The functioning of a cell at various organizational levels is determined by the interactions between macromolecules that promote cellular organelle formation and orchestrate metabolic pathways via the control of enzymatic activities. Although highly specific and relatively stable protein-protein, protein-DNA, and protein-RNA interactions are traditionally suggested as the drivers for cellular function realization, recent advances in the discovery of weak multivalent interactions have uncovered the role of so-called macromolecule condensates. These structures, which are highly divergent in size, composition, function, and cellular localization are predominantly formed by liquid-liquid phase separation (LLPS): a physical-chemical process where an initially homogenous solution turns into two distinct phases, one of which contains the major portion of the dissolved macromolecules and the other one containing the solvent. In a living cell, LLPS drives the formation of membrane-less organelles such as the nucleolus, nuclear bodies, and viral replication factories and facilitates the assembly of complex macromolecule aggregates possessing regulatory, structural, and enzymatic functions. Here, we discuss the role of LLPS in the spatial organization of eukaryotic chromatin and regulation of gene expression in normal and pathological conditions. The eukaryotic cell nucleus contains several functional compartments, such as the nucleolus, speckles, Cajal bodies, and PML bodies . These cLLPS occurs when the concentration of interacting molecules reaches a threshold value, which can be achieved within a local neighborhood due to a gathering of interacting molecules on a platform possessing specific affinity sites for macromolecules capable of forming phase condensates . This prLLPS first drew attention in connection with the assembly of the so-called nuclear bodies, such as the nucleolus, nuclear speckles, Cajal bodies, and ND10 bodies ,38. NuclThe role of LLPS in global nuclear space organization is likely not restricted to the assembly of nuclear bodies. The recent model of eukaryotic cell nucleus organization discriminates the chromatin domain represented by chromosomal territories and the interchromatin compartment (IC) ,43. The 3D genome organization has become a hot topic in molecular biology because it has been demonstrated that for transcription activation remote enhancers should establish spatial contact with target promoters, a process that is only possible at the 3D genome level ,49,50. IChromatin fiber can be regarded as a polymer comprising alternating blocks of different nature . UndSeveral observations show that LLPS is essential for the assembly of both constitutive and facultative heterochromatin. It has long been assumed that the dense packaging of DNA in heterochromatin physically prevents DNA accessibility to transcription factors and components of transcription machinery ,69. ReceFurthermore, some essential genes are located in heterochromatin and are transcribed . It thusAnother nuclear protein that is likely to contribute to LLPS in the pericentromeric chromatin is Scaffold Associated Factor B (SAFB), previously known as nuclear matrix protein . SAFB inAlthough all of the above-discussed observations suggest the role of LLPS in heterochromatin formation, the typical heterochromatin domains possess some features that distinguish them from liquid condensates. These domains do not necessarily have a round shape, and their crucial components, such as HP1, do not rapidly exchange with the nucleoplasmic pull. It is, therefore, possible that after being initially assembled, the LLPS heterochromatic domains eventually undergo gelation ,75. GelaWhatever the mechanism of heterochromatin domain formation , it is likely to be controlled by epigenetic modifications of histone tails. Of note, nucleosome arrays lacking these tails do not produce liquid droplets. Acetylation of histone tails in pre-formed droplets causes dissolving of droplets . BridginAnalysis of Hi-C maps has demonstrated that spatial interactions between remote genomic elements exist preferentially within A and B chromatin compartments but to a much lesser extent between these compartments .Active chromatin is less densely packed as compared to heterochromatin. Within the A chromatin compartment, there are no general organizers, such as HP1 or Polycomb proteins. However, long-distance interactions within the A compartment are common and, furthermore, in some cells, they are more pronounced than long-distance interactions within the B compartment . Most ofBesides transcriptional factories, nuclear speckles also represent LLPS-derived compartments that attract active genes ,94,95. AThe list of liquid nuclear compartments to which various genes may be recruited is not limited by transcription factories and nuclear speckles. A subset of genes is attracted to Cajal bodies ,100, andAn important feature of the transcription control system in higher eukaryotes is the presence of remote enhancers that can be located hundreds of kilobases away from target promoters ,105. TheBeing brought to spatial proximity, liquid compartments associated with an enhancer and a promoter fuse, giving rise to a common compartment rich in proteins necessary for effective transcription initiation  107,116116. Of nA characteristic feature of many eukaryotic promoters is the presence of paused Pol II elongation complexes . The regRecent observations suggest that distinct liquid condensates are formed at gene bodies in the course of transcription ,131. TheThe LLPS process being determined by the structure of cellular proteins and RNAs is affected by genetic mutations in a number of severe diseases, such as neurodegenerative disorders  and cancThe same is true for Rett syndrome (RTT), which is a postnatal neurodevelopmental disorder characterized by mental disability and autism-like symptoms. In RTT, different mutations in the C-terminal IDR and structured methyl-binding domain of the methyl CpG binding protein 2 (MeCP2) suppress the ability of this protein to form condensates in vivo . In partThe expansion of short nucleotide repeats is another example of genetic deficits affecting LLPS. It has been shown that CAG multiplication, which is characteristic of Huntington’s disease and spinocerebellar ataxias, promotes RNA aggregation in vivo and in vitro . CAG-conThe importance of simple physicochemical processes in the organization of living systems has long been known. A good example is the formation of membranes, which is guided by simple physicochemical processes. The role of such processes in the formation of non-membranous structures became apparent only relatively recently after the demonstration of the importance of forces arising under conditions of macromolecular crowding for maintaining the integrity of various structures in the cell nucleus. Separation of liquid phases is another physicochemical phenomenon that plays an extremely important role in the functioning of living systems due to the fact that it is this phenomenon that underlies the compartmentalization of the intracellular space. Compartmentalization is one of the fundamental characteristics of a living cell. Without intention to discuss various theories of the origin of life, we only note that all of these theories postulate the separation of a certain protocell from the surrounding inanimate matter. It is also easy to see that cell compartmentalization became more complicated with evolution. Functional compartmentalization of the eukaryotic cell nucleus is interconnected with the spatial genome organization and underlies the realization of genome activities such as replication and transcription. Various regulatory events, such as establishing enhancer-promoter communication, also rely on dynamic compartmentalization manifested by the assembly of activating compartments via LLPS. To this end, it is not surprising that compromising LLPS results in the development of various diseases, including cancer. The ambitious task is, therefore, to learn how one can modify and control LLPS within living cells. Of course, this control must be targeted. As discussed above, in many cases, the macromolecules undergoing LLPS are gathered on a certain platform, which may be protein, RNA, or even DNA. The clearest method to disrupt liquid condensates of a particular type is, thus, to target this platform. The approach would depend on the nature and stability of a platform, the number of interacting molecules, and the properties of interacting sites. One solution is to suppress the synthesis of a platform (if the platform is RNA or protein). However, the possibility of blocking interacting sites with some drugs may also be considered. Whatever solution is found, we may expect that drugs affecting the integrity of macromolecular condensates will eventually be developed. The cell nucleus is an equilibrium system stabilized by various interactions of different nature.


