Ets1, Etv1, and Etv2 were predicted to be involved in gene expression regulation. In addition, by integrating super-enhancers (SEs) with RNA-seq data, 76 DEGs were associated with H3K27ac-enriched SEs in the MI group, and the functions of these SE-associated DEGs were mainly related to angiogenesis. Our results suggest that histone modifications may play important roles in the regulation of gene expression in the early stage of MI, and the early angiogenesis response may be initiated by SEs.Myocardial infarction (MI) is a fatal heart disease with high morbidity and mortality. Various studies have demonstrated that a series of relatively specific biological events occur within 24 h of MI. However, the roles of histone modifications in this pathological process are still poorly understood. To investigate the regulation of histone modifications on gene expression in early MI, we performed RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) on myocardial tissues 24 h after the onset of MI. The genome-wide profiles of five histone marks  were explored through ChIP-seq. RNA-seq identified 1,032 differentially expressed genes (DEGs) between the MI and sham groups. ChIP-seq analysis found that 195 upregulated DEGs were modified by change of at least one of the three active histone marks , and the biological processes and pathways analysis showed that these DEGs were significantly enriched in cardiomyocyte differentiation and development, inflammation, angiogenesis, and metabolism. In the transcriptional regulatory network,  Myocardial infarction (MI) is a leading cause of death all over the world, mainly caused by acute coronary artery occlusion, which leads to severe myocardial ischemia, and subsequent myocardium necrosis . A consiHistone modification is a post-translational modification including methylation, acetylation, phosphorylation, ubiquitylation, and SUMOylation, etc. , among wSuper-enhancers (SEs) are characterized as clusters of enhancers, which are enriched with transcription factors, cofactors, and epigenetic modifications, and considered to contain high transcription activity . Some enPrevious studies had uncovered gene expression changes at the transcription level after MI. The expression of inflammatory response-related genes were reported to be significantly upregulated after MI, which resulted from the activation of NF-κB and TNF signaling pathways , 17. HowThe animal experiments were approved by the Ethics Committee of Tongji University. The mouse model of MI was established by permanent ligation of the left anterior descending (LAD) coronary artery as described previously . BrieflyMyocardial tissues from the infarct border zones of MI and sham group were collected for RNA isolation. Five biological replicates were used for each group. Briefly, total RNA was extracted with 1 ml of RNAiso Plus reagent . After adding 200 μl of chloroform, the sample was mixed thoroughly and incubated at room temperature for 3 min, then centrifuged at 12,000 g for 15 min at 4°C. The supernatant was transferred to a new Eppendorf tube on ice, mixed with an equal volume of ice-cooled isopropanol, and then centrifuged at 12,000 g for 15 min at 4°C after placing on ice for 10 min. The supernatant was discarded, and the pellet was washed with 75% ice-cooled ethanol three times, and RNA was dissolved in DEPC water.ChIP was carried out with the SimpleChIP® Plus Sonication Chromatin IP kit  according to the manufacturer's protocol. In brief, three biological replicates of myocardial tissues for each group from the infarct border zones were cross-linked with 1% formaldehyde for 10 min, then terminated by adding glycine. After disaggregating the samples into suspensions using Dounce homogenizer, the chromatins were isolated and sonicated to 100–300 bp fragments. Immunoprecipitation (IP) was performed with antibodies against H3K9me3 , H3K9ac , H3K4me3 , H3K27me3 , and H3K27ac . Finally, the chromatins were eluted, and then cross-links were reversed. The immunoprecipitated DNA and DNA input were purified using spin columns.* 150-bp sequencing kit for double-ended sequencing.The concentration and quality of RNA were measured by Qubit 4 Fluorometer . For each group, two biological replicates of isolated RNA above  with RIN > 8 were used for mRNA library construction. RNA sequencing libraries were generated with KAPA mRNA Hyper Prep kits . The libraries were sequenced on an Illumina NovaSeq 6000 platform using a 2FDR value < 0.05 and |Log2 (Fold change)| ≥ 1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted by R package clusterProfiler (v3.12.0) . Trimmomatic (v0.36)  was applv3.12.0) .VAHTSTM Universal DNA Library Prep Kit for Illumina® V3 kit  was used to construct the ChIP-DNA library. Three biological replicates for MI group and two biological replicates for sham group were used for ChIP-seq library construction. The libraries were sequenced on an Illumina NovaSeq 6000 platform. Clean ChIP-seq reads were mapped to the mouse genome (mm10) using bowtie2 . To iden−ΔΔCt method. ChIP-qPCR was performed with 20 μl of reaction volume, containing 10 μl of SYBR Green Master Mix, 1 μl of forward and reverse primers, 2 μl of DNA, and 6 μl of H2O. ChIP DNA enrichment was calculated as % of input, and the primers are listed in Total RNA was reversely transcribed into cDNA with the PrimeScript RT reagent kit . qRT-PCR assays were performed in five biological replicates. The reaction volume of real-time qPCR is 20 μl, containing 10 μl of SYBR Green Master Mix , 4 μl cDNA, and 1 μl forward and reverse primers, the primers are listed in Ranking of super-enhancer (ROSE)  was applP-value < 0.05) (Nppa and Nppb) was significantly increased in the MI group  were subjected to ligation of left anterior descending (LAD) or sham surgery randomly. The mice were sacrificed 24 h after the surgery, and the left ventricular tissues in the infarct border zone and normal tissues in the corresponding position were dissected to examine the expression profile of MI and sham groups by RNA-seq. The quality of the clean reads and mapping rates of RNA-seq are shown in  < 0.05) . Among t < 0.05) . MoreoveMI group , which dafter MI . The uprafter MI .To profile epigenetic changes in the early stage of MI, we selected H3K27ac, H3K9ac, and H3K4me3 (three marks associated with active regulatory regions) and H3K9me3 and H3K27me3 (two marks associated with repressive regulatory regions) antibodies to perform ChIP-Seq . HierarcTo reveal the difference in histone modifications between MI and sham, we drew the average signals of histone marks relative to ±3 kb of the transcription start site (TSS) . The aveP-value < 0.05] with 7,226 open regions . A total of 195 activated genes were enriched for at least one of H3K9ac (145/195), H3K27ac (79/195), or H3K4me3 (59/195) marks (Cxcl2 and Cxcl3) are shown in Cxcl2 and Cxcl3 loci in MI group. ChIP-qPCR and RT-qPCR verified elevated H3K27ac modification in promoter regions and elevated expression of Cxcl2 and Cxcl3, respectively  marks . Accordi5) marks . We also5) marks . KEGG an5) marks . The infectively .P-value = 1e-15), ETV1 , and ETV2  were significantly enriched in promoters of upregulated genes associated with active histone marks  in the MI group. Hypergeometric optimization of motif enrichment (HOMER) was employed to identify top enriched motifs, which showed that motifs for transcription factors including ETS1  and human skeletal muscle myoblasts (HSMM) from ENCODE database  were enriched in cardiomyocyte differentiation and development, inflammation, and angiogenesis. Pathways implicated in the pathological processes of MI were activated, such as inflammation and metabolism-related pathways. Transcription factors, including ETS1, ETV1, and ETV2, may be involved in the transcriptional regulation of molecular events in the early stage of MI. Moreover, a set of SE-associated genes related to angiogenesis were also uncovered.Ccl2, Ccl6, Cxcl2, Cxcl3, was upregulated and modified by H3K9ac, H3K27ac, and H3K4me3 marks. Ccl2 and Cxcl2 were two widely studied chemokines, which belong to the CC and CXC subtypes. Ccl2 played an important role in recruiting and activating mononuclear cells, and inhibiting Ccl2 reduced the infarct size and ameliorated the cardiac remolding after MI  and pathway (KEGG) analysis, and H3K9ac, H3K27ac, and H3K4me3 regulated the expression of critical chemokines in the early stage of MI. After the occurrence of MI, chemokines were generated in injured myocardium, bound to the endothelial surface and extracellular matrix through glycosaminoglycans, and interacted with receptors on the surface of leukocytes to promote trafficking . CC and after MI , 39. Meafarction . Studiesafter MI –43. Our In order to get the transcription factors that coordinated the transcriptional regulatory network in the early stage of MI, we performed motif analysis and identified ETS1, ETV1, and ETV2. All three transcription factors were members of the ETS family. ETS1 was associated with fibrotic remodeling of the myocardium . ETV1 waPgf and Ccl2. Pgf  was reported to promote angiogenesis and myocardial repair post-MI  were first identified as a large cluster of enhancers with the strong binding of transcription factors that drive the expression of cell identity genes . It has  post-MI , 54. Cclal cells , 55. Apaocardium . Taken tOur study also has limitations. We mainly focused on promoters and SEs, while other regulatory elements of gene expression in the early stage of MI were not investigated, which might lead to underestimation of the roles of histone modifications in the early stage of MI. Besides, we only selected five histone marks to study the regulation of histone modification on gene expression in MI; the roles of other histone marks in MI were not investigated. Moreover, the results were based on a mouse model of MI, so further observation and validation in human samples are needed.In summary, our study revealed the histone modification profile in the early stage of MI in mice and demonstrated that the modulation of histone modifications  might be involved in inflammation and angiogenesis by regulating promoters and SEs, and participating in the pathological processes of cardiac remodeling.https://www.ncbi.nlm.nih.gov/bioproject/PRJNA657342) under accession No. SRP277594.All the sequencing results have been uploaded and deposited in the NCBI Sequence Read Archive database (The animal study was reviewed and approved by Ethics Committee of Tongji University.Y-HC and DS conceived and coordinated the study. YZ and JW constructed the MI models of mice and collected the samples. JW, YZ, LN, and LH constructed the library for RNA-seq and ChIP-seq and performed some experiments. BL made the bioinformatic analysis of RNA-seq and ChIP-seq and drew the figures. JW drafted the manuscript. JY and LX conducted and gave advice on the study. Y-HC, JY, DS, and BL reviewed and revised the manuscript. All authors have approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Transcription factors regulate multiple processes during brain development and in the adult brain, from brain patterning to differentiation and maturation of highly specialized neurons as well as establishing and maintaining the functional neuronal connectivity. The members of the zinc-finger transcription factor family Bcl11 are mainly expressed in the hematopoietic and central nervous systems regulating the expression of numerous genes involved in a wide range of pathways. In the brain Bcl11 proteins are required to regulate progenitor cell