We also found that the location of “chromatin organization” and “metabolic” genes is biased similarly in mouse and human genomes, suggesting that exposure to environmental stressors in different species could elicit similar phenotypic effects via self-reconstruction of disrupted chromatin organization.Exposure to environmental stressors is known to increase disease susceptibility in unexposed descendants in the absence of detectable genetic mutations. The mechanisms mediating environmentally-induced transgenerational disease susceptibility are poorly understood. We showed that great-great-grandsons of female mice exposed to tributyltin (TBT) throughout pregnancy and lactation were predisposed to obesity due to altered chromatin organization that subsequently biased DNA methylation and gene expression. Here we analyzed DNA methylomes and transcriptomes from tissues of animals ancestrally exposed to TBT spanning generations, sexes, ontogeny, and cell differentiation state. We found that TBT elicited concerted alterations in the expression of “ Identifying the full spectrum of factors that may predispose to obesity is key for its treatment and prevention. Obesogens are environmental stressors that promote adiposity by altering fat cell number, size, homeostasis and/or hormonal regulation of metabolism, appetite, and satiety3. Although there is a paucity of information regarding human exposure to obesogens, an important recent study revealed that individuals with the highest levels of perfluorinated compounds in plasma had lower resting metabolic rates and regained lost weight more quickly after cessation of a weight-loss program4. A number of environmental chemicals were shown to exert obesogenic effects in animal models, in directly exposed animals and in some cases on their unexposed descendants. However, the mechanisms through which most of these chemicals act remain poorly understood6.There is currently an obesity pandemic; 39.8% of the US adult population is clinically obese  and its heterodimeric partner, the retinoid X receptor (RXR), which together regulate adipogenic commitment and differentiation20. We showed that prenatal TBT exposure leads to increased fat storage and the commitment of multipotent mesenchymal stem cells (MSCs) to the adipogenic lineage in mice24. TBT exposure in adult rats resulted in altered hypothalamic-pituitary-gonadal axis and increased fat storage in the female gonads28. Animals ancestrally exposed to TBT showed increased white adipose tissue (WAT) mass, white adipocyte number and size, increased commitment of MSCs to the adipocyte lineage, and significant hepatic steatosis22. We reproduced these findings in a separate transgenerational experiment and showed that F4 male descendants of TBT exposed dams had increased plasma leptin levels and accumulated significantly more fat than control animals when their diets where switched from a standard diet  to a diet with a slightly higher content of fat . Moreover, their ability to mobilize stored fat during fasting was impaired21.One obesogen that has been extensively studied in our lab and elsewhere is tributyltin (TBT). TBT was used as antifouling agent with strong biocidal activity, as a fungicide, wood preservative and occurs as a contaminant in vinyl plastics21. The initial focus of these analyses was on potential associations between alterations in the DNA methylation state of promoter regions and the expression of genes relevant to metabolic disorders. However, the number of DNA methylation alterations located in promoter regions was negligible and had no obvious relationship to alterations in gene expression. Next, we followed a less gene-centric approach to identify potential larger-scale DNA methylation and transcriptomic alterations. We identified regions in gWAT DNA that were punctuated by either hypo- or hyper-methylated blocks of DNA in TBT samples when compared to controls and denoted these as iso-directional differentially methylated blocks (isoDMBs)21. We observed that alterations in TBT-dependent DNA methylome, transcriptome, and chromatin accessibility were distributed dichotomously with respect to genomic regions of biased base composition. GC-enriched genomic regions were preferentially associated with hypomethylated isoDMBs and increased expression of metabolically-relevant genes in gWAT; these regions were less accessible (as measured by ATAC-seq) in F3/F4 sperm. In contrast, AT-enriched regions were preferentially associated with hypermethylated isoDMBs in gWAT and were more accessible in F3/F4 sperm. We proposed that ancestral exposure to the environmental stressor TBT resulted in disruptions of higher order chromatin organization that (i) subsequently biased genomic traits such as DNA methylation, chromatin accessibility or gene expression, (ii) was propagated through development and across generations, and (iii) predisposed descendants of exposed individuals to metabolic disorders21.To gain a deeper understanding of the molecular mechanisms underlying TBT-dependent transgenerational obesity, we performed integrative analyses of DNA methylomes and transcriptomes of F4 male gonadal WAT (gWAT) and F3/F4 sperm chromatin accessibility from animals ancestrally exposed to TBT and controls21. These tissues included male and female MSCs from F3 and F4 generations and gWAT and liver from F4 males , thus, they represent bona fide biological replicates. We further note that gWAT, liver and MSCs samples consist of heterogeneous cell populations; therefore, future analyses will be needed to fully understand potential specific effects of TBT on each cell population. This analysis allowed us to assess the dynamics of TBT-dependent variations in chromatin organization through mitosis, meiosis, epigenetic reprogramming and developmental transitions. Therefore, we could make strong inferences on how the putative TBT-dependent disruption of chromatin organization was propagated through development and across generations  to assess whether a putative TBT-dependent transgenerational disruption of chromatin organization subsequently biased DNA methylation in opposite directions for AT- and GC-enriched genomic regions in our tissue panel. MBD-seq reads for each biological replicate were mapped to consecutive, non-overlapping 100-bp windows for Genome Reference Consortium Mouse build 38 (GRCm38). We used MEDIPS to compare read coverage between TBT and control groups. Windows with significant read coverage differences were denoted as “Differentially Methylated Regions” (DMRs) Tables . DMRs sh31. MCW tests calculated gene expression bias indices (GEBIs) before and after randomly rearranging data  were used to identify concerted biases for the expression of genes located within isochores between TBT and control groups Table  and S62121  were similar to each other, but quite different from the endoderm-derived liver Fig. . This is36. Such extensive modification of the DNA methylome could explain why the patterns observed for the TBT-dependent variation of F4 MSCs and gWAT DNA methylomes were so different. Since the liver is composed primarily of differentiated cells such as hepatocytes, it is expected that patterns observed for TBT-dependent variation of F4 liver and gWAT DNA methylomes would be similar to each other, and very different from MSCs. However, DNA methylomes for hepatocytes and undifferentiated cells such as MSCs have been shown to be similar in one regard, they both exhibit accentuated heterogeneities39. In the case of undifferentiated cells, it has been observed that DNA methylation is very variable among human and murine clonal cells39. In the case of hepatocytes, it has been observed that DNA methylation is very variable within and between mouse strains, and even for hepatocytes extracted from single individuals38. Thus, it is possible that the disparities and similarities we observed for TBT-dependent variations in DNA methylomes across F4 male tissues reflected known DNA methylome dynamics for the major cell types and their differentiated/undifferentiated states encompassed by F4 male MSCs, liver and gWAT. Our results are also consistent with the possibility that a TBT-dependent transgenerational disruption of chromatin organization subsequently biased DNA methylation, and that the direction of biases was dependent on tissue-specific factors. In this case, the direction of observed biases may depend more on the inherent heterogeneity of the DNA methylome for undifferentiated cells and hepatocytes and its extensive modification during adipogenesis rather than tissue ontogeny.In contrast, TBT-dependent variations in DNA methylation in AT- and GC-enriched genomic regions for F4 male tissues do not recapitulate their ontogenetic relationships. Patterns observed in MSCs and liver were similar between themselves, but different from F4 male gWAT Fig. . The DNA21. Subset I comprises genes for which DMRs located close to their transcription start sites might modulate DNA binding of short-range regulators or the basal transcription machinery. Subset II comprises genes for which overlapping or flanking DMRs might modulate binding of DNA topology modifiers, long- and short-regulators, or the basal transcription machinery. Subset III comprises genes located within chromosomal regions punctuated by hypo- or hypermethylated DMRs or isoDMBs. These could reflect regional changes in higher order chromatin structures that subsequently biased genomic traits like DNA methylation and transcription21. The only subset for which we detected significant concerted biases in all tissues was subset III, consistent with the possibility that observed changes in DNA methylomes and transcriptomes were both dependent on TBT-dependent regional alterations of chromatin organization  could result in metabolic disruption that is phenotypically relevant, per se, or might be revealed by subsequent dietary or environmental challenges.Our proposed interpretation of how a TBT-dependent transgenerational disruption of chromatin organization could predispose to obesity offers a possible general mechanism to understand how other chemicals could elicit a similar non-genetic predisposition to metabolic disorders that can be propagated transgenerationallychromosome organization” and “chromatin organization”. Consistent with our model, we found that genes associated with both GO terms were significantly overrepresented in GC-enriched regions  terms referring to different but overlapping levels of chromatin organization: “ons Fig. . We alsoons Fig. .Figure 3chromosome organization”, “chromatin organization”, “metabolic process”, and “detection of stimulus” terms. Genes associated with “detection of stimulus” terms, which are overrepresented in AT-enriched regions, showed biased transcript abundance for only one tissue , it is possible to test whether human genetic variations associated with metabolic diseases or traits are overrepresented in GC-enriched genomic regions as would be expected from the GC-biased location of metabolic genes. Using data from the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalogome Fig. . Geneticons Fig. . This ar46, a mechanistic understanding of how effects produced in one generation can be transmitted to others has not been convincingly established. Conceptually, there are two main models to explain how non-genetic alterations produced by environmental stressors could be propagated across generations. These can be termed the replicative model and the reconstructive model49. The replicative model posits that altered patterns of epigenetic elements causing transgenerational phenotypes are faithfully replicated through development and across generations49. Considering that epigenetic marks such as DNA methylation or histone modifications undergo intensive reprogramming during development, it is disputed whether the transmission of epigenetic alterations can be explained exclusively, or even predominantly by faithful replication49. Recent whole genome analyses revealed little or no conservation of altered epigenetic marks between generations and even between somatic cells within the same generation51. The reconstructive model proposes that altered patterns of epigenetic elements causing transgenerational phenotypes are not inherited, but rather recreated in each generation by the priming of altered intermediates49. There is not much literature on the reconstructive model to date. However, it has been generally proposed that some altered