Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and mechanisms at genome-wide association study (GWAS) loci. To identify regulatory elements that display differential activity across adipocyte differentiation, we performed ATAC-seq and RNA-seq in a human cell model of preadipocytes and adipocytes at days 4 and 14 of differentiation. For comparison, we created a consensus map of ATAC-seq peaks in 11 human subcutaneous adipose tissue samples. We identified 58,387 context-dependent chromatin accessibility peaks and 3,090 context-dependent genes between all timepoint comparisons  with 15,919 adipocyte- and 18,244 preadipocyte-dependent peaks. Adipocyte-dependent peaks showed increased overlap (60.1%) with Roadmap Epigenomics adipocyte nuclei enhancers compared to preadipocyte-dependent peaks (11.5%). We linked context-dependent peaks to genes based on adipocyte promoter capture Hi-C data, overlap with adipose eQTL variants, and context-dependent gene expression. Of 16,167 context-dependent peaks linked to a gene, 5,145 were linked by two or more strategies to 1,670 genes. Among GWAS loci for cardiometabolic traits, adipocyte-dependent peaks, but not preadipocyte-dependent peaks, showed significant enrichment (LD score regression P<0.005) for waist-to-hip ratio and modest enrichment (P < 0.05) for HDL-cholesterol. We identified 659 peaks linked to 503 genes by two or more approaches and overlapping a GWAS signal, suggesting a regulatory mechanism at these loci. To identify variants that may alter chromatin accessibility between timepoints, we identified 582 variants in 454 context-dependent peaks that demonstrated allelic imbalance in accessibility (FDR<5%), of which 55 peaks also overlapped GWAS variants. At one GWAS locus for palmitoleic acid, rs603424 was located in an adipocyte-dependent peak linked to SCD and exhibited allelic differences in transcriptional activity in adipocytes (P = 0.003) but not preadipocytes (P = 0.09). These results demonstrate that context-dependent peaks and genes can guide discovery of regulatory variants at GWAS loci and aid identification of regulatory mechanisms. Cardiovascular and metabolic diseases are widespread, and an increased understanding of genetic mechanisms behind these diseases could improve treatment. Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and genetic mechanisms for disease traits. A relevant context for cardiovascular and metabolic disease traits is adipocyte differentiation. To identify regulatory elements and genes that display differences in activity during adipocyte differentiation, we profiled chromatin accessibility and gene expression in a human cell model of preadipocytes and adipocytes. We identified chromatin regions that change accessibility during differentiation and predicted genes they may affect. We also linked these chromatin regions to genetic variants associated with risk of disease. At one genomic region linked to fatty acids, a chromatin region more accessible in adipocytes linked to a fatty acid synthesis gene and exhibited allelic differences in transcriptional activity in adipocytes but not preadipocytes. These results demonstrate that chromatin regions and genes that change during cell context can guide discovery of regulatory variants and aid identification of disease mechanisms. Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiometabolic traits, yet most mechanisms remain unclear due to unknown functional variants, genes, cell types, and relevant contexts, especially at the large number of noncoding loci . NoncodiAdipose tissue influences cardiometabolic traits such as body fat distribution, insulin sensitivity, blood cholesterol levels, and inflammation through its roles in lipid storage and hormone secretion ,13. HundGenetic and environmental variation between individuals can contribute to differences in chromatin accessibility . ChromatSCD and EYA2 loci that showed context-dependent and/or allelic effects on transcriptional activity, detecting potential mechanisms by which specific variants alter gene expression to affect cardiometabolic traits.In this study, we identified differences in chromatin accessibility and gene expression between preadipocytes, immature adipocytes, and mature adipocytes in SGBS cells. In addition, we generated a consensus map of subcutaneous adipose tissue chromatin accessibility using 11 samples obtained from METabolic Syndrome in Men (METSIM) participants . We usedWe profiled chromatin accessibility during adipocyte differentiation with ATAC-seq in SGBS cells ,24. We a2 fold change (LFC)>1; false discovery rate (FDR)<5%; To predict regulatory elements involved in adipocyte differentiation, we identified differentially accessible peaks, hereafter called ‘context-dependent peaks’, between each pairwise comparison of the three timepoints (log-10) near genes associated with roles in several cell cycle processes, including positive regulation of DNA replication (-5) for transcription factor motifs for TEAD and GATA, which inhibit adipocyte differentiation We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Chris Cotsapas, PhDAssociate EditorPLOS GeneticsWendy BickmoreSection Editor: EpigeneticsPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this submission, Perrin et al. aim to identify mechanisms downstream of cardiometabolic GWAS loci through transcriptional and epigenetic profiling of a human cell model of preadipocytes and adipocytes. The relative paucity of eQTLs at GWAS loci may relate partly to the context  of eQTL studies. Like other disease-relevant tissues, adipose tissue is heterogeneous; GWAS variants may therefore act specifically in adipocytes, preadipocytes, or other constituent cells. Using their cell line, the authors identify peaks that are accessible at specific developmental stages, and subsequently assess these for disease relevance.The authors begin by profiling chromatin accessibility in SGBS cells  with ATAC-seq at D0 (preadipocyte), D4 (immature adipocyte) and D14 (mature adipocyte) of differentiation. They identify consensus peaks by overlap at each developmental stage. Differentially accessible ("context-dependent") peaks are identified between each pair of timepoints. Most changes in chromatin accessibility appear to occur between day 0 and day 4, then persist to day 14.ATAC-seq peaks that are more accessible in preadipocytes (D0>D4 and D0>D14) are identified, along with a similar set for adipocytes. The paper defines the adipocyte set as D4>D0 and D14>D0, but the D14>D4 and D14>D0 set would seem to be more specific for mature adipocytes; further motivation for this choice, and examination of its effect on their overall results, would be helpful. Both preadipocyte- and adipocyte-dependent peaks were enriched near genes involved in relevant metabolic processes and appropriate transcription factor motifs, suggesting they capture relevant biology.In parallel, the authors generate a consensus map of chromatin accessibility in subcutaneous adipose tissue from 11 healthy donors (17 prior to QC) using similar methods. Sequencing details are somewhat heterogeneous across the study, with variable read length and sequencing method (paired-end vs. single-end). This is addressed to an extent in their QC but remains a potential limitation.To link changes in chromatin accessibility to potential gene regulatory changes, the authors use RNA-seq to identify gene expression alterations at each stage of adipocyte differentiation. They link context-dependent peaks with potential gene targets by consensus of 2/3 methods: (a) overlap with promoter-capture Hi-C regions, (b) overlap with adipose eQTL signals, and (c) Hi-C overlap or eQTL overlap with additional context-dependent expression. Each of these methods has significant limitations in isolation. eQTL signals overlapping context-dependent accessibility regions do not necessarily have a context-dependent effect. Hi-C regions are frequently large, and the authors included overlaps as small as a single base pair. A more stringent overlap threshold may more precisely identify candidate peaks of interest.A total of 5,145 peaks were linked to the same gene by >=2 approaches, with 78 peaks linked to the same gene by all three approaches. The 78 peaks that link to the same gene by all three methods would seem logical candidates for further mechanistic exploration, but the authors focus on the larger set of traits linked by >=2 methods. They identify GWAS signals (proxies with r^2>0.8) that overlap context-dependent peaks and link these to gene targets using their consensus method. While a number of these may represent disease mechanisms, these should be viewed as mechanistic candidates. Simple overlap is not sufficient to establish causal mechanistic links between GWAS variants, putative context-dependent regulatory elements within context-dependent chromatin, and downstream genes. Further work will be required to validate hypotheses that arise from this work.Supporting the relevance of context-dependent chromatin regions, stratified LD score regression is used to demonstrate heritability enrichment for waist:hip ratio adjusted for BMI in adipocyte-dependent and adipose tissue peaks but not in preadipocyte-dependent peaks. Coronary artery disease was also enriched in adipose tissue peaks and SGBS peaks defined at day 0 and day 4. Allelelic imbalance is observed in several peaks, but this analysis was limited by the fact that all SGBS cells have the same genotype.Data from this study will be a useful resource for future studies of GWAS mechanism within adipose tissue, but at this point specific hypotheses are not validated. Demonstration of context-dependent chromatin accessibility is an important first step, but the present study cannot identify specific regulatory elements that are contained within these peaks. The SGBS cell line offers an elegant approach to dissociate preadipocyte and adipocyte biology, but disease relevance may be somewhat limited by the artificial conditions in which the cell line is grown.Reviewer #2: Perrin et al present a nice manuscript regarding ATAC seq and RNA seq in SGBS cells, day 4 and 14compared to ATAC seq from 11 SAT human samples (METSIM). Found > 50 K context dependent chromatic accessibility in all timepoint comparisons. Linked context dependent peaks based on Hi-C data overlap with eQTL and gene expression. Found GWAS for cardiometabolic traits, adipocyte dependent peaks enriched for WHR.Overall the goals are good: Find out how GWAS loci work, Map regulatory elements with Chromatic accessibility (ATAC Seq) and in an appropriate cell/tissue . This group has a track record in this area and the methods seem sound.I think a few things will improve the paper.-16K context dependent peaks linked to a gene -> 5K linked to 1.6K genes  -> 78 linked to same gene through all 3 approaches...I wonder what are these 78 genes? Seems like these are low hanging fruit as it were?-p.10…Applaud the use of many different ways to try to link peaks to genes…But seems like this is necessarily limited by the availability of Hi-C data from preadipocytes. Was this not looking “under the lamp post where there is light”? Similar to having eQTL data from adipocytes but not pre-adipocytes. I think even more focus could potentially be given to adipocytes given this limitation.