Super-enhancers (SEs) are clusters of highly active enhancers, regulating cell type-specific and disease-related genes, including oncogenes. The individual regulatory regions within SEs might be simultaneously bound by different transcription factors (TFs) and co-regulators, which together establish a chromatin environment conducting to effective transcription. While cells with distinct TF profiles can have different functions, how different cells control overlapping genetic programs remains a question. In this paper, we show that the construction of estrogen receptor alpha-driven SEs is tissue-specific, both collaborating TFs and the active SE components greatly differ between human breast cancer-derived MCF-7 and endometrial cancer-derived Ishikawa cells; nonetheless, SEs common to both cell lines have similar transcriptional outputs. These results delineate that despite the existence of a combinatorial code allowing alternative SE construction, a single master regulator might be able to determine the overall activity of SEs. Estrogen receptor alpha (ERα) is a well-studied member of the nuclear receptor (NR) superfamily and functions as a master regulator in several cell types, including breast, ovarian, and endometrial cancer cells ,2,3. TheDNA binding by the receptor/ligand complex can occur directly through the estrogen response element (ERE) or via indirect protein-protein interactions ,9,10,11.In the vicinity of highly expressed genes, multiple TFs are recruited to activate clusters of enhancers within a relatively small stretch of DNA, which are also known as super-enhancers (SEs) or clustered enhancers ,18. SEs We previously presented that the formation of ligand-inducible ERα-driven SEs depends on the presence of canonical EREs, which can initiate further ERα binding events at nearby and even less specific binding sites ,16. ThesComparing different cell types, enhancers show an extremely high level of variability . By testn = 3872) and ~1.9 times more abundant in Ishikawa (n = 2138) cells than those present in both cell lines (n = 1124)  . The pre = 1124) . The resThe first substantial difference observed between the three identified clusters was seen in their enriched DNA motifs . Within In MCF-7 cells, in addition to ERα, forkhead box A1 (FoxA1) is the most influential TF and is present in approximately half of ERα-bound genomic regions even in the absence of E2 ,33. FoxAAll TF families that had their motifs enriched at the sites bound by ERα identified in Ishikawa cells were previously directly associated with ERα in endometrial cancer . TEA domBased on these initial findings, we carried out a detailed investigation of how different TFs contribute to the formation of both cell line-specific and shared ERα-driven SEs. First, for validation, we mapped the matrix of the identified DNA motifs and found that the shared ERα binding sites showed large numbers of ERE and smaller numbers of other elements, whereas the cell line-specific enhancers showed expected motif distribution patterns: Fox and AP2 motifs were enriched at MCF-7-specific ERα binding sites, while TCF, TEAD, and SIX motifs were enriched at Ishikawa-specific ERα binding sites . Furtherp > 0.05; not significant). These results indicated that the two cell lines use different sets of TFs and that motif distribution within SEs may represent a DNA-encoded component of SE-driven transcription regulation.Generally, motif strengths correlated well with motif distribution patterns; however, TEAD motifs did not show significantly different motif strengths between the analyzed clusters (FOXA1 and TFAP2C (encoding AP2γ), ESR1 (encoding ERα) also showed much lower expression in Ishikawa cells than in MCF-7 cells. This result is consistent with the less prominent dominance of ERα in Ishikawa cells than in MCF-7 cells and partially consistent with substitution or supplementation of its function by other TFs  . This heWWC1 locus was the opposed binding strength between ERα and its collaborating factors in Ishikawa cells  per peak, and in some cases, none of the motif matrices used could be identified C. WithinIn the next step, we investigated the transcriptional outputs, namely, the maintained expression levels of the genes possibly regulated by cell line-specific and shared SEs identified in both cell lines upon E2 treatment (Ishikawa cells upon 240 min and MCF-7 cells upon 320 min of E2 treatment) A–C. Notably, the annotation of regulatory sites to proper genes represents a limitation to any study without using e.g., 3C-based methods, and we did not consider transient gene induction events. According to the main characteristic of SEs (these cause high expression), an asymmetric distribution pattern shifted to MCF-7-specific genes was observed for MCF-7-specific SEs. Nevertheless, several genes showed similar gene expression in both cell lines (<2-fold difference), and a number of genes showed higher expression in Ishikawa cells than in MCF-7 cells (>2-fold difference), although the enhancers’ coverage by ERα was not high enough to call them SEs in Ishikawa cells A. The hiAs SEs are responsible for cell identity, understanding their mechanism of action is indispensable for improving our knowledge of the regulation of gene expression in general and cellular identity in particular . The recPrevious studies focused mostly on changes in SEs during differentiation; here we examined how SE regions driven by the same master TF behave in two different cancer cell lines. We identified 392 ERα-driven SEs from the breast cancer-derived MCF-7 cells and 618 ERα-driven SEs from the endometrial cancer-derived Ishikawa cells. By examining their unique components, the first evidence was that despite a small overlapping subset, which likely involves conserved binding sites driven by strong EREs, cell type-specific sites indicated the presence of quite different TF binding motifs. These motifs were indeed bound by their recognition proteins, and through protein-protein interactions, some of them have a great affinity for each other. Moreover, certain TF partners might make ERα a hormone-sensitive coregulator, which increases TF binding affinity upon ligand treatment. However, additional investigations using, for example, mass spectrometry, protein interaction assays, and structural biochemical techniques are needed to identify all major components and oligomers of these cell line-specific complexes.Although in the MCF-7 cell line ERα-dominated SEs are also modulated by the well-known FoxA1 and AP2γ TFs, as described previously, ERα has no coequal TF partner. In Ishikawa cells, we assumed the existence of at least a tripartite protein complex composed of TEAD4, TCF12, FoxM1, or C/EBPβ in which FoxM1 might have the highest affinity for ERα. However, the role of FoxM1 might be only secondary without its own response elements, and there can be additional TFs playing roles in the assembled regulatory complexes. Of course, the overall structure of a complex based on ChIP-seq data cannot be determined, and further specific experiments supporting this model should be applied. In this study, we carried out a theoretical investigation by using a large publicly available data set derived from two cell lines; however, the proteins identified based on their DNA motifs are indeed parts of a complex acting at SEs and some of them (TEAD4 or TCF12) might have a so-far unknown pioneer role.Out of the predicted 392 (MCF-7) and 618 (Ishikawa) ERα-driven SEs, only 99 regions overlapped in the two cell lines where ERα was recruited in an alternating fashion, depending on the presence of cell line-specific co-factors and their motif(s). In Ishikawa cells, three-quarters of these ERα binding sites required the presence of at least two collaborative factors. By contrast, in MCF-7 cells, only half of the binding sites showed the presence of both FoxA1 and AP2γ or even TEAD4/TCF12. This observation may be explained by ERα binding directly to DNA through the ERE, and FoxA1 or AP2γ rather facilitates and/or stabilizes their presence in MCF-7 cells. By contrast, in Ishikawa cells, except for strong canonical EREs, ERα binding occurs almost exclusively through DNA binding collaborating factors. Overall, we observed that depending on the expressed collaborating TFs, ERα behaves in very different ways. One strong ERE might be sufficient for ERα binding but it is basically insufficient for regulation. Nearby motifs—even within the same enhancer or distant ones within the enhancer cluster—will determine the functional ERα TFBSs, and ultimately the whole cistrome. These results explain how gene regulation is carried out by a combination of DNA sequences selected by a combination of expressed TFs. This is a two-level combinatorial code of regulation further supported by specific co-regulators establishing permissive chromatin environment and chromatin interactions.BCAS3, as an amplified and overexpressed gene and ZNF217, the candidate organizer of repressive histone modifiers) (highlighted in blue in ANXA2 and ATF4) , further demonstrating the importance of understanding the function of strong/canonical regulatory elements or complete units, such as SEs . As we used data from ECC-1 isolates of ATCC  that were genotyped as Ishikawa cells ; we thermakeUCSCfile.pl script of the Hypergeometric Optimization of Motif EnRichment (HOMER) package (v4.2) [-callpeak parameter [Raw sequence data were analyzed uniformly with an updated version of our previously published computational pipeline  as folloe (v4.2) , and peaarameter . In ordearameter .coverageBed program of BedTools (v2.23.0) [annotatePeaks.pl with -hist 50 and -ghist parameters (HOMER). Coverage values for average protein density heat maps were calculated on the summit positions of the RD plots.Reads Per Kilobase per Million mapped reads (RPKM) values were calculated on the ±50-bp regions relative to the peak summits by using the v2.23.0) . The numv2.23.0) . Read difindPeaks.pl script and the -style super parameter. In order to generate a “super-enhancer plot”, we used the -superSlope -1000 parameter and the thus generated “Normalized Tag Count” values were plotted. Tag counts  of the ERα (super-)enhancers were ranked by their ChIP-seq coverage. The definition of super-enhancers was based on the original strategy, where the outstandingly “active” enhancers or broader regions in which enhancers are closer than 12.5 kb to each other are over slope 1 in the rank order.Super-enhancers were predicted from the E2-treated ERα ChIP-seq samples applying the HOMER’s findMotifsGenome.pl script of HOMER. It was performed on the ±100 bp flanking regions of the peak summits. The lengths for the motif search were 10, 12, 14, and 16 bp. p-values were calculated by comparing the enrichment within the target regions and that of a random set of regions (background) generated by HOMER.Motif enrichment analysis was carried out by the annotatePeaks.pl with -mbed parameter, BEDtools, and other command-line programs. The clustering of motif distribution patterns was done by Cluster 3.0. The top motif score for each examined region was determined by annotatePeaks.pl with -mscore parameter. Thresholds of the plotted scores were selected before the last markedly high peak of the motif score distribution.For the motif distribution plot, motif matrices shown in  were mapThe primary analysis of DNase-seq data was carried out as described for ChIP-seq data analysis.Raw sequence reads were aligned to the hg19 reference genome assembly (GRCh37) by using TopHat (v2.0.7). The Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were calculated by Cufflinks (v2.0.2) with default parameters .Super-enhancers were extended to ±100-kb relative to their center. The highest expressed protein-coding gene  the promoter of which overlapped with an extended region was assigned to the related super-enhancer. If an extended region could not be annotated to any gene, the closest protein-coding gene with at least 1 FPKM expression value was assigned to it by using the PeakAnnotator program .Read distribution, average protein density, and correlation heat maps were plotted by Java TreeView (v1.1.6r4) . Area-pr