epigenetic marks may escape germline reprogramming events and, together with transmission of ncRNAs in germ cells, cellular signaling or metabolism could prime, in ways yet to be fully understood, the reconstruction of altered epigenetic information in each generation49.How environmental stressors can predispose exposed individuals and their unexposed descendants to altered phenotype or disease in the absence of DNA mutations is unclear. While a number of well-described transgenerational phenotypes exists21. Eight tissues were evaluated: F3 and F4 MSCs , F3 and F4 sperm as well as F4 male gWAT and liver. We observed consistent patterns in all tissues that we interpreted to indicate that ancestral TBT exposure altered chromatin organization and that the altered structures were propagated through development and across generations. We did not observe recurring changes in DNA methylation but did note patterns for TBT-dependent alterations in blocks of DNA methylation and the transcriptome that are largely consistent with important biological variables including sex, tissue ontogeny and differentiation state of cells. Despite the overall consistency of the observed patterns in a broad sense, the specific details are substantially different. This indicates that the propagation of these alterations cannot rely exclusively on faithful replication between tissues and across generations. Rather they are more consistent with a model in which transgenerational propagation of the TBT-dependent predisposition to obesity relies on reconstruction of altered chromatin organizations in each generation. It is unclear whether there are specific altered intermediate changes that prime or facilitate the intergenerational reconstructions of chromatin organization and, if so, what these might be. Potential clues may be found in known chromatin dynamics after fertilization.We studied transgenerational alterations elicited by ancestral TBT exposure for three genomic traits, DNA methylome, transcriptome and chromatin accessibility55. Yet, the differentiation of the genome into heterochromatin and euchromatin is persistently recapitulated57. Heterochromatin is highly compacted, replicates late in the cell cycle, occupies the nuclear periphery, is AT-enriched and gene-poor, and contains higher levels of non-coding DNA29. Euchromatin is relatively uncompacted, replicates early in the cell cycle, occupies the internal area of the nucleus, is GC-enriched, gene-rich and non-coding DNA-poor29. Biophysical analyses of eukaryotic nuclei, together with systematic analyses of the spatial proximity of non-juxtaposed loci using chromosome conformation capture methodology revealed that heterochromatic and euchromatic genomic components occupy distinctive nuclear compartments and that compartment boundaries are largely mediated by phase separation61. Relocation of a locus between euchromatin and heterochromatin revealed that the heterochromatin/euchromatin compartmentalization has multifaceted modulatory effects on replication timing, subnuclear localization, and gene expression62. Moreover, disruptions of the heterochromatin/euchromatin phase separation were recently shown to play a key role in the pathogenesis of certain neurodegenerative disorders63.Through recent technical innovations, we are starting to appreciate that the spatial organization of eukaryotic chromatin is quite intricate and considerably variable between cell types or even between clonal cells maintained in the same environment67. During the MTZ transition, maternally-deposited material in the oocyte progressively wanes as zygotic genes become transcriptionally active66. Processes occurring along the MTZ transition occur at the expense of finite amounts of mostly maternally-deposited material69. During the MTZ transition, sperm-derived chromosomes undergo intensive chromatin remodeling due to the replacement of protamines by maternally-deposited histones and new chromatin is formed after each zygotic division, which requires substantial amounts of chromatin-forming elements66.The earliest developmental signs of a complex chromatin organization, including the distinction between heterochromatin and euchromatin, appear during the maternal-to-zygotic (MTZ) transition between oocyte fertilization and the transcriptional activation of the zygotic genomeY chromosomes, which are mostly heterochromatic and enriched in repetitive DNA70, act as a sink for chromatin-forming elements when such elements are found in limited amounts72. Thus, whether a sperm nucleus contains an X or Y chromosome results in a different deployment of chromatin-forming elements throughout the genome in female and in male zygotes. This leads to sexually dimorphic chromatin organization that can subsequently bias transcriptomal, epigenetic, and phenotypic traits73. Therefore, it could be expected that environmentally-driven quantitative and/or qualitative alterations in chromatin-forming elements deposited in gametes by each parent primed sexually dimorphic alterations in chromatin organization immediately after fertilization. Consequently, sexually dimorphic disruptions in chromatin organization in animals directly or ancestrally exposed to environmental stressors might be a first indication that such stressors could result in alterations of those chromatin-forming elements in gametes needed for the early establishment of chromatin organization just after fertilization.In principle, variations in genetic and non-genetic determinants of chromatin organization at this critical point in development, when there is no distinction between somatic and germ lines, may prime the early establishment of an altered organization that could then be propagated. The sexual dimorphism many species show for the overall genomic content in heterochromatic repetitive DNA exemplifies how variations in such determinants may modulate the early establishment of chromatin organization. 29, one might expect that ancestral TBT exposure resulted in a disruption of the heterochromatin/euchromatin genomic organization. Consistent with this argument, we observed a distinct sexual dimorphism for the heterochromatin/euchromatin distribution of TBT-dependent alterations of DNA methylomes and transcriptomes in MSCs from F3/F4 females and males. This would be expected if transgenerational propagation of the putative TBT-dependent disruption of the heterochromatin/euchromatin organization resulted from alterations in gamete chromatin organization determinants. Since patterns observed for TBT-dependent variations in gene expression for the entire transcriptome in general and “chromosome organization” and “chromatin organization” genes in particular recapitulated sex and tissue ontogeny, it is possible that these variations were causally interrelated. One possibility is that TBT-dependent biases observed for the expression of “chromosome organization” and “chromatin organization” genes were driving the biases observed for the whole transcriptome. This seems unlikely because patterns for TBT-dependent variation for the whole transcriptome in our tissue panel are characterized by opposite directions of change for AT- and GC-enriched genomic regions, and “chromosome organization” and “chromatin organization” genes are significantly overrepresented only in GC-enriched genomic regions. A feasible alternative is that concerted alterations in the expression of “chromosome organization” and “chromatin organization” genes resulted in changes at the protein level that then contributed to maintaining and/or propagating the TBT-dependent disruption of chromatin organization. Although the tissues we analyzed did not encompass germ cells, our results support the possibility that TBT-dependent disruptions of chromatin organization were able to self-propagate. This is because the concerted bias in expression of genes that themselves contribute to the establishment, maintenance, and propagation of chromatin organization offers a mechanism for the altered structure to recreate itself in each generation. Thus, TBT-dependent alterations in expression of genes related to chromatin organization in all analyzed tissues is consistent with the possibility that these alterations comprised the altered intermediates that primed the recreation of altered chromatin organization through development and across generations . Detailed information about animal procedures were described in a previous publication21. MSCs were isolated from femurs and tibias of 8-week-old non-breeding mice. After euthanasia, hind limbs were harvested. Skin, muscle and connective tissue was removed to clean femurs and tibias. Ends of each bone were snipped off with scissors, and warm media  was flushed through the bone marrow cavity with a syringe and a 25 gauge needle. Flushed marrow was collected in a petri dish and broken up by carefully pipetting the media several times. Cell suspensions were strained with a 100 μm nylon strainer to remove cell clumps and bone particles. Cells were centrifuged at 400 × g for 5 min at RT and supernatant was removed. Pellet was resuspended in fresh media, plated in a 10 cm dish and maintained at 37 °C with 5% CO2 (passage 0). Media was replaced every 3 days. Cells were trypsinized when they reached 80–90% confluency (6–8 days). MSCs from 3 non-sibling, non-cousin animals from the same treatment group and gender were pooled to generate each of the 5 biological replicates per treatment group. At passage 2, cells were processed for molecular analyses.Liver and gWAT tissue were isolated from 4 non-sibling, non-cousin randomly selected mice exposed to the diet challenge (33-week-old) and processed as described21. Raw and processed data for MBD-seq and RNA-seq analyses of F3 and F4 female and male MSCs, and F4 male liver are available at Gene Expression Omnibus (GSE136377). Raw and processed data for MBD-seq and RNA-seq analyses of F4 male gWAT were retrieved from Gene Expression Omnibus (GSE105051). The Supplementary Methods file includes a detailed copy of the R script used here.Genomic DNA (gDNA) and RNA were isolated and deep-sequencing downstream analyses performed as describedp value thresholds ranging between p = 0.00001 and p = 1  by merging adjacent DMRs with the same direction of change for each tissue and p value threshold. We calculated and plot mDMR/DMR ratios for each tissue and p value threshold. mDMR/DMR ratio dynamics for each tissue while increasing p value thresholds would approximate the independency of newly discovered genomic regions showing significant MBD-seq read coverage. mDMR/DMRs would approach 1 when, upon threshold relaxation, newly identified DMRs tended to be unrelated to already identified DMRs. mDMR/DMRs would approach a minimum  when, upon threshold relaxation, newly identified DMRs tended to be adjacent to already found DMRs. We set the threshold of significance for TBT-dependent methylome changes at p = 0.001  identified using 46 80 to assign uniquely mapped RNA-seq reads to GRCm38 genes and count reads with the following parameters: GTF.featureType = exon, GTF.attrType = gene_id, allowMultiOverlap = TRUE, nthreads = 24, and strandSpecific = 0. We used cpm and glmQLFTest functions from edgeR v3.2081 to estimate the number of counts per million per gene and the statistical significance of RNA-seq reads differential coverage between TBT and DMSO samples.We inspected TBT-dependent transcriptome variation between TBT and DMSO samples using the featureCounts function from Rsubread v1.28Supplementary Tables DMR ∩ BDMR/ADMR ∪ BDMR, where ADMR ∩ BDMR represents the intersection of DMRs for tissues A and B or the number of DMRs found with the same direction of change in both tissues, and ADMR ∪ BDMR represents the union of DMRs for tissues A and B or the number of DMRs found in at least one of the two tissues (Table We quantified recurrent DMRs (rDMRs) as DMRs with the same direction of change in at least two of the tissues under consideration. No rDMR was found for more than two tissues. For each pair-wise tissue comparisons, we calculated the fraction of rDMRs as Aes Table .29. Isochore coordinates for GRCm38 were retrieved from IsoFinder82. Isochores were assorted in types L1, L2, H1, H2, or H3 depending on their GC content as described29.We analyzed TBT-dependent DNA methylome and transcriptome variation for our tissue panel with regard to genomic regions with a tendency toward uniformity in base composition or isochoresWe quantified the number of