-Nice to see the heritability enrichment (p.12 and Table S13). The striking findings with WHR are important as are the findings related to HDL, TG, and CAD which all point to a “metabolic syndrome” overlap. Overall the data presented on table S13 should probably emphasize normalized count overlapping adipose dependent peaks rather than any context dependent peak. At least when I order the table in this regard it makes a lot of biological sense. Associations with WHR, HDL, birth weight, TG, CVD, bone density Afib are all expected. However, other interesting biological insight is that breast cancer is in the top 25 as are eczema, and many pulmonary traits (FEV1). This is quite interesting. Would be interested to see if genetically programmed WHR is causally related to FEV1 as a later paper. Also the relationship to intraocular pressure is not immediately intuitive though I am not an eye or glaucoma expert. That table also seems a bit strange to me in one way. There are about 290 traits that have the same number of “total count of signals from catalog, n=1277 with same number of “count overlapping any context dependent peak, n=233) giving identical “normalized count overlapping…, n=18.25). Why is this? I guess I would like to see a bit more of the discussion focused on medical relevance and the results less about presenting "numbers" of peaks with overlap (which could be gleaned from the figures)...More about why all this work is medically relevant would help the reader.Minor-I understand looking at enrichment of transcription factor motifs (p.6-7) in ATAC seq peaks…But less so the pathway analyses. Pathway analyses focused on the “nearest” gene (Table S5) will often be misleading as often times the “nearest” gene is not relevant (as elegantly shown in this and other papers).-"Context dependent" is a reasonable phrase but non-specific. Could “differentiation-specific” be used?-Seems like there has been a similar ATAC Seq study in more commonly used 3T3-L1 cells (Discussion) which may be mentioned in the Intro. What else was the overlap in findings from the mouse to the human cell line? We all want to understand if the mouse cell line can be used as it is a bit easier than SGBS (which have a limited supply).-Fig 1a indicates ATAC seq in 17 people but abstract says 11 people were used, I see in the Figure legend a subset of 11 was used but what happened to the other 6 (33% of the samples)? p.7 indicates they were outliers due to PCA. But why even bother mentioning these or including the data. You could simply omit all references to the 6 samples from all analyses by saying they did not meet QC or other standards.-Fig 1b…order is D0, D14, D4 and 1c is D0, D4, D14…why the difference? Shouldn’t order be temporal?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , and click on the Fetch/Validate link next to the ORCID field.  This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.In the meantime, please log into Editorial Manager at If you have a press-related query, or would like to know about making your underlying data available , please see the end of this email. If your institution or institutions have a press office, please notify them about your upcoming article at this point, to enable them to help maximise its impact. Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Chris Cotsapas, PhDAssociate EditorPLOS GeneticsWendy BickmoreSection Editor: EpigeneticsPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Perrin et al. revise their submission examining chromatin accessibility and gene expression in the SGBS model of adipocytes and preadipocytes. They have addressed several concerns raised in initial review and the paper now reads more clearly, with improved justification.The authors continue to define "adipocyte-dependent" peaks as D4>D0 and D14>D0. While this is less specific for mature adipocytes than the D14>D4 and D14>D0 peak set, it is clarified that there were only 233 peaks specific to mature adipocytes. As chromatin accessibility appears to remain relatively stable between D4 and D14, combining immature and mature adipocytes into a single category seems reasonable. Similarly, the decision to focus on peaks that are linked to genes by 2/3 approaches is better motivated.The revised manuscript more clearly acknowledges a few important limitations. Sequencing methods varied somewhat throughout the study, representing a source of technical variation. It is reassuring that clustering of samples did not appear to be significantly affected. They more clearly acknowledge the challenge of translating overlaps with GWAS loci into molecular mechanism and appropriately call for further studies.This study will be an important resource for studies of GWAS mechanisms in adipose tissue. Potential dissociation of preadipocyte and adipocyte biology is particularly attractive, though the usual caveats pertaining to cell lines continue to apply.Reviewer #2: my comments have been addressed**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: Yes**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  22 Oct 2021PGENETICS-D-21-00875R1 Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci Dear Dr Mohlke, We are pleased to inform you that your manuscript entitled "Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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The horse reference genome assemblies, EquCab2.0 and EquCab3.0, have enabled great advancements in the equine genomics field, from tools to novel discoveries. However, significant gaps of knowledge regarding genome function remain, hindering the study of complex traits in horses. In an effort to address these gaps and with inspiration from the Encyclopedia of DNA Elements (ENCODE) project, the equine Functional Annotation of Animal Genome (FAANG) initiative was proposed to bridge the gap between genome and gene expression, providing further insights into functional regulation within the horse genome. Three years after launching the initiative, the equine FAANG group has generated data from more than 400 experiments using over 50 tissues, targeting a variety of regulatory features of the equine genome. In this review, we examine how valuable lessons learned from the ENCODE project informed our decisions in the equine FAANG project. We report the current state of the equine FAANG project and discuss how FAANG can serve as a template for future expansion of functional annotation in the equine genome and be used as a reference for studies of complex traits in horse. A well-annotated reference functional atlas will also help advance equine genetics in the pan-genome and precision medicine era. Twilight and remain the only high-quality genome assemblies for equids. EquCab2.0 has 42,304 gaps comprising 55 Mb (2.2% of the genome) in total, with a scaffold N50 of 46 Mb. In comparison, EquCab3.0 contains 3771 gaps comprising 9 Mb (0.34% of the genome) with a scaffold N50 of 86 Mb. It has 99.7% mammalian Benchmarking Universal Single-Copy Orthologs (BUSCO) , compared to that of 99.0% (4064 complete orthologs) in EquCab2.0 [The horse reference genomes (Equcab2.0  and EquCGRM6) associated with congenital stationary night blindness [RAPGEF5) associated with equine familial isolated hypoparathyroidism [Using these tools, successful identification of the genetic variants responsible for simple Mendelian traits have been identified, including a novel variant in glutamate metabotropic receptor 6  region [Twilight to achieve this goal while still aligning well with the reference sequence. A unique aspect of this biobank is that horses were extensively phenotyped, both antemortem by experienced veterinarians and postmortem by veterinary pathologists. This not only ensured that there was no evidence of clinical or subclinical disease in these animals, but it also provided insight into the cellular composition of the tissues selected for assays. These tissues are stored at −80 °C in a biobank at UC Davis and are available to all equine FAANG researchers.The first stage of the equine FAANG initiative was to generate a biobank of reference tissues from comprehensively phenotyped animals. Burns et al.  and Donn) region , the decHere, we briefly discuss some of the most relevant findings from ENCODE and their implications for functionally annotating the equine genome.The transcriptome is the collection of all transcripts in an organism. It includes protein-coding mRNAs as well as noncoding RNAs. During the second phase of ENCODE, 62% of the human genome was found to be transcribed with 31% of transcribed bases located in intergenic regions . Many ofAs part of ENCODE, GENCODE was initially founded to provide high-quality reference gene annotation for the human genome and subsequently expanded into a long-running partnership between several groups and institutes. In its most recent release based on GRCh38, a total of 60,649 genes have been identified in the human genome, of which 19,955 are protein coding, with an average isoform-to-gene ratio of 3.9 . It was In addition to protein-coding transcripts, the transcriptome also consists of many noncoding RNA species, including both small and long noncoding RNAs. The functions of these RNAs have been extensively examined and implicated in important biological pathways ,26,27,28Taken together, these findings from ENCODE demonstrated the importance of noncoding RNAs and of alternative splicing in cell-specific expression and regulation. Both Ensembl  and RefSThe FAANG initiative proposed RNA-seq assays for both mRNA and smRNA to identify and quantify these transcripts in a tissue-specific manner . These aTo facilitate data generation for the remaining biobanked tissues, we proposed a unique “Adopt-A-Tissue” model for mRNA-seq. Researchers were invited to “adopt” a tissue or tissues fitting their research interests, which meant they would cover the assay and sequencing costs. All library preparations and sequencing were performed at the same two locations  to minimize variability. This approach allowed the community to contribute to the initiative together while still being able to limit technical variations across laboratories during library constructions . Owing tMore recently, long-read sequencing assays such as PacBio Isoform sequencing (Iso-seq) have emerged as powerful tools to determine the splicing patterns of transcripts. To address the poor isoform annotations currently available for the horse genome, Iso-seq assays are being performed in 8 tissues  across eight PacBio Sequel 8M SMRT cells. By combining a wide variety of assays, the equine FAANG initiative aims to deliver a comprehensively annotated transcriptome for the horse genome.In mammalian cells, DNA molecules are packed by histone proteins to form nucleosomes and are subsequently compacted into chromatin ,38. CompEchoing their strong functional implications, accessible chromatin was also shown to be associated with noncoding variants identified in GWAS studies of common traits. Maurano et al. examined 5654 noncoding variants identified in the GWAS studies of 207 diseases and 447 quantitative traits and found 76.6% of these variants lie either within a DHS or in complete linkage disequilibrium (LD) with another SNP in DHS . The datAlthough DNase-seq has proven successful in identifying accessible chromatin, its laborious protocol, slow turn-around time, and large sample size requirements severely limit large-scale applications ,51. BuenUsing ATAC-seq on cryopreserved nuclei from eight tissues across pig, cattle, and mouse, Halstead et al. showed a lack of conservation of sequence and accessibility in accessible sites across evolutionary distance, with 20% shared sites between pig and cattle and only 10% between mouse and ungulates . TherefoHistone proteins form the basic building blocks of hierarchical chromatin structures and have been recognized to play an important role in modulating gene expression through post-transcriptional modifications ,59,60,61Barski et al. first comprehensively assayed histone modifications across the human genome using high-throughput sequencing . ConsistTaken together, the four histone modifications discussed