proliferation as well as differentiation, migration, and functional integration of neural cells. Mutations of the human Bcl11 genes lead to anomalies in multiple systems including neurodevelopmental impairments like intellectual disabilities and autism spectrum disorders. Transcription factors (TFs) play a crucial role during development as well as in the adult organism. Early on during embryogenesis the precisely regulated temporal and spatial expression of TFs is required to establish the body plan laying the foundation for the different tissues as well as regulating physiological functions. The fine-tuned expression of TFs is of particular importance during development and maintenance of the brain . TFs notHere we focus on the zinc-finger transcription factor family Bcl11 expressed mainly in the hematopoietic and the central nervous systems. The crucial role of the Bcl11 genes in brain development and function became apparent by recent reports describing patients carrying heterozygous mutations of Bcl11 and their implications in intellectual disabilities and neurological disorders . EmerginThe Bcl11 genes were first identified by their functions in the immune system, Bcl11a also known as Ctip1 as a proto-oncogene and Bcl11b or Ctip2 as a tumor suppressor gene . The BclAlternative splicing of Bcl11 mRNAs results in at least four isoforms of human and mouse Bcl11a as well as 2 and 3 isoforms of Bcl11b in human and mouse, respectively . The resBoth Bcl11 genes are expressed during embryonic development as early as E10.5 and continuing to be expressed throughout life in several tissues like the brain, immune system, and skin . In the Little is known about the upstream control of Bcl11 expression in the brain. Best studied is the regulation of Bcl11b expression by Satb2, a chromatin regulating protein binding to the matrix attached region (MAR) of the Bcl11b locus . Satb2 b+ T-cells recognizing TGGGC as DNA binding motif  of orphan nuclear receptors . Both prse SIRT1 , 2005. Mse SIRT1 . Using ase SIRT1 .Up to now the described complex comprised of Bcl11 proteins and chromatin remodeling factors were studied mostly in the hematopoietic system which does not exclude a role of these factors in the transcriptional regulation of the nervous system. However, during neuronal development the SWI/SNF or BAF complex consisting of several subunits including the Bcl11 proteins (BAF100a/b), seems to play a more crucial role. First discovered as tumor suppressor genes involved in a range of cancers  some of Conditional mutagenesis of Bcl11a in the developing dorsal spinal cord of the mouse demonstrated impaired morphological differentiation interfering with the formation of the somatosensory circuitry within the dorsal spinal horn. This connectivity is in part established by the Wnt pathway component Frzb/Sfrp3, which was identified as the first functional downstream target of Bcl11a during CNS development . Bcl11a Although Bcl11a deficient superficial neurons appear to be correctly specified at birth, changes were observed in the populations of projection neuron subtypes within Bcl11a deficient deep cortical layers. Here, supernumerary subcerebral neurons are born at the expense of callosal and corticothalamic neurons . In contThe hippocampus consisting of the dentate gyrus (DG), the Cornu ammonis (CA), and the subiculum has an important function in learning and memory as well as emotional behavior . The DG,Ablation of Bcl11b during postnatal development causes a dramatic mossy fiber phenotype implying a role in axon outgrowth and pruning . Mossy fSince Altman and colleagues first reported about adult neurogenesis in the 1960s  its exisThe Bcl11b adult-induced mouse model provides further evidence for the importance of adult neurogenesis . Adult-iGiven the Bcl11b mutant phenotype in mice, specifically changes of the dentate gyrus-CA3 connectivity, strongly suggests behavioral consequences. Indeed, analyzing hippocampal specific behavior of Bcl11b mouse models demonstrated impairment of spatial learning and working memory , 2016. TRecent reports examining patients carrying heterozygous Bcl11 mutations not only revealed severe defects of the immune system but also neurodevelopmental disorders including intellectual disabilities . In the Notably, autism spectrum disorders could also be caused by impaired remodeling of the BAF complex . Bcl11 pExamining the adult and aging human as well as rodent brain suggests Bcl11b to be involved in a number of neurodegenerative disorders like Alzheimer’s disease (AD) , HuntingIn the nervous system Bcl11 proteins regulate multitudes of signaling pathways during development, maturation and aging establishing and maintaining a functional brain circuitry. Both Bcl11 proteins are expressed in a number of diverse cells regulating their proliferation, differentiation and maturation by conserved mechanisms. However, numerous unanswered questions remain concerning the molecular mechanisms of Bcl11 regulation. How do the widely expressed Bcl11 genes direct the development and maintenance of highly specialized cells like projection neurons in the brain? Most importantly, what are the partners of Bcl11 in these processes, e.g., downstream genes and co-factors of specific binding complexes to ensure the specific fate of cells? Answers could be provided by comparative transcriptome analyses of single cells determining target genes and signaling pathways as well as proteomics to determine the composition of specific protein complexes involved. Interestingly, human Bcl11 mutations result in neurodevelopmental impairments including autism spectrum disorder. Reiterating human Bcl11 mutations in mouse models could further contribute to a better understanding of the involved molecular mechanisms. RS wrote main part of the manuscript. CW contributed to the text and made figures. SB contributed to the text.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Jmjd2b resulted in differentiation of TSCs, suggesting an indispensable role of JMJD2B/KDM4B in maintaining the stemness. Through the integration of transcriptome and ChIP-seq profiling data, we show that JMJD2B is associated with a loss of H3K36me3 in a subset of embryonic lineage genes which are marked by H3K9me3 for stable repression. By characterizing the JMJD2B binding motifs and other transcription factor binding datasets, we discover that JMJD2B forms a protein complex with AP-2 family transcription factor TFAP2C and histone demethylase LSD1. The JMJD2B–TFAP2C–LSD1 complex predominantly occupies active gene promoters, whereas the TFAP2C–LSD1 complex is located at putative enhancers, suggesting that these proteins mediate enhancer–promoter interaction for gene regulation. We conclude that JMJD2B is vital to the TSC transcriptional program and safeguards the trophoblast cell fate via distinctive protein interactors and epigenetic targets.Trophoblast stem cell (TSC) is crucial to the formation of placenta in mammals. Histone demethylase JMJD2  family proteins have been previously shown to support self-renewal and differentiation of stem cells. However, their roles in the context of the trophoblast lineage remain unclear. Here, we find that knockdown of  Abnormal development of trophoblast may result in inadequate placentation, which can lead to miscarriage, growth restriction, preeclampsia, other pregnancy disorders, and disease predisposition2. The trophoblast lineage is emerged from the first cell fate specification event in the embryo at the late morula stage, which results in the formation of trophectoderm (TE) and inner cell mass (ICM). TE cells contribute to the formation of the placenta, whereas ICM cells give rise to the embryo proper. Cells committed to the embryonic lineages are strictly excluded from the trophoblast fate, and vice versa5. The restricted lineage specification is recapitulated in the in vitro derived embryonic stem cell (ESC) and trophoblast stem cell (TSC) from ICM and TE, respectively. TSC serves as an excellent in vitro stem cell model for the study of trophoblast development. The multipotent and self-renewal properties of TSC are associated with the trophoblast-specific transcriptional program, including the expression of key transcription factors CDX2, EOMES, ELF5, TFAP2C, ETS2, SOX2, and ESRRB. It has been shown that many of these transcription factors interact with each other, for example, TFAP2C interacts with SOX2 and ELF57, to reinforce their expression through a positive feedback loop and to maintain the stem cell circuit by cross-regulation11.Placenta is an essential organ for fetal growth in mammals, providing an interface for the exchange of nutrients, oxygen, metabolites and other molecules between the fetal and maternal bloodstream. It is primarily formed by highly specialized cell types of the trophoblast lineage12. DNA hypomethylation can facilitate cell fate switch from embryonic to trophoblast lineage, as evidenced by the presence of trophoblast marker expressing cells in Dnmt1−/− embryos and in DNA methylation-deficient ESCs11. The differential DNA methylation of transcription factor Elf5 was proposed to be responsible for safeguarding the commitment of trophoblast cell fate in TSCs. However, the restricted expression of other key trophoblast stemness genes, such as Cdx2 and Eomes, was not regulated by DNA methylation in both ESCs and TSCs11, suggesting the involvement of other epigenetic mechanisms in trophoblast lineage specification and maintenance. Importantly, repressive histone modification H3K9me3 has been shown to have a crucial role in the transcriptional regulation of trophoblast lineage genes, which contributes to the trophoblast/embryonic lineage barrier in ESCs. It has been shown that genetic abrogation of histone methyltransferase Setdb1  in ESCs resulted in trans-differentiation to TSC-like cells, which reveals the importance of H3K9me3 for the repression of the trophoblast transcriptional program in ESCs14. In addition, a negative correlation between promoter H3K9me3 and gene expression of several key stemness factors was observed during TSC differentiation15, further supporting the epigenetic regulatory role of H3K9me3 in trophoblast development. However, little is known about the global epigenetic alterations in association with the transcriptional program in the maintenance of TSCs.Lineage specification and the associated gene expression pattern during cell differentiation are tightly associated with epigenetic regulations. It is noted that the global DNA methylation level in TE cells is relatively lower than that of ICM cells17. All four members of the JMJD2 family have the ability to demethylate H3K9me2/3 and/or H3K36me2/3. It has been reported that JMJD2 family members are involved in the regulation of stem cell self-renewal and differentiation. JMJD2C maintains the expression of pluripotent gene Nanog in ESCs by preventing the deposition of repressive H3K9 methylation18. A recent genome-wide study showed that JMJD2A and JMJD2C act together at active gene promoters for the maintenance of the transcriptional program in ESCs19. JMJD2A was also reported to be essential for the derepression of Myog during skeletal muscle cell differentiation20, whereas JMJD2B was involved in the osteogenic differentiation of mesenchymal stem cell through mediating H3K9me3 demethylation at the DLX promoter21. Importantly, several studies using ESC models demonstrated the binding of JMJD2 proteins at trophoblast lineage gene loci, such as Cdx2, Eomes, Tfap2c, Hand1, Ovol2, and Gcm123, suggesting