hypermethylated and hypomethylated DMRs overlapping at least one base of any isochore. For each tissue and isochore type, we calculated hyper-/hypomethylated DMR ratios as (u/d)/(U/D), where u and d represent the number of hypermethylated and hypomethylated DMRs overlapping isochores of each type, respectively, and U and D represent the number of hypermethylated and hypomethylated DMRs overlapping any isochore, respectively Table . For eac30. For each tissue under consideration, we selected those genes that were deemed as expressed in both treatments, i.e., genes for which RNA-seq read coverage was higher than 0 in at least two biological replicates per treatment . Subset II includes genes that overlap or flank at least one DMR. Subset III includes genes located within iso-differentially methylated blocks (isoDMBs), which represent genomic regions punctuated by iso-directional TBT-dependent DMRs. GRC3m8 genes associated with GO terms “chromosome organization” (GO:0051276), “chromatin organization” (GO:0006325), “metabolic process” (GO:0008152), and “detection of stimulus” (GO:0051606) were retrieved from the Gene Ontology Consortium database84.To ascertain whether TBT-dependent transcriptome variation tended to be significantly biased in a concerted way for genes sharing relevant traits, we performed Monte Carlo-Wilcoxon matched-pairs signed-ranks tests (MCW)nt Table . We defint Table , or theint Table . GRCm38 30. We performed MCW tests using transcript abundance mean (M) and coefficient of variation (CV), independently30. mGEBI and cvGEBI represent bias indices calculated using transcript abundance mean and CV, respectively. For each subset of genes and tissue, mGEBI was calculated following five steps. First, we subtracted transcript abundance mean for DMSO samples  from TBT samples  for each gene, i.e., ∆Mi = Mi,TBT − Mi,DMSO. Second, we assigned ranks to all genes in the dataset after sorting them according to the absolute value of ∆Mi from lower to higher. Third, we assigned signs to each rank according to the sign of ∆Mi. Fourth, we sum signed ranks corresponding to genes for the subset under analyses. Fifth, we calculated mGEBIs by dividing the sum of sign ranks for each subset by the maximum value the sum would take if the genes in the subset in question were the ones with highest ∆Mi values. Thus, mGEBI for each subset would range from 1 to −1 if the genes in this subset were the ones showing the highest or the lowest ∆Mi values, respectively. cvGEBI were calculated in the same way after subtracting for each gene transcript abundance CV for DMSO samples  from TBT samples , i.e., ∆CVi = CVi,TBT − CVi,DMSO. To assess whether observed GEBIs were significantly different from those expected just by chance, we compared them with GEBIs calculated after randomly rearranging signed ranks for each tissue 10,000 times (Table MCW test proceeds by comparing gene expression bias indices (GEBI) calculated for the whole transcriptome or a subset of related before and after randomly rearranging data to simulate the effect of chance on the variation in gene expression for a dataset with the same size and expression rangees Table .82. GRCh37 isochore coordinates were converted into Genome Reference Consortium Human build 38 (GRCh38) coordinates using Lift-Over utility from Galaxy v1.0.685. Isochores were assorted in types L1, L2, H1, H2, or H3 depending on their GC content as described29. GRCh38 genes overlapping isochores were identified using the tool “Search for features within Genomic Regions” from HumanMine v5.186. GRCh38 genes associated with GO terms “chromosome organization” (GO:0051276), “chromatin organization” (GO:0006325), “metabolic process” (GO:0008152), and “detection of stimulus” (GO:0051606) were retrieved from the Gene Ontology Consortium database84. For each GO term and isochore type, we calculated GO enrichments as (x/n)/(X/N), where x and X represent the number of genes associated with each GO term overlapping isochores of each type or any isochore, respectively, and n and N represent the number of genes overlapping isochores of each type or any isochore, respectively. To assess whether observed GO enrichments were significantly different from those expected by chance, we compared them with GO enrichments calculated after randomly rearranging isochore type tags 10,000 times (Table GRCm38 isochore coordinates and overlapping genes were retrieved as described in the previous section. Isochore coordinates for Genome Reference Consortium Human build 37 (GRCh37) were retrieved from IsoFinderes Table .44. For each disease/trait and isochore type, we calculated GWAS SNP enrichments as (x/X)/(l/L), where x and X represent the number of SNPs for each trait mapping to isochores of each type or any isochore, respectively, and l and L represent the cumulative length of isochores of each type or all isochores, respectively. To assess whether observed GWAS SNP enrichments were significantly different from those expected by chance, we compared them with GWAS SNP enrichments calculated after randomly rearranging isochore type tags 10,000 times (Table GRCh38 locations for single nucleotide polymorphism (SNPs) associated with diseases/traits “Body mass index”, “Obesity-related traits”, “Type 2 diabetes”, “Diastolic blood pressure”, “Systolic blood pressure”, “Adolescent idiopathic scoliosis”, and “Post bronchodilator FEV1/FVC ratio in COPD” were retrieved from the NHGRI-EBI GWAS Cataloges Table .G*Power v3.1.5 was used to perform a priori power analysis to determine the number of animals required for DNA methylome and transcriptome analyses. Since gene expression changes in tissues are typically ≥ 1.5 fold with SEM of ≤ 10% (effect size d = (μ1−μ2)/σ = 3.92), we set type I and II errors (α and β) at 0.05 and the effect size d = 3.92, the minimum sample size required for a Power (1−β) of 0.95 was calculated to be ≥ 4.pupper and plower as the fraction of the 10,000 chance simulations rendering measures that were higher or equal, or lower or equal than observed ones, respectively  if the highlighted area is above or below the 0 value, respectively, and no significantly different from measures expected by chance (p ≥ 0.05) if the highlighted area spanned the 0 value.To help comparing visually observed Supplementary materialSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7Supplementary Table S8


Streptomyces tsukubaensis stands out among actinomycetes by its ability to produce the immunosuppressant tacrolimus. Discovered about 30 years ago, this macrolide is widely used as immunosuppressant in current clinics. Other potential applications for the treatment of cancer and as neuroprotective agent have been proposed in the last years. In this review we introduce the discovery of S. tsukubaensis and tacrolimus, its biosynthetic pathway and gene cluster (fkb) regulation. We have focused this work on the omic studies performed in this species in order to understand tacrolimus production. Transcriptomics, proteomics and metabolomics have improved our knowledge about the fkb transcriptional regulation and have given important clues about nutritional regulation of tacrolimus production that can be applied to improve production yields. Finally, we address some points of S. tsukubaensis biology that deserve more attention. Streptomyces tsukubaensis and its secondary metabolite tacrolimus were discovered in 1984, during a screening performed by the Fujisawa Pharmaceutical Co. . S. tsukubaensis was isolated from a soil sample in the Tsukuba region (Japan) and tacrolimus was identified in its culture broths, becoming the first immunosuppressant discovered with macrolide structure . DH. DH40]. bcluster ,43,44,45tructure . Neverthynthases . The tacynthases , but theynthases .l-pipecolate from l-lysine [For the cyclation of the macrolide, FkbL generates l-lysine , which il-lysine ,49,50. Fl-lysine ,52. The l-lysine ,53. Bothl-lysine ,45,54. Ifkb cluster revealed three potential regulators: fkbN, fkbR and allN . FkbN is a large regulatory protein of the LAL family (Large ATP binding regulators of the LuxR family). The LAL regulators are large proteins (872–1159 amino acids) that contain a LuxR-type HTH DNA binding region near the C-terminal end of the protein and an ATP binding motif in the N-terminal end [Streptomyces hygroscopicus [Streptomyces venezuelae [Streptomyces hygroscopicus [S. hygroscopicus var. ascomyceticus [Streptomyces griseus [Streptomyces natalensis [Streptomyces noursei [Streptomyces nodosus [Streptomyces avermitilis [The first sequence analyses of the inal end ,56. Simiscopicus , PikD ofnezuelae , GdmR1 ayceticus , FscRI i griseus ,61, PimMtalensis ,63, NysR noursei , AmphRIV nodosus  and PteFrmitilis ,67.Streptomyces: genome sequencing revealed about 40 LTTRs in S. coelicolor [S. clavuligerus (77% identity), ThnI from Streptomyces cattleya (39% identity), AbaB from Streptomyces antibioticus or ClaR from S. avermitilis [The second regulatory protein FkbR belongs to the family of the LysR-type transcriptional regulators, also named LTTR, which are very common autoregulatory genes in bacteria . In factelicolor . FkbR, aelicolor ,71. Othermitilis ,73.allN. This gene is located in the 5’ end of the extended version of the tacrolimus gene cluster and encodes a protein that has similarity with regulatory proteins involved in nitrogen metabolism, particularly with regulators of AsnC family [The third putative regulatory gene of the tacrolimus gene cluster is C family . This geC family ,46.S. tsukubaensis was performed by gene disruption and complementation studies. Whilst the inactivation of fkbN resulted in the lack of tacrolimus production, disruption of fkbR reduced tacrolimus yields to 20% of that of the parental strain and the inactivation of allN did not affect tacrolimus production [fkbN and fkbR encode positive regulators whilst allN has no influence on tacrolimus production [S. tsukubaensis L19 [fkbN or fkbR in the wild type strain using the ermE* promoter produced an increase of the final yield of tacrolimus of 55% and 30%, respectively, using a culture medium optimized for tacrolimus production. These results agree with the observations published by Mo and coworkers on the effect of FkbN in Streptomyces sp. KCTC 11604BP [Functional analysis of the role of FkbN, FkbR and AllN in oduction . Thus, ioduction . In addinsis L19 . Overexp 11604BP .fkbN is present in both the extended and the short version of the fkb cluster but fkbR is only present in the extended cluster version [fkbN (but not with that of fkbR) [fkbR is constant and low throughout the culture whilst that of fkbN increases before the onset of tacrolimus production and is maintained during the production phase (There are important differences between FkbN and FkbR that we summarize here as follows: (1)  version ; (2) Fkb version ,76,77; (of fkbR) ,38; (4) on phase  [36,38,7rppA chalcone synthase reporter systems and qRT-PCR showed that the inactivation of fkbR or fkbN prevents transcription of certain genes in the S. tsukubaensis fkb cluster such as fkbG or fkbB, implying that some fkb genes are regulated by FkbN while others are not [fkbN inactivated mutant have confirmed that FkbN controls the expression of most of the genes of the fkb cluster [fkbN inactivation: (a) Genes clearly induced by FkbN coinciding with the onset of tacrolimus biosynthesis  and whose expression is significantly reduced in the fkbN mutant  and (b) Genes poorly expressed through the culture time and not affected by fkbN inactivation  , which are FkbN-independent, was demonstrated.Early studies using the  are not . However cluster . Two typnd fkbR) . Thus, tfkb cluster allowed the identification of 6 transcriptional units: fkbR, tcs6-fkbQ-fkbN, fkbOPADM, fkbBCLKJIH, fkbG and allAKRD. It was concluded that fkbR is transcribed as a leaderless mRNA and that fkbN forms an operon along with tcs6 and fkbQ whose transcription depends on two different promoters, one FkbN-dependent and the other FkbN-independent [S. tsukubaensis L19 by Zhang and coworkers [allNPOS and allM. More recently, differential RNA-seq (dRNA-seq) transcriptional profiling has been performed in S. tsukubaensis by Bauer and coworkers [allOS and allNP are transcribed as independent mRNAs [The use of tiling probes covering the ependent . These roworkers , who repoworkers , who ident mRNAs .fkbR seems to be transcribed as a leaderless mRNA and is not directly regulated by FkbN [tcs6-fkbQ-fkbN and fkbR in S. tsukubaensis L19 