in this manuscript represent major regulatory elements and can provide valuable information regarding tissue-specific regulatory activities in the horse genome. Using genome-wide chromatin immunoprecipitation sequencing (ChIP-seq) for these four marks in eight prioritized tissues in the two female FAANG horses, Kingsley et al. reported over one million putative regulatory sites . The utiMYC [LYZ [APP) [HBB [CCCTC-binding factor (CTCF) is a well-studied zinc finger protein that serves a central role in the formation of chromatin topology and remodeling. It was first discovered as a repressive transcription factor in chicken for c-MYC  as well MYC [LYZ . It was YZ [APP) . In 1999PP) [HBB . InsulatPP) [HBB . This siPP) [HBB , a typicPP) [HBB ,79. Many seemingly contradictory functions of CTCF have attracted extensive efforts to understand the mechanisms of its multivalent roles. CTCF is highly conserved across species ,81 and eThe introduction of Hi-C technology that enabled genome-wide interrogation of long-range interactions  quickly WAPL results in enlarged chromatin loops between incorrectly orientated CTCF motifs [WAPL knockout cells are a result of aggregated CTCF loop anchors, or a “cohesin traffic jam” [These findings led to a proposed extrusion model ,95, wherF motifs . Allahyafic jam” .Given its central role in chromatin loop formation, CTCF binding sites can be considered an intermediate between the 1D genomic sequence and 3D chromatin topology. Although there is no simple rule to determine the functional outcome of a disrupted CTCF binding site, as it largely depends on its interaction with surrounding regulatory elements, there is no doubt that a catalog of CTCF binding sites in a given cellular context can provide valuable information when decoding the functional implications of DNA variants. Following the practices established by the FAANG community, characterization of CTCF binding sites using ChIP-seq is being performed on eight prioritized tissues for both sexes. Analyses to identify both tissue and sex-specific CTCF binding and integrate all of the FAANG ChIP-seq data into chromatin state annotations are currently underway.While the associations between individual histone marks and regulatory activities are noteworthy, combinations of histone marks have proven to be more reliable in the fine-scale predictions of regulatory elements. For example, Creyghton et al. observed that the H3K27ac mark could distinguish active enhancers from inactive/poised enhancers, which are both marked by H3K4me1 . BernsteThe recognition of chromatin states and introduction of computation tools such as ChromHMM  providedEquus [Centromeres are enigmatic structures because, contrary to other genetic loci, their function is not determined by the underlying DNA sequence but depends on epigenetic factors. The Centromere Protein A (CENP-A) is a centromere-specific variant of histone H3 that epigenetically identifies, maintains, and propagates centromere function . The chaEquus ,108,109.Equus . The position of the ECA11 satellite-less centromere, identified as the CENP-A binding domain, is not fixed in the horse population but slides within an about the 500 kb region, giving rise to different positional alleles or “epialleles” ,111,112.The absence of satellite DNA at these centromeres also provides a unique opportunity to understand whether some typical features of mammalian centromeres depend on the presence of satellite DNA. In particular, it was possible to demonstrate that satellite DNA was not necessary for segregation fidelity of the centromere  and was The rich repository of tissues from different developmental origins available through the FAANG project will allow us to answer other important questions on centromere biology using the ECA11 centromere as model system. We will test whether the centromere position is conserved during development or if it can slide during tissue differentiation. In addition, thanks to the large amount of data regarding the functional annotation of the horse genome, generated within the FAANG effort, we will be able to map the epigenetic marks available through the consortium in the ECA11 centromeric region. The results will indicate whether chromatin markers and transcriptional activity at ECA11 centromere vary across tissues and individuals, and with respect to centromere position. Furthermore, CENP-A has been shown to bind at TF binding sites and promoters, suggesting potential regulatory activities . TherefoJust three years after starting the tissue and data collection for the equine FAANG initiative, the community has completed over 400 experiments from more than 50 tissues using a variety of assays targeting different features of the horse regulatory landscape . Data arWith over 80 tissues collected from four healthy and comprehensively phenotyped animals, we will be able to generate a map of gene expression and regulation throughout the horse body, providing unique opportunities to investigate tissue-specific gene expression and gene networks. However, this tissue collection presents a serious challenge for data analyses. Heterogeneity both within tissues as a result of cell-type differentiation and across tissues as a result of tissue infiltration or contamination during collection, can confound analysis of tissue-specific expression and regulation. The prevalence of this issue was recently reported by Sturm et al. . To mitiWhile the equine FAANG biobank represents a wide variety of tissue types, the four horses these tissues were collected from represent only a narrow subset of the horse population, as well as developmental stages. These horses were intentionally selected to be of the same breed as the reference genome assembly in order to better annotate the reference genome assembly. However, caution should be taken with interpretation and extrapolation of these data to other breeds or developmental stages. Regardless, this initiative will serve as a template and reference point for the future expansion of the transcriptome and epigenome of equids. FAANG represents a notable international collaborative effort in the equine community that has brought together equine researchers and practitioners from around the globe. Most importantly, FAANG collaborators have been vocal proponents of open science and broad data accessibility within the equine community. The growing number of publicly available datasets is accelerating discoveries and powering large-scale analyses. Well-annotated and carefully documented FAANG data with accompanying comprehensive metadata will serve as a reference point for many future discoveries in horse.


The genome of a eukaryotic organism is comprised of a supra-molecular complex of chromatin fibers and intricately folded three-dimensional (3D) structures. Chromosomal interactions and topological changes in response to the developmental and/or environmental stimuli affect gene expression. Chromatin architecture plays important roles in DNA replication, gene expression, and genome integrity. Higher-order chromatin organizations like chromosome territories (CTs), A/B compartments, topologically associating domains (TADs), and chromatin loops vary among cells, tissues, and species depending on the developmental stage and/or environmental conditions (4D genomics). Every chromosome occupies a separate territory in the interphase nucleus and forms the top layer of hierarchical structure (CTs) in most of the eukaryotes. While the A and B compartments are associated with active (euchromatic) and inactive (heterochromatic) chromatin, respectively, having well-defined genomic/epigenomic features, TADs are the structural units of chromatin. Chromatin architecture like TADs as well as the local interactions between promoter and regulatory elements correlates with the chromatin activity, which alters during environmental stresses due to relocalization of the architectural proteins. Moreover, chromatin looping brings the gene and regulatory elements in close proximity for interactions. The intricate relationship between nucleotide sequence and chromatin architecture requires a more comprehensive understanding to unravel the genome organization and genetic plasticity. During the last decade, advances in chromatin conformation capture techniques for unravelling 3D genome organizations have improved our understanding of genome biology. However, the recent advances, such as Hi-C and ChIA-PET, have substantially increased the resolution, throughput as well our interest in analysing genome organizations. The present review provides an overview of the historical and contemporary perspectives of chromosome conformation capture technologies, their applications in functional genomics, and the constraints in predicting 3D genome organization. We also discuss the future perspectives of understanding high-order chromatin organizations in deciphering transcriptional regulation of gene expression under environmental stress (4D genomics). These might help design the climate-smart crop to meet the ever-growing demands of food, feed, and fodder. A eukaryotic genome comprises several chromosomes, which vary along their length, contain supra-molecular complexes of chromatin fibers, and are intricately folded in a three-dimensional (3D) structure. The genome is not randomly positioned in the nucleus, but it is packed into higher-order chromatin structures that play important functional roles. Understanding the organization of the nuclear genome is seeking significant attention nowadays, as several processes like DNA replication, transcription, genome integrity, etc. involved in growth, development, and stress tolerance are regulated through the nuclear genome organization. Eukaryotic genome organization can be observed at three levels i) linear genome: the nucleotide sequence deciphered by DNA sequencing, ii) epigenome: representing the additional information added due to the modified bases and/or histone proteins which help regulate gene expression, and iii) 3D structure of the genome: representing the arrangement of chromatins/chromosomes in the nucleus . These gIn eukaryotes, chromatin is packed into nucleosomes wherein histone proteins make up the largest component. DNA wrapped around a histone octamer  sealed by a linker histone (H1) builds the structural constituent nucleosome to form chromatin. The chromatin-related research is progressing with unprecedented speed and resolution, deciphering the complex and dynamic chromatin architecture during cellular processes including DNA replication, recombination, repair, transcription, mitosis, and meiosis. Chromatin structures are highly dynamic, which undergo cyclic compaction and de-compaction during the cell cycle, cell differentiation, developmental processes, and defense responses. Chromatin accessibility to the regulatory elements like RNA polymerase II (RNA Pol-II) is affected by chromatin compaction/de-compaction, which fine-tunes the regulation of gene expression . DiffereBeing sessile, plants face numerous abiotic and biotic stresses throughout their life. Our understanding of chromatin organization in model species has advanced significantly in the past decade . Highly Despite the absence of insulator protein CTCF in plants, TADs have rarely been observed in Arabidopsis. TAD-like domains and motifs at the TAD boundaries have been identified in rice . Moreovevia DNA methylation . During the last few decades, linear genomes and epigenomes of eukaryotes have been extensively studied towards understanding the regulation of gene expression. It is now evident that the information and function of a genome are modulated under varying environmental conditions not only by the epigenetic modifications