativgression . In thisgression . We foungression . In BT-5 the PLS . Accordithat JUN  and ARIDthat JUN  are necethat JUN  and H anEpigenetic regulatory mechanisms, including ATP-dependent chromatin remodeling, are essential for maintaining tissue homeostasis and the wound healing response . The MUCNOTCH1 gene, which is of importance for the progression of CSCs  occupancy of JUN and ARID1A complexes, (ii) increases in H3K27ac and H3K4me3 levels, and (iii) opening of chromatin accessibility. The importance of JUN and BAF in this setting was further supported by the dependence on ARID1A for NOTCH1 activation  pioneer transcription factors in the dedifferentiation of CRPC and TNBC cells . Among t of CSCs . MUC1-C/49 cells . In suppNOTCH1 pELS revealed an unexpected role for MUC1-C in chromatin remodeling and provided the basis for extending these investigations to dELSs. Along these lines, AP-1 can operate both proximally to the TSS and at distal enhancer sites  patterns of JUN and ARID1A occupancy, and (ii) intensities of H3K27ac and H3K4me3 signals. MUC1-C was also necessary for driving chromatin accessibility of the pELS and dELS in both cell types. A common finding in our studies of the NOTCH1, EGR1, and LY6E genes is that, in addition to activating H3K27ac signals, MUC1-C was associated with increases in H3K4me3 as compared with H3K4me1. H3K4me3 is a characteristic signal of active promoters and is also detectable at active enhancers bound by RNA Pol II (NOTCH1, EGR1, and LY6E is associated with predominant H3K4me3 signals at enhancer-like signatures. Further studies will be needed to address the mechanistic basis for these findings and whether they are related to MUC1-C–induced regulation of H3K4 methyltransferases or demethylases that contribute to aberrantly increased transcriptional activity.Our studies of the er sites . Given tA Pol II . Our worEGR1 and LY6E stemness-associated genes informs new avenues for investigation of how they may contribute to the CSC state, which will be a focus of our subsequent studies. Additional studies will also be needed to address the diversity of MUC1-C–driven AP-1 target genes, particularly in the context of lineage-specific TFs, which can dictate cell type–specific recruitment of BAF. Along these lines, the observations that silencing MUC1-C and ARID1A is associated with the regulation of common sets of AP-1 target genes indicate that MUC1-C may broadly induce global remodeling of chromatin through AP-1 and the esBAF complex in driving stemness (The identification of MUC1-C–induced stemness . CSCs hastemness , which cSupplementary FigureClick here for additional data file.


This article comments on:Patrick RM, Huang X-Q, Dudareva N, Li Y. 2021. Dynamic histone acetylation in floral volatile synthesis and emission in petunia flowers. Journal of Experimental Botany 72, 3704–3722. Biosynthesis of volatile organic compounds (VOCs) in plants depends on the biosynthetic pathways of the primary metabolite. These regulations require molecular coordination at different levels on both primary and secondary metabolic networks by specific transcription factors that temporarily activate transcription and scent emission in plant flowers. However, very little is known about the epigenetic regulation of VOC biosynthetic genes. Now, Plants synthesize a plethora of secondary metabolites that offer a wide range of functions, including plant growth, development, and defence against biotic and abiotic environments  and benzenoid (C6–C1) compounds are produced from catalysis of Phe to trans-cinnamic acid through l-phenylalanine ammonia-lyase (PAL) action. The formation of the volatile phenylpropanoid-related (C6–C2) compounds originates directly from Phe (ODORANT 1 (ODO1) and EMISSION OF BENZENOIDS I and II (EOBI and EOBII) in petunia down-regulates the expression levels of many VOC biosynthetic genes and, as a result, a decrease in the emission of most volatiles was observed (The flowers of gulation . The majHistone acetylation neutralizes the positively charged Lys residues on histone tails. As a result, the interaction between DNA and histones weakens, making chromatin more accessible to transcription factors. The transcription factors will activate or repress gene expression . The enzHistone acetylation modulates chromatin dynamics in plant responses to internal and external cues to regulate downstream gene expression as signalling outputs .The histone acetylation and the transcriptomic analysis on the second day after anthesis of petunia flower points toward the molecular processes that contribute to VOC biosynthetic genes. This work could be used as a reference point to reveal the role of histone modifications in coordination of terpenoid biosynthesis genes or volatile, fatty acid derivative compounds in other plants. Epigenetic factors are relevant modulators of rapid responses to the environment, enabling plants to adapt to stress events more efficiently and preparing the offspring for future challenges. Moreover, volatile secondary metabolites are essential for plant biotechnological applications including food, health, and industrial products, and the perfume industry (The regulation of VOCs and the release of petunia floral scent are mediated by histone modification. Furthermore, H3K9 acetylation is correlated with transcription activation of VOC biosynthetic genes (One possibility is the regulation of the rhythmic release of VOCs by transcription factors encoded by clock genes (Another scenario is that specific transcription factors, such as ODO1, EOBI, and EOBII, recruit HAT complexes to the promoters of genes involved in the phenylpropanoid/benzenoid biosynthetic pathway independently of circadian rhythms. During flowering development in Arabidopsis, histone acetylation and several HATs play a significant role by controlling hormone responses and orchestrating gene expression profiles in several floral organs . The spe


Caenorhabditis elegans, ARC-C identifies approximately 15,000 significant interactions between regulatory elements at 500-bp resolution. Of 105 TFs or chromatin regulators tested, we find that the binding sites of 60 are enriched for interacting with each other, making them candidates for mediating interactions. These include cohesin and condensin II. Applying ARC-C to a mutant of transcription factor BLMP-1 detected changes in interactions between its targets. ARC-C simultaneously profiles domain-level architecture, and we observe that C. elegans chromatin domains defined by either active or repressive modifications form topologically associating domains (TADs) that interact with A/B (active/inactive) compartment-like structure. Furthermore, we discover that inactive compartment interactions are dependent on H3K9 methylation. ARC-C is a powerful new tool to interrogate genome architecture and regulatory interactions at high resolution.Nuclear organization and chromatin interactions are important for genome function, yet determining chromatin connections at high resolution remains a major challenge. To address this, we developed Accessible Region Conformation Capture (ARC-C), which profiles interactions between regulatory elements genome-wide without a capture step. Applied to  The development and application of chromosome conformation capture methods have been instrumental in shaping our understanding of genome topology . The basThe Hi-C method enables genome-wide assay of chromatin interactions . In mostChromatin at regulatory elements is known to be relatively accessible to nucleases, which has enabled their mapping using sensitivity to DNase I or Tn5 transposition . A higheSupplemental Fig. S1; Methods). Ends are then repaired and DNA ligated to join ends in close proximity. The library is then made in nucleus using Tn5 tagmentation (cis-informative” read pairs (those mapping to the same chromosome) to construct chromosome-wide contact maps and to call significant interactions following bias correction .The steps of ARC-C are illustrated in entation , amplifiCaenorhabditis elegans L3 chromatin, preparing libraries from three biological replicates. Data from all replicates were highly concordant . To increase the power to profile interactions, all cis-informative reads were pooled, resulting in 12 million read pairs for analysis . As expected, cis-informative read pairs were enriched at regulatory elements—43.7% of reads overlap regulatory elements (REs), which comprise 21.1% of the genome—and REs are from Cis-informative read pairs are produced by ARC-C with an efficiency similar to those of capture methods .We applied ARC-C to C. elegans genome topology using Hi-C identified and characterized large roughly 200-gene self-interacting domains on the X Chromosome regulated by the dosage compensation complex (DCC)  . Large ddetected . The X Cdetected .Supplemental Figs. S4, S5). ARC-C maps and insulation profiles from the autosomes are also highly similar to Hi-C maps including detection of the previously reported preferential interactions within the large central and two distal chromosome blocks of autosomes  domains .We next investigated the potential formation of smaller TAD-like domains. Although these were not clearly visible in the contact matrix, we considered that genomic domains defined by chromatin modifications might form self-interacting domains, because in other animals, marking patterns within individual TADs and small compartmental domains are relatively uniform . In C. e domains . Active  domains . Both do domains . The levSupplemental Fig. S6C). These results indicate that active and H3K27me3 domains have TAD structure, because they are enriched for self-interactions and insulated. The compact nature of the C. elegans genome and the relative weakness of these domains compared to TADs in Drosophila and mammals may have prevented their detection from de novo analyses.To investigate whether the active and H3K27me3 chromatin domains are spatially separated and form TAD-like structures, we