In the developing vertebrate retina, retinal progenitor cells (RPCs) proliferate and give rise to terminally differentiated neurons with exquisite spatio-temporal precision. Lineage commitment, fate determination and terminal differentiation are controlled by intricate crosstalk between the genome and epigenome. Indeed, epigenetic regulation plays pivotal roles in numerous cell fate specification and differentiation events in the retina. Moreover, aberrant chromatin structure can contribute to developmental disorders and retinal pathologies. In this review, we highlight recent advances in our understanding of epigenetic regulation in the retina. We also provide insight into several aspects of epigenetic-related regulation that should be investigated in future studies of retinal development and disease. Importantly, focusing on these mechanisms could contribute to the development of novel treatment strategies targeting a variety of retinal disorders. During retinal development, distinct neuronal subtypes are generated sequentially, and in a precise order, from the same pool of retinal progenitor cells (RPCs) . ProgeniThe retina is a highly accessible part of the vertebrate central nervous system. As such, it constitutes an ideal organ in which to perform experimental manipulations to better understand the epigenetic regulation of neural development. The mature retina is composed of seven cell types Fig. : rod andIn eukaryotic cells, histones are proteins that function to condense the DNA into nucleosomes. The major histones are the core histones, H2A, H2B, H3 and H4, along with the linker histones H1/H5 . ChromatStx3), a gene expressed in mature rod cells, is enriched with bivalent modifications , while in differentiated rods, H3K27me3 is depleted at the promoter [Adding further complexity to chromatin state logic, studies have found that the active H3k4me3 and repressive H3K27me3 modifications may coexist on genomic loci . Such “bTo facilitate an understanding of the dynamics of histone marks in a biologically meaningful way, a machine-learning algorithm, called chromatin Hidden Markov Modeling (chromHMM) was developed to catalogue different histone marks into groups or “states” . While tH2A.Z in embryo bodies (EBs) results in impairment of neural differentiation in the presence of retinoic acid (RA), suggesting that H2A.Z plays a critical role in cell fate transitions and lineage commitment [H2A.X−/− mice subjected to hypoxic conditions, retinal angiogenesis decreased when compared to wild-type mice [Histone variants are histones that replace core histones to further modulate nucleosome structure and activity. H2A.B, H2A.Z, H2A.X, macroH2A, and H3.3 are the most common variants and these are highly conserved across species . While lmmitment . Histonemmitment . In H2A.ype mice . This deype mice .H1c/H1e/H10 knockouts result in decreased heterochromatin area, larger nuclei and impairment of the structural integrity of rods [Hist1h1c in Ins2+/+ and Ins2+/− mice (a diabetic retinopathy model) resulted in increased expression of autophagy markers, such as LC3B-I and LC3B-II, and repressed the diabetes-induced transcription of proinflammatory factors [Hist1h1c in the retina resulted in ectopic induction of autophagy and diabetic retinopathy-like phenotypes. In this model, the number of cells in the GCL decreased and the INL and IPL thickness were reduced, suggesting that Hist1h1c levels may contribute to the regulation of autophagy, inflammation, and neuron loss, similar to diabetic retinopathy [In addition to the core histones and their variants, data also point toward a possible role of the linker histone (H1) and its variants during retinal development. Studies on rod photoreceptor development in mice have shown that the total level of histone H1c increases, concomitant with the increase in the length of the nucleosomal repeat from 190 to 206 bp . Triple  of rods , possibl of rods . Knockdo factors . On the inopathy . ConsideSeveral enzymes orchestrate post-translational modification (PTM) of histones through the addition or removal of different chemical groups on histone tails, and these are critically important in regulating chromatin structure and function. Most histone-modifying enzymes are ubiquitously expressed, yet it has become clear that their roles are context-specific . Further3CO) groups, while methyltransferases catalyze the addition of a methyl (CH3) group to lysine residues on histone tails [Drosophila melanogaster as master regulators of Hox gene expression and found to play important roles in embryonic stem (ES) cell self-renewal, cell fate choice, proliferation, apoptosis, cell cycle regulation, plasticity and regeneration [Histone acetyltransferases and methyltransferases are two main classes of histone “writers” that act as molecular switches to maintain the active or silenced states of target loci, respectively . Histonene tails . Of thosneration –31. PcG neration , 33. Metneration , 35.Bmi-1 deficiency resulted in misregulated proliferation and self-renewal of RPCs [Rybp allele leads to retinal coloboma (a failure of optic fissure closure), while overexpression of Rybp in the lens causes abnormal fiber cell differentiation, and lens opacification [PRC1 and PRC2 complex functions have been studied during retinal development. For instance, the PRC1 component Bmi-1 is expressed in immature RPCs and differentiated cones, where it is required for cone maintenance . Indeed, of RPCs . Rybp is of RPCs , 39; how of RPCs . Loss offication . Moreovefication . Samd7, fication . Furtherfication .Ezh2 in retinal progenitor cells results in microphthalmia, reduction in postnatal progenitor proliferation, and accelerated differentiation of rods and Muller glia [Pnr, Nrl, and rhodopsin is also changed during embryonic stages following Ezh2 knockout [Ezh2 in retinal progenitors at embryonic stages also results in gradual photoreceptor degeneration throughout postnatal life, possibly through the de-repression of Six1 and photoreceptor-related genes [The roles of the PRC2 and its core subunits have also been investigated during retinogenesis –45. Enhaler glia , 45. Beyed genes .Eed at postnatal stages results in severe defects in retinal lamination, a depletion of RPCs and an increase in the proportion of early-born amacrine cells at the expense of late-born cell types such as Müller glia [Eed conditional knockouts also possess reduced RPC proliferation and increased cell death at postnatal stages, which resulted in disruption of late-born retina cells such as Müller glia, bipolar, and rod photoreceptor generation [Drosophila where mutation in E(z) and Su(z)12, the Drosophila counterparts of the vertebrate PcG components Ezh1/Ezh2 and Suz12, respectively, result in eye discs that are substantially smaller than the wild-type [Drosophila, PcG targets the majority of master regulator transcription factor genes involved in eye development  and similarly, in humans and mice PRC1 and PRC2 also target retinal specification and patterning genes  [The core component of PRC2, Eed (Embryonic ectoderm development), also plays a crucial role during retinal development. Conditional knockout of ler glia . In condler glia . Similarneration . These rild-type . In Drosh, Otx1) . These dG9a knockout results in retinal defects including the formation of photoreceptor rosettes, mislocalization of RPCs, prolonged RPC proliferation and elevated cell death [Another repressive enzyme is G9a, a nuclear histone lysine methyltransferase (HMT) that mainly catalyzes histone H3 lysine 9 mono- and di-methylation, which are linked to transcriptional silencing . G9a andll death . WhetherMll1 in mouse results in deficits in visual function, morphological defects in the retina that include altered cell type composition, a reduction in INL thickness, likely due to reduction of the progenitor cell pool, and defects in horizontal cell morphology and survival [Drosophila trithorax-related (Trr), and functions as an H3K4 monomethyltransferase [Drosophila eye imaginal discs by reducing H3K4 monomethylation and thereby affect multiple growth-promoting pathways [Ppp2r5c and Rhodopsin via H3/H4 acetylation [p300/CBP resulted in misregulation of photoreceptor-related genes via reduction of histone H3/H4 acetylation [p300/CBP in the developing lens placode resulted in aphakia (absence of the lens) [Six3, Sox2, Otx1, and Pitx3, which together play pivotal roles in regulating lens formation [TrxG proteins mediate histone methylation and chromatin remodeling activity, which leads to the maintenance of chromatin in an active mode; moreover, TrxG family proteins mainly act via nucleosome remodeling or histone modification, the latter of which includes H3K27ac, H3K4me3 and dimethylation of Lys36 on histone H3 (H3K36me2) . Mixed-lsurvival . Mll3/4 nsferase . Trr actpathways , 58. Givtylation , while ttylation . Condititylation . Beyond he lens) . This phormation .Collectively, the studies discussed above demonstrate the importance of histone writers in modulating several aspects of retinal development. While most of them seem to be essential for retinal proliferation, their roles during retinal differentiation and cell type specification vary, which highlights the need for in depth analysis of their genome-wide roles in regulating distinct retinal cell fate determination events during development.