in the linear DNA sequence but also by altering the 3D chromatin organization within the nucleus . Gene achylation , histonehylation , and chrhylation . Differehylation . Advancehylation . Studieshylation . The acchylation  and the hylation  indicatehylation . Some ofhylation . HoweverThis review presents the recent advances in 3D genomics methods and focuses on understanding the 3D genome organization of plants with reference to the available knowledge of nuclear genome organization in the animal system. We also discuss the developments in chromosome conformation capture technologies, their relevance in understanding genome structure (genome assembly) and functions. Future perspectives of 3D genomics, with special reference to its application in plant/crop improvement, and the constraints currently being faced are also discussed.cis-acting elements to form chromatin loops , and environmental cues . In germcis-regulatory elements to modulate gene expression by recruiting TFs  was directly visualized by cytological and microscopy techniques . During In plants, chromosomes show different morphology including Rabl (in the honor of Carl Rabl), Rosette, and Bouquet configuration. In Rabl configuration, the chromosomes are folded at the centromere making a polarized separation of centromeres and telomeres . Such coRecent studies reveal that gene expression is associated with chromatin positioning (CT) in the nucleus. Some of the chromatin domains are associated with the nucleolar periphery and named nucleolus-associated chromatin domains (NADs) . In addiin situ Hi-C technique reported the existence of global A/B compartments across the tissues, while the local A/B compartment was reported to be dynamic and tissue-specific associated with differential expression of genes  and specific epigenetic marks , which atrans-associated factors  influence 3D compartmentalization independent of their cis-effect on local chromatin composition and activity. In Arabidopsis, several local structural features like positive strips which interact frequently with the neighboring chromatin were observed  mapping indicated that TAD is constituted of metaTADs and subTADs, which are dynamic to form active and inactive nuclear compartments . Wijcherobserved . Such poobserved . In planobserved . Moreoveobserved .cis-regulatory elements of target genes form chromatin loops to control gene expression.Similarly, TAD-like domains enriched in and associated with highly expressed genes were observed in maize, tomato, foxtail millet, and sorghum . Hi-C ancis-acting elements and the genes that are brought into close spatial proximity, which are vital for gene regulation  and non-coding RNAs (ncRNAs) are involved in the formations of repressive chromatin loop [FLC locus which is bound by histone modifiers like H3K4 methyltransferase and H3K36 methyltransferase  combined with mediator and RNA Pol-II were reported to promote the formation of enhancer-promoter loops to modulate transcriptional activity of the target genes in human cell lines  [Figure nsferase . Bindingin situ hybridization (FISH) boosted the progress in understanding how the spatial organization of CTs affects gene expression within the nucleus   visualizes 3D chromatin folding at targeted genomic regions with ultrahigh-resolution (5 nm × 5 nm × 30 nm) (in situ hybridization (FISH)  , wide-fipy (CFM) , chrom-ecroscopy , Hi-M  . Dependin (FISH) ; Oligo-Fn (FISH) ; MB-FISHn (FISH) ; GISH, gdization , ii) stadization , iii) imdization , and iv)dization  which indization , lacO-LaP system , and traP system . ChromatP system . The lacthe lacO . A novelthe lacO    , split-pIA-Drop)   . While GIA-Drop) , SPRITE IA-Drop) , DamC deIA-Drop) . On the IA-Drop) .in situ Hi-C (in situ Hi-C is performed in the intact nuclei with DNA–DNA proximity-ligation (Bridge Linker Hi-C (BL Hi-C) combines restriction enzyme (RE) targeting and two-step proximity ligation (Digestion-Ligation-Only Hi-C (DLO Hi-C) digestion and ligation are performed twice without biotin labeling and pulldown (Bridge linker-Alul-Tn5 Hi-C), was developed by combining Alul restriction with biotinylated linker-mediated proximity ligation -based approaches  as well itu Hi-C . Captureitu Hi-C , in situligation , micro-Cligation , while Dligation  for chroligation  bridges ligation , while ipulldown . The recpulldown , and OCEpulldown  were usepulldown . Cut-C ipulldown . A simplligation . With ju looping . While m looping .The 3D genomic techniques have considerably advanced over the last decade. However, their efficiency in plant 3D genomic studies is comparatively less probably because of the cell wall. Some of the derivative techniques have successfully been used in plants for 3D genomic studies. The basic 3C technique allows “one-to-one” chromosomal interactions between two loci in the genome utilizing microarrays. Hence, the 4C technique was developed as a “one to all” strategy, which allows genome-wide screening for the interactions between one specific locus with all other loci in the genome  using NGin situ Hi-C was used in rice, sorghum, tomato, and Foxtail millet . Then PETs are used for high-throughput sequencing  sequencing combines chromatin immunoprecipitation (ChIP) with 3C-type analysis for comprehensive and efficient analysis of long-range chromatin contacts bound by a protein like promoters at lower-kilobase resolution . In ChIAquencing . ChIA-PEquencing . The lonquencing , ChIA-PEquencing .However, depending on the scientific needs, several modifications in Hi-C and ChIA-PET have been adopted. Recent studies using ChIA-PET unraveled chromatin interactions associated with gene expression in maize and rice . Maize CHi-C combined with ChIA-PET (chromatin immunoprecipitation) known as Hi-ChIP  was succChromatin configuration generally varies in different tissues and cells with changing environmental factors. The chromatin architecture and variations observed by Hi-C/ChIA-PET indicate the population average of cells. Therefore, the chromatin conformation of an individual cell should be captured and studied at the single-cell level. The difference in the 3D genome architecture of cells could be detected by the single-cell 3D genome mapping technique (single-cell Hi-C) . AdvanceSingle-cell 3D genome mapping of mammalian cells demonstrated variation in TADs in different cells, whereas the chromatin compartments and lamina-associated domains remained stable . Single-Hierarchical 3D genome organization is observed in yeast, animals, and plants. Higher-order chromatin architectures like CTs and chromatin compartments are fairly conserved among the cell types, tissues, and species . HoweverSimilar to the compartments and sub-compartments observed in animals , the larin situ Hi-C reported stable genome architecture with chromosomal decondensation during cold stress in rice seedlings  is replicated earlier than the heterochromatin  in animals . Pope etHi-C analysis of tomato and maize genomes showed a large number of long-range chromatin loops to be formed, linking them with interstitial active chromatin regions and suggesting spatial clustering of the expressed genes . The intTo ensure survival, proper growth, development, and reproduction, plants need to adapt to the prevailing environmental conditions, antedate potential changes, while maintaining the necessary flexibility to respond to other fluctuations. Light, temperature, water, etc. fluctuate on a seasonal basis throughout the year. Long-term adaptation and short-term reaction to environmental factors are underpinned by the changes in gene(s) expression . The chavia ATP hydrolysis, while the other utilizes specialized enzymes to (de)methylate DNA or post-translationally modify histone proteins. The SWI2/SNF2 family of chromatin remodeling complexes (part of a large superfamily of helicases−translocases) use ATP energy to gain access to the DNA sequences  and Actin-Related Protein 6 (ARP6), the components of SWR1 chromatin-remodeling complex  (Histone deacetylase 1 (OsHDAc1) was reported to repress (OsIDS1) . In soyb(OsIDS1) . Acetyla(OsIDS1) . In addi(OsIDS1) . The acc(OsIDS1) .Varying temperature (low or high temperature) significantly affects plant growth and crop yield. Chromatin remodeling is one of the molecular mechanisms implicated in temperature sensing and regulating gene expression . RepressTemperature and light influence the developmental trajectory/morphology of the plant. The light-regulated modulations in chromatin architecture were initially reported based on the photomorphogenesis responses . StudiesFLC repression during the vegetative stage of plant development  associated with transcriptionally active chromatin  to work together with Histone Deacetylase 2C (HDAc2C) by directly binding to Cold Regulated  genes’ promoter  and Cullin 4 (CUL4) to degrade HDAc2C, which leads to hyperacetylation of histone H3 at COR chromatin. This promotes CBF proteins binding at the COR promoters via HOS15 to activate COR genes expression. Moreover, the GCN5 promotes H3 acetylation at COR genes  . When thelopment . Clf mutn of FLC . Some ofFLC , while the other regions containing active/expressed genes are called Loose Structural Domains (LSDs)  . Moreoves (LSDs) .Chromatin domains are a prominent feature in the mammalian genome and are referred to as TADs. Interaction frequency within a TAD is higher than that between TADs, which reduce significantly at the domain boundaries . BordersDistal regulatory elements can interact physically with genes through the formation of loops, which is well-studied in the β-globin gene revealing a causal relationship between looping and gene activation . The mosThe discoveries made with the use of 3D genomics techniques including hierarchical chromatin structures like chromatin loops, TADs, A/B compartments and sub-compartments, chromatin territories have revolutionized structural and functional genomic analyses. However, there are certain limitations of these techniques. Most of the 3D genomics analyses indicate chromatin configuration of population average which varies in different tissues/cells with changing environmental conditions. Therefore, the need of the day is to study chromatin conformation at the single-cell level. Though FISH provides easy visualization of repetitive sequences and dynamics at an individual locus , its strChromatin conformation has considerable effects on gene expression and regulation, and regions with strong chromatin interaction generally show functional dependency . AlteratUntil recently, studies on 3D genome organization have been challenging tasks due to technical difficulties; however, technological advances have enabled us to take up such studies with unprecedented resolution and accessibility. Technological developments in the 3D genomics techniques like ChIA-PET , CaptureFine structures of the 3D genome are also being investigated by combining improved CRISPR technologies with ultra-high resolution microscopy in mammals. Locations of transcriptionally active and inactive regions in the nucleus were determined using sgRNAs (targeting 16 MS2 binding motifs) and catalytically inactive Cas9 (dCas9) protein . MoreoveCertainly, there is still a lot to examine and learn, which would require more contact maps at higher resolution particularly for the plant genomes differing in size and gene density. To gain more knowledge, a comparison of the contact maps of the same genome under different environmental conditions and/or developmental stages (4D genomics) of a single cell or single cell type would be desirable. Moreover, the use of synchronized cells would help to understand the changes in chromatin architecture during the cell cycle. Furthermore, the participation of