visualized and compared interaction frequencies in aggregate within active or H3K27me3 domains and their neighboring chromatin. Supporting the spatial separation of both domain types, we found enrichment for interactions within domains, visible as a central square of higher signal A,E. To fSupplemental Fig. S7B). We conclude that C. elegans active and H3K27me3 chromatin domains form TADs and that TADs of the same type interact with each other to form a compartment-like structure similar to A/B (active/inactive) compartments of other animals. We note that compartment strength appears to be higher than that of TAD strength. This may be related to the apparent lack of insulator proteins in C. elegans  interact with each other to form an A/B (active/inactive) compartment-like structure. For these analyses, we aggregated ARC-C signal either between active domains or between H3K27me3 domains. This revealed that domains show preferential interactions with those of the same type B,E. Althns e.g., . A possiThe mechanisms of compartment formation are not well understood, and few factors that affect compartment structure are known. We considered that histone modifications may be important because they largely differ within active and inactive compartments . H3K9 meC. elegans autosomes contain most of the H3K9 methylation and associate with the nuclear lamina, whereas the central region contains little H3K9 methylation  and SET-25 . These results indicate that H3K9 methylation, predominantly found on the distal arms, facilitates the compartment structure of H3K27me3 domains across the chromosomes, even in regions that lack H3K9 methylation. Given the known role of H3K9 methylation in tethering distal chromatin to the nuclear envelope . Altered genes were predominantly up-regulated, and gene expression increases were associated with genes normally marked by H3K9 methylation irrespective of whether the gene was located in an active or H3K27me3 domain . We conclude that H3K9 methylation is an important mediator of inactive compartment structure, but that compartment structure itself has little effect on gene expression.Despite the strong effect on H3K27me3 compartment strength, the loss of H3K9 methylation did not have a widespread or specific effect on gene expression in H3K27me3 domains, because C. elegans . The interactions involve 9733 different regions, 95% of which overlap an annotated promoter or enhancer . Therefore, ARC-C has the power to map interactions between regulatory elements at 500-bp resolution.We next investigated the ability of ARC-C to profile interactions between regulatory elements. We previously defined 15,714 promoter and 19,231 enhancer elements in  elegans . To idenPromoters are most prevalent among the significant interactions, accounting for 62% of interacting elements, and they are involved in 86% of the interactions A. Half oC. elegans, we screened for transcription factors and chromatin regulators for which binding sites show significantly enriched interactions in the ARC-C contact map (Methods). Of 105 proteins tested, 60 chromatin regulators or TFs had this property . Similar assessment of two Hi-C maps . As expected from the role of the condensin I DC complex in mediating X Chromosome interactions between rex sites . We next analyzed direct BLMP-1 targets, defined as the subsets of BLMP-1 binding regions associated with a gene that was up-regulated or down-regulated in blmp-1 mutants . Chromatin interactions between targets down-regulated in blmp-1 mutants were significantly reduced , showing that ARC-C can detect specific changes in mutants. The results suggest that BLMP-1 may promote spatial proximity between targets that require it for expression, for example, by directly mediating interactions. Valid coverage at the down-regulated targets is also reduced 1.2-fold in blmp-1 mutants, suggesting that BLMP-1 contributes to facilitating open chromatin at these sites.To evaluate the ability of ARC-C to detect changes in regulatory interactions, we chose to analyze BLMP-1, a TF for which binding sites significantly interact D. blmp-1elopment . We perfcis-informative read pairs for profiling architecture and regulatory element interactions. For application to larger mammalian genomes, an enrichment step for ligation events  would be beneficial.In its present form, ARC-C works well for profiling chromatin interactions in relatively small genomes, because sequencing 200 million fragments per duplicate library produces enough Here, we used ARC-C in whole animals, so the cell types from which the detected interactions came are unknown. In addition, interactions that occur in a small number of cells are likely to have been missed. The future application of ARC-C to specific purified cells would address these issues, allowing in vivo investigation of cell type–specific architecture.C. elegans revealed unappreciated domain and compartment structure and proteins that are candidates for mediating organization. The use of ARC-C and study of these candidates should accelerate studies of transcriptional regulation and the relationship with genome architecture.In conclusion, ARC-C provides a new ability to study genome topology and regulatory interactions at high resolution in a single genome-wide assay. Our application of ARC-C in C. elegans strains were maintained at 20°C as previously described (blmp-1(tm548) (met-2(n4256) set-25(n5021) (escribed . The fol1(tm548) , and GW65(n5021) .Strains for ARC-C and ChIP-seq were grown in liquid culture at 20°C using standard S-basal medium with HB101 bacteria. Animals were first grown to the adult stage, bleached to obtain embryos, and the embryos hatched without food in M9 buffer for 24 h at 20°C to obtain synchronized starved L1 larvae. L1 larvae were grown in a further liquid culture at 20°C then harvested at the L3 stage. Worms were collected, washed in M9 buffer, floated on sucrose, washed again in M9, then frozen into small pellets by dripping worm slurry into liquid nitrogen, which was stored at −80°C until use.JMJD-2, SCC-1, ZFP-1, BLMP-1, TOP-2, and SIN-3 chromatin immunoprecipitations in L3 larvae and library preparations were conducted as in met-2 set-25 mutant worms were grown at 20°C on NGM plates, harvested at the L3 stage, and flash frozen in liquid nitrogen. RNA was extracted from frozen worms using TriPure (Roche) and purified with the Zymo Research RNA Clean and Concentrator kit (R1013) after DNase I digestion. Libraries were prepared with the TruSeq RNA Library Prep Kit (Illumina). Reads were aligned to genome assembly ce10 with gene annotation WS235 using STAR . Genes were annotated as being marked by H3K9me2 or H3K9me3 if their average signal was 1.5× above the median, using L3 ChIP-seq data sets available under the NCBI Gene Expression Omnibus  accession numbers GSE49728 for H3K9me2 and GSE49720 for H3K9me3  mutants at L3 stage were obtained from GEO (GSE55225) .Raw RNA-seq data of wild-type and SE55225) . Reads wSE55225) . Read cog DESeq2 , requiriC. elegans nuclei, we found that 50–200 units/mL DNase I gives optimal recovery of informative read pairs using the digestion conditions indicated below .A key element of ARC-C is to digest nuclei with DNase I in situ under conditions in which cutting results in maximal enrichment of informative read pairs at accessible chromatin. This concentration should be empirically determined for the sample type of interest. For 2, 0.5 mM spermidine, 0.15 mM spermine, 1 mM DTT, protease inhibitors), resuspended in 7 mL Buffer A, then the material dounced 20 strokes in a 7 mL stainless steel tissue grinder (VWR 432-5005). The dounced material was spun at 100g for 5 min; the supernatant, which contains nuclei, was transferred to a new tube. The pellet was resuspended in Buffer A and again dounced with 20 strokes. After spinning, the two supernatants containing nuclei were pooled.Frozen worm pellets were ground into a fine powder in which worms were broken into approximately 10 fragments. Then 1 mL of worm powder was fixed in 10 mL of 1% formaldehyde in PBS for 10 min at room temperature (RT) with gentle shaking then quenched for 5 min with a final concentration of 125 mM glycine. Fixed worm fragments were then washed with Buffer A , and chromatin was digested with 50 and/or 100 units/mL DNase I for 10 min at 25°C. The reactions were then quenched with a final concentration of 25 mM EDTA and 5 mM Tris at pH 7.5. Nuclei were washed twice with 1 mL of ice-cold Nuclear Washing Buffer . Nuclei were then resuspended in 100 µL of end repair master-mix  and incubated for 30 min at 20°C with rotation. Thereafter, 400 µL of ligation master-mix was added  and the mixture was incubated overnight at 4°C with rotation. Nuclei were pelleted, resuspended in 50 µL of tagmentation master-mix , and incubated for 30 min at 37°C. Next, 5 µL of 1% SDS and 2 µL of NEB Proteinase K [800 units/mL] was added and left for 15 min at 65°C before DNA from the mixture was purified using Qiagen MinElute columns. Large fragments (>500 bp) were removed from the purified DNA using two rounds of 0.6vol AMPure XP beads. Before PCR amplification, a test qPCR amplification using 1/20th of the input DNA was performed, and the cycle number for PCR was determined by the midpoint of the exponential phase of the amplification curve. The resultant DNA was amplified with NEBNext Ultra II Q5 Master-mix under the following PCR conditions: for 5 min at 72°C, for 30 sec at 98°C, and cycling for 10 sec at 98°C, for 30 sec at 63°C, and for 1 min at 72°C using the determined cycle number. The library was size-selected with AMPure XP beads to a final range of 200–700 bp (insert size: ∼70–570 bp). Before sequencing, libraries were quality tested by qPCR at the gap-3 promoter. DNase I overdigestion or underdigestion led to poor enrichment of regulatory elements. Good libraries generally had gap-3 promoter enrichment values of greater than fourfold. Libraries were sequenced on the Illumina platform paired-end (100 bp). Four libraries were sequenced from wild-type L3 larvae , and two biological replicate libraries were sequenced for both blmp-1 and met-2 set-25 L3 larvae.Aliquots of 10 million fixed nuclei were spun down at 1000Supplemental Figure S2 lists libraries analyzed in the paper and sequencing statistics. Adapter sequence was trimmed by cutadapt  as a function of mapping distance. The vast majority of pairs mapping <500 bp apart were in the forward–reverse configuration (nonligated fragments), whereas >600 bp the proportions were stably at ∼25% each .ARC-C libraries were sequenced 62–150 bp (100 bp for most samples) from both ends. cutadapt , and seqcutadapt , which aContact maps were made from informative read pairs by binning the genome into fixed-width  nonoverlapping bins and counting the number of read pairs between each pair of bins. The maps were then normalized by matrix balancing using the Knight–Ruiz algorithm . Concordhttps://www.ncbi.nlm.nih.gov/sra) accession number SRX77040 and processed using HiCUP v0.5.9  and expanded them to 500 bp or until neighboring intervals began to touch; a small number of elements that were within 100 bp were merged first. The rest of the genome was covered with evenly placed 500-bp nonoverlapping fixed-width intervals; hence, the entire genome was covered by a combined set of 192,257 intervals of average size (494 bp). We used this procedure instead of generating fixed nonoverlapping bins to avoid individual Res being split into two bins.We first segmented the genome into bins of ∼500 bp using the following procedure. We first took annotated regulatory elements  . To address this and avoid normalizing out interaction signals, we developed an approach similar to that used for Capture Hi-C, which has similar coverage biases and used “off-peak” interaction frequency to account for coverage . We modei∈I, the number of cis-informative read pairs ccis,i were counted. Intervals in the top 10% of the coverage distribution were regarded as peaks, and intervals in the bottom 10% were removed. An off-peak cis-informative coverage cioffpeak, was calculated for every kept interval, counting the number of contacts not involving peak intervals. We calculated a scaling factor for the interval's representation/visibility as vi = )0.87 (see below for derivation of the exponent). Chromosome-wide average distance-dependent contact frequency F(d) in the distance range of 1 kb to 1 Mb was modeled by fitting a spline function in a two-pass process (following Fit-Hi-C) . Given the total number of cis-informative contacts (N) of the chromosome, we considered a null distribution in the form of a binomial, where the observed number of contacts, ni,j ∼ binomial. Significant interactions were called at an FDR level of 0.05 and were post-filtered requiring support by more than five read pairs.For every interval it-Hi-C) . For eveX, should be able to transform the unnormalized contact matrix A into a normalized contact matrix B by B = diag(X)*A*diag(X), such that each row or column of B sums up to the same value, thus eliminating the unevenness of representation across different bins. It has been well-established that a correct set of factors can be found using the method “matrix balancing” (MB) (c) and square root coverage (c0.5), have been proposed for use in the place of MB-derived correction factors  and found that the reciprocal of c indeed overcorrects, but the reciprocal of square root coverage undercorrects. The most accurate approximation is achieved at around an exponent of 0.87. Therefore, we used the reciprocal of (coffpeak)0.87 as the correction factor.The appropriate correction factors for adjusting the representation bias, ng” (MB) , and an ng” (MB) . Our goa factors . It was  factors . We examcis-contacts among its binding sites within a distance range from 20 kb to 1 Mb, and local maps of 21 × 21 bins centered at the contacts were extracted and aggregated. For the case of BLMP-1 regulated targets, all possible cis-contacts between BLMP-1 binding sites that involves a BLMP-1 regulated target were aggregated . The log2 fold change of the central point over the mean of the rest of the points in the aggregated map was calculated to measure the relative increase in contact frequency over local background. To assess statistical significance while controlling for accessibility and the distance between the pair of NFBSs, 1000 sets of random contacts were generated, each containing the same number of contacts with matching accessibility and distance as the NFBS contacts. Each of the 1000 random sets was aggregated and a relative increase in contact frequency was calculated in the same way as the NFBS set, forming a distribution of values against which the NFBS value was compared and a P-value generated, which was corrected for multiple testing using the FDR method. The rex–rex APA analyses used normalized and background-frequency-corrected contact maps of 10-kb resolution and a distance range of 100 kb to 4 Mb. Data sets , except for BLMP-1, which used MACS2  because such binding events are thought to represent non-sequence-specific TF binding or ChIP artifacts , maps of each contact region containing a domain of at least 5 kb together with up to 25 kb of the flanking domains of opposite type were extracted and aggregated. Where neighboring domains were <25 kb, only the domain was extracted. The aggregated contact was scaled to a square of 9 × 9 bins, and the flanking intervals were scaled to five bins wide. The logmet-2 set-25 ARC-C data for each of the domain sets as described above.To test the effect of local H3K9 methylation on TAD and compartment strength, we separated active and H3K27me3 domains into highly and lowly marked sets. For each active and H3K27me3 domain, the average coverage of H3K9me1, H3K9me2, and H3K9me3 was calculated using wild-type L3 ChIP-seq data . For eacblmp-1 ARC-C data. To normalize overall open chromatin measurements between blmp-1 and wild-type maps, we first fitted blmp-1 ACA measures as a linear function of the respective measures in wild type, that is, log2(blmp1_FC) = a + b * log2(N2_FC). The residuals were then used to measure the difference in contact frequency in blmp-1 relative to wild type. Statistical significance was assessed by 10,000-time bootstrapping the distribution of residuals, and P-values were adjusted by FDR .We used the procedure described above for wild type to measure the relative contact frequency between NFBSs in n = 5124), linked genes for which both were in the bottom 30% of all CV values (n = 2224), and linked genes for which both were in the top 70% of CV values (n = 879). To assess the statistical significance of the correlations, we generated control sets by sampling the same number of random pairs using genes from the respective linked gene sets while requiring the distribution of distance between the random pairs to match that of the observed set. Difference in correlation between the observed and the random control set was tested using a t-test: all (P = 7.77 × 10−27), bottom 30% (P = 4.38 × 10−06), and top 70% (P = 5.69 × 10−20).For genes linked by promoter–promoter interactions, we calculated Pearson correlation coefficients of gene expression across cell types using data from https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE144673. The code used in this paper is available at GitHub (https://github.com/nh3/ctk) and as Supplemental Code.The ARC-C, ChIP-seq, and RNA-seq data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


The cardiac neural crest cells (cNCCs) is a transient, migratory cell population that contribute to the formation of major arteries and the septa and valves of the heart. Abnormal development of cNCCs leads to a spectrum of congenital heart defects that mainly affect the outflow region of the hearts. Signaling molecules and transcription factors are the best studied regulatory events controlling cNCC development. In recent years, however, accumulated evidence supports that epigenetic regulation also plays an important role in cNCC development. Here, we summarize the functions of epigenetic regulators during cNCC development as well as cNCC related cardiovascular defects. These factors include ATP-dependent chromatin remodeling factors, histone modifiers and DNA methylation modulators. In many cases, mutations in the genes encoding these factors are known to cause inborn heart diseases. A better understanding of epigenetic regulators, their activities and their roles during heart development will ultimately contribute to the development of new clinical applications for patients with congenital heart disease. Neural crest cells are multipotent stem-like cells that are exclusive to vertebrate embryos. They are formed at the ectoderm-neural ectoderm border through epithelial-to-mesenchymal-transition during embryonic development. After formation, they quickly migrate ventrally to various destinations where they differentiate into different cell types based on local instructive cues . The carThe critical roles of cNCCs in PAA development and OFT septation are well established in mammalian development . AbnormaIn this section, we will summarize the known functions of epigenetic regulators during cNCC development that have been reported in recent years. These factors are classified into three groups: ATP-dependent chromatin remodeling factors, histone modifiers and DNA methylation modulators.The ATP-dependent chromatin remodeling complexes utilize energy from ATP hydrolysis to alter nucleosome structure or conformation, thereby regulating the accessibility of DNA to transcription factors and other regulators . There a(i)Arid1A, Arid1B, Brg1, Brm, Snf5/Ini1, or BAF57, which encode the subunits of the BRG1-associated factors (BAF) chromatin-remodeling complex , which encodes an ATPase subunit of BAF complex, plays crucial roles in cardiovascular development. In zebrafish, Brg1 is required for neural crest induction and differentiation  