Polycomb Repressive Complexes 1 and 2  are conserved epigenetic regulators that promote transcriptional gene silencing. PRC1 and PRC2 converge on shared targets, catalyzing repressive histone modifications. Additionally, a subset of PRC1/PRC2 targets engage in long-range interactions whose functions in gene silencing are poorly understood. Using a CRISPR screen in mouse embryonic stem cells, we found that the cohesin regulator PDS5A links transcriptional silencing by Polycomb and 3D genome organization. PDS5A deletion impairs cohesin unloading and results in derepression of a subset of endogenous PRC1/PRC2 target genes. Importantly, derepression is not linked to loss of Polycomb chromatin domains. Instead, PDS5A removal causes aberrant cohesin activity leading to ectopic insulation sites, which disrupt the formation of ultra-long Polycomb loops. We show that these loops are important for robust silencing at a subset of PRC1/PRC2 target genes and that maintenance of cohesin-dependent genome architecture is critical for Polycomb regulation. Through a genetic screen, the authors find that the cohesin regulator PDS5A is required for Polycomb target gene silencing. Derepression upon cohesin dysregulation is linked to loss of Polycomb loops without change in repressive chromatin domains. Drosophila melanogaster to vertebrae2. Moreover, aberrant activity of Polycomb repressive complexes and other epigenetic regulators contribute to diverse diseases, including cancer initiation and metastasis, highlighting the importance of understanding the pathological mechanisms10.In metazoans, precise epigenetic regulation of gene expression enables the development of diverse cell types despite the same underlying genomic blueprint. Gene expression is primarily controlled by DNA binding transcription factors directing the transcriptional apparatus. However, epigenetic mechanisms modulate chromatin to directly and indirectly regulate transcription. Polycomb repressive complexes are chromatin-modifiers and serve as prototypes of epigenetic gene regulation via histone modifications. Decades of research have cemented their roles in establishing and maintaining cell identity throughout development across organisms, ranging from 14. In vertebrates, PRC1 complexes have diversified into distinct subcomplexes based on incorporation of one of six paralogous PCGF proteins (PCGF1-6). PCGF2 or PCGF4 dictate assembly of canonical PRC1 (cPRC1) which specifically incorporates CBX (chromobox-containing protein) subunits. CBX subunits endow cPRC1 with the capacity to bind H3K27me3, which promotes cPRC1 recruitment to PRC2 target genes and transcriptional silencing16. PCGF1, 3, 5, and 6 form variant PRC1 (vPRC1) complexes which harbor RING1 and YY1- binding protein (RYBP), or its paralogue YAF2 instead of CBX, and rely on H3K27me3-independent mechanisms of chromatin targeting. Thus, PRC1 and PRC2 are generally considered to exert their repressive functions in a synergistic manner, but they have different mechanisms of targeting, signaling, and repression (reviewed in17).Polycomb group (PcG) proteins are conventionally grouped into Polycomb repressive complexes 1 and 2 (PRC1 and PRC2). All PRC1 complexes share RING1A/B as their catalytic core subunit, which deposits monoubiquitination at lysine 119 of histone H2A (H2Aub1) at its targets, whereas PRC2 contains the histone methyltransferase EZH1/2, which catalyzes mono-, di- and trimethylation at lysine 27 of histone H3 (H3K27me1/2/3) at targets20. Indeed, loss of PRC2 or cPRC1 does not substantially compromise the repression of Polycomb target genes in mouse embryonic stem cells (ESCs) expressing vPRC119. These findings have propelled vPRC1 to the center of coordinating and establishing a repressive Polycomb chromatin domain. Although cPRC1 contributes minimally to H2Aub1 deposition, it possesses the unique capacity to mediate long-range 3D interactions between Polycomb target genes27, which has been shown to contribute to gene silencing in flies28.Recent studies have established that vPRC1 can act upstream of PRC2 and cPRC1, and that its deposition of H2Aub1 is critical for Polycomb-dependent gene silencing30. To circumvent this limitation, we previously developed a Polycomb in vivo Assay that reports the activity of distinct PRC1 complexes. Briefly, we generated ESCs that can recruit ectopic cPRC1 or vPRC1 to an integrated TetO repeat sequence flanked by fluorescent reporters  fusion triggers the assembly of cPRC1 at the TetO sites, Polycomb-dependent histone modifications and reporter gene silencing. Binding of the TetR fusion is released upon addition of Doxycycline (Dox), and we found that more than 70% of cells maintained cPRC1-induced, but not vPRC1-induced, silencing in the presence of Dox. We showed that sequence-independent propagation of cPRC1-induced silencing requires H3K27me3 and H2Aub1, suggesting that it relies on PRC1/PRC2 feedback. However, the mechanism and players required for heritable PRC1/PRC2-mediated silencing, and whether they are the same at all target genes, remain incompletely understood.The redundant functions of vPRC1 and cPRC1 complicate dissecting their individual mechanisms by genetic analysisers Fig. 31. For iHere we performed a CRISPR-mutagenesis screen to identify novel regulators of silencing by PRC1/PRC2. We discovered that the cohesin regulator PDS5A is required for repression of canonical Polycomb target genes. Unexpectedly, loss of PDS5A does not substantially impact repressive Polycomb chromatin domains, but instead disrupts ultra-long chromatin loops between PRC1/PRC2 target genes. Our work uncovers a subset of Polycomb target genes that require distal 3D interactions for transcriptional silencing.Ring1b. We found that CRISPR mutation of Ring1b had a negligible effect on silencing induced by TetR-CBX7 (in the absence of Dox), but strongly impaired the epigenetic maintenance of silencing  confirmed that ectopic CBX7 tethering nucleated a Polycomb chromatin domain with high levels of RING1B, SUZ12, H3K27me3, and H2Aub1 surrounding the TetO nucleation