a repressive role of JMJD2 to the trophoblast cell fate in ESCs. Whether JMJD2 family is epigenetically involved in the maintenance of stemness in TSCs remains to be elucidated.JMJD2  family belongs to a group of Jmj-domain-containing proteins, which is evolutionarily conserved from budding yeast to mammalsIn this study, we show that JMJD2B is a key JMJD2 family member regulating the TSC transcriptional program. JMJD2B is persistently bound at the majority of active gene loci during TSC differentiation, whereas H3K9me3 is mainly associated with non-trophoblast lineage genes and keeps them silent. Unlike in ESCs, we do not observe any global negative correlation between JMJD2B and H3K9me3 in TSCs. In a subset of silent genes, JMJD2B is co-enriched with H3K9me3 and is associated with the loss of H3K36me3 mark. By characterizing the JMJD2B binding motifs and other transcription factor binding profiles, we discover a novel interaction between JMJD2B, AP-2 family transcription factor TFAP2C, and histone demethylase LSD1 at active gene promoters. Taken together, our data reveal that JMJD2B regulates the transcriptional program via distinctive interacting partners and epigenetic mechanisms that are crucial to the lineage identity of TSCs.14. To ask whether the JMJD2 family is essential in TSCs, we treated TSCs with JIB-04, which is a non-competitive inhibitor of α-ketoglutarate and serves as a pan-selective inhibitor of Jumonji histone demethylases, including KDM5/JARID1 family, KDM4/JMJD2 family, and KDM6B/JMJD326. Although the IC50 values of JIB-04 ranged from 230 to 1100 nM for different histone demethylases24, we observed that TSCs exhibited growth inhibition and underwent apoptosis at 250 nM and 500 nM concentrations, respectively (data not shown). Therefore, we attempted to treat TSCs at a concentration ranged from 0–100 nM, which was reported to show biological responses on cell proliferation and gene expression28. Interestingly, it was observed that stemness-associated genes, including Cdx2, Eomes, and Elf5, were significantly downregulated by 2.5–3.5 folds at 50 nM JIB-04 treatment for 48 h, and their expressions were further decreased at 100 nM treatment in a dosage-dependent manner , whereas Jmjd2a-depleted TSCs showed no morphological changes  sequences were designed to target the exon 3 and 5 of the Jmjd2b gene, which could introduce frameshift mutations or truncation at the JmjC domain. The transfection condition of gRNA was optimized to achieve over 95% transfection efficiency, which presumably resulted in genome editing at the Jmjd2b locus in the majority of TSCs. Both CRISPR knockdown TSC lines showed depletion of the JMJD2B without altering the expression of JMJD2A and JMJD2C  and induction of differentiation markers  in the CRISPR knockdown cells, even though the mRNA levels of these markers were not as strong as the in vitro differentiated TSCs (as compared to the day 2 or day 6 in vitro differentiated samples)  and D6 (most differentiated) samples. Notably, several inactive pathways, such as Notch signaling, gonadotropin-releasing hormone receptor pathway, and integrin signaling, were enriched, suggesting that JMJD2B could have a dual function by activating stemness factors while suppressing pro-differentiation signals at the undifferentiated state. In addition, similar Gene Ontology (GO) terms were observed between D2 and D6 samples, which were distinct from the D0 sample. The strong dynamic changes of GO annotation at the early differentiation process (D0 vs. D2/6) implies that JMJD2B could have a unique regulatory role in the undifferentiated TSCs. Once TSCs undergo differentiation, the functions of JMJD2B could remain stable.To investigate the epigenetic regulation mediated by JMJD2B in TSC maintenance, we profiled the genome-wide JMJD2B occupancy and its association with H3K9me3 in TSCs by JMJD2B- and H3K9me3-ChIP followed by high-throughput sequencing (ChIP-seq). Globally, we identified 272,434, 225,041, and 304,499 JMJD2B binding sites in the undifferentiated (D0), 2-day differentiated (D2), and 6-day differentiated (D6) TSCs, respectively. The general overview of JMJD2B-mediated functions was revealed by Genomic Regions Enrichment of Annotations Tool (GREAT) analysis using the top 65,000 confidence peaks in each TSC sample , followed by an increase in the expression of early differentiation markers  at D2 TSC, and late differentiation markers for TGCs  and syncyiotrophoblast (Syna) at D6 TSC, respectively.Since the H3K9me3-enriched loci were not associated with biological processes that are relevant to the trophoblast lineage, we attempted to reveal the global correlation between the H3K9me3 enrichment and transcriptional repression in TSCs. The mRNA expression profiles of the corresponding TSC samples were generated by RNA-seq. It was observed that 2,486 upregulated genes and 2,154 downregulated genes in the early differentiation phase (from D0 to D2), and 3,261 upregulated genes and 2,754 downregulated genes in the late differentiation phase (from D2 to D6)  in each of the three stages of TSCs. Strikingly, JMJD2B was enriched at the promoters of the vast majority of actively transcribing genes, as opposed to the H3K9me3 pattern  and two differentiation factors (Ascl2 and Tpbpa) in a panel of 17 selected trophoblast lineage genes showed a negative correlation between H3K9me3 and gene expression during TSC differentiation  showed no correlation with the H3K9me3 pattern. We also observed that genes with persistence or absence of H3K9me3 during TSC differentiation showed a wider range of transcriptional output (including up- and down-regulation) when compared to genes with differential H3K9me3 enrichment  which showed negative correlations between gene expression and H3K9me3, their JMJD2B patterns remained unchanged , have higher FPKM values in TSCs , did not contain any prominent trophoblast lineage genes. GO analysis showed that this subset of genes was strongly associated with a wide range of embryonic developmental processes  were mainly involved in immune response and metabolic processes  of the silent genes bound by JMJD2B in the undifferentiated TSCs, it is noticed that the majority of them  were also marked by H3K9me3. Unexpectedly, about 40%  of these genes remained JMJD2B/H3K9me3 co-enriched and silent throughout the TSC differentiation process  promoter JMJD2B binding at D2 and D6 TSC, respectively   that are enriched at the murine TSC enhancer elements38. Even though H3K9me3 is associated with the expression of a few key trophoblast factors in TSCs, we notice that its modulation is independent of the histone demethylase JMJD2B. This is supported by our findings that the low H3K9me3 levels at Cdx2 and Eomes remain unchanged in the Jmjd2b-knockdown TSCs. Interestingly, we observed a persistent co-enrichment of JMJD2B and H3K9me3 at transcriptionally silent genes during TSC differentiation. It has been reported that the Drosophila H3K9me3-bound Heterochromatin Protein 1a (HP1a) interacts with dKdm4a (orthologs of JMJD2A) at heterochromatin. Mutation of dKdm4a leads to increased enrichment of H3K36me3 at both heterochromatic and euchromatic targets45. Similarly, we also observe a negative correlation between JMJD2B and H3K36me3 patterns at the gene bodies of H3K9me3-associated non-trophoblast lineage genes, suggesting that JMJD2B prevents inappropriate gene activation thought its H3K36 demethylation activity. How JMJD2B functions by targeting H3K36me3 but not H3K9me3 in TSCs is not clear. Nevertheless, previous works on other histone demethylases showed that the binding of co-factors or the presence of other histone modifications can affect the substrate specificity from one histone lysine residue to another47. In addition, it is noteworthy that JMJD2 proteins show redundant functions in stem cell maintenance and differentiation. Knockout ESC lines and mouse models showed that individual JMJD2 members are dispensable for pluripotency and in vivo embryo development48. Nevertheless, JMJD2A and JMJD2C act together to safeguard the ESC identity by enabling transcriptional competency via constitutive removal of H3K9me3 and H3K36me3 at the active gene promoters19. The Drosophila dKdm4a and dKdm4b also acted synergistically for the prevention of developmental arrest. Only the double homozygous mutants exhibited motility at the second instar larval stage, while single knockout mutants were viable and fertile49. Therefore, it is likely that the H3K9me3 pattern at key trophoblast factors is regulated by multiple JMJD2 members. However, a single knockdown of Jmjd2b triggers TSC differentiation, which highlights the essential role of JMJD2B in the regulation of the transcriptional program governing TSC properties. It is thus important to elucidate how this JMJD2B-associated network shares common targets with other JMJD2 proteins in TSCs.A previous study showed that H3K9me3 was associated with the repression of several key trophoblast factors during TSC differentiation35, androgen receptor51, MLL complex37, peroxisome proliferator-activated receptor gamma (PPARγ)36, histone deacetylases (HDACs)52 and polycomb repressive complex 2 (PRC2)22. Notably, some of the transcriptional regulations mediated by the JMJD2-containing complexes are independent of histone demethylation, but conferred with the modulation of the protein partner activity36. Our data indicate that JMJD2B interacts with TFAP2C and LSD1 as a transcriptional activator complex, which is predominantly located at active gene promoters. Previous findings on the interactome of TSCs showed that TFAP2C is one of the interacting partners of LSD110. However, the function of TFAP2C–LSD1 complex remains unclear. Through the analysis of the binding patterns of TFAP2C and LSD1, we found that the TFAP2C–LSD1 complex occupies at a significant portion of inactive promoters, which is in contrast to the JMJD2B–TFAP2C–LSD1 complex. This suggests that the interaction with JMJD2B converts the protein complex into a transcriptional activator. Interestingly, we notice that a majority of the TFAP2C–LSD1 complex occupies gene enhancers. It has been reported that both TFAP2C and LSD1 are required for chromatin looping, which facilitates promoter-enhancer interaction54. Besides, TSCs with TFAP2C depletion cannot be maintained7, whereas LSD1-depleted TSCs are prone to differentiate41. LSD1 also occupies active enhancers in undifferentiated ESCs and its demethylase activity is inhibited by the presence of acetylated histones56. During ESC differentiation, enhancer histone acetylation is decreased owing to the reduced occupancy of the OCT4-p300 complex, which subsequently permits H3K4 demethylation by LSD1. Therefore, we speculate that JMJD2B may facilitate long-range chromatin interactions with the TFAP2C–LSD1 containing complexes, and possibly modulate its activity, to maintain the stemness transcriptional program in TSCs.Although the presence of JMJD2B at gene promoters is generally associated with active gene expression in TSCs, we notice that a number of promoters with JMJD2B binding per se remained silent. This suggests that the JMJD2B binding does not equate at all to its activity. In fact, JMJD2 family proteins reside in large multimeric complexes and act as either transcriptional co-activators or co-repressors depending on the associated protein partners. Several protein complexes containing JMJD2 proteins have been reported, including nuclear receptor corepressor (N-CoR)In summary, we propose a model for the dual function of JMJD2B