and shortened OFT at E10.5. In NCCs, BRG1 suppresses the expression of Ask1 (an apoptosis factor) and cip1p21 (a cell cycle inhibitor) to decrease cell death and promote cell proliferation. Furthermore, BRG1 also promotes Myh11 expression to support differentiation of NCCs into SMCs. These results suggest that Brg1 maintains cNCC progenitor pool by inhibiting apoptosis and promoting proliferation, regulating cNCC migration to the OFT and helping cNCC differentiation into vascular SMCs  at E10.5 , TOF and patent ductus arteriosus PDA; . More thntiation . Brg1-nual crest . Brg1 haat birth . While tg1± mice . To addrapproach . NCC spelar SMCs . In addiat E10.5 . Collect(ii)CHD7 gene, a member of CHD family, is the major cause of CHARGE syndrome  important for NCC development Baz1B (bromodomain adjacent to zinc finger domain 1B), which encodes WSTF (Williams Syndrome transcription factor). WSTF is a major subunit of two distinct ATP-dependent chromatin remodeling complexes: WICH (WSTF-ISWI chromatin remodeling complex), a subclass of the SWI/SNF class and WINAC (WSTF including the nucleosome assembly complex), a subclass of the ISWI class  is a congenital disorder characterized by developmental delays, learning challenges and cardiovascular disease. It occurs in approximately one per 7,500 births, and associated heart defects include supravalvular aortic stenosis and pulmonary stenosis. Most cases of WS are caused by the spontaneous deletion of a specific region on chromosome 7. One gene within that region is WI class . While Bpatients . In Xenontenance . Thus, tTwo of each core histones  form an octameric structure called the nucleosome core, which associates with wrapped DNA to organize into a nucleosome, the basic building block of chromatin. The core histones are highly evolutionary conserved, and they can be modified in a variety of ways, including methylation, acetylation, phosphorylation and ubiquitination . These pLysine can be mono-, di-, or tri-methylated, whereas arginine can be mono- or di-methylated. Histone methylation is associated with both transcriptional activation and repression . We will(i)H3K4 methylation and H3K27 demethylationKMT2D (histone-lysine N-methyltransferase 2D) and KDM6A  encodes for a ubiquitously expressed H3K4 methylase, which is predominantly associated with active gene transcription H3K9 methylation and demethylationEHMT1 (euchromatic histone-lysine N-methyltransferase 1), a gene encoding H3K9 methyltransferase, is the major cause of KS  is a rare genetic disorder characterized by childhood hypotonia, intellectual disability, and distinctive facial features. Approximately 40% of KS patients have CHDs, including ASD/VSD, TOF, aortic coarctation, bicuspid aortic valve, and pulmonic stenosis. Mutations in se of KS , 2006. Ereported . TherefoJmj (jumonji) gene, functions as histone demethylase. Jmj expression is detected in the heart throughout all the cardiac developmental stages. Homozygous Jmj mutant mice die soon after birth with various cardiovascular defects such as VSD and DORV (JmjD2A (Jumonji domain containing 2A) in chick embryos leads to down-regulated expression of neural crest specifier genes , resulting in abnormal neural crest derivatives at later embryonic development stages , and JmjD2A knock-down represses Sox10 transcription by inhibiting H3K9me3 demethylation H3K36 methylationWHSC1 and WHSC2) and LETM1 (leucine zipper and EF-hand containing transmembrane protein 1). The most established role of WHSC1 is that of an H3K36me3-specific histone methyltransferase, which is associated with both transcriptional activation and inhibition. In mouse embryonic hearts, loss of WHSC1 leads to reduced H3K36me3 at the Pdgfra locus, up-regulated transcription of Pdgfra and WHS-like defects including congenital cardiovascular anomalies  is a rare congenital neurodevelopmental disorder characterized by prenatal and postnatal growth deficiency, intellectual disability, seizure, craniofacial malformation and heart defects . The assnomalies . Therefo the PAs .Hdac3, which encodes histone deacetylase 3, results in perinatal lethality and severe cardiovascular defects including IAA-B, PTA, VSD, DORV, and aortic arch hypoplasia. The cardiovascular abnormalities are probably due to deficiency in smooth muscle differentiation, indicating that HDAC3 is required for differentiation of cNCCs into SMCs that are involved in septation of the distal OFT in mice  also play important roles in neural crest development, there is no evidence for their requirement in cNCCs DNMT3A and 3BDnmt3A homozygous null mice typically die 4 weeks after birth, and Dnmt3B homozygous null mice die in utero . Furthermore, DNMT3A directly represses the transcription of Sox2 and Sox3 through promoter DNA methylation  syndrome is a rare autosomal recessive disease with just less than 70 known cases at present . Approxito E16.5 . Sectionpatients . In contructures . This maructures .(ii)Folic acidFolR1 and Rfc1 are required for Sox2 methylation on neural crest region in mouse embryos, and knockdown of FolR1 and Rfc1 results in severe reduction of Sox10 expression and associated orofacial defects (MTHFS is detected (Folic acid is a B-vitamin crucial for vertebrate development . Folate  defects . Finallydetected . UltimatWe summarize the major genes discussed above in Despite the significant progress made on epigenetic regulation of cNCC development, there remain some particularly challenging questions warranting further investigation. First, many epigenetic regulators are expressed in different cell types, and how these factors execute their specific functions in cNCCs and other cardiac cell types remain highly elusive. One likely explanation is that these regulators interact with cell-type-specific transcriptional regulators to control expression of cell-type specific genes. In this case, understanding their interaction is particularly important, as it holds the key to designing specific therapeutic strategies for a particular cell type. Second, numerous examples have shown that epigenetic regulators from different classes can act in concert to regulate gene expression. How such harmony is achieved in cNCCs and other cell types remains largely unknown. This question is particularly challenging, as it requires a good understanding on how regulatory mechanisms act individually and in tandem. Third, a single epigenetic regulator may have distinct functions. For example, we recently showed that in addition to its well-known function in nucleosome remodeling, CHD7 can also recruit histone modifying enzymes to target loci . This laSY, JL, and KJ wrote the review manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Understanding how enhancers drive cell-type specificity and efficiently identifying them is essential for the development of innovative therapeutic strategies. In melanoma, the melanocytic (MEL) and the mesenchymal-like (MES) states present themselves with different responses to therapy, making the identification of specific enhancers highly relevant. Using massively parallel reporter assays (MPRAs) in a panel of patient-derived melanoma lines (MM lines), we set to identify and decipher melanoma enhancers by first focusing on regions with state-specific H3K27 acetylation close to differentially expressed genes. An in-depth evaluation of those regions was then pursued by investigating the activity of overlapping ATAC-seq peaks along with a full tiling of the acetylated regions with 190 bp sequences. Activity was observed in more than 60% of the selected regions, and we were able to precisely locate the active enhancers within ATAC-seq peaks. Comparison of sequence content with activity, using the deep learning model DeepMEL2, revealed that AP-1 alone is responsible for the MES enhancer activity. In contrast, SOX10 and MITF both influence MEL enhancer function with SOX10 being required to achieve high levels of activity. Overall, our MPRAs shed light on the relationship between long and short sequences in terms of their sequence content, enhancer activity, and specificity across melanoma cell states. Yet, sutor (TF) . Anothertor (TF) . The mostor (TF) . Massivetor (TF) . Howevertor (TF) . This isHere, we study enhancer location, specificity, and regulatory grammar in melanoma using a variety of MPRA strategies. Melanoma exhibits pronounced heterogeneity within and between patients . Two maiIn this study, we analyzed regions with MEL- and MES-specific H3K27ac chromatin marks proximal to differentially expressed genes. We designed MPRA experiments to test those regions at three different levels in a panel of patient-derived malignant melanoma (MM) lines. Our results precisely locate the origins of enhancer activity within the larger H3K27ac domains. In addition, we can accurately predict their subtype specificity and ultimately identify a set of rules governing MEL and MES enhancer activity. Furthermore, we show that a melanoma deep learning model  by selecting regions that are specifically acetylated in either the MEL or the MES melanoma cell state and located around differentially expressed genes in a panel of 12 melanoma lines: 3 MES lines  and 9 MEL lines that cover a spectrum from pure melanocytic to intermediate melanoma  , encompassing the entire H3K27ac regions. The tiles are 190 bp long with a 20 bp shift between consecutive tiles. The tiling for library A starts at nucleotide position 1 of the H3K27ac ChIP-seq regions while library B starts at position 11, resulting in a final tiling resolution of 10 bp when both sublibraries are taken into account.We also sought to probe the effect of mutations within putative TFBSs in the selected H3K27ac regions. We used Cluster-Buster CBust;  with posWe first transfected all MPRA libraries in the most melanocytic line, MM001 . Of the Next, we examined the activity of all 190 bp sequences tiled along the entire H3K27ac regions. We confirmed that the majority of active tiles (92.2%) are located within an ATAC-seq