site whereas reference loci including selected Polycomb and non-Polycomb target genes were unaffected , which allow analysis of mutant phenotypes at a single-cell levellls Fig. , and thep-value < 0.005)  and PRC2 , as well as a negative regulator (Carm1) of the Polycomb repressive deubiquitinase complex (PR-DUB)34. This indicates that our screening approach identified known genes required for TetR-CBX7-induced silencing (Cbx7) and its epigenetic maintenance   Fig. . NotablyDox Fig. . Thus, s35. Several auxiliary cohesin proteins are critical for dynamic regulation of DNA interactions. For instance, cohesin-dependent extrusion of chromatin loops involves STAG1/2, WAPL and PDS5A/B which associate at the interface between SMC3 and RAD21 and control ATPase activity and/or ring opening45. In addition to Pds5a, our CRISPR screen revealed enrichment of Stag2, albeit below the significance cutoff . Since cohesin function is essential for cell division it is possible that other auxiliary cohesin proteins have been missed in our screen48. To separately evaluate the requirement of other cohesin release factors for Polycomb-dependent silencing, we used independent CRISPR-Cas9 mutagenesis targeting Pds5b, Wapl, Stag1 and Stag2 in TetR-CBX7 reporter ESCs and observed that similar to Pds5a maintenance of GFP silencing was impaired , which form a tripartite ring structure that entraps DNAPds5a knockout ESCs using CRISPR-Cas9 (Pds5a KO). In addition, we obtained a loss-of-function (LOF) ESC line harboring a disruptive gene-trap in the second intron of the Pds5a gene (Pds5aGT KO)49. Since gene-trap disruption is reversible, we also generated a matched control ESC line in which Pds5a expression was restored (Pds5aGT WT). Pds5a knockouts as well as the rescue were confirmed by western blot , respectively    and/or SUZ12 (PRC2) peaks within 3 kb of their TSSs were classified as “non-PcG target genes”.To evaluate how endogenous Polycomb target genes are affected by PDS5A deletion, we performed transcriptome profiling of .5) Fig. . DEGs wePds5aGT KO ESCs and Pds5a KO ESCs  analysis of upregulated genes revealed terms related to developmental processes such as neurogenesis and pattern specification  of PcG proteins and Polycomb-dependent histone modifications in matching gene-trap ESCs to limit the influence of potential secondary changes due to long-term cohesin deregulation. Specifically, we analyzed enrichments of RING1B, SUZ12, H3K27me3 and H2Aub1 in Pds5aGT WT and Pds5aGT KO ESCs and validated the results with ChIP-qPCR of wild-type and Pds5a KO ESCs  in Pds5a KO ESCs, excluding potential structural effects of the gene trap cassette. After quality control, we combined sequencing reads of Hi-C replicates amounting to a total of ~365 million valid unique cis-contacts per genotype  compared to between compartments (A/B or B/A)  sizes were on average larger in ops Fig. . We alsosed Fig. . These c26, suggesting that Polycomb-associated long-range interaction account only for a small fraction of the 12,425 loops detected in ESCs. Further classification of Polycomb loops based on additional PcG proteins and associated histone modifications revealed that virtually all of them  arise from long-range interactions between genomic sites harboring PRC1/PRC2 target genes, consistent with recent findings26  and unchanged/downregulated  PRC1/PRC2 target genes. Interestingly, anchor sites in wild-type ESCs that involve upregulated PRC1/PRC2 target genes are engaged in stronger loops compared to the anchor sites that involve unchanged/downregulated genes  loops, which are substantially reduced in nes Fig. . Strikinnes Fig. . These rPds5a KO ESCs are largely uncoupled from changes in Polycomb chromatin domains.To understand what defines the subset of Polycomb loops that are vulnerable to cohesin dysregulation, we first compared chromatin modifications between anchor sites of upregulated and unchanged/downregulated PRC1/PRC2 target genes. We noticed that at Polycomb loops of upregulated PRC1/PRC2 target genes, only one of the two anchor sites was associated with loss of gene silencing. To investigate potential differences in the repressive chromatin modifications, we separated the two anchor sites into unchanged (left) and upregulated (right) and compared PcG protein occupancy and associated histone modifications Fig. . Anchor Next, we explored if sensitivity to cohesin dysregulation is linked to the distance between Polycomb loop anchor sites. Comparison of loop sizes of upregulated and unchanged/downregulated PRC1/PRC2 target genes revealed a striking difference: loops between anchor sites of upregulated PRC1/PRC2 target genes were substantially longer than loops between anchor sites of unchanged/downregulated or all shared PRC1/PRC2 target genes Fig. . Not sur63 in 250 kb bins across the genomes of wild-type and Pds5a KO ESCs. Insulation scores in most bins  were unchanged upon PDS5A deletion , as well as from Dlx2 (v1) and the Hoxd gene cluster (v3), reveal increased mid- to long-range interactions in Pds5a KO ESCs, consistent with aberrant extension of cohesin loops   remained unchanged.Next, we tested if loss of Polycomb long-range interactions by genetic deletion of a loop anchor region would impair PRC1/PRC2 target gene silencing comparable to PDS5A deletion. 