has been reported [ by FkbN . In factreported .fkbN mutant by Ordóñez-Robles and coworkers [fkb cluster that might be targets of FkbN such as ppt1, encoding a 4′-phosphopantetheinyl transferase that is known to be involved in CDA formation in S. coelicolor [ppt1 is involved in tacrolimus production in S. tsukubaensis L19 [ppt1 and fkbN share a common transcriptional response to glucose, glycerol and N-acetylglucosamine additions (see below). The study identified acyl-CoA dehydrogenase and methoxymalonate biosynthesis coding genes that were negatively affected by the fkbN inactivation and thus, might be involved in tacrolimus biosynthesis. On the contrary, some PKS coding genes located in a chromosomal region that has been predicted to encode a cluster for the production of a bafilomycin-like compound [fkbN inactivation, which might reflect competition for precursors between these two clusters for the biosynthesis of secondary metabolites.It has been reported that cluster-situated regulators (CSR) can regulate genes located outside their own cluster ,79 and, oworkers  revealedelicolor . This geelicolor . In agrensis L19  and latecompound  were uprS. natalensis [Using the information-theory of Schneider , a putattalensis .fkb cluster regulation is necessary to achieve higher tacrolimus production yields. In this sense, the identification of transcriptional start sites (TSS) is useful for the introduction of artificial promoters without affecting the structure of mRNAs. Bauer and coworkers [fkb cluster through the formation of RNA secondary structures [allAKRD operon was reported to be transcribed with a rather long untranslated 5’ region  that is predicted to form a secondary structure.In-depth knowledge of the oworkers  reportedructures . In factS. tsukubaensis NRRL 18488 and 8% in Streptomyces clavuligerus KCTC 10561BP [Despite the efficacy of tacrolimus in the treatment of organ transplantation, its use in clinical therapy is expensive. This is mainly due to the low production yields of the producer strains used but also to the formation of byproducts such as ascomycin (FK520) or FK525, which are structurally similar to tacrolimus but differ in the nature of some radical groups . The pre 10561BP ,87. The  10561BP .Streptomyces sp. MA6858 (ATCC 55098) was formulated by Yoon and Choi [S. tsukubaensis [In the last decades, the research on tacrolimus production enhancement has been mainly focused on culture media optimization and genetic engineering of the strains. For a recent review on the improvement of tacrolimus biosynthesis through synthetic biology approaches see ,91. The and Choi ; later, and Choi  developeubaensis ,95. Thisubaensis .Considering that the availability of precursors is a limiting factor in the biosynthesis of secondary metabolites, precursor supply is a straightforward strategy to increase antibiotic yields . A summaNevertheless, the addition of precursors in industrial fermentations can be a non-efficient strategy from an economical point of view ; thus, S. tsukubaensis NRRL 18488 [Finally, the addition of stressing agents, such as dimethylsulfoxide (DMSO) or sodium thiosulfate, has been shown to stimulate polyketide production in different bacteria ,110 as wRL 18488 .S. tsukubaensis, several metabolomic studies have been performed in the last decade. Huang and coworkers [S. tsukubaensis D852 including 865 chemical reactions and 621 metabolites to predict targets for genetic manipulation. These models reconstruct the organism metabolism from the genome annotation, taking into account genes encoding enzymes and transporters. By this means it was predicted that some of those modifications in the primary metabolism pathways leading to the accumulation of erythrose-4-phosphate, α-ketoglutarate, fumarate, succinate, pyruvate, phosphoenolpyruvate, NADPH, chorismate and malonyl-CoA have a positive effect on tacrolimus production. This implies that both the pentose phosphate pathway and the TCA cycle are positively correlated with tacrolimus production. Regarding the biosynthetic cluster, the overexpression of genes involved in the formation of the starter unit DHCHC, pipecolate and in different modification reactions  were supplied to the culture media of S. tsukubaensis D852 [aroC and dapA  increased production of the macrolide by 40% and 23%, respectively. See a summary of the distinct gene modifications that produce a positive impact on tacrolimus production in The inactivation or overexpression of a particular gene involved in a certain biosynthetic pathway can affect other metabolic pathways and also the growth of the microorganism. For this reason, global studies covering the whole transcriptome, proteome or metabolome are usually preferred. In oworkers ,111 devesis D852 . In thissis D852 ) allowedS. tsukubaensis NRRL 18488 which uncovered new targets for genetic manipulation (see gcdh (glutaryl-CoA dehydrogenase) and overexpression of tktB (transketolase), msdh  and ask (aspartate kinase).The GSMM developed by Huang and coworkers  is a psetion see  that resS. tsukubaensis TJ-04 in two media of similar composition but resulting in different tacrolimus productivity. They analyzed the concentration of a wide range of metabolites and compared them between the two media to identify key metabolites that correlate positively with tacrolimus production. In good agreement with the results of Huang and coworkers [The approached used by Xia and coworkers  consisteoworkers ,111, intS. tsukubaensis NRRL 18488 grown under soybean oil feeding. The positive effect of this carbon source on growth and on tacrolimus production has been already reported in other producing strains [More recently, Wang and coworkers  performe strains ,100,101  strains  and inclStreptomyces species in the last two decades, including S. tsukubaensis [S. tsukubaensis, the negative regulation of tacrolimus biosynthesis by phosphate was reported in 2013 [fkbN and also in primary metabolism genes that might be involved in tacrolimus precursor supply such as STSU_30046, encoding an acetoacetate-CoA ligase [Understanding how a biosynthetic cluster is regulated is important to develop strategies to improve secondary metabolite production. Our group has studied the phosphate regulation of antibiotic production in different ubaensis ,115,116.ubaensis . This reubaensis . When phubaensis ,119,120. in 2013  and late in 2013 . In the  in 2013 . PutativA ligase .S. tsukubaensis; the glucose effect being stronger than that of glycerol [fkb cluster; thus, it was concluded that transcriptional repression plays a role in this regulatory mechanism. In addition, the effect of these carbon sources can be exerted at the intermediary metabolism level: glucose addition increased transcription of genes involved in glycolysis, pyruvate and oxaloacetate formation but downregulated genes involved in the TCA cycle. These results are coherent with the previous assumption that the TCA cycle is positively correlated with tacrolimus production whilst glycolytic metabolites show a negative correlation [A second regulatory mechanism governing secondary metabolite production is carbon repression. Similar to phosphate, the presence of ready-to-use carbon sources in the media reduces or blocks antibiotic production and this can happen at the transcriptional or at the posttranslational level ,123. Theglycerol . Both glrelation .fkbN increases in a two-step fashion before tacrolimus is detected in the broths [fkbN is under phosphate control [fkbN transcription is not strongly self-regulated [hrdA or bldN) and transcriptional regulators  were downregulated under glucose or glycerol addition conditions [S. coelicolor and S. griseus [S. tsukubaensis strain that overproduces tacrolimus [In the MGm-2.5 medium used in the work, transcription of e broths : a sligh control  . The transcriptional response to N-acetylglucosamine addition is very similar to that exerted by glucose, which is not surprising since both carbon sources share a common catabolic pathway from fructose-6-phosphate.A third example of the nutritional regulation of secondary metabolite production is that exerted by ectively ,130. We S. tsukubaensis unveils potential candidates for the rational engineering of industrial strains. It has also improved our knowledge about other aspects of its physiology such as the possible members of the PHO regulon in this species or the mechanisms operating in the presence of repressing carbon sources. These findings are worthy to detect potential targets for the bypass of nutritional repression of secondary metabolism in Streptomyces.Overall, the application of transcriptomics to nutritional studies in S. tsukubaensis and its secondary metabolite tacrolimus were discovered. Despite the importance of this immunosuppressant macrolide in current clinics, there are still many aspects to be elucidated about the transcriptional and nutritional regulation of tacrolimus biosynthesis, and further studies are necessary to improve the yield and reduce the costs of its industrial production. In this sense, the omic approaches constitute an important basis to understand the producer microorganism physiology from a genome- [omic studies performed in S. tsukubaensis have given important clues such as the positive correlation of the pentose phosphate pathway and TCA cycle with tacrolimus production or the identification of targets for genetic manipulation. These types of studies can be applied not only to the overproduction of tacrolimus but also to the awakening of cryptic clusters [S. tsukubaensis’ genome contains several clusters for the production of secondary metabolites which might encode useful compounds. One of the potential products encoded is predicted to be similar to bafilomycin [omic results in the framework of the strain and culture media used since there are important physiological differences depending on the strain and the culture conditions. Therefore, the comparison of different models can broaden our perspective of tacrolimus production and S. tsukubaensis’ physiology.It has been more than 30 years since  genome- , proteomclusters . In factilomycin  and two ilomycin ,135. Nevfkb cluster such as the role of the allMNPOS subcluster in the strains that contain it. Although not strictly required for tacrolimus production, the all subcluster might be involved in the generation of macrolide variants with useful properties. Thus, the overexpression of these genes under promoters regulated by FkbN seems an interesting study. In addition, the ppt1 and scoT genes, which are affected by the inactivation of fkbN, might be potential targets for tacrolimus biosynthesis improvement. Considering the transcriptional regulation of eshA and atrA under tacrolimus producing and repressing conditions, both genes seem good candidates for genetic engineering of the strains.There are still some interesting points to address in the study of the fkbN is also interesting given that it is the main transcriptional activator of the fkb cluster. The identification of transcriptional regulators that bind to its promoter region is a good approach to identify new targets for genetic engineering of the strains that overexpress fkbN and therefore, to increase tacrolimus production. Finally, the post-transcriptional regulation of the fkb cluster deserves further attention. As reported by Bauer and coworkers [S. tsukubaensis . Long 5’-UTRs might be involved in the formation of secondary structures that regulate transcription of the cistrons and might be potential targets for manipulation.The transcriptional regulation of oworkers , a high