peak  and idencis-regulatory topics  gene, expressed specifically in MEL lines , indicating that ATAC-seq peaks are found within the selected acetylated regions and maintain the same differential signal. Active enhancers are found in the majority (14/18) of the MEL-specific acetylation regions with ATAC-seq signal. This trend is also visible in the ATAC-seq-based library, with most of the active enhancers detected in ATAC-seq peaks (13/19)  . HoweverIn conclusion, our enhancer selection resulted in a high rate of active enhancers in MM001 and the design of our MPRA libraries allowed us to precisely pinpoint the origins of, at least part of, the enhancer activity.Next, we transfected all libraries in the MES line MM029. The activity profiles in this line show that, as expected, the majority of MES enhancers display activity at both the H3K27ac and ATAC-seq level . In regiCOL5A1) gene, up to 100 kb upstream of the TSS, were found to be specifically acetylated in MES lines, and we included a total of seven into the library  and three MEL-intermediate lines . These three lines have high SOX10 and MITF expression , yet also show both marker expression and phenotypic characteristics typical for the MES subtype  . Furtherr = 0.64; MES vs. intermediate r = 0.89). The only difference resides in the strength of the enhancer activity, where MM001 has weak activity and intermediate lines have moderate activity for MES regions.Indeed, the enhancer activity profile for the H3K27ac library obtained in intermediate lines correlates well with MM001, except for MM074, which moderately correlates with all lines . InteresThe comparison of enhancer activity between subtypes highlights the particular case of the SOX10_–56-D region, a MEL-specific H3K27Ac region with enhancer activity in all cell lines . Based oWith the ATAC-seq region library, the correlation between lines remains the same with a clear separation of the MEL and MES lines and the same pattern in the proportion of active enhancers . The oveAt the tiling level, intermediate lines show an increased MES enhancer activity comparable to that in the MES lines . This reIn summary, MEL enhancers display a consistent and specific activity in MEL lines while MES enhancers are not only active in MES lines but also in intermediate lines and to a lesser extent in MM001. This behavior suggests that enhancer activity is driven by the expression of TFs responsible for the MEL and MES subtypes. SOX10, MITF, and TFAP have been identified as drivers of the MEL state and AP-1 and TEAD of the MES state . MM029 eTo better understand which TFs are important for MEL enhancer activity, we included tiles with MITF, SOX, and TFAP binding site mutations in our tiling libraries. SOX10 and MITF both represent established lineage TFs in melanocytes and in melanoma, and TFAP2 has been found to co-occur with MITF in active regulatory elements in melanocytes . SOX10 wand activity. Motif enrichment analysis on both the active and most accessible tiles in MEL lines identifies the E-box motif (MITF) as highly enriched in both active and accessible sequences while the SOX motif is only enriched in accessible tiles  with siRNA was performed on MM057 and MM087, shifting these lines to a MES phenotype, and was followed by ATAC-seq after 0, 24, 48, or 72 hr . After cTo further investigate a possible interaction between SOX10 and MITF and see if a MEL enhancer can be generated with only those two TFBSs, we designed sequences consisting of combinations of SOX dimer and MITF motifs spread over a 259 bp background sequence  and identified a modular and motif-centric definition of enhancer elements . The useDrosophila , comparable to other studies leveraging a similar approach . In contosophila . Those cosophila . This meThe influence of the size of the candidate enhancer has previously been assessed, although to a limited extent, and only by choosing sizes that are not representative of chromatin structures . Here, wMany genes are regulated by an ‘array’ of enhancers thought to cooperate to regulate target gene expression by forming a chromatin hub . For sevIntriguingly, we find a gradient of MES enhancer activity across our panel of cell lines, with MM001, intermediate, and MES lines having low, medium, and high activity, respectively. These enhancer profiles do not agree with their acetylation, ATAC-seq, and transcriptome profiles, which all show a strong intra-subtype homogeneity. This discrepancy was more pronounced with short 190 bp sequences.Our results suggest that AP-1 is responsible for the activity of MES enhancers, agreeing with its predominance in allele-specific chromatin accessibility variants in melanoma . Note thThe analysis of MEL regions revealed that both SOX and MITF binding sites independently contribute to enhancer activity. TFAP binding sites did not show any influence on enhancer activity in the tested H3K27ac regions and showed only a small enrichment in SOX10-dependent ATAC-seq peaks displaying activity . These results contradict previous observations of TFAP2 and MITF co-occurrence in active regulatory elements in melanocytes and of TFAP2 pioneer function during cell fate commitment in the neural crest . These dWith this study, we show that melanoma subtype-specific enhancers can be identified, even down to sizes of 190 bp. This opens up promising avenues to use information gathered from MPRAs to identify small, specific enhancers for enhancer and gene therapies. Our assay combining SOX and MITF motifs showed that new MEL enhancers can be designed by addition of the corresponding TFBSs to a random background sequence and that the level of activity can be precisely controlled by varying the number of TFBSs. Using genomic regions confirmed as MEL enhancers and adjusting the motif sequences and numbers could further improve enhancer design.The selection of H3K27ac ChIP-seq regions harboring potential state-specific enhancer activity was done as follows . DiffereAll PCR amplifications performed in this study make use of the KAPA HiFi HotStart ReadyMix . The amplification of the selected regions was done from MM074 genomic DNA in a 50 µl reaction. PCR fragments were then purified on a 0.8% agarose gel and the sequence was confirmed via Sanger sequencing. 53 regions could be successfully amplified . For theFor the generation of the CHEQ-seq intron library, amplified regions were reamplified with primers containing adaptors to allow recombination within the intron of the CHEQ-seq vector . The CHEQ-seq vector containing a random 18 bp BC downstream of the synthetic intron was linearized by inverse PCR with the primers ‘CHEQ-seq_lin_Intron_For’ and ‘CHEQ-seq_lin_Intron_Rev.’ Reamplified regions were mixed in equimolar ratio and introduced in the CHEQ-seq vector via In-Fusion reaction with a vector to insert ratio of 1:2. The In-Fusion reactions were dialyzed against water in a 6 cm Petri dish with a membrane filter MF-Millipore 0.05 µm  for 1 hr. Reactions were recovered from the membrane, and 2.5 µl of the reaction was transformed into 25 µl of Lucigen Endura ElectroCompetent Cells . Transformed bacteria were cultured overnight in a shaker before maxiprep.The sequences constituting the ATAC-based library were selected from ATAC-seq peaks from MM001, MM029, MM047, MM057, MM074, MM087, and MM099 overlapping with H3K27ac library regions. Among this subset, only peaks that overlap with regions identified as active in the H3K27ac library or that are assigned as MEL- or MES-specific regulatory regions (respectively represented by regions belonging to topics 4 and 7) in our previously published cisTopic analysis of ATAC-seq data from 16 human melanoma cell lines were retained . The finUsing a custom script, tiles were generated by selecting 190 bp from the start (library A) or position 11 (library B) of the H3K27ac selected regions and by switching every 20 bp in 3′ direction. Tile generation is stopped when the position of the final nucleotide of the tile is superior to the final nucleotide of the H3K27ac region. To generate mutated tiles, we first scanned all selected H3K27ac regions with CBust  and the Adaptor sequences were added to the tiles: ‘Adaptor_LibA_5'’ and ‘Adaptor_LibA_3'’ for library A and ‘Adaptor_LibB_5'’ and ‘Adaptor_LibB_3'’ for library B. The use of different adaptors for each library will result in the insertion of the library B 20 bp downstream of the library A, providing a slightly different surrounding context that combined with a high number of barcodes per enhancer aims at reducing experimental noise. Final libraries were synthesized via Agilent’s Oligonucleotide Library Synthesis Technology .Oligonucleotide libraries were resuspended in endotoxin-free TE buffer pH 8 to a final concentration of 20 nM. For each library, 10 PCR reactions were performed with 2 µl of resuspended library for 12 cycles with primers ‘Lib_A_amp_For’ and ‘Lib_A_amp_Rev’ or ‘Lib_B_amp_For’ and ‘Lib_B_amp_Rev.’ The PCR product was first cleaned up using MinElute (QIAGEN) with five PCR reactions per column, then pooled together and cleaned up a second time with 1.6× SPRI beads . The CHEQ-seq vector containing a random 17 bp BC upstream of the synthetic intron was linearized via inverse PCR with primers ‘CHEQseq_lin_A_For’ and ‘CHEQseq_lin_A_Rev’ or ‘CHEQseq_lin_B_For’ and ‘CHEQseq_lin_B_Rev.’ Amplified libraries and the corresponding linearized vector were combined in an NEBuilder reaction with a vector to insert ratio of 1:3.25. Dialysis, transformation, and maxiprep were performed similarly to the H3K27ac library.https://sites.google.com/site/anshulkundaje/projects/blacklists (hg19). We ran cisTopic  for models with a number of topics between 2 and 25. The best model was selected on the basis of the highest log-likelihood, resulting in 16 topics. We binarized the topics using a probability threshold of 0.99 and continued with topic 11, which contained regions that are accessible in baseline conditions but lose accessibility following SOX10 knockdown. A total of 1461 enhancers were chosen to be tested. To increase the resolution of the library, we tiled the sequences to 190 bp using a 120 bp sliding window across the enhancers, resulting in 6696 tiles. In addition, 100 shuffled negative control sequences were generated similarly to the tiling libraries  on two melanoma cells lines (MM057 and MM087)  were inserted in the sequence with a motif in the center and the other motifs placed upstream and downstream with a distance of 25, 50, or 75 bp . PacBio sequencing library preparation for both libraries was done by the Genomics Core Leuven (KU Leuven). Sequencing was done with a PacBio Sequel for long-read sequencing  with both libraries sequenced with one SMRT cell. We obtained 66,407 and 105,590 reads with three passes for CHEQ-seq 5′ and CHEQ-seq intron, respectively.Enhancer–BC assignment was done with a custom script. Briefly, enhancers and random BCs were independently extracted from the reads with Cutadapt . EnhanceA PCR amplification of the enhancer-specific BC, together with the random BC, was done with the primers ‘Enh-BC_ATAC_Stag = X_For’ and ‘Enh-BC_ATAC_Rev.’ Illumina sequencing adaptors were added during a second round of PCR with the primers ‘i5_Indexing_For’ and ‘i7_Indexing_Rev.’ After sequencing in NovaSeq600 for 50 cycles in read 1 and 49 cycles in read 2, enhancer BCs and random BCs were extracted from read 1 and read 2, respectively, with Cutadapt before being filtered for quality (Q > 30). Following assignment, 44 (95.7%) sequences could be identified, with an average of 34,815 BCs per enhancer.Cloned sublibraries were amplified via PCR with the primers ‘Enh-BC_Tiling-A_Stag = X_For’ (for sublibrary A and SOX10-KD library), ‘Enh-BC_Tiling-B_Stag = X_For’ (for sublibrary B), and ‘Enh-BC_Tiling_Rev.’ Illumina sequencing adaptors were added during a second round of PCR with the primers ‘i5_Indexing_For’ and ‘i7_Indexing_Rev.’ After sequencing in NovaSeq600 for 251 cycles in read 1 and 51 cycles in read 2, whole-length enhancers and random BCs were extracted from read 1 and read 2, respectively, with Cutadapt before being filtered for quality (Q > 30). Enhancer reads were mapped and linked to random BCs as previously described for CHEQ-seq 5′/intron for H3K27ac ChIP-seq regions. Following assignment, 7356 (99.2%), 7344 (99.8%), and 6773 (99.7%) sequences could be identified in the sublibraries A and B and the SOX10-KD library, respectively, with an average of 3096, 3021, and 8056 BCs per enhancer.This CHEQ-seq library was prepared for sequencing similarly to the CHEQ-seq enhancer tiling A sublibrary. After sequencing in NovaSeq600 for 50 cycles in read 1 and 49 cycles in read 2, enhancer BCs and random BCs were extracted from read 1 and read 2, respectively, with Cutadapt before being filtered for quality (Q > 30). Following assignment, 249 (99.6%) sequences could be identified, with an average of 276 BCs per enhancer.The cell lines used in this study were patient-derived melanoma lines obtained from the laboratory of Pr. Ghanem-Elias Ghanem . All lines have been identified by RNA-seq and ATAC-seq. Mycoplasma test was performed systematically before each experiment that would provide data for this study.-1 penicillin/streptomycin (Thermo Fisher Scientific). Cell cultures were kept at 37°C, with 5% CO2.All MM lines were cultured in Ham’s F10 nutrient mix  supplemented with 10% fetal bovine serum (Thermo Fisher Scientific) and 50 μg mlThe MPRA libraries were electroporated in 4–6 million cells each using the Nucleofector 2b or 4D  with 6 µg of plasmid DNA and program T-030 or Y-001 and DS-132, EH-116, or CM-134, respectively. For the CHEQ-seq 5′ and intron H3K27ac and CHEQ-seq ATAC libraries, one replicate was performed per cell line except for MM087, where three replicates were performed. For STARR-seq ATAC and the CHEQ-seq SOX-MITF combination library, one replicate was performed per cell line. For the CHEQ-seq enhancer tiling libraries, two replicates were performed per cell line except for MM087, where four replicates were performed. For the CHEQ-seq SOX10-KD library, three replicates were performed in MM087. Medium was changed 24 hr after electroporation. 48 hr post-electroporation, cells were detached from the plate using trypsin (Thermo Fisher Scientific). One-fifth of the cells was used for plasmid DNA extraction . The remaining cells underwent RNA extraction using the innuPREP RNA Mini Kit 2.0 , followed by mRNA isolation using the Dynabeads mRNA purification kit  and cDNA synthesis using the GoScript RT kit and oligo dT primer . For STARR-seq samples, a junction PCR was done for 12 cycles with the primers ‘STARR-seq_Junction_cDNA_For’ or ‘STARR-seq_Junction_plasmid_For’ and ‘STARR-seq_Junction_Rev’ followed by a PCR to amplify the enhancer for four cycles with the primers ‘STARR-seq_enhancer_Stag = X_For’ and ‘STARR-seq_enhancer_Rev.’ For CHEQ-seq samples, a PCR was performed to amplify the random BC from the plasmid DNA or cDNA samples for 16 cycles with the primer pairs ‘Cheq-seq_barcode_Intron_round1_Stag = 0_For’/‘Cheq-seq_barcode_Intron_round1_Rev’ for the CHEQ-seq intron H3K27ac library or ‘Cheq-seq_barcode_5'_round1_Stag = X_For’/‘Cheq-seq_barcode_5'_round1_Rev’ for all other libraries. To add Illumina sequencing adaptors, all samples were finally amplified by PCR for six cycles with the primers ‘i5_Indexing_For’ and ‘i7_Indexing_Rev.’ After confirmation of the fragment size with a Bioanalyzer, samples were sequenced at the Genomics Core Leuven (KU Leuven).Read processing following sequencing was performed with a custom bash script. First, random BCs were extracted using Cutadapt and filtered for a quality (Q-score) > 30. The number of reads per uniquely identified BC was counted and the name of the enhancer was assigned to the BC sequence based on the enhancer–BC assignment list for each library. Unassigned BCs were filtered out to obtain a final data frame containing the name of the enhancer, the BC sequence, and the number of reads.https://github.com/aertslab/Melanoma_MPRA_paper; swh:1:rev:a90ee6e58d16fbaf367e4e68dc3a3d6fd3bc9e85). Enhancers were first filtered based on the number of BCs identified in the sequencing reads. Thresholds of 5 (for the H3K37ac libraries and enhancer tiling libraries), 10 (for ATAC libraries), or 20 (for the SOX-MITF combinations library) BCs per enhancer were selected based on the complexity of the library and the sequencing saturation of the enhancer–BC assignment samples. For the remaining enhancers, BC counts were aggregated per enhancer and then a count per million (CPM) normalization was applied. Plasmid (input) and cDNA (output) samples were merged by keeping only enhancers remaining in both samples after filtering. Input normalization was done by dividing CPM normalized cDNA counts by CPM normalized plasmid counts, resulting in an FC value. For libraries with shuffled sequences, a basal expression normalization was further applied by dividing the FC value of the enhancer by the median FC value of the shuffled sequences. The MPRAnalyze method was tried as an alternative to our method and gave nearly identical results in the case of the H3K27ac CHEQ-seq intron library   were excluded.Differential motif enrichment between the active tiles and the remaining tiles for the enhancer tiling libraries and the SOX10-KD library was performed via HOMER findMotifs . For theEnhancer sequences for the enhancer tiling and SOX-MITF combinations libraries were scored with the DeepMEL2+ GABPA version of DeepMEL2 as described previously . To accoVisualization of nucleotide contribution to DeepMEL2 prediction score was done with DeepExplainer as described previously .Three MES-specific ATAC regions , three MEL-specific ATAC regions , and two inactive ATAC regions  were selected. For each sequence, TFBSs for AP-1, TEAD, MITF, SOX, and ZEB were manually identified from the DeepExplainer profile and mutated the same way as for the enhancer tiling library. For ZEB binding sites, the CAGGTG site was mutated into CGGATG. A total of 24 sequences were ordered from Twist Bioscience pre-cloned in the pTwist ENTR plasmid . RegionsMM001 and MM099 were seeded in 24-well plates and transfected with 400 ng pGL4.23-Enhancer plasmid + 40 ng pRL-TK renilla plasmid (Promega) with Lipofectamine 2000. One day after transfection, luciferase activity was measured via the Dual-Luciferase Reporter Assay System (Promega) by following the manufacturer’s protocol. Briefly, cells were lysed with 100 µl of Passive Lysis Buffer for 15 min at 500 rpm. 20 µl of the lysate were transferred in duplicate in a well of an OptiPlate-96 HB  and 100 µl of Luciferase Assay Reagent II were added in each well. Luciferase-generated luminescence was measured on a Victor X luminometer (PerkinElmer). 