65 PRC1/PRC2 target genes lost long-range interactions and displayed transcriptional upregulation upon PDS5A deletion. From this list, we selected Cs Figs.  and  6d.sis Fig. . NotablyIrx2 are required for Foxd1 silencing suggesting that Polycomb loops directly contribute to maintenance of repressive chromatin domains at a subset of PRC1/PRC2 target genes. Thus, Polycomb repression takes place in a delicate spatial equilibrium with cohesin-dependent nuclear architecture, that is essential to maintain robust silencing at PRC1/PRC2 target genes.Taken together our results demonstrate that long-range interactions with Here, we used a CRISPR-mutagenesis screen to identify novel regulators of cPRC1-induced gene silencing which revealed the cohesin regulator PDS5A. Subsequent independent deletion in ESCs confirmed that PDS5A, PDS5B, WAPL, STAG1 and STAG2 are genetic dependencies in reporter gene silencing and that PDS5A is required for repression of a subset of endogenous PRC1/PRC2 target genes. Notably, loss of PRC1/PRC2 silencing upon PDS5A deletion is mostly uncoupled from changes in Polycomb chromatin domains. Instead, PDS5A loss affects cohesin-dependent genome architecture. We speculate that PDS5A deletion promotes aberrant chromatin loop extrusion leading to breached TAD boundaries, increased cohesin occupancy at gene regulatory elements and formation of ectopic insulation sites. In turn, new insulation sites perturb competing loops mediated by cPRC1. Finally, our results argue that ultra-long Polycomb loops are critical for robust silencing at a subset of PRC1/PRC2 target genes.Drosophila revealed several mutants in the wapl gene64. In mammalian cells, PDS5A and PDS5B are required for Polycomb-dependent silencing50, and Pds5a KO mice exhibit developmental abnormalities, including skeletal malformations65, that resemble the patterning defects of Polycomb mutant mice67. Our results provide a mechanistic explanation linking changes in 3D genome architecture to defects in Polycomb-dependent gene silencing. While previous work demonstrated that cohesin activity counteracts Polycomb-mediated long-range interactions26, we show that by disrupting Polycomb loops, PDS5A defects uncover the architectural role of PRC1 in gene silencing. Since loss of PRC1/PRC2 target gene silencing is uncoupled from changes in Polycomb chromatin domains, our data argue that Polycomb-dependent long-range interactions are required for and directly contribute to transcriptional repression.Previous reports have linked genetic mutations in genes encoding cohesin regulators to defects in Polycomb-dependent gene silencing, but the underlying mechanisms remained unclear. For example, a genetic screen for dominant suppressors of Polycomb-dependent silencing in Drosophila28. To our knowledge, the contribution of cPRC1-mediated loops to gene silencing in mammalian cells has been controversial22. PRC1/PRC2 target genes are generally located within the active (A) compartment in the nucleus of ESCs and thereby in spatial proximity to actively transcribed genomic regions68. We speculate that Polycomb-dependent silencing of PRC1/PRC2 target genes involves multiple parallel mechanisms including repressive histone modifications and long-range interactions. By uncoupling loss of Polycomb silencing and loop interactions from changes in repressive chromatin modifications, our data argue that 3D organization by itself has repressive function potentially by tethering PRC1/PRC2 target genes away from transcriptional co-activators and/or the transcriptional machinery. Therefore, when combined with repressive chromatin modifications, which promote Polycomb feedback mechanisms, spatial aggregation by Polycomb loops would effectively enhance robust gene silencing.The capacity of cPRC1 to form 3D chromatin interactions that contribute to gene silencing has been demonstrated in The relative contribution of each of these mechanisms is likely locus-specific and may vary in different cell types. For example, alternative incorporation of paralogous cPRC1 subunits, such as CBX proteins or PHC proteins may influence the specific regulation of Polycomb 3D network formation as a mechanism of repression. Hence, future studies are needed to discern how cPRC1 and Polycomb-dependent genome architecture control target gene silencing in the context of different cell types and in disease.49. TetR-CBX7 reporter ESCs with 7x TetO DNA binding sites flanked by GFP and BFP reporter genes were previously described ref. 31. Pds5aGT KO and its corresponding wild-type ESCs with genetrap insertion in the non-disruptive orientation were acquired from the Haplobank repository (Cell IDs: 10388IH and 10388MH)49.All diploid ESC lines used in this study were derived from originally haploid HMSc2 termed AN3-122 and were passaged every 48 h by trypsinization in 0.25% 1x Trypsin-EDTA . In order to reverse of TetR-CBX7 fusion protein binding 1 µg/ml Doxycycline  was added to cell culture medium.All ESCs were cultivated without feeders in high-glucose-DMEM (Corning 10-013-CV) supplemented with 13.5% fetal bovine serum (Corning 35-015-CV), 10 mM HEPES pH 7.4 , 2 mM GlutaMAX , 1 mM Sodium Pyruvate (Corning 25-000-Cl), 1% Penicillin/Streptomycin , 1X non-essential amino acids , 50 mM β-mercaptoethanol  and recombinant LIF. Cells were incubated at 37 °C and 5% COPds5a (5'-TGTCTCTGCAGAGTGGAACG- 3') or Ring1b (5'- GTGTTTACATCGGTTTTGCG -3') were transduced into parental reporter ESCs by electroporation using NEON transfection system . 36 h post transfection cells were FACS sorted for hCas9-BFP and 1000-2000 cells seeded for clonal expansion on a 15 cm plate. 7–10 days later colony forming clones were individually picked, the targeted loci genotyped and loss of function confirmed by western blot.Generation of mutant TetR-CBX7 ESC lines was achieved by CRISPR-Cas9 technology using a modified version of the vector plasmid pX330-U6-Chimeric_BB-CBh-hCas9 (Addgene #42230) that yields a BFP marker for selection (Gift by J. Zuber). Plasmids expressing hCas9 together with sgRNAs targeting Pds5b, Stag1, Stag2, Wapl, each and a scramble control (5' GATCCATGTAATGCGTTCGA 3') were cloned in custom sgRNA vector including a G418 selection cassette and introduced into TetR-CBX7 hCas9 expressing mESCs via electroporation.For the generation of population mutants of TetR-CBX7 ESC cells we generated a stably expressing hCas9 clonal cell line that yields a hygromycin selection marker for hCas9. Further four guides  and cloned into the CRISPR-Cas9 pX330-U6-Chimeric_BB-CBh-hCas9 (Addgene #42230) BFP-selection marker modified version described above (Gift by J. Zuber). The 2 sgRNA CRISPR-Cas9 plasmids were transduced wild-type mESCs via electroporation using NEON transfection system . 