Dlx5 promoter region, ranging from −774 to −95 bp that contains two putative C/EBPβ binding elements (site-1: −517 to −510 bp and site-2: −164 to −157 bp), in the suppression of Dlx5 transcription. Consequently, chromatin immunoprecipitation analysis confirmed the importance of site-1, but not site-2, in C/EBPβ binding and transcriptional suppression of Dlx5. In conclusion, we elucidated the underling mechanism of Dlx5 downregulation in IBMX-induced adipogenesis. IBMX activated cAMP/PKA/CREB signaling and subsequently upregulated C/EBPβ, which binds to the Dlx5 promoter to suppress Dlx5 transcription.Distal-less homeobox 5 (Dlx5) is a negative regulator of adipogenesis. Dlx5 expression is decreased by adipogenic stimuli, but the mechanisms of Dlx5 downregulation by adipogenic stimuli have not yet been determined. Here, we tested the impact of cAMP/PKA (protein kinase A) signaling induced by 3-isobutyl-1 methyl xanthine (IBMX), forskolin, and 8-CPT-cAMP on the expression of Dlx5 in 3T3-L1 preadipocytes. Significant downregulation of Dlx5 mRNA expression and protein production levels were observed via cAMP/PKA-dependent signaling. Forced expression of cAMP-responsive element-binding protein (CREB) and CCAAT/enhancer-binding protein β (C/EBPβ) was sufficient for downregulation of Dlx5 expression and revealed that CREB functions upstream of C/EBPβ. In addition, C/EBPβ knockdown by siRNA rescued Dlx5 expression in IBMX-treated 3T3-L1 preadipocytes. Luciferase assays using a Dlx5-luc-2935 reporter construct demonstrated the requirement of the  Obesity has become a rising issue related with various health problems in modern society . ObesityRunx2 promoter and enhances Runx2 expression. Dlx5 also mediates Bmp2-induced expression of Sp7, another key transcription factor for osteoblastic differentiation [Distal-less homeobox 5 (Dlx5) is a well-known transcription factor involved in bone development. Dlx5 upregulates osteogenesis by activating Runx2, a master transcriptional regulator for osteoblast differentiation [ntiation . We prevntiation . We also3T3-L1 preadipocytes are a well-established in vitro cell culture model to study adipogenic differentiation . 3T3-L1 In the early stages of adipogenesis, accumulation of cAMP stimulates the activation and expression of C/EBPβ and δ ,14. It hIn the present study, we characterized the underling mechanisms of Dlx5 downregulation in IBMX-induced adipogenesis. IBMX activated cAMP/PKA/CREB signaling and subsequently upregulated C/EBPβ, which binds to the promoter region of Dlx5 and suppresses Dlx5 transcription.Because our previous study demonstrated that an adipogenesis induction mixture suppresses Dlx5 expression , we firsDlx5 gene. qRT-PCR results demonstrated that all the reagents inducing cAMP accumulation or reagents mimicking cAMP action significantly decreased Dlx5 mRNA level , or the adenylate cyclase activator forskolin -CPT-cAMP,16. 3T3-NA level C. WesterNA level D. IBMX iNA level E,F. ThesIt has been demonstrated that cAMP/PKA signaling increases the activity and expression of CREB and C/EBPβ . TherefoTo determine whether CREB and C/EBPβ play a role in cAMP/PKA signaling-induced downregulation of Dlx5, we examined Dlx5 expression levels after forced expression of CREB or C/EBPβ. As shown in To further confirm that C/EBPβ is required for inhibition of Dlx5 expression, we knocked down C/EBPβ in 3T3-L1 cells using C/EBPβ siRNA and incubated the cells for 24 h in the presence or absence of IBMX. C/EBPβ silencing efficiency was confirmed by qRT-PCR and Western blot analysis D,E. C/EBDlx5 promoter sequence (−2935 to +123 bp). 3T3-L1 cells were transiently transfected with Dlx5-luc-2935 and/or the C/EBPβ expression vector and incubated in growth medium for 24 h. Renilla luciferase was applied as a control for normalization of transfection efficiency. Similar to the inhibitory effect on Dlx5 mRNA expression, C/EBPβ overexpression clearly suppressed Dlx5 promoter activity  contains putative C/EBPβ binding elements. As depicted in Dlx5 promoter regions demonstrated that the relative ratio of C/EBPβ antibody bound fraction to input in the C/EBPβ binding site-2 was less than 3% of that of the C/EBPβ binding site-1  in 3T3-L1 preadipocytes, activation of cAMP/PKA signaling by IBMX, forskolin, and 8-CPT-cAMP suppressed Dlx5 expression; (ii) activation of cAMP/PKA signaling induced C/EBPβ expression in a CREB-dependent manner; (iii) C/EBPβ induction is sufficient to inhibit Dlx5 expression; and (iv) C/EBPβ binds to the Forskolin induces cAMP accumulation by activating the adenylate cyclase. IBMX, prostacyclin, and 8-CPT-cAMP are well known adipogenic inducers through the cAMP induction and PKA activation pathway ,20. WhenIt is well established that PKA is directly involved in CREB activation by inducing phosphorylation of Ser133, which is necessary for transcriptional activation . CREB haC/EBPβ is an important regulator of mitotic clonal expansion of preadipocytes as well as the adipogenic differentiation . The actDlx5 promoter region-dependent manner. Subsequently, Dlx5 promoter reporter assay results were further supported by ChIP experiments, which showed that IBMX treatment or overexpression of CREB and C/EBPβ in 3T3-L1 cells increased the binding of C/EBPβ but decreased that of RNA pol II and acetyl histone H3 to the Dlx5 promoter region −550 to −426 bp, which encompasses a putative C/EBPβ binding site (−517 to −510 bp). These results suggest that enhanced binding of C/EBPβ to the Dlx5 promoter leads to histone modification status for transcriptional repression and reduces recruitment of RNA pol II. However, further studies are necessary to confirm the direct binding of C/EBPβ to the Dlx5 promoter by repeating Dlx5 promoter reporter assays using a construct with mutations in the putative C/EBPβ binding site (−517 to −510 bp) or by performing electrophoretic mobility shift assays.Dlx5 promoter reporter assays further support the transcriptional repressor role of C/EBPβ by showing that C/EBPβ suppresses Dlx5 promoter activity in a −774 to −95 bp Taken together, these results demonstrate that the adipogenic differentiation inducer IBMX suppresses Dlx5 transcription in 3T3-L1 preadipocytes in a cAMP/PKA/CREB/C/EBPβ-dependent manner. Considering the negative regulatory role of Dlx5 in adipogenesis, this finding suggests that Dlx5 inhibition contributes to the enhancement of adipogenesis triggered by C/EBPβ expression during adipogenic differentiation.IBMX, forskolin, 8-CPT-cAMP, and H89 were purchased from Sigma . Anti-Dlx5 antibody was purchased from Millipore . Anti-RNA polymerase II (pol II), anti-actin antibody, and HRP-conjugated secondary antibodies were obtained from Santa Cruz Biotechnology . Anti-C/EBPBβ, anti-acetyl-histone H3 (H3), and anti-CREB antibodies were obtained from Cell Signaling Technology . Dulbecco’s modified Eagle’s medium (DMEM) and fetal bovine serum (FBS) were purchased from HyClone . The C/EBPβ expression vector was kindly provided by Prof. JB Kim from Seoul National University. Construction of the CREB expression vector was described previously .3T3-L1 cells were cultured in DMEM supplemented with 10% FBS, 100 U/ml penicillin, and 100 µg/mL streptomycin. After 2 days of confluence, the cells were treated with the indicated reagents  for the indicated time periods. To induce exogenous expression of CREB or C/EBPβ, 3T3-L1 cells were transfected with expression vectors  using lipofectamine 2000 reagent  and incubated for 24 h.TC values compared to the control. The sequences (5′→3′) of forward and reverse primers for each gene were as follows: Dlx5, TCT CTA GGA CTG ACG CAA ACA and GTT ACA CGC CAT AGG GTC GC; CREB, AGC TGC CAC TCA GCC GGG TA and TGG TGC TCG TGG GTG CTG TG; C/EBPβ, GAC TTT ATG GGC AGC TTT GC and GGC TTT GTC TCT GCT TTT GC; and GAPDH, TCA ATG ACA ACT TTG TCA AGC and CCA GGG TTT CTT ACT CCT TGG.Total RNA isolation and qRT-PCR was performed as described previously . Each saTM Protein Extraction Solution  according to the manufacturer’s instructions. Western blot analysis was then performed as described previously [Whole cell extracts were prepared using PRO-PREPeviously .plus SMART pool, L-043110-00-0005 and Non-targeting siRNA, D-001810-10-05) were purchased from Dharmacon , and transient transfection of siRNA was performed using Dharmafect (Dharmacon).C/EBPβ siRNA and control siRNA (ON-TARGETDlx5 promoter region sequence upstream of the luciferase gene of the pGL3-basic vector (Dlx5-luc-2935), and serial deletion constructs  were kindly provided by Prof. HM Ryoo from Seoul National University. To measure luciferase activity, 3T3-L1 cells were plated at an initial density of 1 × 104 cells per well into 96-well plates. After overnight culture, the cells were transiently transfected with the indicated plasmid using lipofectamine 2000. In each transfection, 0.2 µg of expression vector (C/EBPβ or pcDNA) and 0.2 µg of reporter plasmids were used. The Renilla luciferase-expressing plasmid was also added to normalize transfection efficiency. Luciferase activity was measured after 24 h-incubation using a Dual-Glo luciferase assay kit .The Dlx5 promoter reporter plasmid, containing the −2935 bp to +123 bp mouse Dlx5 promoter regions containing putative C/EBPβ binding elements. The forward and reverse primer sequences are as follows (5′→3′): Dlx5 promoter region-1 (−550 to −426 bp) CCG TCG GAG GAG GGG GGA GAG G and GCT CCG TGC TGT TTG AAG ACA ACG; and Dlx5 promoter region-2 (−168 to −24 bp) CTC TTT AAG CAA TGC TTT GTT GTG C and GGC GCA GCA CAG CCT TGG TTA AAT C.ChIP assays were performed as described previously using anti-C/EBPβ, anti-RNA polymerase II (RNA pol II), and anti-acetyl histone H3 (acetyl H3) antibodies . DNA elut-tests and a p-value less than 0.05 was considered statistically significant.Quantitative data are presented as the mean ± SD. Statistical analysis was performed with Student’s


SLC25A13 gene, encoding for the mitochondrial aspartate/glutamate carrier isoform 2 (AGC2), catalyses the electrogenic exchange of aspartate for glutamate plus a proton, thus taking part in many metabolic processes including the malate-aspartate shuttle. By the luciferase (LUC) activity of promoter deletion constructs we identified the putative promoter region, comprising the proximal promoter (−442 bp/−19 bp), as well as an enhancer region (−968 bp/−768 bp). Furthermore, with different approaches, such as in silico promoter analysis, gene silencing and chromatin immunoprecipitation, we identified two transcription factors responsible for SLC25A13 transcriptional regulation: FOXA2 and USF1. USF1 acts as a positive transcription factor which binds to the basal promoter thus ensuring SLC25A13 gene expression in a wide range of tissues. The role of FOXA2 is different, working as an activator in hepatic cells. As a tumour suppressor, FOXA2 could be responsible for SLC25A13 high expression levels in liver and its downregulation in hepatocellular carcinoma (HCC). Mitochondrial carriers catalyse the translocation of numerous metabolites across the inner mitochondrial membrane, playing a key role in different cell functions. For this reason, mitochondrial carrier gene expression needs tight regulation. The human  SLC25A13 gene, is a member of the mitochondrial carrier family [SLC25A12 gene. The human aspartate/glutamate carrier isoform 2 (AGC2), encoded by the r family . Humans - for external glutamate plus a proton [+ from cytosol to mitochondria [2+ reacting with AGCs on the external side of the mitochondrial membrane [2+-binding motifs [SLC25A12 are responsible for a disease characterized by developmental delay, epilepsy, hypotonia, hypomyelinization and decreased N-acetylaspartate in brain [SLC25A13 result in type II citrullinemia, which manifests with a dislike for carbohydrates, the inability to consume alcohol, citrullinemia and hyperammonemia leading to encephalopathy and neuropsyachiatric symptoms [Both AGC1 and AGC2 exhibit a high substrate specificity for aspartate and glutamate and catalyse an electrogenic exchange of intramitochondrial aspartatea proton . AGCs archondria  and is vchondria , especiamembrane ,9. Thus membrane ,11,12,13g motifs . In spitg motifs ,15. Furtin brain ,17, whersymptoms ,19,20. Isymptoms ,22. SLC25A12 is primarily activated by the transcription factor CREB (cAMP response element binding protein), upregulated in neuronal differentiation and downregulated in neuroinflammation [SLC25A12 gene in HCC cells [SLC25A13 (AGC2) gene expression and its transcriptional control are still completely unknown.Due to the relevance of the mitochondrial carrier family members in metabolism, their activity must be regulated in different tissues and under various physiological and pathological conditions. It is not surprising, therefore, that the molecular mechanisms involved in gene expression regulation of several mitochondrial carriers, as well as the changes of their gene expression occurring during cell differentiation or in different tissues, diseases and cancer, have been thoroughly studied ,29,30,31ammation . FurtherCC cells . In contSLC25A13 and identified two transcriptional factors, USF1 and FOXA2, that act as positive regulators of the SLC25A13 gene expression. In particular, we have shown that FOXA2 works as a transcriptional activator and this might be a possible mechanism responsible for SLC25A13 high expression