RNAs in the formation/maintenance of chromatin structures, if any, would also need to be studied. Finally, several outstanding questions will need to be answered including, but not limited to: i) do different chromatin structures exist in a cell type under changing environmental conditions and/or developmental stages? ii) Do TADs/TAD-like structures exist only in the nuclei of plant species with larger genome sizes? iii) Can the changes in gene expression modify chromatin configuration? As soon as we would get answers to these questions, several other new questions will be required to be answered. Indeed, the experiments designed to answer some of these questions are on the go in laboratories worldwide, and we believe that the next 5 years of research on 3D genomics would be more exciting than they had been in the past.Sequencing and assembly of genomes for model animal and plant species were some of the ground-breaking biological research findings of the second half of the 20th century. After preparing the draft genome for the model organisms, the scientific attention moved to annotate and decipher the biological functions of protein-coding genes to get the answer to many relevant biological questions. After understanding the biological function of specific gene/protein and protein complexes, which has provided better understanding in all the fields of biology, now it has become clear that the DNA/genome sequence itself is not the absolute determinant of phenotypic traits. Subsequently, researchers around the world started investigating the so-called ‘junk DNA’ (which in some cases embodies the vast majority of the eukaryotic genome) that might play regulatory roles in gene expression. Hence, during the last 3 decades, efforts were made to explore the non-genetic/epigenetic/3D genomic mechanisms/features responsible for phenotypic plasticity observed in living beings.With the advances in 3D genomics technologies, chromatin loops are being detected with the help of Hi-C/ChIA-PET which identify enhancer−promoter interactions affecting gene expression . Moreove


The 3D genome organization is crucial for gene regulation. Although recent studies have revealed a uniquely relaxed genome conformation in totipotent early blastomeres of both fertilized and cloned embryos, how weakened higher-order chromatin structure is functionally linked to totipotency acquisition remains elusive. Using low-input Hi-C, ATAC-seq and ChIP-seq, we systematically examined the dynamics of 3D genome and epigenome during pluripotent to totipotent-like state transition in mouse embryonic stem cells (ESCs). The spontaneously converted 2-cell-embryo-like cells (2CLCs) exhibited more relaxed chromatin architecture compared to ESCs, including global weakening of both enhancer-promoter interactions and TAD insulation. While the former correlated with inactivation of ESC enhancers and down-regulation of pluripotent genes, the latter might facilitate contacts between the putative new enhancers arising in 2CLCs and neighboring 2C genes. Importantly, disruption of chromatin organization by depleting CTCF or the cohesin complex promoted the ESC to 2CLC transition. Our results thus establish a critical role of 3D genome organization in totipotency acquisition. Zscan4 cluster genes and murine endogenous retrovirus with leucine tRNA (MERVL) repeats  compared to those of later-stage embryos and somatic cells ,8. SimilThe stable mouse ESC line with the MERVL::tdTomato reporter (2C-E14) were generated as previously described . BrieflyAnalysis of mitotic cells was performed as previously described . BrieflyESCs and 2CLCs were purified by fluorescence-activated cell sorting (FACS), and 100 000 cells were used for each sisHi-C library preparation. The sisHi-C libraries were prepared as previously reported with minor modifications . BrieflyFor ULI-NChIP-seq, 10 000 or 40 000 cells were used per reaction. The ULI-NChIP procedure was performed as previously described ,18. Brie2 and 0.1% Igepal CA-630) for 10 min on ice to prepare the nuclei. Immediately after lysis, nuclei were spun at 500 g for 5 min to remove the supernatant. Nuclei were then incubated with the Tn5 transposome and tagmentation buffer at 37°C for 30 min . After the tagmentation, the products were purified with 2.0× VAHTS DNA Clean Beads . PCR was performed to amplify the library for 14 cycles using the following PCR conditions: 72°C for 3 min; 98°C for 30 s; and thermocycling at 98°C for 15 s, 60°C for 30 s and 72°C for 3 min; following by 72°C 5 min. After the PCR reaction, libraries were purified with the 1.2× VAHTS DNA Clean Beads. Barcoded libraries were pooled and sequenced on an Illumina HiSeq X Ten platform in the paired-end mode.The ATAC-seq libraries were prepared as previously described with minor modifications . BrieflyRaw reads were trimmed to 50 bp and aligned to the mouse genome (mm9) using bowtie2 (version 2.3.4.1) with default parameters. Unmapped reads and PCR duplicates were removed. Peaks were called using MACS2 (version 2.1.1.20160309) with parameters ‘-q 0.05 –nomodel –nolambda –broad –extsize 300 -B –SPMR -g mm’. Average intensity profiles were generated using deepTools (version 2.5.4).P values were generated in edgeR (exactTest) and then adjusted to the false discovery rate (FDR). The significantly up-regulated and down-regulated genes were identified as genes with FDR < 0.05, log2(fold change) greater than 1.5 and less than −1.5, respectively.RNA-seq libraries were prepared using the Smart-seq2 method as previously described . Barcodehttps://github.com/dekkerlab/cworld-dekker). To calculate PC1 values, we used the ‘‘matrix2compartment.pl’’ script using contact maps binned at 500 kb resolution as input. Bins with positive or negative PC1 values were defined as A or B compartment, respectively. The compartment strength was then calculated as (AA + BB)/2AB as previously described .Raw reads were first processed with HiC-Pro (version 2.11.1). Briefly, raw reads were trimmed to 50 bp and then aligned to the mouse genome (mm9) using the Bowtie 2 end-to-end algorithm and ‘-very-sensitive’ option. To rescue the chimeric fragments spanning ligation junctions, the ligation site was detected and the 5′ fraction of the reads was aligned back to the reference genome. Unmapped reads, multiple mapped reads and singletons were then discarded. Pairs of aligned reads were then assigned to MboI restriction fragments. Read pairs from uncut DNA, self-circle ligation and PCR artefacts were filtered out, and the valid read pairs involving two different restriction fragments were used to build the contact matrix. Valid read pairs were then binned at a specific resolution by dividing the genome into bins of equal size. The binned interaction matrices were then normalized using the iterative correction method in HiC-Pro with default parameters to correct for biases such as GC content, mappability and effective fragment length. For each chromosome, we obtained the expected Hi-C contact values by calculating the average contact intensity for all loci at a certain distance. We then transformed the normalized Hi-C matrix into an observed/expected (O/E) matrix by dividing each normalized observed value by its corresponding expected value. We identified A and B compartments and TADs from Hi-C data using cworld  using the DharmaFECT 1  reagent following the manufacturer's instructions. The control siRNA (siNC) was derived from sequences in the Actin. Primer sequences are available in Total RNA was isolated using RNeasy Plus Mini Kit , cDNA was generated using HiScript II Q RT Super Mix with gDNA wiper , and qPCR was performed using the AceQ qPCR SYBR Green Master Mix . Relative quantification was performed using the comparative Ct method with normalization to Statistical analyses were performed with the R software. Statistics were calculated using the Wilcoxon signed rank test unless indicated otherwise.Zscan4 family genes, Dux, Dub1, Zfp352 and MERVL repeats were highly expressed in 2CLCs , which has been established as a faithful indicator of the 2C-like state . Consistin 2CLCs , and RNAin 2CLCs . Importain 2CLCs . Pathwayin 2CLCs , consistin 2CLCs .Tdpoz family genes in the B-to-A switching compartments   to deterartments , it is wartments , indicatartments   and ESCs (n = 3625) were comparable, of which the vast majority (n = 2957) were stable  in the MERVL::tdTomato ESCs  containing both the MERVL::tdTomato reporter and an auxin-inducible degron (AID) system targeting CTCF. Endogenous CTCF in this cell line is also fused with an eGFP tag to indicate the protein level of CTCF . As shown Figure  and B, tn Figure  and H, an Figure , signifin Figure  and H. Wn Figure , comparan Figure . Our anan Figure , demonstIn addition to epigenetic modifications, 3D chromatin architecture has been increasingly recognized as another regulatory layer of gene expression, which is tightly linked to cell identity. ESC differentiation has been shown to coincide with formation or strengthening of TAD boundary loops, indicating a relatively relaxed chromatin architecture in ESCs compared to differentiated cells ,40. Furtde novo calling of loops, preventing detailed analysis of ESC- and 2CLC-specific loops. Also, it remains to be elucidated how chromatin relaxation occurs in spontaneously converted 2CLCs. As shown in We revealed that chromatin architecture of ESCs became more relaxed during the spontaneous 2C-like transition, with globally weakened chromatin loops. This included not only TAD boundary loops but also chromatin loops between ESC enhancers particularly super-enhancers and the promoters of their neighboring genes, correlating with ESC enhancer inactivation and transcriptional down-regulation of many pluripotent genes controlled by ESC super-enhancers. The down-regulation of pluripotent genes in 2CLCs was previously reported to take place only at the protein level but not the mRNA level ,14. NeveWe further demonstrated a causal relationship between relaxed chromatin architecture and totipotency acquisition, by showing that disruption of 3D organization of chromatin in ESCs could facilitate 2C-like transition. We also observed formation of putative 2C enhancers during ESC to 2CLC transition. Importantly, up-regulated genes during the 2C-like transition tend to have ESC TAD boundaries separating them from putative 2C enhancers nearby. Therefore, disruption of TAD boundary loops in ESCs may facilitate the contacts between putative 2C enhancers that are likely sporadically activated in ESCs and the promoters of nearby 2C genes, thus promoting their expression, which in turn fully activates the 2C-like transcriptional program. Consistently, a recent study showed that cohesin pre-depletion in donor cells could enhance SCNT efficiency at least partially through up-regulation of the minor ZGA genes in donor cells, many of which are also 2C-specific genes .In sum, we demonstrated that the spontaneous pluripotent to totipotent-like state transition in mouse ESCs not only coincides with global weakening of 3D genome conformation, but also can be promoted by disruption of chromatin loops. Further investigations are still required to elucidate the molecular mechanisms underlying the global weakening of higher-order chromatin structure during totipotency acquisition, as well as to fully understand how 2C-specific genes are activated upon disruption of chromatin loops.The RNA-seq, ATAC-seq, ChIP-seq, CUT&Tag, and Hi-C sequencing data reported in this paper have been deposited with the NCBI GEO under accession number GSE159623.https://github.com/shenlab423/2CLC-Project-Code).Scripts for data analysis are available on GitHub (gkab1069_Supplemental_FilesClick here for additional data file.