36 h post transfection cells were FACS sorted for BFP and cells seeded for clonal expansion on a 15 cm plate. Single colony forming clones were isolated 7–10 days later and subjected to genotyping of the Irx2 Polycomb loop anchor site targeted for excision. Genotyping primers flanking the region as well as overlapping the borders of the excision target site were designed .For cell cycle profiling ESCs were trypsinized and genomic DNA was stained with Hoechst 33342  for 30 min at 37 °C and 5% COOne thousand cells were seeded and grown to form colonies at low density on 15 cm tissue culture dishes for 7 days. On day 7, dishes were washed with 100 mM tris (pH 8) and AP activity assay was performed using the VECTOR Blue AP Substrate Kit  according to the manufacturer’s instructions. Following AP staining, stained colonies were fixed in 4% formaldehyde overnight. Plates were rinsed with 1x PBS the following day and images taken on a brightfield microscope (EVOS XL Core system).To assess pluripotency of PDS5a loss of function mutants, we applied intracellular staining of OCT3/4, SOX2 and SSEA1. Single cell suspensions of wild-type and Pds5a KO ESCs were permeabilized and fixed using the fixation/permabilization buffer (R&D systems), washed twice with 1x PBS and stained using the H/MM pluripotent Stem Cell Multi-Color Flow Cytometry kit (R&D Systems) according to vendor’s protocol. Flow cytometry data was collected on an Attune NxT equipped with Attune NxT v3.1 acquisition software. Final data analysis was performed using FlowJo (10.7.1).2, 25 mM KCl, 0.05 mM EDTA, 10% Glycerol, 1 mM DTT, 1 mM PMSF, 1× Complete Mini protease inhibitor, Roche) resuspended in RIPA buffer . Lysates were homogenized by sonication using a Bioruptor Pico (Diagenode) and concentration determined by Bradford assay (Biorad). 4x non-reducing Laemmli SDS sample buffer , 10 mM final DTT and 0.5% final BME were added to 20 µg total protein/sample and boiled at 95 °C for 5 min. Samples were separated on NuPAGE 4–12% Bis-Tris gels (Invitrogen) in Bis-Tris running buffer  and transferred on a Merck Chemicals Immobilon-FL Membrane (PVDF 0.45 µm). After blocking the membranes  the blots were incubated o/n with the primary antibodies in 5% non-fat dry milk in 1× PBS and 0.1% Tween 20. Antibodies used: PDS5a (Millipore Sigma #SAB2101764) 1:1000; PDS5B (Bethyl Laboratories A300-537A) 1:1000; RING1B  1:1000; SMC3 (Bethyl Laboratories A300-060A) 1:2000; SUZ12  1:1000; LAMIN B1 (Abcam ab16048) 1:15000; H2AK119ub  1:20000; H3K27me3 (Diagenode p069-050) 1:1000; H3 (Abcam ab1791) 1:10000; a-TUBULIN (Sigma-Aldrich T9026) 1:500. Next, membranes were incubated with corresponding secondary IRDye 800CW Goat anti-Rabbit IgG (H + L) (LICOR) or IRDye 680RD Goat anti-Mouse IgG (H + L) (LICOR) antibodies and imaged on an Odyssey CLx Near-Infrared Imaging System (LICOR).10 million ESCs were subsequently lysed in Buffer A  (20 mM HEPES (pH 7.9)), 1.5 mM MgCl2, 25% glycerol, 2 mM EDTA, 1 mM DTT, 1 mM PMSF and protease inhibitor cocktail  for 15 min. Nuclei were isolated by adding NP-40 to a final concentration of 0.75% and gentle resuspension. After centrifugation (500 g for 5 min) the nuclear pelleted was then resuspended in 100 µl LSB. 100 µL of High-Salt Buffer (HSB)   was added dropwise while vortexing at low speed to reach a final concentration of 200 mM NaCl. Further, samples were vortexed at low to medium speed at 4 °C for 30 min and centrifuged at 21,000 g for 10 min. The resulting supernatant was collected as the nuclear soluble fraction. The pellet containing the chromatin-bound protein fraction was washed twice in the 200 mM NaCl buffer, resuspended in SDS-PAGE loading buffer, sonicated using a probe sonicator and boiled at 95 °C for 10 min. Finally, both nuclear soluble and chromatin bound fractions were subjected to SDS-PAGE and western blot.Nuclear soluble and chromatin bound fractions were isolated as described previously31.For the genetic CRISPR-Cas9 mutagenesis screen, EF1a promoter driven hCas9 with a hygromycin resistance marker (modified version of Addgene #52961) was stably integrated via lentiviral transduction into the previously described TetR-CBX7 reporter ESC cell line, which contains 7x TetO DNA binding sites flanked by GFP and BFP reporter genes32. For retroviral library generation, the barcoded plasmid library of sgRNAs, containing neomycin resistance for selection, was packaged in PlatinumE cells (Cell Biolabs) according to the manufacturer’s recommendations.For CRISPR-Cas9 mutagenesis, a sgRNA library targeting 6560 nuclear factors with four sgRNAs per gene and 112 nontargeting controls was utilized8 TetR-CBX7 reporter ESCs were infected with a 1:10 dilution of the harvested virus-containing supernatant PlatinumE cell medium for 24 h in the presence of 2 μg/ml polybrene . The 3 × 108 ESCs were divided into three sets of 1 × 108 million ESCs (10 × 15 cm plates of 10 million cells each) that were treated as three separate replicates throughout entirety of the mutagenesis screen and sgRNA NGS sequencing. 24 h post infection, neomycin-resistance selection was started on the infected cells by addition of G418 (Gibco) at 0.5 mg/ml. After 24 h of selection, each replicate was expanded from 10 15-cm dishes to 20 dishes. Subsequently, for the duration of the neomycin selection cells were always maintained at a minimum of 3 × 108 cells. After 5 days and completion of G418 selection, half of the cells were cultured in ESC medium containing Dox for 3 days and the other half without Dox. GFP-positive cell populations of both Dox-treated and untreated populations were sorted on a FACSAria II cell sorter (BD Biosciences) and flow cytometry data analyzed with FlowJo software. Unsorted mutant populations were served as background controls. Genomic DNA was isolated from GFP-positive sorted and unsorted cells, their sgRNA cassettes amplified by PCR and subjected to NGS sequencing on an Illumina HiSeq 2500. Data analysis was performed as previously described in32 and gene enrichment determined using MAGeCK33.3 × 106 ESCs were trypsinized and collected by centrifugation. Resulting cell pellets were washed in 1x PBS and resuspended in 1x DNA/RNA protection reagent . Subsequently, cells were lysed and total RNA extracted following the mammalian cell protocol including optional on-column DNase I treatment.5 × 10For RNA-seq library preparation, 1 µg of total RNA per sample was enriched for poly-A using the NEBNext Poly(A) mRNA Magnetic Isolation Module  and final RNA-seq libraries generated using the NEBNext Ultra II Directional RNA Library Prep kit  . Final libraries were sequenced as 150 bp paired-end reads on the Illumina HiSeq platform.For RT–qPCR experiments, total RNA was extracted using the Monarch Total RNA Miniprep Kit (NEB) following the mammalian cell protocol including optional on-column DNase I treatment. 