levels in liver and downregulation in HCC as well as its expression pattern in different tissues.In this work, we have investigated the transcriptional regulation of To analyse the promoter activity of the 5′-flanking region of the human SLC25A13 gene, various truncated versions of its promoter (from −1223 to −19 bp) were amplified and cloned into the luciferase (LUC) reporter vector pGL3 basic. These constructs were transfected into HepG2, SK-N-SH and HEK293 cell lines to determine the sequence elements required for SLC25A13 promoter activity. The results represented in SLC25A13 gene promoter activity that was further increased with the deletion of −593 to −443 bp (C5). Finally, deletion from −442 to −272 bp (C6) led to a drop in luciferase activity  and found that the −442/−272 bp fragment upstream transcription starting site contains a minimal promoter working in different cell lines.In all cell lines, deletion from −767 to −594 bp (C4) almost restored activity A in HepGNext, we performed an in silico analysis of the 1200 bp of the human SLC25A13 promoter region. This analysis revealed the lack of typical TATA box and the presence of a CCAAT box (−225/−229), the initiator element (INR) at −58/−51 bp, a downstream core promoter element (DPE) at −40/−35 bp, a canonical E-box (CACGTG) at –416/−411 bp as well as a number of putative cis-regulatory elements for several transcriptional factors. In addition, we measured SLC25A13 mRNA and AGC2 protein basal levels with real-time polymerase chain reaction (PCR) and Western-blot analysis, respectively, in HepG2, SK-N-SH and HEK293 cell lines. SLC25A13 gene expression. To this aim, different experiments were performed. First, we carried out a ChIP-qPCR (chromatin immunoprecipitation-PCR) analysis on chromatin extracted from HepG2 cells by using anti-USF1 antibody. As shown in SLC25A13 gene promoter—of an active cis-element bound by HepG2 nuclear extracts   in genesextracts . These rSLC25A13 gene expression during differentiation of liver cells.Del Arco et al. showed that AGC2 was differently expressed during differentiation in rat liver . BecauseSLC25A13 gene expression. Firstly, HepG2 cells were transfected with siRNA targeting not only FOXA2 but also FOXA1 and FOXA3—the other members of FOXA family [SLC25A13 gene promoter activity and ACG2 gene expression than the other FOXA genes, we focused our further analyses on this isoform. HepG2 cells were transfected with C2 construct in the presence or absence of pcDNA3 vector containing FOXA2 coding sequence (pcDNA3-FOXA2) and the relative luciferase activity was measured. Reporter activity was enhanced by about 100% in cells transfected with pGL3basic-C2 and pcDNA3-FOXA2 compared to cells transfected with pGL3basic-C2 alone (SLC25A13 gene promoter was confirmed using the ChIP-qPCR assay. SLC25A13 required for SLC25A13 gene activation (results not shown). All together these findings strengthen the link between SLC25A13 and FOXA2 expression patterns and suggest FOXA2 to be a transcriptional factor responsible for SLC25A13 upregulation in liver.The construct C2, showing a high luciferase activity in HepG2 cells, contained a FOXA transcription factor element at −928/−918 bp. Since FOXA transcription factors are actively involved in the control of hepatic and pancreatic gene expression and metabolism , we focuA family —having tC2 alone C. In conC2 alone C. ConsisC2 alone D. FurtheC2 alone . In addiSLC25A13 gene was also under-expressed in cancer. To this end, we measured expression levels of both FOXA2 and AGC2 in normal and hepatocellular carcinoma (HCC) cells. Interestingly, Wang et al.  reportedSLC25A13 gene we overexpressed FOXA2 in SK-N-SH. First, we checked that none of the FOXA family isoforms  were present in SK-N-SH. Indeed, real-time PCR performed on mRNA extracted from SK-N-SH cells showed the absence of FOXA1, FOXA2 and FOXA3 transcription factors  sequences interact with HLH transcription factors, such as USFs, in TATA-less and pyrimidine-rich promoter [SLC25A13 gene transcription, such as the pre-initiation complex. However, USF1 could also be involved in coordinating the regulation of lipid and glucose metabolism by a mechanism not yet understood. USF1 is decreased by fasting and increased by refeeding [+ from cytosol to mitochondria [SLC25A13 in foetal (low) and adult (high) cells in parallel with differential expression USF. It might be speculated that increasing expression of USF in adult cells compared to foetal cells also affects AGC2 expression.The physiological role of AGC2 and its different tissue distribution (particularly high in liver) suggest that its expression might be regulated at a transcriptional level in a specific tissue and cell-type manner. In order to elucidate human promoter ,39. Conspromoter . Given tefeeding . In cultefeeding ,44,45. Ichondria . FurtherSLC25A13 gene expression, although to lower extent than FOXA2. The FOXA subfamily of winged helix/forkhead box (Fox) transcription factors is involved in the regulation and differentiation of metabolic tissues, such as liver, pancreas and adipose tissue, acting as factors, the binding to promoters of which enables chromatin access to other tissue-specific transcription factors [SLC25A13 gene expression in some non-hepatic cells, such as SK-N-SH cells. This contention is supported by the experimental observations reported in this study and in particular by the lack of FOXA transcription factors in SK-N-SH cells and by the very high increase in gene reporter activity obtained by FOXA2 overexpression in these cells. Therefore, this study may explain at the molecular level, at least in part, the differences in AGC2 expression levels between liver and other tissues [It is known that USFs work in tandem with general or specific transcription factors, including FOXAs ,47. By m factors . In part factors ,50. In f factors ,52,53. E factors , glucose factors  and tyro factors , as well factors ,50. Our  tissues . Interes tissues . SLC25A13 gene expression. It is likely that both factors interact each other as cotransfection of a FOXA2 binding site mutated version with both USF1 and FOXA2 transcription factors causes no increase of SLC25A13 promoter activity. Noteworthy, USF1 and FOXA2 single or double gene silencing significantly reduces the NAD+/NADH ratio, that is, negatively affects AGC2 function by regulating its transcription. Obviously, it cannot be excluded that other co-factors and transcription factor-binding sites participate in the regulation of the SLC25A13 gene expression, beyond the central role played by FOXA2 and USF1.In the light of our findings, it is clear that USF1 and FOXA2 are positively regulators of SLC25A13 gene expression which result to be very different than those controlling SLC25A12 at least in hepatocarcinoma. AGC2 (at the level of mRNA and protein) is significantly downregulated in liver cancer cells, whereas AGC1 is upregulated by an epigenetic mechanism consisting in histone hyperacetylation [SLC25A13 expression and function, in agreement with the reported role of FOXA2 as tumour suppressor in HCC [https://www.oncomine.org) database used for an in-depth analysis of SLC25A13 and FOXA2 mRNA levels reveal a strong downregulation of both genes in different types of tumours  when compared to normal tissues [This study sheds some light on the mechanisms underlying the tylation . Our finr in HCC ,60. In t tissues ,62,63. SSLCA25A13 downregulation in HCC are not clear. However, it appears that AGC2 is not essential for cancer cells’ proliferation as SLCA25A13 gene silencing does not affect HCC cell proliferation [SLC25A13 does not compromise HCC cell survival. Moreover, preliminary quantification data by NMR in SLC25A13 silenced HCC cells indicate a decrease of glutamate levels compared to control cells (data not shown), suggesting that SLC25A13 gene silencing induces the conversion of glutamate in α-ketoglutarate via mitochondrial glutamate dehydrogenase (GDH) thus sustaining TCA cycle.The reasons for feration . On the feration . Thus, iferation . As cancferation , a downrSLC25A13 gene and have demonstrated, for the first time, that the transcriptional factors FOXA2 and USF1 are involved in the positive regulation of SLC25A13 gene transcription. Furthermore, we have shown that FOXA2 works as an enhancing factor and this is most likely the mechanism responsible for SLC25A13 high expression levels in liver and its downregulation in HCC.In conclusion, we have characterized the promoter of the human Progressive deletion fragments of the −1233/−19 bp region of the AGC2 gene promoter, with or without mutations in FOXA or USF binding sites, were obtained by PCR. They were cloned into the pGL3 basic-LUC vector upstream of the LUC gene coding sequence . FOXA2 (pcDNA3-FOXA2) and USF1 (pcDNAs-USF1) expression vectors were obtained by cloning the human FOXA2 cDNA (Accession No. NM_004496.2) and the human USF1 cDNA (Accession number NM_007122), respectively, into the pcDNA3.1 vector . The sequences of primers used to generate the mutated FOXA and USF sequences of the SLC25A13 gene in the −932/−909 bp and −424/−404 bp regions, respectively, are shown in v/v) fetal bovine serum, 2 mM L-glutamine, 100 U penicillin and 100 lg/mL streptomycin at 37 °C in 5% CO2. SK-NSH cells  were grown in RPMI 1640 medium  at 37 °C in 5% CO2. Primary human hepatocytes were grown in a hepatocyte culture medium according to the manufacturer’s instructions . Transient transfection was performed as reported [® Reporter Assay System (Promega). The luminescence was measured using GloMax plate reader (Promega).HepG2 and HEK293 cells  were maintained in high glucose DMEM (Dulbecco’s modified Eagle’s medium) containing 10%  targeting human FOXA1 , FOXA2  and FOXA3  were transfected in HepG2 cells using siPORT NeoFX Transfection Agent (Thermo Fisher Scientific). A siRNA  with no significant similarity to human, mouse or rat gene sequences was used as negative control. 6 HepG2, HEK293 and SK-N-SH cells or purchased HCC cells ; normal adult and foetal liver RNAs were purchased from Origene ; and reverse transcription was performed as reported [Total RNA was extracted from 1 × 10reported . Real-tireported . Assay-oWestern blot analysis was performed as reported previously . Briefly7 of HepG2 cells were fixed by 1% formaldehyde at 37 °C for 10 min; afterwards, the cells were lysed and sheared by sonication in a 1% SDS lysis buffer to generate cellular chromatin fragments of 400–500 bp. The chromatin was immunoprecipitated for 14–16 h at 4 °C using specific antibodies to FOXA2  and to USF1 (sc-229X Santa Cruz Biotechnology). After reverse cross-linking, chromatin immunoprecipitates were purified, then 2 µL of each sample was analysed by PCR (35 cycles) using a forward FOXA2 primer (5′-CCTGATCACCACTGTCTTAT-3′) and a reverse primer (5′-CCGGGTCAAAAGACTTCC-3′) suitable to amplify the −1040/−864 bp region of the SLC25A13 promoter and a forward USF1 primer (5′-CCTCAGGTGACGGGCTTG-3′) and a reverse primer (5′-GTGATCCAGCGGCCTCTG-3′) suitable to amplify the −498/−343 bp region of the SLC25A13 promoter.ChIP experiments were performed as previously reported . Briefly5 cells obtained by centrifuging at 2000 rpm for 5 min was used. Afterwards, cells were lysed in NADH/NAD Extraction Buffer by freeze/thawing for 2 cycles of 20 min on dry ice followed by 10 min at room temperature. Then samples were centrifuged at 13,000 rpm for 10 min to remove insoluble material. Part of the cell lysate was incubated at 60 °C for 30 min to produces NADH. Enzyme mix cycling buffer and developer were added according to the manufacturer’s protocol. Plates were incubated for 3 h and absorbance was read at 450 nm using the GloMax plate reader (Promega).NAD+/NADH ratio was determined by using a NAD/NAD Quantification Kit (Sigma–Aldrich) according to the manufacturer’s instructions. Briefly, HepG2 cells were seeded in 24-well plates and gene silencing experiments were carried out as described above. Wells were washed once with ice cold PBS. For each assay a pellet from 2 x10Results are presented as means ± S.D. of, at least, four independent experiments. Differences between means of samples were compared using the one-way ANOVA test. Subsequently, where indicated, the Student’s t-test was used as a post hoc test to compare differences between means of samples and relative controls. A 5% or 1% significance level and 95% (*) or 99% (**), respectively, confidence intervals were used in the statistical analysis.