in safeguarding the TSC identity Fig. . JMJD2B 57 and cultured in TS basic medium  with 20% fetal bovine serum , 1 mM sodium pyruvate (Gibco), 1 × penicillin–streptomycin-glutamine (Gibco), and 50 μM β-mercaptoethanol , 25 ng/mL hFGF4  and 1 μg/mL heparin (Sigma)), with 70% medium pre-conditioned on embryonic fibroblasts57. TSC differentiation medium consisted of the TS basic medium without hFGF4 and heparin. J1B-04  was prepared in DMSO and stored at − 20 °C as a stock solution. TSCs were cultured at 37 °C with 5% CO2. The culture medium was changed every 2 days.TSC line was derived from ROSA26 (129 Sv) mice by Dr. Rossant groupJmjd2b genomic sequence, were synthesized and separately annealed with the Alt-R CRISPR-Cas9 tracrRNA (IDT) to form the duplexed oligo. The Cas9-expressing TSCs were transfected with 90 nM duplexed oligo using TransIT-X2 (Mirus Bio). Two Jmjd2b-knockdown TSC lines were selected and verified by T7 endonuclease I digestion and Sanger sequencing  using TransIT-X2  according to the manufacturer’s instructions. Samples were analyzed 72 h after transfection. For CRISPR-Cas9 genome editing, TSCs were transduced by lentivirus generated from the LentiCRISPRv2-mCherry plasmid. The mCherry positive Cas9-expressing TSCs were isolated by fluorescence activated cell sorting (FACS) using BD FACSAria II cell sorter . Two Alt-R CRISPR-Cas9 crRNAs , which targeted the Dynein or β-Actin. All samples were analyzed in duplicates and three independent experiments were performed.Total RNA was extracted using MiniBEST Universal RNA Extraction Kit  and reverse transcribed by Transcriptor First Strand cDNA Synthesis Kit  according to the manufacturer’s instructions. qRT-PCR was performed on a StepOnePlus System  using the SYBR Premix Ex Taq (Takara) and primers listed in Supplementary Table TSCs seeded on gelatin-coated coverslips were fixed with 4% paraformaldehyde for 10 min at room temperature. Cells were permeabilized by 0.1% Triton X-100 for 10 min. Immunostainings were performed after blocking in 1% BSA for 1 h using the following antibodies and dilutions: anti-EOMES  1:250; anti-ASCL2  1:200; anti-HAND1  1:100. Secondary antibodies were anti-IgG conjugated with Alexa Fluor 555 . Cells were stained with 0.1 µg/ml DAPI for 1 min. Photographs were taken on an Olympus BX53 epifluorescence microscope at 400X magnification with the following exposure time: 18 ms for DAPI; 400 ms for EOMES; 500 ms for ASCL2; 700 ms for HAND1.ChIP was performed with anti-Jmjd2b (Abcam ab191434), anti-H3K9me3 (Abcam ab8898), or anti-H3K36me3 (Abcam ab9050) using MAGnify Chromatin Immunoprecipitation System (Invitrogen) according to the manufacturer’s instructions. Chromatin was sonicated by Bioruptor Plus UCD-300 . ChIP with IgG was served as a negative control. Immunoprecipitated DNA was analyzed by qPCR with the relevant primers listed in Supplementary Table Cells were lysed in lysis buffer containing 50 mM Tris–HCl pH6.6, 150 mM NaCl, 1% Triton X-100, 0.1% SDS, 0.1% Na deoxycholate and 1% NP-40 and 1 × complete protease inhibitor cocktail (Roche), 1 μg/ml antipain, 1 μg/ml leupeptin, 1 mg/ml pepstatin, 2 μg/ ml aprotinin, 1 mM PMSF on ice for 30 min. The cell lysates were loaded on a 8–10% SDS–polyacrylamide gel for SDS-PAGE, followed by electro-transfer to a PVDF membrane. The membrane was blocked with 5% skim milk in TBS-Tween-20  for 2 h and then incubated with either anti-GAPDH , anti-JMJD2A , anti-JMJD2B , or anti-JMJD2C  at 4 °C overnight, followed by incubation with horseradish peroxidase-conjugated anti-rabbit IgG secondary antibody. Immunoreactive protein was visualized using ECL prime solution .TSCs were lysed in 100 μL ice-cold non-denaturing lysis buffer  with 5U DNase (Thermo Fisher Scientific) at 37 °C for 15 min and then on ice for 90 min. Anti-JMJD2B antibody (Abcam ab191434), anti-LSD1 (Abcam ab17721), or anti-TFAP2C antibody (Santa Cruz Biotechnology sc-12762) was conjugated with Dynabeads Protein A/G beads (Invitrogen) and mixed with 1 mg protein lysate at 4 °C overnight. The Dynabeads-antibody-antigen complex was washed 3 times with PBS for 5 min at 4 °C. The immunoprecipitated protein was eluted in protein loading buffer  at 95 °C for 10 min. The eluted proteins were subjected to Western blotting with anti-JMJD2B , anti-LSD1 antibody , anti-TFAP2C antibody  or anti-EOMES .Mus musculus mm10 (Ensembl release 89) genomic assembly was performed using Tophat2. Duplicate reads were removed by MarkDuplicates from Picard (Broad Institute). Transcriptome assembly and differential gene expression were calculated in fragments per kilobase of transcript per million mapped reads (FPKM) unit by Cufflinks. Differentially expressed genes (DEGs) were selected with more than 1.5-fold changes in FPKM values between samples. To determine gene transcriptional status, zFPKM transformation was performed using R package. zFPKM value over − 3 was regarded as transcriptionally active genes as recommended by Hart et al.33. Gene ontology (GO) analysis was performed using the two-side hypergeometric test from the ClueGO plugin of Cytoscape software. Enriched GO terms with Benjamini–Hochberg FDR-corrected q-value < 0.05 were selected.Total RNA sample was used to construct sequencing library using KAPA Stranded mRNA-Seq kit , followed by paired-end sequencing using HiSeq 1500/2000  at the Center for Genomic Sciences (University of Hong Kong). Two biological replicates of each time point sample were subjected to RNA-seq. The raw sequencing reads were filtered against low quality bases , partial Illumina adaptor sequences, and rRNA/tRNA contamination by Trimmomatics and Bowtie2. Read alignment against –4. Signal density heatmap and profile of ChIP-Seq signals were generated by deepTools. Scores per genome regions were calculated by ComputeMatrix and then utilized by plotProfile and plotHeatmap for graph plotting. The GO biological processes and PANTHER pathway analyses were conducted using Genomic Regions Enrichment of Annotations Tool (GREAT). The threshold for region-based enrichment term was set twofold for GO biological processes and onefold for PANTHER pathway analysis. Enriched GO terms with Benjamini–Hochberg FDR-corrected p-value < 0.05 were selected. Motif analysis was performed using HOMER. Published TSC ChIP-seq datasets were obtained from European Nucleotide Archive: H3K4me1, H3K27ac, CDX2, EOMES, and ELF5 (PRJNA179294)38; H3K36me3 (PRJNA170844)34; TFAP2C (PRJNA298763)10; SOX2 (PRJNA139231)8; LSD1, ESRRB, and DAX1 (PRJEB9375)39. ChIP-seq datasets from non-TSCs: TFAP2C (PRJNA232935)8; LSD1 (PRJNA174296)42 (PRJNA219405)43. All the peaks with mm9 coordinates were converted to mm10 by UCSC LiftOver tool. Promoter was defined as − 3 kb to + 500 bp relative to the transcriptional start site (TSS); gene body was defined by Ensembl annotation (GRCm38.89); putative enhancer was defined by the H3K4me1 and H3K27ac overlapped regions outside the promoters. Overlapping analyses of the genomic regions were performed using Bedtools (version 2.26.0). Regions that consist of at least 1 bp intersection between the tested samples were considered as overlapped. Randomized controls were generated by shuffling peaks within the same chromosomes using Bedtools. Control peaks of individual shuffled ChIP-Seq datasets were intersected with the JMJ2B target regions and the maximum total number of intersected target regions was counted from 10,000 shuffling times.ChIP DNA fragments were used to construct sequencing library using MicroPlex Library Preparation Kit v2 (Diagenode), followed by paired-end sequencing using HiSeq 1500/2000  at the Center for Genomic Sciences (University of Hong Kong). Two biological replicates of each time point sample were subjected to ChIP-seq. Raw reads were processed as described in the RNA-seq analysis section. Peak calling and differential binding events were analyzed by MACS2 using the default cutoff value (0.1) of FDR-corrected q-value. Top 65,000 statistically significant peaks  were selected for all the ChIP-seq datasets, which is corresponding to a mean FDR-corrected q-value cutoff at 3 × 10A statistical significant difference was calculated by two-tailed unpaired Student’s t-test or one-way ANOVA with Tukey’s test.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server  to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal ( The authors summarize the data produced by phase III of the Encyclopedia of DNA Elements (ENCODE) project, a resource for better understanding of the human and mouse genomes. Operationally, functional elements are defined as discrete, linearly ordered sequence features that specify molecular products  or biochemical activities with mechanistic roles in gene or genome regulation 5. Commencing with the ENCODE Pilot Project in 2003 (which focused on a defined 1% of the human genome sequence4) and scaling to the entire genome in a production phase II that began in 20071, ENCODE has applied a succession of state-of-the-art assays to identify likely functional elements with increasing precision across an expanding range of cellular and biological contexts. To capitalize on the value of the laboratory mouse, Mus musculus, for both comparative functional genomic analysis and modelling of human biology, a Mouse ENCODE Project of more limited scope was initiated in 20096. An accompanying Perspective7 provides further context for the evolution of the ENCODE Project and describes how ENCODE data are being used to illuminate both basic biological and biomedical questions that intersect genome structure and function.The human genome comprises a vast repository of DNA-encoded instructions that are read, interpreted, and executed by the cellular protein and RNA machinery to enable the diverse functions of living cells and tissues. The ENCODE Project aims to delineate precisely and comprehensively the segments of the human and mouse genomes that encode functional elements2 that conform to ENCODE standards  incorporating new assays such as RNA-binding-protein localization and chromatin looping; (ii) increasing the depths at which current assays such as transcription factor chromatin immunoprecipitation and sequencing (ChIP–seq) interrogate reference cell lines; and (iii) collecting data over a greatly expanded biological range, with an emphasis on primary cells and tissues. In addition, ENCODE has now incorporated and uniformly processed the substantial data from the Roadmap Epigenomics Projectards see .cis-regulatory elements (cCREs) in both the human and mouse genomes. We have also developed a new web-based interface called SCREEN to facilitate access to the human and mouse registries and to facilitate their application to diverse biological problems.Here, we describe the generation of nearly 6,000 new experiments  in phase III that have extended previous phases of ENCODE in order to define and annotate diverse classes of functional elements in the human and mouse genomes  to facilitate access to the human and mouse registries and to empower their application to diverse biological problems.To enhance the utility and accessibility of ENCODE data for studies of gene regulation, in this report, we have now:We sought to develop the human Encyclopedia of DNA Elements along three axes by: (i) expanding established chromatin structure and histone modification assays to new and diverse cellular contexts, chiefly primary cells and tissues; (ii) adopting and scaling up additional biochemical assays to address gaps in the annotation of DNA-encoded elements, particularly transcribed elements; and (iii) increasing the molecular depth of assays for transcription factors (TFs), co-factors, and other chromatin-associated proteins to deeply annotate prioritized reference cell types Fig. , Table 16, the third phase of ENCODE expanded by more than fourfold the number of cell types and tissues assayed and more than twofold the number of experimental datasets produced . Furthermore, all ENCODE data are now also available as resident data sets within a major public computing cloud (https://registry.opendata.aws/encode-project/). We have continued to expand the repertoire of tools for data analysis, adopting widely used external tools whenever possible and developing new tools as needed (https://www.encodeproject.org/software/).Overall, compared with our previous reports18. The transcriptome is deeply complex, and both new isoforms of known genes and short RNA species such as enhancer RNAs continue to be discovered18.The universe of transcribed elements—the transcriptome—has become a common tool for the molecular phenotyping of cells and tissues and serves as a framework for diverse computational analyses of cellular states19 that can (i) position transcriptional start sites (TSSs) with single-nucleotide resolution; (ii) generate accurate quantitative and reproducible measurements of promoter-specific RNA expression; and (iii) precisely connect 5′-transcription initiation sites with splicing isoforms, thus providing a previously unavailable connection between promoter regulation and spliced products over long genomic intervals. RAMPAGE also enables the annotation of previously intractable classes of RNA transcript that emanate from repetitive elements20. We deepened human transcriptome annotations by combining RAMPAGE with short (below 200 nucleotides (nt)) and long (more than 200 nt) RNA sequencing (RNA-seq) performed on approximately 200 biosamples 22. The RNA sequences and structures recognized by RBPs are encoded by the underlying genomic sequence, and thus represent a class of functional sequence elements not previously explored by ENCODE . These data open a window into the post-transcriptional roles and mechanisms of RBPs in determining the levels of specific transcripts.Genes that encode RNA-binding proteins (RBPs) are one of the largest gene families in the human genome, comprising approximately 10% of all protein-coding genes25; however, these collections are far from complete, particularly for factors with extended recognition sequences. Notably, sequence motifs alone do not capture which motif instances are occupied in vivo, nor do they identify indirect localization events wherein one or more TFs are associated with an ‘anchor’ factor that is directly bound to the genome26. To enable detailed analysis of both in vivo recognition motifs and combinatorial occupancy patterns for human transcriptional regulators, we applied ChIP–seq to densely map the locations of 662 chromatin-associated proteins, including classical RNA Pol II-associated factors such as TFIID, in reference cell types  assay that requires only small numbers of input cells, we expanded ENCODE human DHS maps by more than 200 different cell types and states, chiefly primary cells and tissues11  was tripled to more than 300, enabling delineation of 4.4 million consensus human DNase I footprints within DHSs, enhanced annotation of tissue selectivity, and identification of functional variants that directly affect regulatory factor occupancy12. These extensive indexes of DHSs and footprints, systematically annotated by their tissue-selective patterns of activation, provide unprecedented resources for detailed studies of gene regulation and investigation of genetic variants associated with diseases and complex traits.We have expanded the biological range and molecular resolution of ENCODE DNase I hypersensitive sites (DHSs) and DNase I footprint annotations. DHSs are the hallmark of active or poised 27, was adopted to profile chromatin accessibility genome-wide in 66 mouse tissues and cell types that spanned 8 developmental stages ges Fig. . More th30. In this phase, we standardized ChIP–seq assays for 11 histone modifications and 2 common histone variants   Table , which m37 —a core histone modification that is characteristic of transcribing promoters41—to annotate active and poised promoters43. Similarly, H3K27ac, combined with relative paucity of H3K4me3 surrounding a DHS, has been strongly associated with active enhancer function at the underlying DNA element44.The comprehensive discovery and annotation of We have applied these simple core biochemical signatures, integrated with the GENCODE annotation of TSSs, to create an initial registry of human and mouse cCREs that show signatures of activity, or of being poised for activity, in one or more ENCODE biosamples. Using the classification system Fig.  detailed46. Whereas a majority of enhancer-like elements map to promoter-distal regions , many known enhancers lie in close proximity to a TSS47. Previously, ENCODE had analysed promoter-containing regions by using a generous fixed-interval definition 1. That arbitrary cutoff had the effect of commingling the TSS and minimal-promoter function with promoter-proximal enhancer function. To better identify promoter-proximal enhancer-like cCREs and to help to distinguish them from active promoter signatures, we adopted a GENCODE TSS-aware approach that focuses on the dominant histone ChIP–seq signal, with additional parameters imposed around known TSSs  in the pertinent ChIP–seq dataset. The pertinent ChIP–seq dataset allows the cCREs to be classified into general groups. Specifically, we defined three major annotation groups using the following categorization schema for both human and mouse :1) Active and poised enhancer-like elements: cCREs annotated with enhancer-like signatures (cCRE-ELS) have high DNase and H3K27ac signals and, if they fall within 2,000 bp of an annotated TSS, they must also have low relative H3K4me3 signal. We further partitioned ELSs into two subclasses on the basis of broader proximity to the TSS:1a) Proximal enhancer-like elements: cCREs with proximal enhancer-like signatures (pELS) fall within 2 kb of a TSS.1b) Distal enhancer-like elements: cCREs with distal enhancer-like signatures (dELS) fall more than 2 kb from the nearest TSS.2) Active and poised promoter-like elements: cCREs annotated with promoter-like signatures (cCRE-PLS) possess high DNase signals and high H3K4me3 signals. They are partitioned into two subclasses on the basis of their proximity to a TSS.2a) Canonical promoter-like elements (cCRE-PLS): these fall within 200 bp (centre-to-centre) of an annotated GENCODE TSS that has high DNase and H3K4me3 signals.2b) Other high-H3K4me3 elements: cCREs with this annotation have high DNase with high H3K4me3 but low H3K27ac signals and do not fall within 200 bp of an annotated TSS. These elements may denote either poised canonical promoters, non-canonical promoter-like elements, or elements with other functions that lie within the high-H3K4me3 signal region around a canonical promoter.3) CTCF-only elements: CTCF-only cCREs have high DNase and CTCF signals but low signals for H3K4me3 and H3K27ac. These isolated CTCF elements are candidates for insulators and looping functions in which CTCF participates. Other regulatory elements (ELS and PLS) can also be bound by CTCF, where this protein may also participate in those roles.Tiers of data supportPlacing cCREs into predicted functional groups on the basis of their epigenetic features ideally would be done with full knowledge of each feature in each biosample. However, as the breadth of biological systems expands, with a concomitant increase in the number of biosamples examined, it becomes very difficult to maintain full ascertainment of all features in all biosamples . The resTier 1a cCREs are fully defined, being supported by high DNase signal plus high H3K4me3, H3K27ac or CTCF signal within the same biosample and with all measurements complete in that biosample. These cCRE annotations are derived from the 25 human (and 15 mouse) biosamples with all four features determined .Tier 1b cCREs are also supported by high DNase signal plus high H3K4me3, H3K27ac or CTCF, within the same biosample, although unlike tier 1a, they may lack some or all other data in that biosample.Tier 2 cCREs are provisionally defined cCREs, given that the supporting data are available only in different biosamples. Tier 2 cCREs are supported by high DNase signal in one or more biosamples that lack data for the pertinent H3K4me3, H3K27ac, or CTCF features that were ultimately used to make the cCRE call. They are regarded as provisional because the pertinent histone mark or CTCF data came from a different biosample that lacked DNase data. Tier 2 cCREs can be promoted to tier 1 as additional pertinent data are determined within a single biosample, and this reclassification will be performed for each new build of the registry.A detailed description of cCRE classifications into groups and tiers is in 48  for each segment. The cCRE-containing segments were judged to encode regulatory activity if lacZ expression was consistently and specifically observed in the target tissue at E11.5  conducted using the ENCODE reference human cell lines GM12878ely Fig. . Furtherome Fig. . By contcis. The assays also do not account for poised elements that exhibit DNase I hypersensitivity but are gated functionally by additional trans-acting signals or cell contexts. Furthermore, we acknowledge the possibility that not all open chromatin regions marked by high levels of H3K27ac function as enhancers; therefore, these regions will not test positive in the functional characterization experiments conducted here. These caveats are likely to be addressed in part by genome and epigenome editing approaches that enable in situ manipulation of regulatory DNA and associated chromatin.Overall, these initial functional assessments indicate that at least one-third of the cCRE-ELS compartment encodes transcriptional control elements that produce positive results in contemporary cell transfection assays, while a smaller number marked by stronger biochemical signatures are active in the more stringent transgenic mouse embryo system. However, it is important to acknowledge that each assay system has inherent limitations. None of the aforementioned methods interrogates cCREs in their native chromosomal context, nor do they test for combinatoric interactions among cCREs in cis-regulatory elements by ENCODE; http://screen.encodeproject.org)  Box . SCREEN org) Box . Box 2 Interactive use of cCREs via SCREENhttp://screen.encodeproject.org). SCREEN connects every cCRE with all available ENCODE epigenomic and transcriptomic data as well as external data from FANTOM and Cistrome (http://cistrome.org). A series of videos introducing and illustrating many of the capacities of SCREEN is available . Homologous cCREs between human and mouse are linked through SCREEN, facilitating evolutionary comparisons. To facilitate more extensive downstream analysis, all underlying data in SCREEN can be downloaded or accessed programmatically via an associated GraphQL application program interface (API).SCREEN catalogues the 0.9 million human cCREs and 0.3 million mouse cCREs in the registry. Users can find cCREs of interest by searching for genes, genomic intervals, or GWAS phenotypes Box  Fig. 1. SCREEN is organized into three ‘apps’—the cCRE app, the gene expression app, and the GWAS app—that provide different perspectives on the registry Box  Fig. 1. 