(LSD1) proteins act as erasers of lysine methyl marks on histones [The main types of histone “erasers” are histone demethylases and histone deacetylases. These enzymes remove acetyl or methyl groups from histone lysine residues –66. The histones , 64. Hishistones . Bromodohistones .kdm6b results in a decrease of Bhlhb4 and Vsx1 expression, critical transcription factors in the differentiation of rod-ON bipolar cells and cone-OFF bipolar cells. The loss of the Kdm6b in mouse retina leads to failure in the differentiation of these two subsets of bipolar cells [Xenopus eye, Kdm6b is expressed in the inner regions of optic cup, but its role in the optic cup has not been functionally investigated [Members of the Jumonji protein family possess a conserved JmjC domain which functions in histone demethylation via a mechanism involving an oxidative reaction that requires α-ketoglutarate (αKG) and iron Fe(II) as cofactors . Twenty-ar cells , 70, whiar cells . In the stigated .Xenopus, the lysine-specific histone demethylase 5C (Kdm5c) is also expressed in the embryonic eye and knockdown of Kdm5c results in smaller and deformed eyes, perturbed retinal lamination and abnormal RPE formation [Jmjd6 (Ptdsr) in mouse results in severe disruption in eye formation, with defects ranging from impaired retinal neuron differentiation to complete unilateral or bilateral anophthalmia [FbxI10 results in neural tube closure defects, an expanded retina and retinal coloboma [In ormation . Knockouhthalmia . Fbxl10 coloboma .rd1 mice, a model of retinitis pigmentosa, and application of the histone methyltransferase inhibitor, 3-deazaneplanocin A (DZNep) to postnatal retinal explants results in a reduction of calpain activity (a marker of dying cells), delayed photoreceptor loss and improved the light-adapted electroretinogram. These data suggest that modulation of histone modifications could be a potential therapeutic candidate for treating retinal degenerative diseases [LSD1 (Kdm1a) was the first protein lysine demethylases discovered . In the diseases .Hes1 and Vsx2 via concomitant increases in acetylation of histone 4 lysine 12 (H4K12) and H3K9 [hdac1 mutation resulted in reduction in cyclin D and E expression and failure of RPCs to exit the cell cycle [hdac1 function is vital for retina and optic stalk differentiation; in hdac1 mutants, retinal lamination is disrupted and differentiated RGCs and photoreceptors are absent [Sirt1 in mouse resulted in abnormal closure of the optic fissure, thinner and disorganized plexiform layers, and defects in the formation of photoreceptor inner and outer segments. These defects were thought to potentially result from hyperacetylation of p53 [Histone deacetylases also play a role in retinal development. It has been shown pharmacological inhibition of Hdac1 activity in mouse impairs rod photoreceptor differentiation . Mechaniand H3K9 . In zebrll cycle . hdac1 fe absent . Sirt1 in of p53 . In summBrg1 in mice results in microphthalmia and defects in photoreceptor differentiation [Brn3b expression [chd7 morphants, eyes are smaller and retinal organization is disrupted with a reduction in RGC numbers and a lack of photoreceptor layer maturation [Chd7 knockout in mice also resulted in dysmorphic eyes with unrecognizable optic cups and small lenses [Chromatin remodeling complexes play pivotal roles in neural development and differentiation . During ntiation . Snf2h  have provided insights into the relationship between chromatin structure and gene expression during retinal development and in distinct retinal cell types. Indeed, the genome-wide mapping of accessible chromatin with ATAC-seq in rods and cones of mature mice demonstrated that in rods, thousands of loci are selectively closed relative to those in blue and green cones. This may be due to the regulatory role of ll types . The LIMt retina . In RPCs targets . It was  targets . The 3D  targets . Integraar cells .Analysis of chromatin accessibility and gene expression have also been recently performed in developing human retinae and human (h) iPSC-derived retinal organoids, expanding our understanding of human retinal development as well as the potential uses and limitations of iPSC-derived retinal organoids and their constituent cell types in modeling and treating human retinal disease. For example, integration of RNA-Seq data from the developing human retina with DNase-seq data at two fetal stages (Day 74 and Day 125) revealed a strong association between chromatin accessibility and gene expression, which could then be used to predict putative gene regulatory networks regulating human retinal development . A compaAbca4−/−Rdh8−/− double knockout (dKO) mice are a photosensitive model of stress-induced photoreceptor degeneration, which is widely used for modeling age-related macular degeneration (AMD) pathology [Abca4−/−Rdh8−/− dKOs manifest a global decrease in chromatin accessibility over time, which results in gene expression changes that drive apoptosis. Importantly, while the global chromatin accessibility decreased in this model after bright light, the accessibility of genomic regions surrounding key inflammatory response genes increased following photobleaching, suggesting that changes in chromatin accessibility could contribute to reactive gliosis and apoptosis in the retina [From a disease perspective, athology . Followie retina . Genomice retina .DNA methylation contributes to dynamic regulation of chromatin architecture and the regulation of gene expression in many developing organs and tissues. DNA can be covalently modified through addition or removal of a methyl group predominantly to the cytosine base found in the dinucleotide sequence 5′CpG3′. DNA methylation is a heritable epigenetic mark established and maintained through the activity of DNA methyltransferase (DNMT) enzymes. Methylation is reversible where the methyl group can be removed through several mechanisms, including via the activity of ten-eleven translocase (Tet) proteins . DNA metIn the retina, mature retinal neurons possess distinct cell-specific DNA methylation patterns . For exadnmt1 and dnmt3 (dnmt3bb.2) expression in the developing zebrafish retina revealed that both genes are essential for neuronal differentiation and retinal lamination [dnmt1 in zebrafish also resulted in progressive degeneration of the ciliary marginal zone (CMZ), an endogenous stem cell containing zone of the peripheral retina [dnmt2 (trdmt1) in zebrafish, resulted in mild microphthalmia, retinal lamination defects, RPE malformation, and reduced expression of retinal cell type markers like atoh7, neurod, and zash1a, suggesting that DNA methylation may be involved in the differentiation of retinal neurons [Dnmt1 in the developing mouse retina hampered the differentiation and maturation of retinal neurons and led to rapid photoreceptor degeneration [Dnmt1 conditional knockout in the RPE resulted in the absence of cone outer segments [Dnmt1, Dnmt3A, Dnmt3B resulted in severe retinal phenotypes such as the lack of photoreceptor outer segments, reduced outer plexiform layer size, reorganization of synaptic layers, and overall defects in visual function [Functional analyses of DNA methylation enzymes have implicated DNA methylation in a variety of processes regulating retinal development. Morpholino knockdown of mination , 112. Lol retina , as welll retina . Morphol neurons . Indeed,neration . Furthersegments . Mouse tfunction .tet2 and tet3 identified a requirement for Tet activity during retinal neuron formation, possibly by modulating Wnt and Notch pathway activity [tet3 in Xenopus resulted in anophthalmia; tet3 depletion leads to misregulation of pax6, rx and six3 expression, suggesting a potential role during eye induction [As stated above, DNA methylation is transient and demethylation can be initiated through the activity of the Tet enzymes which convert the methyl group to hydroxymethylcytosine and subsequently to formylcytosine and carboxycytosine, which is then removed through base excision repair . These iactivity . Morpholnduction .Astyanax mexicanus cave morph evolved from a surface morph and has lost its eyes [dnmt3bb.1 is overexpressed at 54 h post-fertilization in the eye, which results in global repression of key eye development genes, such as opn1lw1, gnb3a, and crx. A single intravitreal injection of 5-azacytidine, a DNA methyltransferase inhibitor, resulted in larger eyes, well-defined lens and the formation of retinal layers in the eyeless cave morph, suggesting that DNA methylation may play a key role in eye regression in cavefish [Changes in DNA methylation patterns at loci regulating eye formation may be important evolutionarily. Regression of eyes is a morphological feature of cave-adapted animals such as cavefish; loss of the eye may result as an adaptation to conserve energy in a nutrient-deficient environment . The Astits eyes . In the cavefish .Finally, several clinical studies highlight the potential importance of DNA methylation in ocular pathologies Table . ConsideRNA-based regulatory mechanisms play an important role in numerous developmental processes in eukaryotes. Besides small non-coding RNAs (ncRNAs), mammalian genomes encode numerous long non-coding RNAs (lncRNAs), which are > 200 nucleotides in size , 125, lnMath5, Isl1 and Pou4f2 [Not surprisingly, lncRNAs are expressed in various ocular tissues and in the retina, they are expressed in specific cell types during development –136. Ford Pou4f2 . Many lnd Pou4f2 –141, retd Pou4f2 , 141. Med Pou4f2 , 142–144d Pou4f2 –147. FutMitochondrial function is co-regulated by the nucleus; however, mitochondria can also modulate nuclear gene expression, mainly via mitochondrial plasticity and the formation of metabolites that act as cofactors or substrates for DNA and histone-modifying enzymes . For exaIn this review, we have discussed the current understanding of epigenetic regulation during retinal development and focused on histones, histone modifications and variants, DNA methylation and lncRNAs, as well as the potential for crosstalk between epigenetic regulation and mitochondrial function. Importantly, while epigenetic regulation clearly plays a critical role during retinal development and in ocular disease, there is far more that needs to be learned. Indeed, for some of these epigenetic regulatory mechanisms, there is little known about if and how they operate during retinal development. Moreover, considering the cross-talk between histone variants, PcG and bivalent modifications in other cell types, it is almost certain that decoding the role of histone variants and deposition mechanisms will shed new light on cell fate determination in the retina. Furthermore, the interplay between these epigenetic mechanisms is essential for cell plasticity and likely contributes to regenerative mechanisms found in the retinae of many non-mammalian vertebrates . U