100 µl of the Stop & Glo Reagent was added to each well, and the luminescence was measured again in order to record renilla activity. Luciferase activity was estimated by calculating the ratio luciferase/renilla. This value was further normalized by the ratio calculated on blank wells containing only reagents. Three biological replicates were done per condition, except for all MLANA_5-I and IRF4_4-I sequences in MM001, where four biological replicates were done.ChIP-seq data for MITF, SOX10, JUN, and JUNB were CPM normalized with the bamCoverage function  . For anaMITF and SOX10 ChIP-seq in 501mel were downloaded from the GEO entry GSE61965 . TFAP2A H3K27ac ChIP-seq data used in this study were processed in a previous study  as folloATAC-seq data used in this study were processed in a previous study  as follohttps://www.ncbi.nlm.nih.gov/geo/) under accession number GSE180879.MPRA data generated for this study have been submitted to the NCBI Gene Expression Omnibus  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 3 peer reviewers, including Jian Xu as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Richard White as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Analysis of long and short enhancers in melanoma cell states" 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:1) Provide more details on the filtering and selection criteria for the identification of MES- and MEL-specific regions. A simple flowchart to illustrate the major selection criteria might be helpful.2) Consider improving the nomenclature for various enhancer regions, as commented by Reviewer #1.3) For the identification of candidate TFs and their combinations responsible for differential enhancer activity in MEL vs MES cells, DeepMEL2-based analysis does not seem to provide robust predictions. This is also exemplified by the analysis of binding sites for MITF and AP-1, as commented by Reviewer #2. Therefore, the authors may use available ChIP-seq data for candidate TFs and quantitatively compare signals at the respective enhancers in different cell states. This analysis may be further extended to other differentially enriched H3K27ac and/or ATAC-seq peaks to evaluate whether the identified TFs are generally associated with differential H3K27ac and/or ATAC-seq signals between cell states.4) Provide information about AP-1 expression in MEL-intermediate cells with and without SOX10 knockdown, as commented by Reviewer #2.5) While altering TF binding sites at native gene loci may be technically challenging (and would be good for future studies), it would be helpful to include an analysis of SOX10, MITF or AP-1 mutations in the context of a larger MPRA sequence on a few test cases to determine the effect on enhancer activity, as commented by Reviewer #3. This would be informative for future investigators that the MPRA assay would be a useful method for figuring out which TF sites would be good to go after in vivo.6) Additional discussion on the functional roles of SOX10 and MITF in melanoma pathophysiology would be helpful to highlight the broad relevance of findings in the current study.7) Consider revising figures to increase readability.Reviewer #1 (Recommendations for the authors):1. It would be helpful to provide more details on the filtering and selection criteria for the identification of 35 MES- and 18 MEL-specific regions as H3K27ac ChIP-seq based library. Is there any normalization steps for ChIP-seq and ATAC-seq signals across samples? How are enhancer target genes defined? A simple flowchart to illustrate the major selection criteria might be helpful.2. Line 102, 18 MES-specific ATAC-seq peaks were selected from 35 MES-specific H3K27ac peaks. Are there H3K27ac peaks without selected ATAC-seq peaks? If so, why were those H3K27ac peaks excluded for the current study?3. It is advised to improve the nomenclature for various enhancer regions. Instead of using Gene_1, Gene_2, etc, it would be more informative to show the location and distance of the enhancers to the TSS of putative gene targets. For example, Gene_+10kb indicates an enhancer located 10kb upstream of the TSS while Gene_-10kb indicates 10kb downstream of TSS. The distance could be determined by distance between the peak summit of ATAC-seq (or H3K27ac if they overlap) and TSS.4. For the identification of candidate TFs and their combinations responsible for differential enhancer activity in MEL vs MES cells, DeepMEL2-based analysis does not seem to provide robust predictions. Therefore, the authors may also use available ChIP-seq data for candidate TFs and quantitatively compare the normalized ChIP-seq signals at the respective enhancers in different cell states. This analysis may also be extended to include other differentially enriched H3K27ac and/or ATAC-seq peaks to evaluate whether the identified TFs (e.g. SOX10-MITF for MEL and AP1 for MES) are generally associated with differential H3K27ac and/or ATAC-seq signals between cell states.Reviewer #2 (Recommendations for the authors):This manuscript is mostly convincing, but a few aspects of data need to be clarified to strengthen the main conclusions.1. In Figure 1a, a MITF binding site was predicted within IRF4 enhancer and a serial of tiles with mutated MITF motif were constructed based on the prediction. However, in comparison with the MITF ChIP-seq track, the predicted MITF motif apparently located outside of the MITF ChIP peak. This is also true for predicted AP-1 binding sites in COL5A1 and HEG1 enhancers. How accurately can those predicted TF bind sites based on DNA sequence represent the TF binding on chromatin in vivo?2. SOX10 KD shifts MEL-intermediate cell to MES phenotype and shapes the landscape of chromatin accessibility. Upon SOX10 KD, does AP-1 expression increase and gained ATAC-seq peaks display high predictions score for topic 19?3. Authors conclude that AP-1 binding alone drives MES state specific enhancer activity. However, MES enhancers, such as COL5A1_5 region, show strong enhancer activities by reporter assay in MEL-intermediate cell as well, despite that the endogenous loci remain close in those cells . How much AP-1 protein is expressed in MEL-intermediate cells?Reviewer #3 (Recommendations for the authors):To address weaknesses:1) To better contextualize the role of specific TFs in regulating a given gene in Weakness 1 – Altering or deleting TF binding sites or subregions of specific enhancers, especially at the scale of test sequences discussed in this study, at the native gene locus seems destined for future studies, although could be argued is the truest measure of the role of an individual TF site/enhancer subregion. As a potentially more intermediate/approachable assay, could an analysis of SOX or MITF or AP-1 mutations in the context of a larger MPRA sequence (e.g. 510 bp or in the H3K27Ac 1-2 kb sequence) in addition to the tiled versions  be done on a few test cases to capture enhancer context better. As the authors note on page 19, lines 496-497, ~500 bp sequences may be needed to capture sufficient complexity of the enhancer.2) Brief mention of the demonstrated functional role of SOX10 and MITF protein in melanoma onset/growth/survival could be included . The binding sites for these TFs are clearly relevant in the assays shown, but the larger context/support for these TFs role in melanomagenesis could thus be better highlighted. Similarly, SOXE dimer sites have been noted to be over-represented in melanoma-specific DNase I hypersensitive sites previously  and supports the papers conclusions.Data availability appears appropriate. Reviewer #1 (Recommendations for the authors):1. It would be helpful to provide more details on the filtering and selection criteria for the identification of 35 MES- and 18 MEL-specific regions as H3K27ac ChIP-seq based library. Is there any normalization steps for ChIP-seq and ATAC-seq signals across samples?DESeq function from R/Bioconductor package DESeq2 v1.4.5 was used to detect differentially active regions between the two invasive and nine proliferative samples. P-values were adjusted for multiple testing according to Benjamin-Hochberg. On the H3K27ac signal, applying a threshold adjP ≤0.05 and log2FC ≥|1| led to 13,671 regions that were more active in invasive samples and 7,146 regions more active in proliferative samples. This strategy was then compared with differential peak calling by MACS2 algorithm , with the proliferative samples as treatment and invasive samples as control. This supported the differentially called regions resulting in final sets of 13,453 invasive and 6,669 proliferative regions.We apologise for this omission and now provide the following details. H3K27ac ChIP-seq data used in this study were processed in a previous study  as follows: ChIP-seq reads were mapped to the genome (hg19 with GENCODE v18 annotation) using Bowtie2 v2.1.0. The coverage of candidate regulatory regions was computed using BEDTools. Subsequently, read counts were transformed using a regularized log transformation. The Downstream analysis and filtering process resulting in the selection of the 35 MES- and 18 MEL-specific regions are described in the “Methods > MPRA design and cloning > H3K27ac based library” section and illustrated with a flowchart in Supplementary Figure 1.We have limited ourselves to the selection of 65 H3K27ac regions (down to 53 after some amplification failures) because we had to design primers and perform PCR amplification from genomic DNA for each sequence individually.6/; or made by bamCoverage  .ATAC-seq data used in this study were processed in a previous study  as follows: Reads were mapped to the human genome (hg19-GENCODE v18) using Bowtie 2 (v2.2.6) . Mapped reads were sorted using SAMtools (v1.8) , and duplicates were removed using Picard MarkDuplicates (v1.134). Reads were filtered by removing mitochondrial reads and filtering for Q> 30 using SAMtools. Peaks were called using MACS2 (v2.1.2) (Gaspar 2018). Blacklisted regions (ENCODE) and peaks overlapping with alternative chromosomes and ChrM were removed. Summits were extended by 250 bp up- and downstream using slopBed  , providing human chromosome sizes. Peaks were normalized for the library size, and overlapping peaks were filtered using the peak score by keeping the peak with the highest score. Normalized bigWigs were either made from normalized bedGraphs using as scaling parameter  1 × 10This extra information has been added to the “Methods > ChIP-seq, ATAC-seq and RNA-seq public data” section.How are enhancer target genes defined?The assignment of target genes to the selected regions was indeed not clearly mentioned. Following i-cisTarget analysis and gene filtering, genes are ordered by differential expression in MES vs MEL lines. The TSS of the top and bottom genes is used as the starting point to manually search for H3K27ac regions specific for MES and MEL respectively. Identified regions, within ~150kb of the TSS, are associated with the differentially expressed gene, even if some of them are closer to or within another gene.CDH1 enhancers. CDH3 TSS was used as a starting point to search for H3K27ac regions. Two differentially acetylated regions were found in an intron of CDH1 downstream