52. Another ENCODE companion paper expanded this model to connect cCREs with genes and thereby to construct large-scale regulatory networks that serve as a valuable resource for disease studies38. A two-dimensional, epigenetic state segmentation model, IDEAS53, served as the basis for regulatory region annotation and target gene assessments in mouse haematopoiesis28. In the developing mouse limb, IDEAS elements from bulk epigenomic data were deconvolved into specific cell type assignments by using single-cell RNA-seq16.In addition to the Registry of cCREs described in this report, one of the ENCODE companion papers developed a machine learning model that draws on the depth of ENCODE data in selected reference cell types to predict enhancers from self-transcribing active regulatory region sequencing (STARR-seq) data55. However, the sensitivity of these approaches for detecting candidate functional elements compared with the assays we describe here performed on deeply sequenced bulk samples has yet to be determined.ENCODE element annotations aim to delineate specific segments of the human and mouse genomes that encode a potential biological function. We aim to predict the activities of ENCODE sequence elements within a given biological context or of the different combinations of elements that become active in different biological contexts. It has become apparent that, by virtually any metric, elements that govern transcription, chromatin organization, splicing, and other key aspects of genome control and function are densely encoded in many parts of the human genome sequence. However, most of these elements are actualized sparingly in a cell type- or state-selective manner, complicating assessment of the completeness of the ENCODE Encyclopedia, or what remains to be discovered. Functional elements that are active only in rare cell types are likely to be underrepresented in the current ENCODE Encyclopedia because many assays used heterogeneous whole tissue samples. Advances in single-cell genomics technologies may help to bridge these gaps by deconvolving in silico the epigenome or transcriptome profiles from a tissue sample into its constituent cell typescis-acting elements. Furthermore, the target genes for candidate distal enhancers in the registry have yet to be defined, which is currently among the on-going goals of ENCODE. It is anticipated that emerging functional genomic strategies involving genome or epigenome editing will provide considerable insights into the functional roles of biochemically marked elements.Despite the very large number of biochemically defined elements within the ENCODE Encyclopedia, their functional annotation is currently limited to a few broad categories . Conventional assays of regulatory function, from transgenic mice to high-throughput reporter systems, have substantial technical and conceptual limitations, including their failure to capture combinatoric interactions of multiple 56. The value of ENCODE-defined elements for interpreting genome-wide association studies was already apparent in earlier phases of the project and has improved in parallel with the expanding space of biological contexts sampled by ENCODE assays, strengthening the hypothesis that many noncoding risk variants function via transcriptional regulatory mechanisms2. We expect that a comprehensive catalogue of functional elements, with more precise and accurate functional annotations, such as cell type-specific usage, transcription factor binding, and regulatory target genes, will provide an even more powerful tool for realizing the translational potential of the human genome for the diagnosis and treatment of diverse diseases.Ultimately, we anticipate that the ENCODE Encyclopedia will help researchers to decode the molecular mechanisms that underpin the genetic bases of human traits and diseasesWe have complied with all relevant ethical regulations regarding animal research and research involving humans. Each individual project that contributed data to ENCODE had their own institutional board that approved the study protocol.https://www.encodeproject.org/biosamples/ENCBS689AWK/. Cell lines were not tested for mycoplasma contamination.All human biosamples were collected with open access consent that met relevant IRB standards. All mouse biosamples were approved by the respective institutional animal care and use committees. Details  for individual biosamples are publicly available on the ENCODE portal. A representative example can be found here: 57 and CAGE58 . To survey transcriptomes across human and mouse biosamples, we performed a variety of RNA-seq experiments in ENCODE phase III − RNA-seq, CRISPR RNA-seq, CRISPRi RNA-seq, shRNA-knockdown RNA-seq and siRNA-knockdown RNA-seq; (ii) small RNA-seq surveys RNAs less than 200 nt; and (iii) microRNA-seq surveys microRNA levels by selecting for species less than 30 nt. Additional assay details, along with detailed experimental protocols, are available at the ENCODE Portal60 .In earlier phases of ENCODE, we surveyed transcriptome data mainly for immortalized cell lines using two approaches developed in Consortium laboratories—RNA-seqII Table , which c− RNA. The pipeline consumes RNA-seq reads in FASTQ format; alignment is performed with STAR, and gene and transcript quantifications are performed by RSEM against a gene annotation file, which contains by default GENCODE annotations. STAR also outputs normalized RNA-seq signals for both the + and − strands. Further details are available at https://github.com/ENCODE-DCC/long-rna-seq-pipeline.There are two distinct ENCODE uniform RNA-seq pipelines, one for RNAs longer than 200 nt and the other for RNAs shorter than 200 nt. The long RNA pipeline is appropriate for processing libraries generated from mRNA, rRNA-depleted total RNA, or poly(A)For all RNA-seq experiments, data quality is evaluated by calculating the number of aligned reads and replicate concordance.https://www.encodeproject.org/documents/0651efa6-7fd7-4b33-ab11-b05348c9f1c0/@@download/attachment/295491.pdf. Additional assay details are available at https://www.encodeproject.org/data-standards/rampage/.RAMPAGE captures 5′-complete cDNA to allow the identification and quantification of TSSs and transcript characterization. Production documents were generated for each experiment, and a representative experimental protocol is available at The ENCODE RAMPAGE pipeline is appropriate for libraries generated with RNAs longer than 200 nt, and it consumes reads in FASTQ format and produces alignments and normalized signals for both the + and − strands with STAR. Peaks, representing TSSs, are called from the alignments using GRIT, and output in BED, bigBED, and GFF formats. Quality control (QC) is performed for the peaks, and the irreproducible discovery rate (IDR) is used to identify reproducible peaks between replicates.Data quality is evaluated by calculating read depth and replicate concordance.23. By modifying steps in CLIP-seq and iCLIP protocols, eCLIP requires fewer amplification cycles and results in fewer redundant reads. Additionally, with the eCLIP protocol, size-matched inputs are generated to serve as controls for peak calling and other downstream analyses. The experimental protocol is available at https://www.encodeproject.org/documents/842f7424-5396-424a-a1a3-3f18707c3222/@@download/attachment/eCLIP_SOP_v1.P_110915.pdf.Enhanced crosslinking and immunoprecipitation (eCLIP) identifies transcriptome wide RBP occupancy siteshttps://www.encodeproject.org/eclip/.Additional assay details are available at https://www.encodeproject.org/documents/fb70e2e7-8a2d-425b-b2a0-9c39fa296816/@@download/attachment/ENCODE_Approved_Nov_2016_RBP_Antibody_Characterization_Guidelines.pdf.We require all eCLIP antibodies to undergo primary and secondary characterizations. Detailed RBP antibody standards are available at https://github.com/gpratt/gatk/releases/tag/2.3.2.Data were processed by the Yeo laboratory using their eCLIP pipeline. In brief, adaptor trimmed reads were mapped to the human genome using STAR, and redundant reads were removed. Peaks were called using CLIPper. The pipeline is available at https://www.encodeproject.org/documents/3b1b2762-269a-4978-902e-0e1f91615782/@@download/attachment/eCLIP_analysisSOP_v2.0.pdf.The pipeline description is available at Data quality is evaluated by calculating the number of unique fragments, IDR, and the fraction of reads in peaks (FRiP).61. Recombinant RBPs are purified and incubated with randomized RNAs. The RBPs are then captured, and bound RNAs are sequenced. The experimental protocol is available at https://www.encodeproject.org/documents/aa71cabf-aaee-4358-a834-c6ee002938b8/@@download/attachment/RBNSExperimentalProtocol_Feb2016_96well.pdf.RNA Bind-n-Seq characterizes RBPs and their motifs in vitrohttps://www.encodeproject.org/rbns/.Additional assay details are available at Kmer enrichment was calculated by comparing the frequency of kmers in the pull-down groups to those in the input groups. The estimated binding fraction was calculated using streaming kmer analysis. Motif logos were created by aligning enriched kmers that met specific threshold criteria. The pipeline is available at https://bitbucket.org/pfreese/rbns_pipeline/src/master/.Bind-n-Seq data were processed by the Burge laboratory. In brief, reads were separated into ‘input’ and ‘pull-down’ groups. https://www.encodeproject.org/documents/c8b3442a-7e63-4847-af11-c72597bf65b3/@@download/attachment/RBNS_Computational_Pipeline_Aug_2016_update_Dec2018.pdf.The pipeline description is available at kmer enrichments, and the Coomassie gel size and purity test of the recombinant protein.Data quality is evaluated by calculating the number of recovered reads per concentration, 62, many of which have been implicated in developmental disorders and cancer progression63. The experimental protocols are available at https://www.encodeproject.org/documents/be2a0f12-af38-430c-8f2d-57953baab5f5/@@download/attachment/Epigenomics_Alternative_Mag_Bead_ChIP_Protocol_v1.1_exp.pdf  and https://www.encodeproject.org/documents/18580e80-0907-4258-a412-46bcc37bd040/@@download/attachment/Ren%20Lab%20ENCODE%20Chromatin%20Immunoprecipitation%20Protocol%20MicroChIP.pdf . Additional assay details are available at https://www.encodeproject.org/chip-seq/histone/.Histone ChIP–seq surveys the interaction between DNA and histone proteins, selecting for a specific protein variant or post-translational modification through immunoprecipitation followed by sequencing. We also profiled a panel of 22 proteins involved in the deposition or recognition of histone modificationshttps://www.encodeproject.org/documents/4bb40778-387a-47c4-ab24-cebe64ead5ae/@@download/attachment/ENCODE_Approved_Oct_2016_Histone_and_Chromatin_associated_Proteins_Antibody_Characterization_Guidelines.pdf.We required all commercial histone antibodies to be validated by at least two independent methods, and antibody lots to be analysed independently. Detailed histone mark antibody standards are available at P value. Peaks are also called from the alignments, using MACS2. Additionally, the pipeline calls peaks from the pooled alignments of each experiment’s isogenic replicates. Sets of replicated histone mark peaks are generated by comparing the pooled and individual peaks using overlap_peaks. Further detail and basic workflows are available at https://github.com/ENCODE-DCC/chip-seq-pipeline.The ENCODE consortium histone ChIP–seq data pipeline takes into account the different binding distributions of the respective