TBX3 in cardiac conduction velocity. Whether these noncoding variants affect expression of TBX3 or neighboring genes and how they affect cardiac conduction is not understood. Here, we use high-throughput STARR-seq to test the entire 1.3 Mb human and mouse TBX3 locus, including two cardiac conduction-associated variant regions, for regulatory function. We identified multiple accessible and functional regulatory DNA elements that harbor variants affecting their activity. Both variant regions drove gene expression in the cardiac conduction tissue in transgenic reporter mice. Genomic deletion from the mouse genome of one of the regions caused increased cardiac expression of only Tbx3, PR interval shortening and increased QRS duration. Combined, our findings address the mechanistic link between trait-associated variants in the gene desert, TBX3 regulation and cardiac conduction.Genome-wide association studies have implicated common genomic variants in the gene desert upstream of  The majority of trait-associated genetic variants  is found in noncoding genomic DNA, and is thought to affect the function of romoters . Expressromoters . Yet, thTBX3 have been strongly associated with both PR interval and QRS duration  conducts the electrical impulse from atria to ventricles and synchronizes ventricular activation, thus orchestrating the rhythm of the heart. Disrupted AVCS function can lead to life-threatening cardiac arrhythmias and hypertrophy . Common duration , ECG parduration . Presumafunction , or MED1tivation  and recetivation .TBX3+ AVCS cardiomyocytes in the heart and the heterogeneous cellular composition of the AVCS genomic experiments is hampered by the very small proportion of the AVCS . Along tTBX3 and MED13L affect the regulation of gene expression and AVCS function. Utilizing mouse AVCS-specific assay for transposase-accessible chromatin sequencing (ATAC-seq) and human and mouse TBX3/TBX5 locus-wide self-transcribing active regulatory region sequencing (STARR-seq), we identified multiple regulatory regions within two risk loci upstream of TBX3 that are activated by factors involved in AVCS development and drive gene expression in vitro. We assessed the presence of associated variants in these REs and tested their effect on RE activity. Using modified BACs containing the mouse orthologues of the respective risk loci, we show that each region independently drives reporter expression in the AVCS in transgenic mice. We deleted the mouse homologous regions of both human risk loci using CRISPR/Cas9-mediated genome editing and show that deletion of the proximal domain resulted in a two-fold increase in the expression of Tbx3, but not Tbx5 or Med13l, specifically in the AVCS, and in decreased PR interval and increased QRS duration. Thus, we used a systematic approach to identify REs driving TBX3 expression in the small number of cells that make up the AVCS, and provide evidence that a genomic noncoding region associated with PR interval is involved in the regulation of Tbx3 expression and AVCS function in vivo.In this study, we aimed at circumventing the unavailability of AVCS cells to elucidate how the PR interval- and QRS duration-associated variants in the gene desert in-between TBX3/TBX5 gene cluster  1). A second region (VR2) ranging from −85 kb to −6 kb upstream of TBX3 harbors SNPs associated with only PR interval. SNPs associated with QRS duration were also found downstream of TBX3. The location of these variants within the TBX3 TAD suggests that they may affect putative REs that regulate the expression of TBX3 and possibly TBX5 in the heart, causing affected conduction parameters.Common genomic variants associated with PR interval and QRS duration have been identified in the region of the human  cluster . Using pormation ) and phyc tissue . Sequencinterval  and QRS interval  are locaTbx3 expression in the AVCS that are possibly affected by genetic variation within VR1 and VR2, we performed ATAC-seq    on Tbx3+ capture , are acc capture . To idenhe locus . Among the locus . Intereshe locus , and forhe locus . AnalysiTbx3 locus are enriched for binding sites for Smad/Gata and Tcf (Wnt) transcription factor binding, involved in AVCS gene regulation    to scan ARR-seq) . The seqARR-seq) . Regions4 (tandem repeat of a 380 bp Tbx2 enhancer responding to Smad- and Gata4 factors) , allowing us to assess the response of sequences within the libraries with high transfection efficiency specifically to the co-transfected transcription factors. Co-transfection of the libraries with pcDNA3.1 as control (m/hSTARR_ctrl) revealed multiple active sequences, indicative of REs capable of driving reporter gene expression in COS-7 cells . To findfactors)  and TOP/factors) . A subsefactors) , indicatTBX3 locus are accessible specifically in AVCS tissue and respond to Smad/Gata and Wnt-signaling to activate reporter gene expression, indicating they are potentially involved in TBX3 regulation in the AVCS in vivo.To further justify the legitimacy of the chosen >1.5 threshold for fold activity and to validate that the identified regions were indeed activated by either SG4- or Wnt-factors, we performed transcription factor motif enrichment analysis and found that the 216 mSTARR_SG4 sequences were enriched for binding motifs for Smad and Gata factors when compared to an equal number of randomly selected mSTARR_Wnt active sequences. Vice versa, binding motifs for Tcf factors are enriched in the 257 mSTARR_Wnt regions when compared to mSTARR_SG4 regions , enhancer prediction based on various cardiac-specific epigenomic datasets (hEMERGE), and conservation with active and AVCS-specific accessible genomic regions from the mouse genome (mouse orthologous regions)  . The preregions) , is mark2 >0.5; To assess whether these RE candidates harbor common variants associated with PR interval or QRS duration, we filtered for genomic location within VR1 or VR2 and found seven candidate variant REs within human VR1 and 13 candidate variant REs within VR2 , or varihRE5 drove basal luciferase reporter activity in both HL-1 and COS-7 cells ; hRE5 decreased reporter activity  increased basal reporter activity in HL-1 cells when compared to the reference allele, whereas the risk allele of hRE9, hRE11 and hRE15-16 decreased basal reporter activity in both HL-1 and COS-7 cells , or only Wnt-response (hRE15-16). Combined, these results show that multiple candidate REs within both VR1 and VR2 hold regulatory capacity in vitro, and that the activity of a subset of these regions (in both VR1 and VR2) is affected by the risk allele. We next analyzed whether potential TF binding motifs were disrupted or de novo created by the respective SNP in the variant region of the functionally affected REs to elucidate, thereby possibly explaining the differential effect of the risk allele on luciferase activity. None of the variants within the functionally affected fragments matched known TF binding motifs.We measured luciferase reporter activity of the major and minor haplotype of these RE candidates and found that all RE candidates except for activity . Due to -7 cells . The ris-7 cells . The SG4Tbx3 expression in the ventral, right-sided and dorsal  aspect of the AV canal  from the murine genome . To elucidate whether deletion of VR1 affects AVCS function postnatally, we performed surface ECGs on adult +/+Tbx3 and ΔVR1/ΔVR1Tbx3 mice and found no difference in PR interval or QRS duration between genotypes  from the murine genome (∆VR2Tbx3). Again, we validated the deletion by PCR and Sanger sequencing . Postnatally, however, the observed number of ∆VR2/∆VR2Tbx3 pups was approximately half of the expected number  and Cacna1g (p=0.004), ion channel-encoding genes involved in AVCS function, and decreased expression of Scn5a (p=0.02), the cardiac sodium channel Nav1.5-encoding gene involved in conduction, compared to hearts of wildtype littermates , forelimbs, liver and lungs of E13.5 embryos, and found increased expression only in the SAN (p=0.013). Expression in other tissues was not affected . ∆VR2/∆VR2Tbx3 mice showed shortened PR intervals compared to +/+Tbx3 mice genomic experiments do not yet exist. Here, we functionally characterized two variant regions in the gene desert upstream of cis-REs, substantially impacting gene regulation and contributing to disease susceptibility , a histone mark associated with transcriptional repression , while Tbx5 was not deregulated. This suggests that the Tbx3/Tbx5 balance model does not hold for the AV node. Several other transcription factors have been implicated in AVCS gene regulation, which together with Tbx3 and Tbx5 form a complex gene regulatory network that has not yet been fully elucidated  or strong STARR-seq signal   . NeverthThe lack of readily available AVCS cell lines prompted us to transfect our STARR-seq libraries in COS-7 cells, a monkey kidney-derived fibroblast cell line. Although seemingly irrelevant for assessing regulatory activity of potential AVCS-involved REs, the high transfection efficiency of COS-7 cells allowed us to screen multiple libraries on a large scale and acquire sufficient coverage. The inactivity of a cardiac-specific gene program in COS-7 cells furthermore allowed us to specifically assess the response of genomic fragments to the cotransfected SG4- and Tcf-factors. Our observation that binding motifs for these factors are specifically enriched in AVCS cardiomyocytes, indicated these factors may be involved in regulation of the AVCS gene program. Motif enrichment in AVCS-specific accessible regions was performed by matching AVCS-specific accessible regions to motifs present in the HOMER database. Such databases have their limitations, as they are incomplete and possibly inaccurate as they derive their input from experimental procedures that are condition- and cell type-dependent. Nevertheless, the observed enrichment supported previous studies implicating Bmp-signalling (Smads), Gata4/6 and Wnt-signalling (Tcf) in AVCS patterning .Tbx3 expression, as multiple other factors are involved in AVCS development  showed a discordant direction of effect between basal activity in COS-7/HL-1 and SG4-/Wnt-stimulated activity. This observation suggests that potentially multiple factors act on these variant REs, with SG4- or Tcf factors counteracting the effect of regulatory networks active in COS-7 or HL-1 cells. Although we could not identify specific TF binding motifs affected by the respective SNPs in these REs, studying TF occupancy in more detail specifically in AVCS cells could elaborate on the precise mechanisms through which these variant REs act. By focusing on SG4- and Wnt-responsive REs, we potentially overlook a proportion of relevant sequences involved in the regulation of elopment . Recent elopment  will pro−8), SNPs with a p-value<0.05 were included according to the relevance of sub-threshold SNPs described in ref (2 >0.5) with the top SNPs for both variant regions in our analysis  12.5-E14.5 embryos and enriched for AV conduction system tissues by microdissection. Single-cell suspensions were obtained using 0.05% Trypsin/EDTA . Cells were sorted on a FacsAria flow cytometer (BD biosciences) and gated to exclude debris and cell clumps, and sorted for Venus+ cells. Approximately 75.000 Venus+ cells were collected and used as input for ATAC-sequencing, which was performed as previously described  and fragments of approximately 500–1000 bp were selected by gel extraction followed by cleanup using the Qiaquick Gel extraction kit . 1.5 µg of size-selected DNA fragments was used as input for library preparation using the NEBNext DNA Library Preparation Kit  and NEBNext Multiplex Oligos for Illumina (NEB E7335S) according to manufacturer’s protocol. Recombination of adapter-ligated DNA fragments, transformation and library DNA isolation was performed as described  supplemented with 10% FBS  and 1% Pen/Strep . Cells were transfected with 150 µg library DNA and 75 µg of pcDNA (control), SG4  or Wnt  using Polyethylenimine 25 kDa  in a ratio of 1:3 (DNA:PEI). Medium was refreshed 6 hr after transfection. 