The deacetylation of histone H3K27 as well as the transcription factors Hnf1α and Foxo1 was involved in CBP/p300 HAT inactivation-repressed expressions of β cell identity and functional genes. These findings highlight the dominant role of CBP/p300 HAT in the maintenance of β cell identity by governing transcription network.Loss of β cell identity and functional immaturity are thought to be involved in β cell failure in type 2 diabetes. CREB-binding protein (CBP) and its paralogue p300 act as multifunctional transcriptional co-activators and histone acetyltransferases (HAT) with extensive biological functions. However, whether the regulatory role of CBP/p300 in islet β cell function depends on the HAT activity remains uncertain. In this current study, A-485, a selective inhibitor of CBP/p300 HAT activity, greatly impaired glucose-stimulated insulin secretion from rat islets in vitro and in vivo. RNA-sequencing analysis showed a comprehensive downregulation of β cell and α cell identity genes in A-485-treated islets, without upregulation of dedifferentiation markers and derepression of disallowed genes. A-485 treatment decreased the expressions of genes involved in glucose sensing, not in glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation. In the islets of prediabetic  In mice, the ablations of transcription factors such as Pdx1, MafA, Nkx6.1, Pax6, NeuroD1, and Foxo1, lead to loss of β cell identity by repressing β cell functional genes and inducing genes disallowed in β cells7. It has been demonstrated that hyperglycemia, oxidative stress, and inflammation inhibit transcription factor activities and perturb β cell identity in mice and human10. However, the mechanism underlying the intrinsic transcription networks-mediated the maintenance of β cell identity remains largely understood.It is well established that type 2 diabetes is generally due to the progressive decline of islet β cell function, with loss of β cell identity11. Histone acetylation is widely regarded as an active epigenetic marker of gene transcription through unwrapping the interaction between DNA and chromatin and enhancing the chromatin accessibility12. Of interest, acetylation of histone H3 lysine 27 (H3K27Ac) is a hallmark of active enhancers13, which are highly associated with cell type-specific gene expression patterns14. Histone acetylation is determined by competing activities of histone acetyltransferases (HATs) and histone deacetylases (HDACs). Among the HATs members, CREB-binding protein (CBP) and p300 are essential for H3K27Ac15. CBP and p300 act as multifunctional transcriptional co-activators with extensive sequence homology and functional similarity. Both CBP and p300 possess a HAT domain and several protein-protein interaction domains16, which make them prone to serve as transcriptional scaffolds for recruiting a number of transcription factors17.Chromatin modification plays an essential role in specific gene expression programs of differentiated cells21. p300 is recruited to Glut2 gene promoter and coactivates Glut2 expression via the interaction with transcription factor Hnf1α22. p300 also facilitates Klf11-regulated Pdx1 transcription in β cells23. Mice with an activating mutant CBP (S436A) display increased β cell proliferation but diminished glucose-stimulated insulin secretion (GSIS)24. Triallelic knockout of CBP/p300  in pancreatic endocrine progenitors impairs proliferation of neonatal α and β cells in mice25. These data implicate an essential regulatory role of CBP and p300 in islet β cells. However, it is still unclear whether the HAT domain is crucial for CBP/p300-regulated β cell function.It has been reported that CBP and p300 synergistically promote insulin gene transcription by interacting with transcription factors NeuroD1, Pdx1, and Klf1126. In this current study, A-485 treatment markedly suppressed GSIS in rat islets in vitro and in vivo. RNA sequencing (RNA-seq) revealed a comprehensive down-regulation of β cell identify genes in CBP/p300 HAT-inhibited rat islets. ChIP sequencing (ChIP-seq) analysis provided insight into the involvement of H3K27 acetylation in CBP/p300 HAT-governed transcription networks of islets.A-485 is a newly discovered selective inhibitor of CBP/p300 HAT activity through bounding to the catalytic active site of CBP/p300 and competing with acetyl coenzyme A, displaying 1000-fold more potent than previously described control compounds including C646To investigate the effect of CBP/p300 HAT on β cell function, isolated rat islets were pretreated with A-485 for 16 h, and then stimulated with various concentrations of glucose or 35 mM KCl for 1 h. As shown in Fig. p < 0.05), among which 807 genes were downregulated while only 148 genes were upregulated  islets25. Amazingly, only 11.9% of A-485-repressed genes (96/807) were uniformly downregulated in CBPHet; p300KO islets , as well as Ucn3, a mature β cell marker28, were all conspicuously downregulated following CBP/p300 HAT inhibition. In the gene set identified by Qiu et al.29, we found that 98 β cell enriched genes were regulated by A-485, among which 92.9% were significantly downregulated  under the condition of CBP/p300 HAT inhibition. Consistent with the result of RNA-seq, mRNA expressions of β and α cell identity genes  were lowered in A-485-treated rat islets Fig.  and isol37. CBP/p300 HAT activity is essential for β cell function maturity as A-485 treatment leads to diminished GSIS  in human, are responsible for the regulation of insulin secretion under basal condition38. Attractively, our RNA-seq showed that three inactivating-mutated pathogenic genes of CHI, including ATP-sensitive potassium channel subunits Kcnj11, Abcc8, and hydroxyacyl-coenzyme A dehydrogenase (Hadh), were obviously repressed by A-485  based on the downregulated gene set identified by RNA-seq. Attractively, Hnf1α, a possible target of CBP and p300 in mice isletssis Fig. . It has ets Fig. . In consets Fig. . Thus, i1, protecting β cells when facing the metabolic stress partially through inducing NeuroD1 and MafA expressions46. Foxo1 mRNA expression was without significant change in A-485-treated rat islets , MODY2 (Gck), MODY3 (Hnf1α), MODY4 (Pdx1), MODY5 (Hnf1β), MODY6 (NeuroD1/Beta2), and MODY7 (Klf11), which regulate islet-enriched gene expression in a coordinated manner23. Klf11 interacts with the coactivator p300 to mediate Pdx1 activation24. Pdx1 interacts with p300 to activate target genes in pancreatic β cells, including insulin gene55. NeuroD1/Beta2, Hnf4α, and Hnf1α gene mutations have been reported to impair their interactions with p30057. In our study, MODY genes Pdx1, NeuroD1, Gck, Abcc8, and Kcnj11 were downregulated by A-485, indicating that CBP/p300 are not only coactivators of MODY genes, but also their transcriptional activators.It has been reported that CBP/p300 interact with transcription factors such as Pdx1, NeuroD1, and Klf11 to coactivate insulin gene transcription37. Glut2 and Gck are two glucose sensors in β cells. Glucose phosphorylation by Gck is the rate-limiting for glucose metabolism to trigger insulin secretion58. In this study, A-485 treatment decreased Glut2 and Gck expressions, without altering the expressions of genes related to glycolysis and oxidative phosphorylation. These results suggest that the inhibition of CBP/p300 HAT leads to decreased glucose-stimulated insulin secretion mainly via attenuating glucose flux into β cells, rather than directly disrupting glucose oxidation. Inappropriate basal insulin secretion is a feature of functional immature β cells43. However, little is known about the negative regulation of basal insulin secretion. Inactivating mutation of Hadh gene gives rise to basal and non-glucose-stimulated hyperinsulinemia in both clinical cases59 and gene defect mice42. In the present study, A-485 treatment inhibited Hadh expression in rat islets in vitro and in vivo. Pre-diabetic 4-week-old db/db mice displayed simultaneous repressions of Hadh, Glut2 and MafA as well as CBP/p300. These results implicate a possible role of Hadh in basal insulin hypersecretion of pre-diabetic status. CBP/p300 may be involved the decreased expressions of β cell functional genes in the development of type 2 diabetes.A mature pancreatic β cell is sensitive to glucose stimulation13, which is associated with islet functional gene expressions60. However, to what extent the change of H3K27 acetylation affects the expression of β cell identity genes is not well established. Surprisingly, only 226 of 807 genes downregulated by A-485 displayed decreased levels of H3K27Ac, including β and α cell identity genes Ins1, Slc2a2, NeuroD1, and MafB. H3K27Ac levels of many other islet identity genes repressed by CBP/p300 HAT inhibition were unaffected, suggesting an involvement of alternative mechanisms. More than 100 non-histone transcription-regulating proteins, including transcriptional factors, transcriptional co-activators, and nuclear receptors, are regulated by acetylation55. Interestingly, our study revealed the involvement of acetylated Hnf1α and Foxo1 in CBP/p300 HAT-mediated gene expression. A-485 disrupted the interaction of Hnf1α and p300 through downregulating the acetylation level of Hnf1α, which might attenuate their co-activation for target gene expressions in islet β cells. In addition, inhibited CBP/p300 HAT activity accelerated Foxo1 protein degradation, indicating another non-histone acetylation mechanism underlying CBP/p300 HAT-mediated transcription network.Mechanistically, H3K27Ac enrichment has been established as an active marker in cell type-specific enhancer elementsdb/db mice, CBP/p300 display a significant decrease with key β cell function genes. CBP/p300 are essential for the expressions of β cell identity and functional genes by acetylating H3K27 as well as the transcription factors Hnf1α and Foxo1, and establishing a HAT-governed hierarchical transcription network  for 7 days. All the animals were housed under 12/12 h light/dark cycles with ad libitum feeding, in full accordance with the protocols approved by the Animal Care Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine.8-week-old male Sprague-Dawley rats and 8-week-old male C57BL/6 mice were obtained from Shanghai Slack Experimental Center. 