immunoprecipitation targets across the genome. The ChIP–seq pipelines consume raw reads in FASTQ format; alignment of the reads is performed with BWA to generate alignment BAMs. Signal tracks are produced from the alignments using MACS2; these are output in two separate bigWigs, which represent fold-change over control and signal Data quality is evaluated by calculating read depth, non-redundant fraction (NRF) , and PCR bottlenecking coefficients (PBC1 and PBC2).https://www.encodeproject.org/documents/20ebf60b-4009-4a57-a540-8fd93407eccc/@@download/attachment/Epigenomics_CR_ChIP_Protocol_v1.0.pdf (Bernstein laboratory), https://www.encodeproject.org/documents/6ecd8240-a351-479b-9de6-f09ca3702ac3/@@download/attachment/ChIP-seq_Protocol_v011014.pdf and https://www.encodeproject.org/documents/a59e54bc-ec64-4401-8cf6-b60161e1eae9/@@download/attachment/EN-TEx%20ChIP-seq%20Protocol%20-%20Myers%20Lab.pdf (Myers laboratory), and https://www.encodeproject.org/documents/f2aa60f2-90a6-4e4b-863a-c6831be371a2/@@download/attachment/ChIP-Seq%20Biorupter%20Pico%20TruSeq%20protocol%20for%20Syapse-c5bdc444fe0511e69d6a06346f39f379.pdf (Snyder laboratory). Additional assay details are available at https://www.encodeproject.org/chip-seq/transcription_factor/.ChIP–seq surveys the interaction between DNA and DNA regulatory proteins such as transcription factors and chromatin remodellers through immunoprecipitation followed by sequencing. The experimental protocol is available at https://www.encodeproject.org/documents/c7cb0632-7e5f-455e-9119-46a54f160711/@@download/attachment/ENCODE_Approved_May_2016_TF_Antibody%20Characterization_Guidelines.pdf (TF antibodies) and https://www.encodeproject.org/documents/35a9f776-dd6a-44e3-8795-50ead83f34f7/@@download/attachment/Guidelines_for_Use_of_Epitope_Tags_in_ChIP-seq_Jan_2017.pdf (epitope-tagged proteins).We required antibodies to undergo primary and secondary characterizations for each lot. For epitope-tagged proteins, we developed a protocol that includes genomic DNA characterization followed by immuno-characterization. Additional details are available at P value. Peaks are also called from the alignments using SPP. Additionally, the pipelines call peaks from the pooled alignments of each experiment’s isogenic replicates. For TF experiments, the pooled peaks are compared with the peaks called for each replicate individually using IDR and thresholded to generate a conservative set of peaks and an optimal set of peaks. Further detail and basic workflows are available at https://github.com/ENCODE-DCC/chip-seq-pipeline.The ENCODE consortium has developed a TF ChIP–seq data pipeline that takes into account the different binding distributions of the respective immunoprecipitation targets across the genome. The ChIP–seq pipelines consume raw reads in FASTQ format; alignment of the reads is performed with BWA to generate alignment BAMs. Signal tracks are produced from the alignments using MACS2; these are output in two separate bigWigs, which represent fold-change over control and signal Data quality is evaluated by calculating read depth, NRF, PCR bottlenecking coefficients (PBC1 and PBC2), replicate concordance using IDR, and FRiP.27. Experimental protocols are available at https://www.encodeproject.org/documents/404ab3a6-4766-45ca-af80-878a344f07b6/@@download/attachment/ATAC-Seq%20protocol.pdf  and https://www.encodeproject.org/documents/4a2fc974-f021-4f85-ba7a-bd401fe682d1/@@download/attachment/RenLab_ATACseq_protocol_20170130.pdf . Additional details can be found at https://www.encodeproject.org/atac-seq/.ATAC–seq surveys open chromatin regions through the insertion of primers into the genome via transposase followed by sequencinghttps://github.com/ENCODE-DCC/atac-seq-pipeline). In brief, trimmed reads were aligned to the genome using Bowtie2. Signal files and peak calls were generated using MACS. The pipeline also calls peaks from the pooled alignments of each experiment’s replicates. The pooled peaks were compared with the peaks called for each replicate individually using IDR and thresholded to generate a conservative set of peaks and an optimal set of peaks. In the near future, this pipeline will be incorporated as one of the ENCODE uniform processing pipelines.Experiments were processed using the Kundaje laboratory’s ATAC–seq pipeline , number of resulting peaks, fragment length distribution, FRiP, and TSS enrichment.https://www.encodeproject.org/documents/926174f5-d14c-4e77-bc52-5517b56daac0/@@download/attachment/Culturedcells_SOP_nuclei_DNase_crosslink_RNA_V1.pdf (cultured cells) and https://www.encodeproject.org/documents/c6ceebb6-9a7a-4277-b7be-4a3c1ce1cfc6/@@download/attachment/08112010_nuclei_isolation_human__tissue_V6_3.pdf (tissues). Additional details are available at https://www.encodeproject.org/data-standards/dnase-seq/.DNase-seq surveys open chromatin regions through genomic cleavage by endonuclease DNase I followed by sequencing. For ENCODE phase III, the DNase-seq protocol was updated, allowing for smaller quantities of input material. Experimental protocols are available at 64), which outputs alignments in BAM format. Alignments from sets of technical replicates are merged and filtered before peak calling with HOTSPOT2, which generates peaks in BED format. Input FASTQs must meet minimum criteria to be processed, and various quality control metrics are also generated at each step. Further detail and basic workflows are available at https://github.com/ENCODE-DCC/dnase_pipeline.The ENCODE DNase-seq processing pipeline consumes raw sequencing reads from technical replicates of experiments in the form of FASTQ files. Indexing and alignment of the FASTQ reads is performed with the Burrows–Wheeler Aligner  score.https://www.encodeproject.org/documents/9d9cbba0-5ebe-482b-9fa3-d93a968a7045/@@download/attachment/WGBS_V4_protocol.pdf (human) and https://www.encodeproject.org/documents/8f3cbe33-cf8f-4f26-b76b-d14a3b9721bd/@@download/attachment/Ecker_Methyl_Protocol_022315.pdf (mouse). Additional details are available at https://www.encodeproject.org/data-standards/wgbs/.To map DNA methylation, WGBS uses bisulfite treatment to convert unmethylated cytosines into uracils, leaving methylated cytosines unchanged. Through sequencing and alignment to a transformed genome, CpG, CHG, and CHH methylation levels can be extracted. The experimental protocol is available at https://github.com/ENCODE-DCC/dna-me-pipeline.ENCODE WGBS pipelines are available for paired-end and single-end data. In summary, the pipeline maps trimmed reads to a Bismark-transformed genome using Bowtie2. Methylation states at CpGs, CHHs, and CHGs are quantified using Bismark and custom python scripts. Pearson correlation of CpG methylation is then calculated between replicates. Further detail and basic workflows are available at Data quality is evaluated by genomic coverage, C-to-T conversion rate, and correlation of CpG methylation levels between replicates.https://github.com/ENCODE-DCC.DNAme arrays measure methylation at CpGs. Like WGBS, DNA is treated with bisulfite to convert unmethylated cytosines to uracils. After amplification, DNA is hybridized to an array (Illumina Infinium Methylation EPIC BeadChip) with probes for both methylated and unmethylated states. Methylation is then quantified by comparing the signal between the two probes. All ENCODE uniform processing pipelines can be found at 65. Production documents were generated for each experiment, and a representative experimental protocol for Repli-seq is available at: https://www.encodeproject.org/documents/59c9ceae-9f55-41c1-b5ce-78dc7bd59a1e/@@download/attachment/Repliseq_Protocol.pdf.DNA replication timing provides insights into both gene regulation and spatiotemporal genome compartmentalizationhttps://www.encodeproject.org/documents/97c4a9b3-8037-4fa4-a348-f396fcc3ecd1/@@download/attachment/wgEncodeFsuRepliChip.release2.html.pdf.A representative experimental protocol for Repli-chip is available at: 66. Repli-seq data were mapped to the hg19 genome using Bowtie267. Details are available at: https://www.encodeproject.org/pipelines/ENCPL734EDH/.Repli-chip data were processed using LIMMAhttps://www.encodeproject.org/help/data-organization/). Metadata describe experimental assays, biosamples, antibodies, computational analysis. Metadata are organized as JSON objects and can be queried programmatically using a REST API. In order to ensure metadata accuracy, each schema has a set of dependencies to enforce proper modelling when related metadata are submitted. After submission, a system of audits is used to identify inconsistencies in the data. These audits are also used to communicate details of ENCODE data, such as data quality relative to standards, to the public68. Each audit is designated a colour depending on its severity and is displayed on the search page and individual object pages. The metadata contain the protocol, date created, lab, and sequencing platform, which can be used for removing batch effects during integrative analysis.The ENCODE Data Coordination Center (DCC), in collaboration with the laboratories performing the assays and the Data Analysis Center (DAC), has defined a set of metadata to describe the experimental conditions that were used to generate the data, processing steps that were performed to analyse and interpret the data, and metrics to evaluate the quality and reproducibility of the data (https://github.com/weng-lab/ENCODE-cCREs/), with details provided in The scripts for generating the Registries of cCREs and subsequent analyses are available in a GitHub repository (−3). DNase peaks were clustered across all DNase-seq experiments, and we selected the peak with the highest signal  in each cluster as the representative DNase hypersensitive sites (rDHS) for the cluster. All the DNase peaks that overlapped this rDHS by at least one base pair were considered represented by the rDHS and removed for subsequent iterations. We updated the clusters, identified the next rDHS with the highest signal, and removed all the DHSs that it represented. This process was repeated until it finally resulted in a list of non-overlapping rDHSs that represented all DNase peaks. To reduce the number of false positives, we discarded the rDHSs that did not overlap a collection of consensus DHSs (cDHSs), independently derived by taking a consensus across the DHSs across multiple samples11; this cDHS filtering process eliminated 3% of the rDHSs  have the same distributions across biosamples  for the 2.2 million human and 1.2 million mouse rDHSs in each species for each experiment of the four core assays—DNase-seq and H3K4me3, H3K27ac, and CTCF ChIP–seq. For each rDHS, we then determined the maximum Z-score (max-Z) for each of the four core assays across all biosamples. The rDHSs with a high DNase max-Z and another high max-Z for at least one of the other three ChIP–seq marks were defined as cCREs. A Z-score cutoff of 1.64 corresponds to the 95th percentile for a one-sided Gaussian distribution. A high Z-score or a max-Z value is defined as >1.64 throughout, and low otherwise.Using the scheme outlined above, we calculated the Z values across all biosamples but not the Z-scores in a specific biosample, cCREs were classified into seven states and five groups. A state stands for a specific high–low combination of a cCRE’s H3K4me3, H3K27ac, or CTCF max-Z values; seven states are possible because at least one mark needs to have a high signal. For the group classification, we further took into account the genomic distance from the centre of the cCRE