48 hr after transfection, total RNA was isolated using the Qiagen RNeasy maxi prep kit . polyA+ RNA was isolated using Dynabeads Oligo-dT25  and treated with Ambion turboDNase  for 30 min at a maximum concentration of 150 ng/µl. RNA was purified using the Qiagen RNeasy MinElute kit . First-strand cDNA synthesis and library preparation was performed as described (15*10escribed . LibraryPaired-end sequence reads were mapped to mm9 (mSTARR) or hg19 (hSTARR) using BWA . For visPutative mouse cardiac enhancers were predicted by EMERGE  using a Venus/+Tbx3 AVJs was utilized to identify accessible regions within the Tbx3 locus. As genome-wide peak-calling algorithms did not fully call all peaks within the Tbx3/Tbx5 locus, even with less stringent parameters, we used a local, locus-specific threshold value for the selection of peaks. The entire sequence surpassing the threshold was included and considered as potential RE. A similar approach was used to select ventricular ATAC-seq regions  were used as input for the HOMER motif analysis tool  using T4 DNA Ligase . After transformation, plasmid DNA was isolated using the PureLink HiPure Plasmid Midiprep kit  and purified by phenol/chloroform extraction.Murine and human regions selected based on either STARR-seq activity or EMERGE/ATAC-seq prediction for validation were amplified from respective BAC DNA (used for STARR-seq library preparation) in which they reside. Major and minor haplotypes of identified RE candidates were amplified from human DNA from individuals within the CONCOR-genes database. PCR amplification of RE sequences was performed using Q5 Hot Start High-Fidelity DNA Polymerase . Primer sequences are listed in CVCL_0303; 2.5*106 cells/plate) were grown in 12-well plates in Claycomb medium  supplemented with chemically defined HL-1 FBS substitute , Glutamax  and Pen/Strep . COS-7 cells  were grown in 12-well plates in DMEM  supplemented with 10% FBS  and Pen/Strep . Both HL-1 and COS-7 cell lines were routinely tested negative for mycoplasma contamination. HL-1 cells and COS-7 cell lines were easily distinguished based on cellular morphology and contractility (HL-1). Neither HL-1 or COS-7 is found in the database of commonly misidentified cell lines that is maintained by the International Cell Line Authentication Committee.HL-1 cells . 1 µg plasmid DNA was transfected using 4 µL P3000 reagent and 3 µL Lipofectamine. COS-7 cells were transfected using Polyethylenimine 25 kDa  at a 1:3 ratio (DNA:PEI). 1 µg of reporter plasmid DNA was transfected with either 500 ng of pcDNA3.1(+)  as control, or 100 ng Smad1, 100 ng Smad4, 200 ng Alk3 and 100 ng Gata4 expression vectors (SG4) and 100 ng Tcf4, 400 ng pcDNA, and 0.4M LiCl (Wnt). Medium was refreshed 6 hr after transfection. Cells were lysed 48 hr after transfection using Renilla luciferase assay lysis buffer . Luciferase activity measurements were performed in duplo using a GloMax Explorer  with 100 ul D-Luciferin . Measurements were performed by a 1 s delay and 5 s of measurement per sample.Of the variant RE candidates of which the risk allele affected luciferase activity, we took the variant nucleotide and 10 nucleotides both up- and downstream. These 21 bp sequences were used as input for transcription factor binding motif recognition in JASPAR  using de+ embryos were fixed in 4% paraformaldehyde in PBS for 4 hr and processed for immunohistochemistry, as described in + founders were analysed for GFP expression.The modification and analysis of BACs RP24-89K7-GFP, RP23-143N21-GFP and RP23-366H17-GFP has been previously described ,van Wee. BAC RP2Tbx3∆VR1), two genomic sites flanking were targeted by TALEN. TALEN target sequences were designed with TALENT2.0  and assembled according to the Golden Gate cloning protocol . 20 ng/µl of TALEN mRNA was injected in the cytoplasm of one-cell embryos of FVB/N mice. Positive founders were identified by PCR with primers flanking the 69 kb deletion and subsequent Sanger sequencing. The genomic deletion in positive founders was backcrossed for at least two generations with wildtype FVB/N mice before further experiments.Assembled RVDs were cloned in the pC-GoldyTALEN and RCIscript-GoldyTALEN  destinatTbx3∆VR2), oligonucleotides for the generation of two single guide RNAs (sgRNAs) targeting two genomic sites flanking VR2 in the mouse genome were designed using the online tool ZiFit Targeter (BsaI-digested pDR274 using T4 DNA Ligase (Invitrogen). sgRNA (pDR274) and Cas9 (MLM3613) expression vectors were linearized with DraI  and PmeI , respectively. sgRNA and Cas9 in vitro transcription was performed using the MEGAshort T7 kit  and mMessage mMachine T7 Ultra kit , respectively, followed by purification using the MEGAclear kit . 10 ng/µl sgRNA and 25 ng/µl Cas9 mRNA was micro-injected into the cytoplasm of one-cell embryos of FVB/N mice. The genomic deletion in positive founders was backcrossed for at least two generations with wildtype FVB/N mice before further experiments.To delete VR2 from the mouse genome  according to manufacturer’s protocol. First-strand cDNA was synthesized with 500 ng total RNA as input using SuperScript II Reverse Transcriptase . Quantitative real-timePCR was performed in technical duplicates using the Roche LightCycler 480 system. The oligonucleotide sequences for the amplification of the different amplicons are listed in Hprt and Eef2 expression , Tbx2 expression (AVJ) and Isl1 expression (SAN). Expression values in tissues of VR1/+, Tbx3VR2/+, Tbx3VR1/VR1Tbx3 and VR2/VR2Tbx3 mice were compared to those of wild-type littermates (+/+Tbx3).For the quantification of expression levels, E13.5 embryos from Tbx3+/+, Tbx3∆VR1/∆VR1and Tbx3∆VR2/∆VR2 mice  were anesthetized with 4% Isoflurane (Pharmachemie B.V.) and maintained at 1.5–2.0%. Electrodes were placed at the right (R) and left (L) armpit and the left groin (F) and an electrocardiogram (ECG) was recorded  for a period of 5 min. ECG parameters were determined in Lead II (L-R) based on the last 30 s of the recording.2 (1 L/min inflow) and cervical dislocation. The heart was rapidly excised, cannulated, mounted on a Langendorff perfusion set-up as described previously (Tbx3∆VR1/∆VR1and Tbx3∆VR2/∆VR2 mice were compared to those of wild-type littermates (Tbx3+/+).To record ECGs ex vivo the adult mice were sacrificed by COeviously . Neonata In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The manuscript presents an integrated approach for investigating genetic variation identified by GWAS, traveling from human genetics through informatics and genomic analysis to validation in a mouse model. This approach may be broadly applicable. Equally interesting are the specific findings concerning gene regulation at the TBX3 locus, including the identification of several enhancers, at least one of which is shown to be required in vivo.Decision letter after peer review:TBX3 regulation and cardiac conduction" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Ivan P Moskowitz as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Didier Stainier as the Senior Editor The following individual involved in review of your submission has agreed to reveal their identity: Tony Firulli (Reviewer #2).Thank you for submitting your article "Trait-associated noncoding variant regions affect The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Hendrik van Weerd et. al is a very good manuscript detailing a functional genomics approach to understanding regulation of the TBX3 gene and investigating GWAS variants for cardiac rhythm traits at the TBX3 locus. The paper includes an interrogation of common non-coding genomic variants upstream and in cis with the T-box transcription factor TBX3 associated with changes in cardiac conduction velocity.The paper excels in several ways. First, it provides a comprehensive approach to travel from trait-associated variants to functional investigation of the gene regulatory consequences of specific variants, attempting to link them to phenotypic consequences. Second, it advances our understanding of the cis-regulatory landscape at TBX3 and reveals a likely molecular mechanism for GWAS signals at the TBX3 locus – specifically, variation of cis-regulatory element function affecting TBX3 expression in conduction tissues. Third, it considers and tackles some basic difficulties in understanding cardiac conduction system GWAS – the lack of human expression data for cardiac conduction tissues and therefore the lack of eQTL data for such tissues implicated in cardiac rhythm control. The mouse – human pipeline presented provides a potential roadmap for future studies in the CCS or in other tissues that lack human expression data.Revisions:The specific comments are meant to clarify the manuscript. The generation of new data is not necessary for the revision. The following issues should to be clarified in a revision manuscript, either by analysis of current data or by clarification of the text and/or figures:1) In-vivo functional analysis is not of SNPs themselves but of a much larger enhancer deletion. This caveat should be discussed with regards to the molecular mechanism underlying the GWAS association caused by SNPs.2) Some of the observed molecular and functional effects of SNPs or enhancer knockouts are unexpected and are left without a mechanistic explanation. For example, analysis of the in-vivo effects of removal of VR2 generated results for Tbx3 expression and for functional conduction measures that are opposite of expectations. Tbx3, a repressor of fast conduction channels, was upregulated in conduction tissues, generating the expectation that conduction would be slowed in affected regions. However, a decreased PR interval, indicative of more rapid conduction speed, was observed. Can the authors explain why this is the case? At the very least the authors should discuss the unexpected nature of this observation and offer a plausible explanation.SOX9 and SMAD Motifs reported also appear non-canonical. Discussing this at some level is warranted.3) The Motif data presented in a few of the figures may not be accurate. It is clear that the VR2 is functional and published DNA occupancy data shows associated DNA binding from defined TFs within the RE's; however, the sequence consensus shown for the bHLH factors (sans MyoD) do not contain E-boxes, 4) ATAC-seq was performed in embryonic hearts and traits were from adult humans. This comparison may underestimate the overlap were ATAC to be done on adult AVC cells. This caveat should be discussed.5) The authors interrogated the human TBX3 locus for TAD structure and the location of SNPs associated with conduction traits; they identified candidate REs in the AVC by performing AVC ATAC-seq, which should provide an excellent resource for the field.How were the 67 candidate REs at the Tbx3 locus defined relative to the entire dataset?How specific are the identified elements for the AVC as opposed to non-AVC myocardium?The authors show but do not discuss the comparison between the AVC versus ventricular ATAC across the Tbx3 locus. How many are AVC-specific? It looks like many are shared.Did the author's attempt a differential ATAC analysis across the locus?6) The STARR analysis was performed in COS cells to identify regions that activate transcription based on specific TFs previously defined as important for AVC gene expression. As the authors indicate, there is no AVC cell line, requiring use of an unrelated line. None-the-less, the use of COS cells is a significant caveat, and should be discussed clearly in the Discussion. The TF enrichment in this context is a therefore a self-fulfilling prophecy.The number of human and mouse STARR identified candidates, as well as their overlap, should be described up front.7) The description of mouse/human homology for VR1 and VR2 should be described in more detail.8) RE candidate selection is described in Fg3a using a hierarchical approach in schematic form.It