4-week-old male db/db mice by using collagenase digestion and separated by density gradient centrifugation. Freshly isolated rat islets were cultured in RPMI 1640 medium containing 5.6 mM glucose and 5% fetal bovine serum (FBS) at 37 °C and 5% CO2 for 2–3 h. For RNA-Seq and ChIP-Seq sample preparation, incubated islets were transferred into 6-well plates and cultured in RPMI 1640 medium containing 5.6 mM glucose and 0.25% bovine serum albumin (BSA) in the presence or absence of 3 μM A-485  for 16 h or 6 h.Islets of Langerhans were isolated from 8-week-old male Sprague-Dawley rats, C57BL/6 mice or Rattus norvegicus genome by using Hisat2 software. The differentially expressed genes and transcripts (measured by fragments per kilobase of exon per million reads mapped (FPKM) value) were identified by setting a threshold at fold change ≥2.0, p-value <0.001.1 μg total RNA from each sample was used to prepare the sequencing library by KAPA Stranded RNA-Seq Library Prep Kit . Sequencing was performed on IlluminaHiSeq 4000 for 150 cycles. After quality control, raw sequencing data was pretreated into trimmed data and further compared with Total RNA was extracted from isolated islets by a total RNA extraction kit  and reverse transcribed into cDNA using a reverse transcription kit . RT-qPCR was performed with Applied Biosystems 7300 Real-Time PCR machine  using a TB Green Premix Ex Taq  to quantify the expression of genes of interest. Primer sequences are described in Supplementary Table Mice pancreases were fixed in paraformaldehyde and embedded in paraffin for section at 5 μm thickness. After deparaffinage and rehydration, pancreatic sections were incubated with primary antibodies overnight at 4 °C and detected by fluorescein-labeled secondary antibodies. Antibody information was listed in Supplementary Table Blood glucose concentrations were measured by glucometer  from mice tail vein. For insulin release test, mice were fasted overnight, and glucose was delivered via intraperitoneal injection at a dose of 2 g/kg body weight. Blood samples were taken from retroorbital venous plexus at the indicated time points for serum insulin measurement.Isolated islets were pre-incubated in Krebs-Ringer Buffer (KRB) that contained 3.3 mM glucose and 0.25% BSA at 37 °C for 30 min after indicated treatment. Then, ten islets per assay in triplicate were incubated with prewarmed KRB that contained other additions as indicated for a further 60 min at 37 °C. The supernatant was removed for insulin secretion analysis, and the islets were extracted with acid-ethanol for determination of insulin content. Insulin levels of all samples were estimated by rat/mouse ultrasensitive insulin ELISA kits .2. For total DNA extraction  and protein extraction, cells were transplanted in 6-well plate and cultured in RPMI 1640 medium containing 5.6 mM glucose and 0.25% BSA in the presence of 3 μM A-485, 10 μg/ml cycloheximide , or 10 μM MG132 .INS-1 cells were cultured in RPMI 1640 medium with 11.1 mM glucose and 10% FBS at 37 °C and 5% COIsolated islets or INS-1 cells were treated with RIPA lysis buffer for total protein extraction. Degenerated protein lysates were added into SDS-PAGE gel for electrophoresis and transferred to PVDF membranes. Antibody information was listed in Supplemental Table p-value threshold of 10−4. The differentially enriched peaks between control and A-485 groups were identified by a fold change ≥2.0, p-value <0.001. All regions were annotated by the gene whose TSS was nearest to the center of peak region according to the newest UCSC RefSeq database and divided into five classes based on the distance to UCSC RefSeq genes. Data visualization was performed by Integrative Genomics Viewer (IGV).Incubated rat islets were crosslinked with formaldehyde and sonicated to shear the chromatin into appropriate fragments. Immunoprecipitation was performed with H3K27Ac antibody . Sequencing library was prepared using TruSeq Nano DNA Sample Prep Kit (Illumina). Sequencing was performed on IlluminaHiSeq 4000 using HiSeq 3000/4000 SBS Kit for 300 cycles. MACS v1.4.2  software ran with the mapped reads to detect the statistically significant ChIP-enriched peaks compared with respective Input group by a INS-1 cells were treated with 3 μM A-485 for 6 h and harvested for endogenous immunoprecipitation. Protein lysates were incubated with anti-Hnf1α  at 4 °C for 2 h and then with Protein A/G PLUS-Agarose beads  overnight at 4 °C. The binding complexes were harvested by centrifugation, washed with lysis buffer, and then eluted with loading buffer. Western blot was followed using anti-p300 (Santa Cruz) and anti-acetyllysine  antibodies for acetylation and binding detection.t-test for two groups or ANOVA for multiple groups. Significance was established at p < 0.05.Data are presented as Means ± SD. Comparisons were performed by Student’s Supplementary InformationFigure S1Figure S2Figure S3Table S1Table S2Table S3Table S4


VHL, EGLN, EPAS1 and EPO, are well known causative factors that contribute to the development of erythrocytosis. We aimed to investigate additional molecular mechanisms involved in the HIF-EPO pathway that correlate with erythropoiesis. To this end, we conducted an extensive literature search and used several in silico tools. We identified genes encoding transcription factors and proteins that control transcriptional activation or repression; genes encoding kinases, deacetylases, methyltransferases, conjugating enzymes, protein ligases, and proteases involved in post-translational modifications; and genes encoding nuclear transport receptors that regulate nuclear transport. All these genes may modulate the stability or activity of HIF2α and its partners in the HIF-EPO pathway, thus affecting EPO synthesis. The theoretical information we provide in this work can be a valuable tool for a better understanding of one of the most important regulatory pathways in the process of erythropoiesis. This knowledge is necessary to discover the causative factors that may contribute to the development of hematological diseases and improve current diagnostic and treatment solutions in this regard.Erythropoiesis is regulated by several factors, including the oxygen-sensing pathway as the main regulator of erythropoietin (EPO) synthesis in the kidney. The release of EPO from the kidney and its binding to the EPO receptor (EPOR) on erythrocyte progenitor cells in the bone marrow results in increased erythropoiesis. Any imbalance in these homeostatic mechanisms can lead to dysregulated erythropoiesis and hematological disorders. For example, mutations in genes encoding key players of oxygen-sensing pathway and regulation of EPO production (HIF-EPO pathway), namely  Erythropoiesis is a process of red blood cell (RBC) production which is regulated by a negative feedback loop of tissue oxygenation and hormone erythropoietin (EPO) synthesis. Expression of EPO in the kidney in low tissue oxygenation is induced by the hypoxia-inducible transcription factor (HIF) via the oxygen-sensing pathway. EPO is thereafter released to the bloodstream and binds to the EPO receptor (EPOR) on red cell progenitors in the bone marrow, triggering erythropoiesis and resulting in compensation of tissue hypoxia . Based uAny imbalance in these homeostatic mechanisms can result in hematological disorders, such as erythrocytosis. Therefore, the understanding of the main regulatory pathways in the process of erythropoiesis is necessary to discover the causative factors, which can contribute to the development of hematological disorders, and improve the current diagnostic solutions in this regard. The aim of this study was to investigate the molecular mechanisms involved in the oxygen-sensing pathway and regulation of erythropoietin production that correlate with erythropoiesis. We reviewed factors acting on the level of (i) transcriptional regulation and (ii) protein interactions, including post-translational modifications and nuclear transport. All these factors alone or cumulatively modulate the stability or activity of the main players in the HIF-EPO pathway, and thus influence the EPO synthesis and final RBC production. In this respect, we conducted an extensive literature search and used several in silico tools, such as Reactome , String 2-dependent α subunit (HIFα) and constitutively expressed β subunit (HIF1β or ARNT) [2-dependent degradation occurs after hydroxylation of HIFα by HIF-prolyl hydroxylase proteins PHD1, PHD2, and PHD3, and ubiquitination of hydroxylated HIFα by the von Hippel–Lindau disease tumor suppressor protein (VHL)-E3 ubiquitin ligase complex [EPO gene and recruits central transcriptional co-activators, histone acetyltransferase p300 (p300) and CREB-binding protein (CBP), through HIFα transactivation domain (TAD). This process is necessary for full transcriptional activation of the HIF complex [EPO is a key regulator of erythropoiesis and its constant production is necessary to maintain the daily renewal of RBC . The majbunit HIFβ or ARNT complex . Reduced complex . In hypo complex . Three independent HIFα subunits have been identified, namely HIF1α, HIF2α, and HIF3α. HIF1α and HIF2α, which are closely related and extensively studied, are both transcriptional activators with distinct as well as common gene targets, while the role of HIF3α in the regulation of gene transcription is less clear. It has been assumed that HIF3α plays an antagonistic role in the regulation of HIF1α and HIF2α isoforms . Among t2-dependent post-translational modification by asparaginyl hydroxylase Hypoxia-inducible factor 1-alpha inhibitor (FIH-1). FIH-1 mediates hydroxylation of HIFα TAD and thus prevents the binding of co-activators CBP/p300. However, FIH-1 more efficiently hydroxylates HIF1α than HIF2α [The transcriptional activity of HIFα subunits can be negatively regulated also through another Oan HIF2α ; therefoan HIF2α .The key players of well-established mechanisms in the oxygen-sensing pathway are presented in EPAS1) in the kidney and liver is controlled. Moniz et al. (2015) [EPAS1 transcription by acting as a regulator of stability and half-life of transcript encoding HIF2α in human renal cell carcinoma cell line [EPAS1 transcriptional regulation in other types of cells. For example, in mouse adipocyte cells the involvement of transcription factors SP1 and SP3 in EPAS1 expression has been suggested [EPAS1 promoter region at normoxia in neuroblastoma cells. The majority of these proteins showed decreased binding to the EPAS1 promoter at hypoxia, indicating increased EPAS1 transcription as the result of dissociated promoter-associated factors. A mechanism of negative feedback regulation for the EPAS1 gene was also suggested in non-small cell lung cancer cells, where HIF2α mRNA was regulated by DNA methyltransferases (DNMTs). DNMT1 was verified as a HIF2α target gene which further promotes the hyper-methylation of EPAS1 promoter and thus decreases mRNA expression levels [Very little information exists on how the expression of the HIF2α gene (. (2015)  showed tell line . Howeveruggested , while iuggested . Howeveruggested  identifin levels .2-dependent hydroxylation, HIFα proteins can be regulated through other post-translational modifications, such as phosphorylation, acetylation, methylation, ubiquitination, and sumoylation, and many do not require O2 as a co-factor. These modifications can regulate HIFα proteins directly or can occur on their binding partner, affecting HIFα activity or stability indirectly. Due to its important role in cancer development and progression, several studies in this regard have been directed to HIF1α (for review see [Apart from Oview see ). HowevePhosphorylation of HIF2α has been shown to affect HIF2α transcriptional activity as well as its nuclear localization. Several kinases were found in this respect, such as casein kinases (CK) and mitogen-activated protein kinases (MAPK). For example, HIF2α transactivation capacity has been shown to depend on phosphorylation of HIF2α C-TAD at