to the nearest TSS . We defined TSSs as the 5′ ends of all basic transcripts annotated by GENCODE (V24 for human and M18 for mouse). A cCRE was assigned to one of five mutually exclusive groups on the basis of its state and TSS proximity , we assign a confidence tier to each cCRE based on its supporting data Box . Tier 1 We also attempted to make group assignments for cCREs in a particular biosample that was not fully covered by the four core assays, making some approximations. The specific schemes are illustrated in Supplementary Fig. 69 score  per base, ±250 bp from the centre of each cCRE. Homologous human and mouse cCREs were identified by liftOver70 with a minimum match score of 0.5 . We ranked dELS-containing regions from the most to the least significant by the average rank of DNase and H3K27ac signals in the corresponding tissue and then selected regions from three segments of each tissue’s ranked list . We used H3K27ac peaks  that overlapped the cCRE-dELSs to choose the boundaries of the tested regions. In total, we tested 151 regions across the three tissues  for testing using E11.5 transgenic mouse assays. We excluded dELS-containing regions that overlapped any previously tested regions that were already in the VISTA database  as described previously49. In brief, predicted enhancers were PCR amplified and cloned into a plasmid upstream of a minimal Hsp68 promoter and a lacZ reporter gene. The plasmids were pronuclear injected into fertilized mouse eggs, and the transgenic embryos were implanted into surrogate mothers, collected at E11.5, and stained for β-galactosidase activity. A predicted element was scored positive as an enhancer if at least three embryos had identical β-galactosidase staining in the same tissue. Conversely, a prediction was deemed inactive if no reproducible staining was observed and at least five embryos harbouring a transgene insertion were obtained.Transgenic mouse assays were performed in FVB/NCrl strain 50 in human lymphoblastoid cells from + region. We analysed all cCREs and GM12878-specific cCREs stratified by the cCRE group.We downloaded the SNPs tested by MPRA51. Using bedtools intersect, we compared the SuRE peaks with the hg38 cCREs lifted down to the hg19 genome version, counting the number of base pairs overlapping each cCRE or region of interest. We then calculated the total percentage of base pairs for each cCRE group that overlapped a SuRE peak.We downloaded SuRE peaks in human K562 cells from the Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-020-2493-4.Supplementary InformationThis file contains Supplementary Notes 1-13, Supplementary Methods, Supplementary References, The ENCODE Project Consortium list, and Supplementary Figures 1-21.Reporting SummarySupplementary Table 1| ENCODE phase III transcriptome data.Supplementary Table 2| ENCODE phase III RBP experiments.Supplementary Table 3| ENCODE phase III ChIP-seq of DNA associated proteins.Supplementary Table 4| ENCODE phase III chromatin accessibility experiments.Supplementary Table 5| ENCODE phase III histone ChIP-seq experiments.Supplementary Table 6| ENCODE phase III DNA methylation experiments.Supplementary Table 7| ENCODE phase III 3D chromatin experiments.Supplementary Table 8| ENCODE phase III DNA replication experiments.Supplementary Table 9| Input datasets for building the Registry of cCREs.Supplementary Table 10| Human GRCh38 cCREs.Supplementary Table 11| Mouse mm10 cCREs.Supplementary Table 12| VISTA regions used for evaluating epigenetic signals.Supplementary Table 13| The performance (AUPR) of using epigenetic signals to predict VISTA enhancers.Supplementary Table 14| Using epigenetic signals to predict transcript expression.Supplementary Table 15| Relative abundance of cCRE-PLSs vs. cCRE-ELSs in 25 human biosamples with full assay coverage.Supplementary Table 16| Regions and genomic positions of ChromHMM promoter and enhancer states that overlap cCREs 5.Supplementary Table 17| Transposon and repeat content of cCREs.Supplementary Table 18| Overlap of TF ChIP-seq peaks with cCREs.Supplementary Table 19| t-SNE clusters from Extended Data Fig. 4.Supplementary Table 20| cCREs near tissue-specific genes.Supplementary Table 21| RNA-seq experiments included in SCREEN's differential expression tool.Supplementary Table 22| Testing candidate enhancers with mouse transgenic assays.Supplementary Table 23| Genome-wide association studies.


Rhodobacter capsulatus reduces and thereby fixes atmospheric dinitrogen (N2) by a molybdenum (Mo)‐nitrogenase and an iron‐only (Fe)‐nitrogenase. Differential expression of the structural genes of Mo‐nitrogenase (nifHDK) and Fe‐nitrogenase (anfHDGK) is strictly controlled and activated by NifA and AnfA, respectively. In contrast to NifA‐binding sites, AnfA‐binding sites are poorly defined. Here, we identified two highly similar AnfA‐binding sites in the R. capsulatus anfH promoter by studying the effects of promoter mutations on in vivo anfH expression and in vitro promoter binding by AnfA. Comparison of the experimentally determined R. capsulatus AnfA‐binding sites and presumed AnfA‐binding sites from other α‐proteobacteria revealed a consensus sequence of dyad symmetry, TAC–N6–GTA, suggesting that AnfA proteins bind their target promoters as dimers. Chromosomal replacement of the anfH promoter by the nifH promoter restored anfHDGK expression and Fe‐nitrogenase activity in an R. capsulatus strain lacking AnfA suggesting that AnfA is required for AnfHDGK production, but dispensable for biosynthesis of the iron‐only cofactor and electron delivery to Fe‐nitrogenase, pathways activated by NifA. These observations strengthen our model, in which the Fe‐nitrogenase system in R. capsulatus is largely integrated into the Mo‐nitrogenase system.The photosynthetic α‐proteobacterium  Rhodobacter capsulatus fixes nitrogen from air by a molybdenum‐dependent and an iron‐only nitrogenase. Here, we present evidence that the transcriptional activator AnfA is required for AnfHDGK production, but dispensable for biosynthesis of the Fe‐only cofactor and electron delivery. All diazotrophs possess a molybdenum nitrogenase consisting of the NifDK and NifH components. Also, some diazotrophs encode either a vanadium nitrogenase  or an iron‐only nitrogenase , or both Mo‐free isoenzymes  located at position –24/–12 upstream of the transcription start site  was prepared as previously described  does not inhibit nitrogen fixation.Bacterial strains and plasmids used in this study are listed in Table 2.2anfH promoter was narrowed down by nested deletions. For this, appropriate primer pairs were used to PCR‐amplify promoter variants F1 to F6  from plasmid pYP35 . The resulting pBBR_F1‐lacZ derivatives carrying transcriptional lacZ fusions to different promoter variants were designated pBBR_Mut1‐lacZ to pBBR_Mut7‐lacZ, pBBR_Mut2/6‐lacZ, and pBBR_Mut2/7‐lacZ.To generate site‐directed substitution mutations in the r Figure , plasmidR. capsulatus wild‐type strain B10S. Following phototrophic growth of the R. capsulatus reporter strains in RCV medium with 10 mM serine (no Mo added) until the late logarithmic phase, LacZ  activity was determined  as previously described  carrying pYP409 was cultivated with AHT induction before cell disruption and Strep‐Tactin affinity chromatography as previously described  to the anfH promoter) and its variants . After 20 min incubation of labeled promoter variants with increasing amounts of the purified AnfA_DBD protein, bound and free DNAs were separated in 6% polyacrylamide gels. Radioactive bands were detected by phosphor screen exposure.The F1 fragment  by the nifH promoter (PnifH), we exchanged the 266 bp anfA‐anfH intergenic region by the 267 bp fdxD‐nifH intergenic region. For this purpose, we constructed mutagenesis plasmid pYP516 containing the 3′ end of anfA , a gentamicin (Gm) resistance cassette, PnifH, and the 5′ end of anfH . To replace the anfH promoter and to delete the anfA gene in a single step, we constructed mutagenesis plasmid pYP515 containing an anfA upstream fragment (but lacking the anfA coding region), a Gm cassette, PnifH, and the 5′ end of anfH. Plasmids pYP516 and pYP515 were conjugationally introduced into the R. capsulatus strain BS85 (ΔnifD), in which the nifD gene is disrupted by a spectinomycin (Sp) cassette. BS85 does not exhibit Mo‐nitrogenase activity, and hence, any nitrogenase activity observed in this background can be assigned to Fe‐nitrogenase  and YP515‐BS85 .To replace the anfH → PnifH, ΔanfA, ΔnifD), strain KS94A‐YP415  contains the wild‐type anfH promoter upstream of the anfHDGKOR3 operon. In this strain, the anfA and nifD genes are disrupted by Sp and Gm cassettes, respectively.In contrast to strain YP515‐BS85  two 17 bp direct repeats each encompassing inverted repeat sequences, which are promising candidates as AnfA‐binding sites, and (c) a highly conserved RpoN‐binding site. For clarity, the 17 bp sequences will from now on be called distal and proximal AnfA‐binding sites.p Figure . This inanfH promoter (PanfH), we analyzed the effects of nested promoter deletions on anfH expression. For this purpose, we generated transcriptional fusions between PanfH fragments, F1 to F6, and a promoterless lacZ gene . As expected, fragments F4 to F6 resulted in only background LacZ activity consistent with the absence of both AnfA‐binding sites.Fragments F1 and F2 mediated considerable LacZ activity Figure  indicati3.2anfH, we generated pBBR_F1‐lacZ variants carrying site‐directed mutations Mut1 to Mut7 , while combined mutations Mut2/6 and Mut2/7 abolished LacZ activity . For this, the radiolabeled F1 fragment (carrying the wild‐type anfH promoter) and its variants  were incubated with increasing amounts of the Strep‐tagged DNA‐binding domain of AnfA (AnfA_DBD) encompassing the C‐terminal 72 amino acid residues of the full‐length regulator  demonstrating that the isolated C‐terminal domain indeed functions in promoter binding independent of the other AnfA domains  Fe‐nitrogenase as long as the anfHDGKOR3 operon is adequately expressed. To achieve AnfA‐independent anfHDGKOR3 expression, we replaced the anfH promoter (PanfH) by the NifA‐activated nifH promoter (PnifH). Both promoters mediate comparably strong expression of their downstream genes at least under Mo‐limiting conditions  or ΔanfA background. Subsequently, we introduced transcriptional anfH‐lacZ reporter fusions in these strains and, as a control, in the wild‐type strain B10S by chromosomal integration of plasmid pMH187 as previously described  showed the expected high anfH‐lacZ expression in serine cultures, while no expression was observed in ammonium cultures  in these strains, before examination of diazotrophic growth. Regardless of the nifD mutation, these strains were expected to express all the other NifA‐dependent nitrogen fixation genes involved in cofactor biosynthesis and electron transport  and GTA mutations  reduced anfH‐lacZ expression by 60%–80%, while the Mut2/Mut7 combination abolished expression  and Rhodomicrobium vannielii (CAC–N6–GTA) suggesting direct nifB activation by AnfA in these strains. In line with the requirement of NifB for the activity of all three nitrogenases in A. vinelandii, the nifB promoter can be activated by NifA, VnfA, or AnfA, which bind to overlapping sites in the nifB promoter  to fine regulation of NifA‐dependent genes ; Investigation (lead); Writing‐original draft . Yvonne Pfänder: Investigation (supporting). Bernd Masepohl: Conceptualization (lead); Funding acquisition (lead); Writing‐original draft . None required.