should be possible to use the actual locus, display the individual datasets, and overlay them to present a more accurate description of the chosen REs at the locus.Figure 3B appears to display data already published. Were any other candidates tested in-vivo?9) Several of the candidate risk alleles, examined in luciferase activity in Figure 4C and d, show discordant effects on basal activity and TF-stimulated activity. This should be described and considered. Do the variants alter TF binding sites of known activator or repressor TFs that would help explain this discordance? Do any of the analyzed variants alter TF binding sites of the TF analyzed, which may explain their altered regulatory response to TF expression?10) In Figure 6, the schematic shows the location of the TALEN cuts. The authors should delineate VR1 and VR2, as in Figure 5.SOX9 Motif also seems out of place with the canonical consensus: CCTTGAG. SMAD 3&4 likewise are reported to bind CAGAC, CAGCC as well as the 5-bp consensus sequence GGC(GC)|(CG). What is shown in the motif is very different and some discussion of this is warranted there is not confidence that the aforementioned transcription factors would bind these sequences robustly. There is simply low confidence that the algorithm is pulling accurate motif data and the authors should try to address this in the text.11) Figure 1C do the authors have insight as to why the Motif consensus for HAND2, TCF3, and TCF4 do not contain a clear E-box motif such as shown for MyoD? The 12) Figure 2 same issue with consensus data in 2G (note the motif name and the motif do not line up correctly).13) Figure 3 some clarity in the narration would be helpful. Paragraph one of subsection “Identification of variant REs within VR1 and VR2” discuss 9 candidate REs in VR1 and 13 in VR2. Are these regions supposed to be identifiable in Figure 3A (as is the assumption)? The figure shows 6 candidate REs which resolve to 3 with SNP overlaps comparing the figure to the narration is confusing.14) Figure 6C shows only Tbx3 expression Tbx5 and Med13l expression narrated as being in Figure 6C. Panel G would benefit from slightly enlarged images.15) The criteria and datasets used to define the EMERGE regions is not found in this paper nor is there any citation listing where these regions came from. Please clarify.16) What was the criteria used to identify the 9 and 13 (as listed in the text) candidate regulatory elements in VR1/2? Supplementary Table 4 lists only 20 candidate regulatory elements (7 and 13). What happened to hRE1 and hRE2 to exclude them from downstream analysis and inclusion in the table? Were other sites excluded as well? It is also unclear from Figures 2H-K and Figure 3A, and Supplementary Tables 3 and 4 what was used to define the candidate regulatory elements examined in greater detail in Figures 4, Figure 3—figure supplement 1 and Figure 4—figure supplement 1. For example, hRE13 was not examined in Figures 4C/D or Figure 4—figure supplement 1. Overall, I really like the multi-factor approach to defining candidates; however, the key filtering steps taken to get the final list of candidates needs to be more clearly defined. Revisions:The specific comments are meant to clarify the manuscript. The generation of new data is not necessary for the revision. The following issues should to be clarified in a revision manuscript, either by analysis of current data or by clarification of the text and/or figures:1) In-vivo functional analysis is not of SNPs themselves but of a much larger enhancer deletion. This caveat should be discussed with regards to the molecular mechanism underlying the GWAS association caused by SNPs.We agree and have elaborated in the Discussion: “Our current findings further substantiate regulatory redundancy in vivo. […] Therefore, we set out to assess the effect of deletion of the entire variant regions within the TBX3 locus rather than assessing the effect of individual SNPs, and found that the two TBX3 risk loci harbor multiple active REs whose activity is affected by their respective risk alleles.”2) Some of the observed molecular and functional effects of SNPs or enhancer knockouts are unexpected and are left without a mechanistic explanation. For example, analysis of the in-vivo effects of removal of VR2 generated results for Tbx3 expression and for functional conduction measures that are opposite of expectations. Tbx3, a repressor of fast conduction channels, was upregulated in conduction tissues, generating the expectation that conduction would be slowed in affected regions. However, a decreased PR interval, indicative of more rapid conduction speed, was observed. Can the authors explain why this is the case? At the very least the authors should discuss the unexpected nature of this observation and offer a plausible explanation.We agree the result was unexpected. Reduction of Tbx3 also results in PR shortening , suggesting that both more and less than normal Tbx3 causes apparent faster conduction through the AV conduction system. While several channels and gap junction subunits that are also targets of Tbx3 have been indicated to dose dependently contribute to conduction , many more genes will be deregulated in the mutant AV conduction system, leading to changes in a complex genetic network with currently unpredictable output. We have discussed this in the Discussion section, also referring to recent work from the Moskowitz lab regarding the interplay between Tbx5 and Tbx3 in the AV bundle.Tbx3 dose influences the AVCS gene regulatory network and PR interval or QRS duration.”“Not only Tbx3 insufficiency  but also over-expression  can be detrimental, suggesting proper AVCS function depends on strictly balanced Tbx3 levels. […] Further studies are required to unravel the mechanisms through which 3) The Motif data presented in a few of the figures may not be accurate. It is clear that the VR2 is functional and published DNA occupancy data shows associated DNA binding from defined TFs within the RE's; however, the sequence consensus shown for the bHLH factors (sans MyoD) do not contain E-boxes, SOX9 and SMAD Motifs reported also appear non-canonical. Discussing this at some level is warranted.We agree with the reviewer, and have specified interpretations of the Motif analysis. The confidence of such analyses depends on the quality of motifs within the database, which are experimentally derived from different experiments, in various cell types or developmental stages. As such, scanning sequences for motif enrichment using HOMER or other large scale analysis tools should be interpreted with this in mind. Nevertheless, we used the analysis to obtain a global impression of the factors that could be involved in AVCS Tbx3 regulation, while being aware that the output list is far from comprehensive. We have toned down statements regarding the conclusion of this analysis in the Results and Discussion section, and have specified in the Discussion that we used this analysis mainly to obtain an impression of involved factors, and have discussed the caveats of this analysis. Furthermore, we have removed the motif logos from Figure 1C.4) ATAC-seq was performed in embryonic hearts and traits were from adult humans. This comparison may underestimate the overlap were ATAC to be done on adult AVC cells. This caveat should be discussed.We agree with the reviewer that we might underestimate the overlap of GWAS-SNPs with potential REs, as we do not include adult AVC ATAC-seq in our analysis. However, we assume that a large proportion of the potential regulatory elements accessible in adult tissues are also accessible at developmental stages. The embryonic ATAC-seq data is furthermore only one of the filtering steps for selecting candidate REs, as we also use mouse and human STARR-seq, and mouse and human EMERGE prediction, which is based on multiple datasets including some derived from adult mouse tissues and human tissues. Nevertheless, including ATAC-seq data derived from adult Tbx3+ hearts could yield additional regulatory elements that are potentially missed in this configuration and we have added this limitation to the Conclusion.5) The authors interrogated the human TBX3 locus for TAD structure and the location of SNPs associated with conduction traits; they identified candidate REs in the AVC by performing AVC ATAC-seq, which should provide an excellent resource for the field.How were the 67 candidate REs at the Tbx3 locus defined relative to the entire dataset?How specific are the identified elements for the AVC as opposed to non-AVC myocardium?The authors show but do not discuss the comparison between the AVC versus ventricular ATAC across the Tbx3 locus. How many are AVC-specific? It looks like many are shared.Did the author's attempt a differential ATAC analysis across the locus?Tbx3 locus and that it is therefore subjective). As such, we used a local, locus-specific threshold value for the selection of peaks – the entire sequence surpassing the threshold was included and considered as potential RE. A similar approach was used to select ventricle ATAC-peaks, and a random selection of 67 regions from this set was used for the motif enrichment analysis.To increase specificity and minimize the risk of unjustly excluding potential accessible regions for downstream analysis, we used a local threshold value for our selection of ATAC-AVJ regions. We observed that, even with less stringent parameters, genome-wide peak-calling algorithms did not fully call all peaks  The STARR analysis was performed in COS cells to identify regions that activate transcription based on specific TFs previously defined as important for AVC gene expression. As the authors indicate, there is no AVC cell line, requiring use of an unrelated line. None-the-less, the use of COS cells is a significant caveat, and should be discussed clearly in the Discussion. The TF enrichment in this context is a therefore a self-fulfilling prophecy.The number of human and mouse STARR identified candidates, as well as their overlap, should be described up front.We have edited the Results section according to the reviewer’s suggestion and moved the description on the number of human and mouse STARR REs, and their overlap, up front. We have revised Figure 2 accordingly.We agree with the reviewer’s comments regarding the caveat of using the COS7 cell line for the identification of AVCS-specific regulatory elements. However, in the experiments described in this manuscript, the high transfection efficiency of the COS7 cells allowed us to reach high resolution in the STARR-seq output, which requires large numbers of transfected cells. Indeed, due to low transfection efficiencies, STARR-seq in “atrial” HL1 cells failed. The lack of a non-AVCS-myocardium gene program allowed us to specifically focus on the response of regions in the murine/human TBX3 locus to SG4 or Wnt factors.The TF enrichment for the respective stimuli could indeed be seen as a self-fulfilling prophecy. However, the aim of this exercise was to validate the cutoff for regulatory activity rather than identify motifs specifically present in these regions, in other words: to see if we, with a rather low threshold for regulatory activity of FC>1.5 would still extract relevant sequences while minimizing the change