conserved threonine residues by CKIIα. This modification increased the affinity of HIF2α for CBP ,30. On tEPO gene expression. However, hSIRT1 has been demonstrated as a redox-sensing deacetylase [Acetylation of HIFα is a much less studied post-translational modification but can affect the transcriptional activity of modified proteins. Direct interaction and deacetylation of HIF2α with NAD-dependent protein deacetylase sirtuin-1 (hSIRT1) has been found to increase HIF2α transcriptional activity, affecting cetylase . Besidescetylase .Methylation has been mainly shown to affect DNA itself, regulating the transcription of genes. DNA methyltransferases that regulate HIF2α mRNA expression (described above) have been found also to affect the VHL gene expression. Even more, Yang et al. (2021)  proposedSumoylation is a highly dynamic post-translational modification that can be reversed by the action of SUMO-specific proteases. This process has been found to modify many proteins that participate in the oxygen-sensing pathway and EPO expression, including VHL, ARNT, p300, and CBP, resulting in repression of their transcriptional activity or inactivation, wherein SUMO conjugating enzyme UBC9 and E3 SUMO-protein ligases PIAS1 and PIAS4 apparently play major roles ,37,38,39Ubiquitination is another highly dynamic process, in the oxygen-sensing pathway best known for its role in VHL-HIFα proteasomal degradation. Apart from that, ubiquitin modification is more complex with diverse functions and can affect other proteins than HIFα . VHL itsBesides post-translational modifications, the activity of HIFs can be modulated also by other protein interactions. We found protein interactions that can affect HIF2α directly or affect its partners in the well-established oxygen-sensing pathway. Many proteins were found to interact with PHD2 and VHL, and positively or negatively regulate HIF2α hydroxylation or ubiquitination and thus HIF2α stability. HIF2α modulators. NF-kappa-B essential modulator (NEMO) is a HIF2α interacting protein that seems to assist in recruiting CBP/p300 and enhance HIF2α transcriptional activity in human embryonic kidney cell line at normoxia . Kelch-lPHD2 modulators. PHD2 is regulated by interactions with Peptidyl-prolyl cis-trans isomerase FKBP8 (PPIase FKBP8), Protein OS-9 (OS-9), Protein CBFA2T3 (CBFA2T3), and Prostaglandin E synthase 3 (PTGES3). While PPIase FKBP8 has been shown to decrease PHD2 protein stability and half-life , OS-9 pr2-dependent manner, although ID2 is an oncoprotein and the study was performed in glioblastoma cells [VHL modulators. VHL is the substrate recognition component of Cullin Ring Ubiquitin ligase complex (CRL), composed of Elongins B and C (ELOB and ELOC), scaffold protein Cullin-2, and ubiquitin-protein ligase RBX1. These components all play an important role in VHL-dependent ubiquitination and degradation of HIF2α . ELOB anma cells . FIH-1 modulators. Several proteins have been reported also to interact with FIH-1, such as NF-kappa-B inhibitor alpha, Nuclear factor NF-kappa-B p105 subunit, Neurogenic locus notch homolog proteins 1, 2, and 3, and Ankyrin repeat and SOCS box protein 4. It has been suggested that these proteins are substrates for hydroxylation mediated by FIH-1 which can take place in competition with HIFα hydroxylation, which could potentially regulate HIF transcriptional activity . BesidesARNT modulators. Aryl hydrocarbon receptor (AHR), which plays a prominent role in xenobiotic metabolism, has attracted our attention since ARNT can be its dimerization partner. Crosstalk between the AHR and hypoxia pathways was evident in a study, where endogenous AHR ligand suppressed nuclear accumulation of HIF2α and subsequent EPO production in human hepatoma HepG2 cells . A notewProteins that are larger than ∼40 kDa move between the nucleus and cytoplasm through the nuclear pore complex (NPC). This process is regulated by nuclear transport receptors importins and exportins that recognize specific transport signals, the nuclear localization signal (NLS) and the nuclear export signal (NES), respectively. Importinα serves as an adapter protein that recognizes and binds directly to proteins containing either a simple or bipartite NLS and forms a trimeric import cargo complex with importinβ, which mediates the interaction with the NPC. Six human importinα isoforms, which differ in affinities to their targets, have been characterized. However, most proteins can directly bind the importinβ and do not utilize importinα as an adaptor protein. NES are recognized by nuclear export receptors, with CRM1 being the most abundant among seven exportins described so far ,67.The importance of intracellular transport of the oxygen-sensing machinery has been relatively ignored. However, the regulation of translocation between cytoplasm and nucleus represents another level of activity control of HIF subunits and molecular basis for cellular oxygen homeostasis. A classical importin α/β-dependent NLS has been found in HIF2α as well as in ARNT, and both have been shown to bind to importins α1, α3, α5 and α7 isoforms in HeLa and U2OS cells. HIF2α can be targeted also solely by importinβ, even with higher efficiency than by importinα . While HTransport between nucleus and cytoplasm has been reported also for PHD2 and VHL. Although the nuclear import of PHD2 does not occur via classical importin α/β interaction, its nuclear export depends on CRM1 binding . NuclearEPO mRNA but production at this site is physiologically repressed. Transgenic animal studies revealed that HRE in renal cells is located between 9.5 and 14 kb 5′ of EPO, while HRE in the liver lies within 0.7 kb 3′ of the gene. A specific negative regulatory liver element is located between 2.2 and 7 kb 3′ to the gene, and generally active repressive elements exist between 0.4 and 6 kb 5′. Due to the lack of an appropriate renal cell culture system, the mechanisms of hypoxia-induced EPO expression were analyzed with the help of human hepatoma cells. Hypoxia-inducible enhancer has been identified at 120 bp 3′ to the EPO polyadenylation site, containing three important hypoxia-responsive sites: a CACGTGCT sequence at the 5′-end, defined as a HIF binding site (HBS); a CACA repeat, located 7 bp further 3′ of the HBS of the EPO gene; and a direct repeat of two steroid receptor half-sites separated by two base pairs (DR-2 site) at 3′ EPO enhancer [EPO promoter, located at the 5′ flanking region of the gene, has been reported as a weak promoter that still contributes to the hypoxic inducibility of the EPO gene and synergistically cooperates with an enhancer [EPO promoter capable of induction by hypoxia encompasses 117 bp 5′ to the transcription initiation site but lacks the consensus HBS and is negatively regulated by GATA sequence [As already mentioned, the major site of EPO expression is the kidney, while the liver is the main source of EPO during fetal development. The adult liver maintains the potential for high expression of enhancer . EPO proenhancer . The minsequence .EPO expression depends on the cooperation with other transcription factors that associate with the EPO gene, such as Hepatocyte nuclear factor-4α (HNF4α). HNF4α binds to the DR-2 site in the EPO enhancer and probably plays a critical role in activating hepatic and renal hypoxia-inducible EPO transcription [EPO gene in the fetal liver during mouse development and also binds to the DR-2 site of EPO enhancer. Furthermore, nuclear receptor coactivator 3 (NCoA-3), a histone acetyltransferase coactivator, is recruited to the 3′ enhancer of the EPO gene upon hypoxic stimulation in a p300-dependent manner and NCoA-3 knockdown reduced EPO transcriptional activity in hepatoma Hep3B cells [EPO gene contains also NFKB3 sequences, and NFKB3 has been reported to play a key role in hypoxia-regulated EPO gene expression [Besides HIF2 complex and transcriptional co-activators p300 and CBP, cription . Howevercription  demonstr3B cells . EPO genpression ,75.EPO transcription through GATA sites in EPO promoter in hepatic and distal tubular cells [EPO expression has been shown in the study of Yin and Blachard (2000) [EPO gene have been identified. The authors proposed that methylation of CpGs in the promoter represses EPO transcription by blocking the binding of sequence-specific DNA binding proteins, while methylation in the 5′-UTR recruits Methyl-CpG binding protein 2, and directly represses transcription or recruits co-repressors, histone deacetylases, or both.On the other hand, transcription factors GATA-2 and GATA-3 have been shown to negatively regulate the ar cells ,77. Anotd (2000) , where CEPO has been described in a study by Kim et al. (2017) [VHL (ECYT2), EGLN1 (ECYT3), EPAS1 (ECYT4) and EPO (ECYT5) [Described factors may be involved in deregulation of erythropoiesis, including anemia, erythrocytosis and cancer . Anemia . (2017) . Erythro (ECYT5) ,82. Impo (ECYT5) ,84,85,86Also disorders affecting high-altitude accommodation can be mediated by HIF-EPO pathway deregulation. Tight regulation of EPO production is responsible for removal of erythrocytes and normalization of increased hemoglobin (Hb) upon descent, which are typical observations of acclimatization in long-term sojourners at high altitude and in high-altitude residents . AlthougSeveral HIF inhibitors have been developed and investigated up to now, comprising direct inhibitors which affect the expression or function of HIF proteins through inhibition of mRNA expression, protein synthesis, dimerization of α and β subunits, DNA binding, and transcriptional activities of HIF, and indirect inhibitors which regulate proteins of upstream or downstream pathways and thus affect the HIF signaling, such as mTOR inhibitors ,89,90. H2-dependent hydroxylation mechanism that coordinates HIF2α stability and transcriptional activity and EPO synthesis, many other proteins are contributing to post-translational modifications, nuclear transport, and transcriptional regulation of complete oxygen-sensing machinery but have been relatively ignored in this context. It should be noted that besides HIF1α, HIF2α and VHL also play a role in cancer development and progression. As the majority of available information and literature data are directed in cancer studies, their enrolment in the regulatory mechanisms in healthy tissue must be considered with caution. However, further analysis of mechanisms in EPO receptor signaling, Hb oxygen affinity pathway, and iron metabolism should also be investigated in this regard.Mechanisms controlling the HIF-EPO pathway have a prominent role in regulation of EPO synthesis, that are all together affecting the RBC production. Overall, any imbalance in HIF-EPO pathway mechanisms can result in deregulation of erythropoiesis that can lead to several diseases, including anemia, erythrocytosis and cancer. Understanding the main regulatory pathways in the process of erythropoiesis is necessary to identify the causative factors which can contribute to the development of such pathological conditions, and help to improve the diagnostic approach and therapeutic implications. Apart from the well-known O