of CDH3 . Because CDH1 is also part of the differentially expressed genes used to search for regions, both selected regions have been assigned to CDH1. Both genes are more expressed in MEL lines so it is possible that those enhancers regulate them both.An exception was made for The part covering the manual selection of the H3K27ac in the Methods section has been rephrased.A simple flowchart to illustrate the major selection criteria might be helpful.We thank the reviewer for this suggestion and have now added the flowchart as a novel panel in Figure 1—figure supplement 1 (and refer to it in the text).2. Line 102, 18 MES-specific ATAC-seq peaks were selected from 35 MES-specific H3K27ac peaks. Are there H3K27ac peaks without selected ATAC-seq peaks? If so, why were those H3K27ac peaks excluded for the current study?MAP3K14_1) and the 15 others did not display activity in the H3K27ac MPRA assay and did not overlap with topic 4 (MEL-specific) or topic 7 (MES-specific) regulatory regions from our previously published cisTopic analysis of ATAC-seq data from 16 human melanoma cell lines .Out of 53 MES/MEL-specific H3K27ac regions, 37 had ATAC-seq peaks selected in them (for 9 regions 2 ATAC-seq peaks per region were selected). Of the 16 H3K27ac regions where no ATAC-seq peak was selected, one does not contain ATAC-seq peaks  and TSS.TYR TSS.We thank the reviewer for this suggestion. We changed the name format of the enhancer regions for the H3K27ac library to GENE_(-)00-X where “(-)00” is the distance (in kb) between the TSS and the highest peak summit of the H3K27ac region. “X” is the gene annotation of the region with “P” for promoter, “I” for Intron, “E” for Exon, “D” for distal and “3U” for 3’ UTR. This annotation is based on the GENCODE V38 (GRCh37) basic gene annotation. For example, TYR_-9-D (previously TYR_1) in a distal region located 9 kb upstream of the This nomenclature is conserved for the ATAC based library (e.g. TYR_-9-D keeps the same name in both the H3K27ac library and the ATAC library). When 2 ATAC regions are located within the same H3K27ac region, they are distinguished by suffixes A and B (e.g. KIT_1A and KIT_1B are now KIT_114-DA and KIT_114-DB).A description of this new nomenclature has been added to the method section of the paper.4. For the identification of candidate TFs and their combinations responsible for differential enhancer activity in MEL vs MES cells, DeepMEL2-based analysis does not seem to provide robust predictions. Therefore, the authors may also use available ChIP-seq data for candidate TFs and quantitatively compare the normalized ChIP-seq signals at the respective enhancers in different cell states. This analysis may also be extended to include other differentially enriched H3K27ac and/or ATAC-seq peaks to evaluate whether the identified TFs (e.g. SOX10-MITF for MEL and AP1 for MES) are generally associated with differential H3K27ac and/or ATAC-seq signals between cell states.Thank you for the suggestion. We used ChIP-seq data for MITF, SOX10 and AP-1 (JUN and JUNB) generated in melanoma cell lines and compared them to the CHEQ-seq values of the H3K27ac regions tested in this study. As expected, regions with high MITF and/or SOX10 ChIP-seq also have high CHEQ-seq activity in MM001 (MEL) and regions with high AP-1 ChIP-seq also have high CHEQ-seq activity in MM029 (MES). Extending the analysis to all MEL vs MES differentially acetylated regions from Verfaillie et al., 2015, we find a significant enrichment of MITF/SOX10 and AP-1 ChIP-seq signal in MEL and MES regions respectively .These results have been added to the relevant Results section of the paper and in Figure 5—figure supplement 2 and Figure 5—figure supplement 3.Reviewer #2 (Recommendations for the authors):This manuscript is mostly convincing, but a few aspects of data need to be clarified to strengthen the main conclusions.1. In Figure 1a, a MITF binding site was predicted within IRF4 enhancer and a serial of tiles with mutated MITF motif were constructed based on the prediction. However, in comparison with the MITF ChIP-seq track, the predicted MITF motif apparently located outside of the MITF ChIP peak. This is also true for predicted AP-1 binding sites in COL5A1 and HEG1 enhancers. How accurately can those predicted TF bind sites based on DNA sequence represent the TF binding on chromatin in vivo?We decided to mutate only the highly conserved motifs in order to keep the number of tiles manageable . UnfortuDetails regarding the mutation of only highly conserved motifs has been added to the “Methods > MPRA design and cloning > Enhancer tiling libraries” section.2. SOX10 KD shifts MEL-intermediate cell to MES phenotype and shapes the landscape of chromatin accessibility. Upon SOX10 KD, does AP-1 expression increase and gained ATAC-seq peaks display high predictions score for topic 19?SOX10 knock down (KD), several members of the AP-1 family are upregulated in MM087 . How much AP-1 protein is expressed in MEL-intermediate cells?We do not have AP-1 protein expression data for MEL-intermediate lines. However, transcript levels from scRNA-seq data  show expression of FOSL1 in all MEL lines except MM001 , as well as in MES lines . If we lReviewer #3 (Recommendations for the authors):To address weaknesses:1) To better contextualize the role of specific TFs in regulating a given gene in Weakness 1 – Altering or deleting TF binding sites or subregions of specific enhancers, especially at the scale of test sequences discussed in this study, at the native gene locus seems destined for future studies, although could be argued is the truest measure of the role of an individual TF site/enhancer subregion. As a potentially more intermediate/approachable assay, could an analysis of SOX or MITF or AP-1 mutations in the context of a larger MPRA sequence (e.g. 510 bp or in the H3K27Ac 1-2 kb sequence) in addition to the tiled versions  be done on a few test cases to capture enhancer context better. As the authors note on page 19, lines 496-497, ~500 bp sequences may be needed to capture sufficient complexity of the enhancer.We thank the reviewer for their suggestions. To confirm the function of the SOX, MITF and AP-1 in a larger context, we now generated mutant versions of 501 bp ATAC regions and evaluated their activity via luciferase assay in both a MEL and a MES line.We used the ATAC regions instead of H3K27ac regions for the following reasons:1. As opposed to H3K27ac regions (which vary between 1.2 and 2.9 kb), all ATAC region sequences are 501 bp long, resulting in luciferase plasmids with identical size to avoid measurement bias due to variability in transfection efficiency.2. The ATAC regions correspond to the accessible part of the genome where the TFs are bound.3. The ATAC regions recapitulate the enhancer activity of the broader H3K27Ac regions.4. With lengths up to several kb, as is the case for many H3K27ac regions, synthesis becomes impractical.Validation via luciferase assay instead of MPRA allows us to simultaneously study the effect of TFBS mutation in the context of ATAC regions and validate our MPRA results in a different assay, by re-assessing the activity of wild type sequences.Three MES-specific , three MEL-specific  and two inactive ATAC regions  were selected. For each sequence, TFBSs for AP-1, MITF, SOX and ZEB were manually identified from the DeepExplainer (DeepMEL2) profiles and mutated the same way as for the enhancer tiling library. For ZEB binding sites, the CAGGTG site was mutated into CGGATG (mutation of the 2 nucleotides with the highest weight in the hocomoco__ZEB1_HUMAN.H10MO.B PWM). A total of 24 sequences were ordered from TWIST Bioscience pre-cloned in the pTwist ENTR plasmid (Supplementary table 1). Regions were introduced in the pGL4.23-GW luciferase reporter plasmid (Promega) via Gateway LR recombination reaction (Invitrogen) and transformed into Stellar chemically competent bacteria (Takara). Plasmid minipreps are performed using the NucleoSpin Plasmid Transfection-grade Mini kit (Macherey-Nagel) and are sequenced with Sanger sequencing to confirm the correct insertion of the regions in the destination plasmid.We opted to use MM001 (MEL) and MM099 (MES) for these experiments. MM099 was selected over MM029 because of its better transfectability with Lipofectamine 2000.The luciferase assay confirmed both the activity and specificity of the wild type sequences in the target cell type and the importance of each TF for enhancer activity. A significant drop in luciferase activity is observed in MM001 and MM099 when, respectively, MITF/SOX and AP-1 predicted binding sites are mutated. The mutation of ZEB motifs results in an increase in activity of MLANA_5-I and IRF4_4-I in MM001, as predicted by DeepMEL2, whereas in MES enhancer the effect of ZEB is found to be weaker.Results and Methods have been added to the paper and the figure has been added as Figure 5—figure supplement 3 and Figure 6—figure supplement 3.2) Brief mention of the demonstrated functional role of SOX10 and MITF protein in melanoma onset/growth/survival could be included . The binding sites for these TFs are clearly relevant in the assays shown, but the larger context/support for these TFs role in melanomagenesis could thus be better highlighted. Similarly, SOXE dimer sites have been noted to be over-represented in melanoma-specific DNase I hypersensitive sites previously  and supports the papers conclusions.The following text has been added to the “Result > Key transcription factor binding sites explain melanoma enhancer activity” section:“SOX10 and MITF both represent established lineage transcription factors in melanocytes and in melanoma and TFAP2 has been found to co-occurrence with MITF in active regulatory elements in melanocytes . SOX10 was previously shown to be necessary for melanoma formation and maintenance by controlling cell survival and cell cycle . MITF is considered to be a melanoma oncogene  and has been implicated in various cellular processes .”