to falsely exclude regions. As such, we wanted to assess whether the active regions we identified with the FC>1.5 threshold would yield relevant sequences, rather than noise caused by inter-experiment variation. Indeed, we found that the active regions in the respective STARR datasets were enriched for binding motifs of the respective factors that we co-transfected the libraries with, indicating the used threshold was justified.We have revised the Discussion and included some discussion regarding the use of COS-7 cells and the potential caveats. Furthermore, we have revised the Discussion on the interpretation and use of the binding motif enrichment analysis according to the reviewer’s comments, also in line with the question of reviewer 2.7) The description of mouse/human homology for VR1 and VR2 should be described in more detail.We have added a track displaying sequence conservation in the mouse and human genomes, respectively, to Figure 5, and have added information regarding the sequence homology between mouse and human VR1 and VR2 in the Results section. VR2 also harbors the AVC-specific RE that was functionally and structurally conserved between mouse and human.8) RE candidate selection is described in Fg3a using a hierarchical approach in schematic form.It should be possible to use the actual locus, display the individual datasets, and overlay them to present a more accurate description of the chosen REs at the locus.Figure 3B appears to display data already published. Were any other candidates tested in-vivo?We agree with the reviewer that the use of the actual locus with the respective datasets would provide a more accurate description of the workflow, and, more importantly, of the filtering/inclusion steps at each step. We have discarded the schematic illustration of the former Figure 3 and revised the figure to show the actual datasets for each step, being: 1) hSTARR-seq data with final regions; 2) human EMERGE predicted enhancers; and 3) mouse orthologous RE candidates, based on fetal AVCS-ATAC-seq and mouse EMERGE enhancer prediction, translated to the human genome. The figure now also shows which regions have been included for downstream analysis at each step.The LacZ-stained heart showing reporter activity driven by Tbx3-eA from Figure 3B has indeed been published previously, and as such we omitted it from the figure. We believe that the message of this panel, to show that this region  was marked in all datasets used throughout the pipeline, serves as a validation of our approach and as such we kept the genome browser view of this region and moved it to the Figure 3—figure supplement 1. We did not test other RE candidates in vivo.9) Several of the candidate risk alleles, examined in luciferase activity in Figure 4C and D, show discordant effects on basal activity and TF-stimulated activity. This should be described and considered. Do the variants alter TF binding sites of known activator or repressor TFs that would help explain this discordance? Do any of the analyzed variants alter TF binding sites of the TF analyzed, which may explain their altered regulatory response to TF expression?We have added a short paragraph to the Discussion on the discordant direction of effect of variant REs on basal activity and SG4- or Tcf-stimulated activity. “Several of the tested risk alleles  showed a discordant direction of effect between basal activity in COS-7/HL1 and SG4-/Wnt-stimulated activity. This observation suggests that potentially multiple factors act on these variant REs, with SG4- or Tcf factors counteracting the effect of regulatory networks active in COS-7 or HL1 cells. Although we could not identify specific TF binding motifs affected by the respective SNPs in these REs, studying TF occupancy in more detail specifically in AVCS cells could elaborate on the precise mechanisms through which these variant REs act.”Furthermore, we have analyzed whether the variants in the tested REs disrupt or create transcription factor binding sites using JASPAR. To this end, we took the variant nucleotide and 10 nucleotides both up- and downstream, and used these 21bp sequences as input for transcription factor binding motif recognition in JASPAR using default settings. We did not find any disrupted or de novo created binding motifs this way, although we are aware that the JASPAR binding motif database is incomplete and/or inaccurate as described below (#11). We also manually analyzed the variant REs for disrupted/de novo created binding motifs, e.g. T-box/E-box motifs, but did not find any. We have added this to the Results section and added a paragraph in the Discussion section.10) In Figure 6, the schematic shows the location of the TALEN cuts. The authors should delineate VR1 and VR2, as in Figure 5.We edited Figure 6 such that it shows the location of VR1 and VR2, additional to the location of the CRISPR/TALEN deletion sites according to the reviewer’s suggestion.11) Figure 1C do the authors have insight as to why the Motif consensus for HAND2, TCF3, and TCF4 do not contain a clear E-box motif such as shown for MyoD? The SOX9 Motif also seems out of place with the canonical consensus: CCTTGAG. SMAD 3&4 likewise are reported to bind CAGAC, CAGCC as well as the 5-bp consensus sequence GGC(GC)|(CG). What is shown in the motif is very different and some discussion of this is warranted there is not confidence that the aforementioned transcription factors would bind these sequences robustly. There is simply low confidence that the algorithm is pulling accurate motif data and the authors should try to address this in the text.We agree with the reviewer, and have specified interpretations of the Motif analysis. The confidence of such analyses depends on the quality of motifs within the database, which are experimentally derived by different experiments, in various cell types or developmental stages. As such, scanning sequences for motif enrichment using HOMER or other large scale analysis tools should be interpreted with this in mind. Nevertheless, we used the analysis to obtain a global impression of the factors that could be involved in AVCS Tbx3 regulation, while being aware that the output list is far from comprehensive. We have toned down statements regarding the conclusion of this analysis in the Results and Discussion section, and have specified in the Discussion that we used this analysis mainly to obtain an impression of involved factors, and have discussed the caveats of this analysis. Furthermore, we have removed the motif logos from Figure 1C.12) Figure 2 same issue with consensus data in 2G (note the motif name and the motif do not line up correctly).Similar to the response to the question above, we have removed the motif logos from Figure 2G and corrected the misalignment of motif name and p-value. Furthermore, similarly to the clarification to the previous question, we have discussed in the manuscript that the motif analysis here was done primarily to see if the regulatory activity of the identified STARR-seq regions indeed correlated with enrichment of motifs for the respective co-transfected factors, rather than to identify novel factors.13) Figure 3 some clarity in the narration would be helpful. Paragraph one of subsection “Identification of variant REs within VR1 and VR2” discuss 9 candidate REs in VR1 and 13 in VR2. Are these regions supposed to be identifiable in Figure 3A (as is the assumption)? The figure shows 6 candidate REs which resolve to 3 with SNP overlaps comparing the figure to the narration is confusing.We agree with the reviewer that the narration in this figure might be unclear. Figure 3 served as simplified example to illustrate the workflow we used to identify regulatory elements within VR1 and VR2. As such, the regions shown in Figure 3 do not depict the true REs we identified in this manuscript. We have revised this figure and used the actual data to illustrate the workflow, simultaneously showing which regions have been included for downstream analysis at each step.14) Figure 6C shows only Tbx3 expression Tbx5 and Med13l expression narrated as being in Figure 6C. Panel G would benefit from slightly enlarged images.We have corrected the text to include the notion that expression levels of Tbx5 and Med13l have been measured but are not shown in Figure 6C. Furthermore, we have enlarged panel G for clarity, following the reviewer’s suggestion.15) The criteria and datasets used to define the EMERGE regions is not found in this paper nor is there any citation listing where these regions came from. Please clarify.We have added a paragraph in the Materials and methods section describing the use of EMERGE for the prediction of regulatory elements in both the mouse and human genome. Furthermore, we have added Supplementary Tables 10 and 11 to Supplementary file 1, listing the datasets used by EMERGE to generate a genome-wide prediction track of cardiac enhancers in both the mouse and human genome.16) What was the criteria used to identify the 9 and 13 (as listed in the text) candidate regulatory elements in VR1/2? Supplementary Table 4 lists only 20 candidate regulatory elements (7 and 13). What happened to hRE1 and hRE2 to exclude them from downstream analysis and inclusion in the table? Were other sites excluded as well? It is also unclear from Figures 2H-K and Figure 3A, and Supplementary Tables 3 and 4 what was used to define the candidate regulatory elements examined in greater detail in Figures 4, Figure 3—figure supplement 1 and S3. For example, hRE13 was not examined in Figures 4C/D or Figure 4—figure supplement 1. Overall, I really like the multi-factor approach to defining candidates; however, the key filtering steps taken to get the final list of candidates needs to be more clearly defined.TBX3 locus and data to illustrate the pipeline and the filtering steps that were used to identify candidate REs. Potential regulatory elements have been identified throughout the entire human TBX3 locus, based on the three approaches/filtering steps shown in revised Figure 3: 1) human STARR-seq activity, 2) human EMERGE enhancer prediction, and 3) mouse orthologous RE candidate regions, based on fetal AVCS-ATAC-seq and mouse EMERGE, translated to the human genome. We then looked at which of these regions were located within VR1 or VR2, and included these regions in Supplementary file 1—Supplementary Table 4. These regions were then overlapped with the GWAS SNPs for PR interval and QRS duration, and the REs overlapping one or more of these SNPs were used for variant haplotype activity testing using transfection/luciferase assays. We have revised the text accordingly to correspond to the revised figure, and hope that this revision clarifies the filtering steps we used to obtain the final list of RE candidates.We have edited Figure 3, and used the actual hRE1 and hRE2 were initially included as potential regulatory elements. However, their genomic location was outside the set boundaries of VR1 for final downstream analysis and inclusion – we have corrected the manuscript accordingly, stating that 7 and 13 REs have been identified in VR1 and VR2, respectively.Due to technical issues we failed to amplify and clone hRE13 from the genomes of the human haplotypes we used. We have added this notion to the manuscript.