100 ng total RNA was used as input for one-step RT-qPCR using the Luna Universal One-Step RT-qPCR kit NEB, E3005) on a CFX96 Real-Time PCR System (Bio-Rad). See Supplementary Table  on a CFX70. Overlap of STAR-aligned reads with genes was performed using HTSeq count function71 with stranded = reverse option and the GRCm38 version 94 GTF file. The HTseq count matrix was pre-filtered to exclude genes with a read count below 10. Differential gene expression analysis was performed using DESeq272 using the “apeglm” method73 for LFC shrinkage. We applied a threshold of p-adj <0.05 and fold change >0.5 or −0.5 for gene expression changes to be considered significant. Visualization of RNA-seq data was performed using custom R scripts and ggplot2. Gene ontology analysis for significantly deregulated genes was performed using custom R scripts and clusterProfiler refs. 75.Raw paired-end RNA-seq reads were aligned to the mm10 genome using STAR-2.6.1c6 ESCs and HEK293T cells were collected, washed once in 1x PBS and crosslinked for 7 min in 1% formaldehyde. The crosslinking was quenched by addition of 125 mM glycine and incubated on ice. The crosslinked cells were pelleted by centrifugation for 5 min at 1200 g at 4 °C. Nuclei were prepared by washes with NP-Rinse buffer 1  followed by NP-Rinse buffer 2 . Afterwards, the nuclei were washed twice with shearing buffer  and subsequently resuspended in 900 µL shearing buffer with added 1× protease inhibitors complete mini (Roche). Chromatin was sheared by sonication in 15 ml Bioruptor tubes  with 437.5 mg sonication beads  for 6 cycles (1 min on/1 min off) on a Bioruptor Pico sonicator (Diagenode). For each ChIP reaction 4 % HEK293T-derived human spike-in lysate was combined with ESC lysate and incubated in 1x IP buffer , with following appropriate antibodies at 4 °C o/n a rotating wheel: H3K27me3 , RING1B , PDS5a (Millipore Sigma #SAB2101764), SUZ12 , H2AK119ub , PCGF1 (Abcam ab202395), RAD21 (Abcam ab992), CTCF (Millipore 070729). Antibody-bound chromatin was captured using Dynabeads protein G beads (Thermofisher #10004D) for 4 h at 4 °C. ChIP washes were performed as described previously53. ChIPs were washed 5x with 1x IP buffer , or 1.5x IP buffer for H3K27me3 and H2AK119ub, followed by 3x washes with DOC buffer  and 1x with TE/50 mM NaCl. ChIP DNA was eluted 2x in elution buffer  at 65 °C for 20 min, RNase A treated for 30 min at 37 °C, Proteinase K treated for 3 h at 55 °C and crosslinks were reversed o/n at 65 °C. The following day, ChIP samples and corresponding inputs were purified by PCI extraction and DNA precipitation.30 × 10For ChIP DNA quantification in Extended Fig. Libraries were prepared using the NEXTflex ChIP-Seq kit (Bio Scientific) following the “No size-selection cleanup” protocol. Libraries were purified using Agencourt AMPure XP (Beckman Coulter) and amplified using the KAPA Real-Time Library Amplification Kit (KAPABiosystems) following the manufacturer’s instructions.ChIPCap-seq libraries were prepared identically to ChIP-seq libraries. After PCR amplification the libraries were enriched for loci of interest using the MYbaits kit DNA capture target enrichment system (Arbor Biosciences) according to manufacturer’s manual. One hundred twenty nucleotides long MYbaits sequence capture probes (Arbor Biosciences) were custom designed against 25 mm9 genomic loci  and only unique mapped reads were retained.Raw reads were mapped to the custom concatenated mouse (mm10) and spike-in human (hg38) genome sequences using bowtie 2 with “–no-mixed” and “no-discordant” options79. High correlation between replicates was confirmed using multiBamSummary and plotCorrelation functions from deepTools80 before merging for visualization and downstream analysis. Genome coverage tracks (bigWig files) were produced with MACS2’ pileup function81 and heatmaps and profile plots generated with deepTools80. Peaks were called on each replicate independently using MACS281. Peaks overlapping with a custom-build blacklist were discarded to remove sequencing artifacts and only peaks called in both replicates were retained for downstream analysis.For visualization uniquely mapped mouse reads were normalized by random subsampling with samtools using calibration factors calculated from the corresponding hg38 spike-in reads as described previously68 with modifications described in22 as following: 5 million ESCs were crosslinked in 1% formaldehyde for 10 mins before the reaction was quenched by adding 0.2 M final glycine. Cells were permeabilized in lysis buffer  and nuclei isolated in NEBuffer 3 supplemented by 0.3% SDS at 62 °C for 10 min. SDS was quenched with 1% Triton X-100 at 37 °C for 60 min, the nuclei pelleted and resuspended in 250 µl of 1x DpnII buffer with 600 U DpnII (NEB). Following o/n digestion at 37 °C, 200 U were added for 2 h. DpnII was inactivated for 20 min at 65 °C before the DNA ends were filled-in and biotin-marked using Klenow, d(C/G/T)TPs and biotin-14-dATP for 90 min at 37 °C. Proximity ligation was performed using T4 DNA ligase (NEB) for 4 h at room temperature. Subsequently, nuclei were spun down, resuspended in 200 µl mQ water and digested with proteinase K for 30 min at 55 °C in presence of 1% SDS. For crosslink reversal 1.85 M final NaCl was added, and samples incubated at 65 °C o/n. The next day, sample DNA was ethanol precipitated and sheared in 500 µL sonication buffer  on a Bioruptor Pico sonicator (Diagenode). DNA was then concentrated on Amicon ultra 0.5 30 K filter units (Millipore), biotin-pulldown performed using MyOne Streptavidin T1 beads  and used for NGS library preparation. DNA ends were repaired, biotin removed from unligated ends and NEXTFLEX DNA barcoded adapters (Perkin Elmer) were ligated. Desired PCR cycle numbers were determined in test endpoint PCRs using Q5 DNA polymerase (NEB M0491L). Final HiC libraries were generated from 4–6 individual PCR reactions, which were pooled and subjected to cleanup and size-selection using AMPure beads (Beckman Coulter A63882). Samples were first test sequenced to check library quality before selected Hi-C ibraries were sequenced at greater depth 88. Insulation score analysis was conducted as previously described in89. Insulation scores63 were computed at 10 kb resolution using GENOVA. Bins with an insulation score greater than −1 were excluded from the analysis. To qualify as differentially insulated bins, either wild-type or Pds5a KO ESCs had to be lower than −0.2 to exclude very lowly insulated portions of the genome. Insulation gaining and losing regions were defined as bins that had an absolute change in insulation between wild-type and Pds5a KO of 0.2.Hi-C data were analyzed using the HiC-Pro (2.11.1) pipeline91. Resulting NGS libraries were sequenced on an Illumina NovaSeq platform in 150 bp paired-end mode.ATAC-seq on mESCs was performed in biological triplicates according to protocol as described previously92 and aligned to the mm10 genome using bowtie 2 with “–no-mixed” and “no-discordant” options76. Low quality reads were filtered using SAMtools77 and duplicate reads discarded via the Picard toolkit (http://broadinstitute.github.io/picard/).ATAC-seq reads were trimmed using NGmerge93.Enrichment overlap of RAD21 peaks with different chromatin domains chromatin states was calculated using ChromHMM (version 1.24), a 12-state model based on mouse ENCODE project ChIPseq dataFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


It remains poorly understood about the regulation of gene and transposon transcription during human early embryogenesis. Here, we report that broad H3K27ac domains are genome-widely distributed in human 2-cell and 4-cell embryos and transit into typical peaks in the 8-cell embryos. The broad H3K27ac domains in early embryos before zygotic genome activation (ZGA) are also observed in mouse. It suggests that broad H3K27ac domains play conserved functions before ZGA in mammals. Intriguingly, a large portion of broad H3K27ac domains overlap with broad H3K4me3 domains. Further investigation reveals that histone deacetylases are required for the removal or transition of broad H3K27ac domains and ZGA. After ZGA, the number of typical H3K27ac peaks is dynamic, which is associated with the stage-specific gene expression. Furthermore, P300 is important for the establishment of H3K27ac peaks and the expression of associated genes in early embryos after ZGA. Our data also indicate that H3K27ac marks active transposons in early embryos. Interestingly, H3K27ac and H3K18ac signals rather than H3K9ac signals are enriched at ERVK elements in mouse embryos after ZGA. It suggests that different types of histone acetylations exert distinct roles in the activation of transposons. In summary, H3K27ac modification undergoes extensive reprogramming during early embryo development in mammals, which is associated with the expression of genes and transposons. After ZGA, embryonic cells begin to have distinct cell identities, and they differentiate into inner cell mass (ICM) and trophectoderm (TE) at the blastocyst stage2. Mammalian embryos present genome-wide reprogramming of DNA methylation6, histone modification11, chromatin accessibility14 and 3D chromatin structure16. The epigenetic reprogramming is believed to function in the temporal regulation of gene expression. However, most of previous studies only show the correlation between epigenetic dynamics and gene expression. Recently, it is shown that the removal of broad H3K4me3 domains is required for normal ZGA7. Very limited is known about the regulatory mechanisms of the reprogramming of epigenetic information in gene expression in early embryos after the ZGA.Mammalian embryo development begins from a fertilized zygote. After fertilization, most of genes are transcriptionally inactive. Major zygotic genome activation (ZGA) occurs at the late 2-cell stage in mouse, and at the 8-cell stage in human17. They usually serve as indicators of chromatin states. For instance, H3K4me3 is enriched at the active promoters, while H3K27ac usually marks active enhancers18. The landscapes of histone modifications have been extensively investigated in human and mouse early embryos11. Non-canonical H3K4me3 broad domains are observed in both human and mouse embryos before ZGA, which are inherited from oocytes in mouse but are de novo established in human early embryos11. Blocking the removal of broad H3K4me3 domains can inhibit normal ZGA and embryonic development in mouse7. A recent study has also examined H3K27ac distribution in early embryos. Dahl et al. only found canonical H3K27ac peaks, but not broad domains, in mouse oocytes, 2-cell and 8-cell embryos7. The investigation of H3K27ac patterns in human embryos at the 8-cell and blastocyst stages shows that the stage-specific H3K27ac signal is associated with stage-specific gene expression, which is consistent with the key developmental events in human early embryo11. However, it remains unknown about the dynamics and the potential functions of H3K27ac around ZGA stage during human embryogenesis.Histone modifications are involved in the regulation of gene expression during development20. Because active retrotransposons can result in random insertions in the genome, in most circumstances, retrotransposons are repressed in mature cells. Previous studies mainly focused on the epigenetic information such as DNA methylation, PIWI-interacting RNA and H3K9me3 that serve as epigenetic repressors for transposon expression21. However, it remains unknown whether there