Long-range chromosomal interactions bring distal regulatory elements and promoters together to regulate gene expression in biological processes. By performing promoter capture Hi-C (PCHi-C) on human embryonic stem cell-derived cardiomyocytes (hESC-CMs), we show that such promoter interactions are a key mechanism by which enhancers contact their target genes after hESC-CM differentiation from hESCs. We also show that the promoter interactome of hESC-CMs is associated with expression quantitative trait loci (eQTLs) in cardiac left ventricular tissue; captures the dynamic process of genome reorganisation after hESC-CM differentiation; overlaps genome-wide association study (GWAS) regions associated with heart rate; and identifies new candidate genes in such regions. These findings indicate that regulatory elements in hESC-CMs identified by our approach control gene expression involved in ventricular conduction and rhythm of the heart. The study of promoter interactions in other hESC-derived cell types may be of utility in functional investigation of GWAS-associated regions. Human embryonic stem cell-derived cardiomyocytes (hESC-CM) are a widely used model to study cardiac genomics. Here, Choy et al. perform promoter capture Hi-C to map long-range chromosomal interactions of hESC-CMs and to study overlap of such regions with genetic loci associated with cardiac phenotypes. Distal promoter contacts are highly cell-type specific3 and form networks of co-regulated genes correlated with their biological functions1. The cis-regulatory contact upon lineage commitment is a dynamic process that includes acquisition and loss of specific promoter interactions4. Cardiomyocytes differentiated from human embryonic stem cells (hESC-CMs) have become a popular model to elucidate functional genomics of cardiac disease; however, there is little information supporting the notion that genomic regions associated with adult phenotypes (such as heart rate) play important roles in hESC-CMs, which have an immature phenotype. More generally, it is essential, in the context of potential cardiac regenerative medicine approaches, to understand how promoters make specific interactions in hESC-CMs to regulate the gene expression patterns underlying the process of cardiomyocyte development. In this study we identify the promoter interactome of hESC-CMs using promoter capture Hi-C (PCHi-C)5, which allows physical promoter-enhancer contacts in cells to be identified. We demonstrate that the promoter interactome of cardiac genes is reorganised after cardiac differentiation and cardiac enhancers are enriched in the promoter-interacting regions (PIRs). The PIRs also share the same target genes with expression quantitative trait loci (eQTLs) of cardiac left ventricles and overlap genetic variants associated with conduction and rhythm of the heart.Long-range chromosomal interactions play an important role in differentiating cells, where they organise three-dimensional promoter-enhancer networks that regulate lineage-specifying developmental genes5 on three biological replicates of 20 million hESC-CMs. From the three biological replicates, we mapped and obtained 774 million valid, unique and on-target captured read pairs (63.3% capture efficiency) with Hi-C User Pipeline (HiCUP)6, and called 179,880 significant hESC-CM promoter–genome interactions  in hESC-CMs9 using Model-based Analysis of ChIP-Seq (MACS)10. We also determined whether the genes corresponding to the promoters were transcriptionally active in hESC-CMs by analysing published RNA-seq data in hESC-CMs11. The active histone marks of H3K4me3 and H3K36me3 were significantly associated with transcriptionally active promoters involved in hESC-CM interactions and the repressive histone mark of H3K27me3 with the inactive ones  using CRISPR-Cas9 technology and showed that the gene expression of Ednra was up-regulated, indicating that mm172 can function as a repressive regulatory element for Ednra  Portal16. Where PIRs in our data overlapped a GTEx eQTL, we ascertained, separately for cPIRs and hESC PIRs, whether the target gene of the eQTL was the same as the target gene of the PIR, and expressed the agreement as a percentage of all eQTL-overlapping PIR–gene pairs. We tested whether there was evidence for enhanced agreement within the cPIRs or hESC PIRs compared with the random background. Previous data indicate that higher Hi-C read counts provide more robust support for interactions between distal regulatory elements and target genes17, and therefore we explored the effect of different read count cut-offs on the agreement between eQTL–gene and PIR–gene pairs. As expected, the agreement percentage increased with sequencing read counts of promoter interactions , with a similar trend observed in the atrial appendage ), 16.5% of them target the same genes as the eQTLs. eQTLs are linked to trait-associated single-nucleotide polymorphisms (SNPs)19 and therefore are important information for functional elucidation of genetic findings. Given that the top 20% cPIRs for read counts showed the most significant agreement of target genes with ventricular eQTLs, we reasoned that these top 20% of promoter interactions represented the most relevant subset mediating eQTL–gene interactions.Of the 1264 top 20% PIR–gene interactions overlapping with lead left ventricular eQTLs  had 40 nodes, including 12 promoter nodes and 28 cPIR nodes, and 117 edges  and MYH7 genes , in particular defects in the atrial and ventricular septum, via dysfunction of cardiac myofibrils22. MYH7 mutations are a major cause of hypertrophic cardiomyopathy, and have also been reported in CHD families22. The identification of promoter interaction networks in hESC-CMs involving key cardiac genes, such as MYH6 and MYH7 in our data, illustrates the potential of this approach to map the relationships between genes and regulatory elements in genomic regions of cardiac importance.Within the 35,960 top 20% hESC-CM promoter interactions, 6376 unique promoters interacted with 27,123 unique cPIRs at an average degree of one promoter to 13.7 cPIRs . However most cPIRs interacted with only one promoter . Also, 89.9% of the interactions occurred within previously published topologically associating domains of hESCsnes Fig.  and the 23. We therefore explored if the hESC-CM promoter interactome was associated with any of the three cardiovascular phenotype groups by considering their GWAS profiles: (1) CHD25; (2) coronary artery disease 26; and (3) cardiac conduction and rhythm disorders (CRD)27. Our prior hypothesis was that we would find an association between the hESC-CM promoter interactome and CRD, which is a phenotypic group largely deriving from dysfunction that involves myocardial cells, but not for CHD or CAD. For each GWAS, we obtained the p values of SNPs located in cPIRs or hESC PIRs and constructed QQ plots against averaged p values of SNPs located in 1000 sets of randomised cPIRs or hESC PIRs . On the other hand, SNPs located in hESC PIRs showed the highest inflation in GWAS p values of CAD . We then investigated whether all the inflated SNPs (p < 0.001) from each of these GWAS studies overlapped significantly with the top 20% cPIRs or hESC PIRs. Using GoShifter (https://github.com/immunogenomics/goshifter)28, we observed a significant overlap for only CRD GWAS SNPs (Bonferroni-adjusted p = 0.045). This indicated that cPIRs are enriched for regulatory regions controlling genes involved in cardiac rhythm, a phenotype in which cardiomyocytes directly participate.Promoter interactions detected in trait-relevant tissues hold the clues to the putative target genes of non-coding genome-wide association study (GWAS) SNPsIRs Fig. . For SNPp < 1 × 10−5) associated with CRD were interacting with promoters of known heart rate-associated genes27 such as CCDC141 on chromosome 2, GJA1 on chromosome 6 and MYH6 on chromosome 14  channels are important regulators of the calcium-dependent hypertrophy pathway30. The significant cPIR SNPs also coincided with published regions of TFPI on chromosome 2, SLC35F1 on chromosome 6 and HCN4 on chromosome 15 but, upon integration of the chromosomal interaction information, those regions were found interacting with ZSWIM2, PLN and NPTN respectively  forms a complex with plasma membrane Ca2+ ATPases to modulate calcium homoeostasis33. The Np55 variant of NPTN is expressed in the heart34 and the disruption of NPTN has been shown to cause increased heart weight in the International Mouse Phenotyping Consortium database 35. ZSWIM2 is an E3 ubiquitin-protein ligase, a family of proteins that may play a role in degrading mal-processed ion channel proteins in the heart36. It has been reported that genes some distance from a strong candidate locus harbouring a GWAS “hit” SNP may have an important influence on complex phenotypes37. For example, polymorphism in the FTO gene are the strongest GWAS signals for obesity, but the functional mechanism of the association appears chiefly due to the effects of the SNPs on two neighbouring genes, IRX3 and RPGRIP1L37. Therefore, promoter interactions of the neighbouring genes within a GWAS signal may provide crucial information to pinpoint the true target genes.Some of the significant cPIR SNPs  in the GWAS study27. However, by incorporating eQTL data from the GTEx Portal16, the significant cPIR SNPs in chromosome 19 in our data were shown to be significant eQTLs for ACTN4 and CAPN12 in the left ventricle of human hearts  genetic effects are of insufficient size to be detected at conventional significant levels even in large GWAS studies40. By focusing on interactions in regions of borderline significant genetic associations, we were able to identify potentially physiologically relevant target genes of GWAS SNPs through functional rather than proximity considerations. This may, in the future, facilitate the identification of new genes influencing cardiac conduction and rhythm disorders.In addition, we identified a peak in chromosome 19 with significant cPIR SNPs interacting with rts Fig. . FunctioEDNRA, which is a critical gene in chamber myocardium formation during heart development15. Somewhat unexpectedly, we observed that deleting this enhancer resulted in an increase of EDNRA expression, consistent with a number of repressive lineage-specifying promoter interactions detected in the pluripotent state2 and in early hESC-derived neural progenitors4. There remains a possibility that silencers of the same gene could be disrupted as well when a cPIR/enhancer was targeted by CRISPR-Cas9 since enhancers and silencers can be located in the same locus or clustered in the same locus control region41. Further work will be required to elucidate this possibility.The hESC-CM interactomic map of gene promoters generated in this study not only enabled us to understand the promoter–genome networks of established cardiac genes but also those of a number of novel genes possibly playing a role in cardiac development and function. We mapped known conserved cardiac-specific VISTA enhancers to their target genes using the promoter interactome. We validated one of the VISTA enhancers involved in cardiac development, mm172, by deleting it using CRISPR-Cas9 and showed that it indeed was a regulatory element of its interacting gene 42; the most complex interactomic subnetwork of cardiomyocytes in our data were represented prominently among GWAS “hits” for the heart rate, which would be in keeping with this model. Moreover, our integration of interactomics with GWAS data suggest that similar approaches to that we present may prove particularly useful to identify “core” genes. This approach may be extended to other hESC-derived cell types to functionally elucidate GWAS regions.The GWAS SNPs associated with cardiac conduction and rhythm disorders were enriched in cPIRs. While the target genes of GWAS SNPs are usually identified by gene proximity, it is known that non-coding variations can influence genes situated at substantial distance, rendering selection of a candidate in a region problematic. Using promoter interactions in hESC-CMs, we were able to identify distal target genes of GWAS SNPs for a phenotype relevant to the specific cell type and new genomic regions associated with cardiac conduction and rhythm disorders. The hESC-CM promoter interactome, rather than the hESC promoter interactome, also had a significantly higher likelihood, compared with the random background, mapping to the target genes of the left ventricular eQTLs located within the cPIRs. Taken together with the GWAS findings, it suggests that hESC-CMs are a relevant model to study ventricular conduction and rhythm. Recently, an “omnigenic” model of complex disease involving few “core” genes and many “non-core” genes influencing a particular phenotype has been proposedThe hESC line WA09 (H9) was obtained from WiCell, maintained and cultured in monolayers according to WiCell Feeder-Independent Pluripotent Stem Cell Protocols using mTeSR1 Medium (SOP-SH-002). The hESC-CM differentiation was performed on WA09 hESC monolayers (passage 32) at 80–100% confluence using PSC Cardiomyocyte Differentiation Kit . After the treatments of Cardiomyocyte Differentiation Medium A and B (2 days each), the cells were allowed to recover in Cardiomyocyte Maintenance Medium for a day. The cells were then passaged (1:1.5 ratio) into plates coated with fibronectin  and allowed to grow for 6 days in RPMI1640-B27 minus insulin (Gibco) before changing to RPMI1640-B27 complete (Gibco) for further 2 days. The cardiomyocytes were then enriched in glucose-depleted-lactate-enriched medium (Dulbecco’s modified Eagle's medium (DMEM) without glucose ; 4 mM lactate (Sigma); non-essential amino acids (Gibco); GlutaMAX (Gibco)) for 6 days and recovered in Iscove’s modified Dulbecco’s medium (Gibco) containing 20% foetal bovine serum (FBS) for 2 days. The hESC-CMs were cultured in RPMI1640-B27 complete (Gibco) for 7 more days before the quality check (at least 80–90% purity by flow cytometry and quantitative polymerase chain reaction (qPCR) using myosin heavy chain and TNNT2 as markers) and the fixation for PCHi-C.3 and summarised as follows: hESC-CMs were fixed by 2% formaldehyde  for 10 min at room temperature, quenched by cold 0.125 M glycine (5 min at room temperature followed by 15 min on ice), flash frozen in liquid nitrogen and stored at − 80 °C. Hi-C libraries were prepared with in-nucleus ligation and PCHi-C was performed with Agilent SureSelect Target Enrichment System. Then, 22,076 HindIII fragments containing a total of 31,253 annotated promoters for 18,202 protein-coding and 10,929 non-protein genes according to Ensembl v.75 (http://grch37.ensembl.org) were captured. A post-capture PCR amplification step was carried out using PE PCR 1.0 and 2.0 primers with four PCR amplification cycles. High-throughput sequencing reactions were performed on the Illumina HiSeq2500 platform. Sequencing reads were processed and mapped with HiCUP6 and PCHi-C interaction was called using CHiCAGO7 with default parameters.PCHi-C was performed as previously described7, the biologically replicated read counts of the promoter interactions in hESC-CMs  and those previously obtained from WA09 hESCs ; GSE86821; in-solution ligation)4 were normalised using DESeq2 43 and averaged. The peak calling for published H3K4me3, H3K27me3 and H3K36me3 histone ChIP-seq  of hESC(WA07[H7])-CM 9 was performed using MACS 2.1.0 10. The RPKM (reads per kilobase per million mapped reads) for each gene was calculated from published RNA-seq data  of hESC(WA01[H1])-CMs 11. For significant tests of enrichment/agreement percentages, randomly generated promoter–PIR interactions were created to retain the distribution of interaction distances of the observed promoter–PIRs in each set. For each promoter bait, the relative positions of its PIRs were calculated and randomly transplanted to another randomly selected promoter bait. For each observed set, 1000 random sets were generated with each set containing the same number of interactions as the observed set, and a one-tailed permutation test was performed. Each random PIR set was then assessed for its enrichment percentage of VISTA enhancers or agreement percentage of eQTLs in the same way as the observed data and used for comparisons. For the eQTL analysis, single-tissue cis-eQTL data were downloaded from GTex 16. For each tissue, lead SNP-gene pairs were selected based on them having the lowest p values within each LD block calculated using the Ensembl API 44. To assess the agreement percentage of eQTL and PIR target genes for each set of hESC-CM or hESC promoter interactions, the gene promoters that each PIR interacts with were compared to the target genes of eQTLs that were found to be located within the PIR region. The agreement percentage is then expressed as the percentage of PIRs containing at least one eQTL targeting the same gene as the PIR among all eQTL-overlapping PIR–gene pairs. GTex eQTLs were limited to those SNP-gene pairs occurring within one megabase distance and therefore promoter–PIR interactions occurring more than one megabase distance were filtered out for the eQTL agreement assessment45. For GWAS/QQ plot analyses, the overlap of PIRs with GWAS SNPs was determined for each set of observed hESC-CM and hESC promoter interactions. The same was done for each of the 1000 randomly generated interaction sets for each observed set. The p values of the observed overlapping SNPs and those overlapping the randomly generated PIRs were compared, first by interpolating the lists of p values to the shorted list (observed or random) and then taking the mean of each rank-ordered p value list across the 1000 permutations based on QQperm package (https://cran.r-project.org/web/packages/QQperm/index.html). The inflation factor  of observed/random QQ plots were estimated using QQperm (estlambda2) as well. All sequenced reads were mapped to the human reference genome (build hg19/GRCh37).After calling the significant PCHi-C interactions with CHiCAGO2) were cloned into plentiCRISPRv2  according to published paired gRNA design46 using human U6 promoter for gRNA1 and murine one for gRNA2. The cloned plentiCRISPRv2 and second-generation packaging plasmids psPAX2  and pMD2.G  were co-transfected into HEK293T cells (cultured in DMEM (Gibco) supplemented with 10% FBS and 1× penicillin–streptomycin at 37 ˚C and 5% CO2) in the proportion of 4:3:2 using Lipofectamine 2000 (Invitrogen) for lentivirus production. Lentivirus-containing media were collected 48 h after transfection, centrifuged to remove cell debris and 0.45 µm filtered prior to infecting H9c2 cell monolayers in the presence of 10 µg/ml polybrene (Millipore). At 24 h post infection, the transduced H9c2 cells were selected in 1 µg/ml puromycin media for 48 h.The guide RNA (gRNA) sequences  used to delete VISTA enhancer mm172 in rat cardiomyoblast H9c2 2-1  supplemented with 10% FBS and 1× penicillin–streptomycin at 37 ˚C and 5% COEdnra  or 1892 bp (cDNA)); (3) Actb  or 228 bp (cDNA)); and (4) plentiCRISPRv2 ).Genomic DNA (gDNA) was extracted from cells using PureLink Genomic DNA Mini Kit (Invitrogen) and total RNA using RNAqueous-Micro Kit (Ambion). First-strand complementary DNA (cDNA) was synthesised from DNase (Promega) treated total RNA using M-MLV Reverse Transcriptase (Promega) and both random hexamer and oligo(dT) primers (Promega). PCR was performed using OneTaq 2×Master Mix (New England BioLabs) and the following primers: (1) mm172 ); (2) 47) and ACTB , and subsequently a goat anti-rabbit horseradish peroxidase secondary antibody . The membrane was incubated with SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Scientific) before being imaged with a Molecular Imager ChemiDo XRS+System (Bio-Rad).Whole-cell lysates were prepared by resuspending cells in RIPA buffer containing 5× cOmplete Mini Protease Inhibitor Cocktail (Sigma) and homogenised with a 19-gauge syringe. Equal amounts of protein samples were boiled in Laemmli buffer, resolved by a Mini-PROTEAN TGX Precast Gel (Bio-Rad), transferred to a nitrocellulose membrane by a Trans-Blot Turbo Transfer System (Bio-Rad), incubated with primary antibodies against EDNRA .The PCHi-C data for hESC-CMs have been deposited in Gene Expression Omnibus (GEO) with accession number Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4