is an epigenetic activator for transposon expression in early embryos.Besides genes, retrotransposons are also transcribed in human early embryosTo address these questions, we investigated the dynamics and functions of H3K27ac in human early embryos.24. We validated this method by mapping H3K27ac modification in mouse 8-cell embryos. The ChIP-seq results are highly reproducible and consistent with the published data which were obtained from 500 cells  of peaks discovered by our method are also present in the published data .Next, we compared the distribution of broad H3K27ac and H3K4me3 domains (> 10 kb) in human 2-cell and 4-cell embryos. Our data show that about 75% of broad H3K4me3 domains overlap with broad H3K27ac domains at the 2-cell and 4-cell stages Fig. . It sugg7. Here, to answer whether the broad H3K27ac domains also exist in mouse early embryos before ZGA stage, we checked the H3K27ac modification in mouse PN5 zygotes, as well as 2-cell and 8-cell embryos. Our data show that many broad H3K27ac domains with lengths > 10 kb, especially those with lengths > 20 kb, are widely detected in mouse zygotes 28. H3K27ac broad domains from the 8-cell onward are positively correlated with gene expression.The majority of H3K27ac broad domains are removed after the ZGA. However, a limited proportion of human genome are still covered by H3K27ac broad domains from the 8-cell stage onward Fig. . The gen30. The alpha-amanitin-treated late 2-cell embryos were collected for H3K27ac ChIP-seq analysis. Our data show that the H3K27ac landscape in alpha-amanitin-treated late 2-cell embryos is similar to that in zygote rather than that in control late 2-cell embryos . Consistently, genes with nearby distal H3K27ac peaks show higher expression levels than those without  of distal H3K27ac peaks have nearby DHS sites (within 500 bp) in blastocysts  show downregulated H3K27ac signals nearby (within 50 kb around TSS)  Fig. . BesidesSS) Fig. . Taken t38, lncRNAs tend to be stage-specifically expressed in human early embryos   is another kind of non-coding RNA transcribed from enhancer regionsTaken together, P300 is critical for the establishment of H3K27ac and the expression of H3K27ac-associated protein-coding and non-coding genes in human early embryos after ZGA stage.20. In P300 KD human morula, the genes involved in viral process are downregulated, which implies that H3K27ac modification may play important role in the regulation of retrotransposons 6. Our data show that the ICR of a maternally expressed imprinted gene (Meg3) shows maternal allele-specific H3K27ac, and 5 paternally expressed imprinted genes show paternal allele-specific H3K27ac, such as Snrpn  between these two mouse strains. Our data show that maternal H3K27ac signals are much stronger than paternal H3K27ac signals in the H3K27ac-enriched regions in mouse zygote  of P300 functions as a reader of histone H3. The histone binding function of ZZ is essential in catalytic activities of P300. In particular, recognition of H3 by the ZZ domain of P300 promotes acetylation of primarily histone H3K27 and H3K18 sites, but not H3K9 and H3K4. It is possible that some factors can mediate the binding of P300 to H3 in ERVK elements by ZZ domains, which can further promote the acetylation of H3K27 and H3K18, but not H3K9 on ERVKs. It is also possible that comparing to the acetylations on H3K18 and H3K27 sites, H3K9ac is metabolized by a distinct set of enzymes, or the H3K9 sites on the ERVK elements are not accessible by the same set of enzymes.Transposons play important roles in shaping our genome. Previous studies have shown that epigenetic information of DNA methylation, piRNAs and H3K9me3 is involved in the silence of transposons. Until now, it remains unknown whether any epigenetic modifications can serve as activator for transposon expression. Here we report that H3K27ac serves as an active indicator for transposon expression. Our study reveals that the establishment of H3K27ac signal in transposons is associated with the active transcription of transposons , the 2016 Guidelines for Stem Cell Research and Clinical Translation issued by the International Society for Stem Cell Research (ISSCR) and the Human Embryonic Stem Cell Research Ethics Guidelines . These policies and guidelines permit human gametes, and/or human embryos created or genetic manipulated in vitro no more than 14 days, can be used specifically for scientific researches.The regulatory framework about the use of human gametes and embryos for this research is based on the policies of the Human Biomedical Research Ethics Guidelines  and the Institutional Review Board of Reproductive Medicine, Shandong University (#2017LSZ38 and #2017LSZ39). Oocytes donated from patients taking in vitro fertilization treatments were fertilized using donated sperm by intracytoplasmic sperm injection (ICSI). Written informed consent was obtained from all oocyte and sperm donors, respectively. Human embryos are allowed to be manipulated by siRNA injection. All embryos are allowed to be cultured no more than 7 days and can be only used for molecular research analyses.2 in air. Cultured 2-cell, 4-cell, 8-cell embryos, morula and morphological AA grade blastocysts were collected and vitrified around 27 h, 48 h, 3 day, 4 day and 5 day after fertilization, respectively. Only high quality embryos were selected and collected according to the following morphological criteria43: 2-cell embryos with 2 symmetric blastomeres of equal size and with negligible fragmentation; 4-cell embryos with 4 blastomeres of equal size and with negligible fragmentation; 8-cell embryos with 8 blastomeres of equal size and without cytoplasmic fragments; morulae are compacted with > 16 cells; blastocysts with clearly visible blastocyst cavity, well developed ICM with many tightly packed cells, smooth trophoectoderm and thin zona. The embryo vitrification was carried out as described44. Briefly, the embryos were incubated in Vitrification Solution 1 consisting of 8% ethylene glycol and 8% dimethyl sulfoxide in Cryobase  at room temperature for 11 min. After initial shrinkage, embryos with original volume were transferred into Vitrification Solution 2  for 1–1.5 min. Embryos were finally transferred onto Cryotop strip in an extremely small volume of solution (< 0.1 μL) and plunged into liquid nitrogen. After addition of the protective cover, the Cryotop was transferred into liquid nitrogen for storage.Immature MI oocytes were donated from patients who were taking in vitro fertilization (IVF) treatments. They were informed that the donation would not affect their IVF process. Those clinically useless MI oocytes were kept in in vitro maturation (IVM) medium at 37 °C in an atmosphere with 5% CO2 for 18–24 h. The IVM medium consists of M199 medium  with 20% Systemic Serum Substitute  and 75 mIU/mL of recombinant follicle stimulating hormone (Merck Serono). The in vitro matured MII oocytes were fertilized using donated sperm by ICSI. Then, the fertilized zygotes were sequentially cultured in G1.5 medium (Vitrolife) in a humidified atmosphere at 37 °C with 6% COThe vitrified embryos on Cryotop strip were thawed rapidly by taking them from the liquid nitrogen after removal of the protective cover, then immersed in 2.5 mL of 37 °C Warming Solution 1  for 1 min on a heated stage. Embryos were then transferred to 0.5 mL of Warming Solution 2  for 3 min, and then placed into 0.5 mL Cryobase for 5 min followed by fresh 0.5 mL Cryobase for 1 min. Embryos were finally transferred to G1.5 or G2 medium (Vitrolife) to evaluate embryo quality.The thawed embryos with high quality were picked randomly for experimental groups. Zona pellucida was removed by mechanical dissection with a glass needle. Embryos were washed several times by gentle pipetting with a narrow-bore glass pipette to remove the attached cumulus or polar bodies. After washing with 0.1% BSA/PBS for 3 times, embryos were used for ChIP-seq experiments. The numbers of embryos at different developmental stages used for ChIP-seq were shown in Supplementary Table 45. The samples were washed carefully in PBS to remove blood and other contaminations completely. The embryos were cut into pieces for ChIP-seq experiments. Two 6-week embryos were used for two biological replicates.The patients with multifetal pregnancy after assisted reproductive technology treatment would receive reduction surgery at 6–8 weeks of gestation to decrease the abortion risk at middle or late pregnancy. The 6-week human embryos were collected after signed informed consents by patients. The methods for reduction surgery were referred from previous studyWild-type 6–8 weeks old female C57BL/6N mice and male C57BL/6N or PWK/PhJ mice  in healthy status were used in this study. They were housed under a 12 h light/dark cycle in specific pathogen-free conditions. The husbandry and experimental procedures were carried out according to the guidelines of the Institutional Animal Care and Use Committee of Institutes of Genetics and Developmental Biology, Chinese Academy of Sciences.To induce ovulation, female mice were injected intraperitoneally with pregnant mare serum gonadotropin  followed by human chorionic gonadotropin  injection 48 h later. Mouse PN5 zygotes, late 2-cell, 4-cell, 8-cell, morula and blastocyst stage embryos were collected at 27 h, 43 h, 54 h, 68 h, 79 h and 92 h post hCG administration, respectively. To collect MII oocytes, the female mice were killed at 20 h after hCG injection without mating. The oviducts were clipped mechanically with a razor blade to release oocytes. The oocyte-granulosa cell complexes were transferred into M2 medium  containing 0.3 mg/mL hyaluronidase to remove the granulosa cells. The oocytes were further washed several times with M2 medium.Zona pellucida was removed by exposure to acid Tyrode’s solution . Polar bodies were dissociated by gentle pipetting with a narrow-bore glass pipette. After washing with 0.1% BSA/PBS, embryos were used for ChIP-seq. The number of embryos used for ChIP-seq was shown in Supplementary Tables To investigate the function of HDACs and P300 in human early embryo development, the following siRNAs were used for gene knockdown:46,HDAC1 siRNA #1 (CUAAUGAGCUUCCAUACAATT)47,HDAC1 siRNA #2 (GCUUCAAUCUAACUAUCAATT)46,HDAC2 siRNA #1 (UCCGUAAUGUUGCUCGAUGTT)48,HDAC2 siRNA #2 (GCGGAUAGCUUGUGAUGAATT)46,HDAC3 siRNA #1 (GAUGCUGAACCAUGCACCUTT)49,HDAC3 siRNA #2 (CCAAGAGUCUUAAUGCCUUTT)50,P300 siRNA #1 (CCCCUCCUCUUCAGCACCATT)51, and negative control siRNA (UUCUCCGAACGUGUCACGUTT). HDAC1 siRNA #1, HDAC2 siRNA #1, and HDAC3 siRNA #1 were pooled as HDACs siRNA #1. HDAC1 siRNA #2, HDAC2 siRNA #2, and HDAC3 siRNA #2 were pooled as HDACs siRNA #2. 6.6 μM HDACs siRNA #1 or #2 (the concentration of each HDAC component was 6.6 μM) and 10 μM P300 siRNA or negative control siRNA were used for microinjection. The donated IVM oocytes were in vitro fertilized and cultured in G1.5 medium as described above. The siRNA solution was loaded into injection pipette and injected into zygotes before pronuclear fading using Eppendorf PiezoXpert and Eppendorf CellTram vario microinjector. The injected embryos were cultured in G1.5 medium (Vitrolife) in a humidified atmosphere at 37 °C with 6% CO2 in air. The embryos with normal morphology were harvested at appropriate time. The embryos injected with HDACs siRNA were harvested at 8-cell stage, while the embryos injected with P300 siRNA were collected at morula stage. One HDACs siRNA #1-injected 8-cell embryo, one HDACs siRNA #2-injected 8-cell embryo, two negative control siRNA-injected 8-cell embryos, one P300 siRNA #1-injected morula, two P300 siRNA #2-injected morulae and three negative control siRNA-injected morulae were collected for RNA-seq. Four P300 siRNA #1-injected morulae and four P300 siRNA #2-injected morulae were collected for H3K27ac ChIP-seq (Supplementary Table P300 siRNA #2 (CAGAGCAGUCCUGGAUUAGTT)52; mouse P300 siRNA #2: GGACUACCCUAUCAAGUAATT53. The P300 KD zygotes were collected at 8 h post injection. Four control siRNA-injected mouse 8-cell embryos, two P300 siRNA #1-injected 8-cell embryos, two P300 siRNA #2-injected 8-cell embryos, two P300 siRNA #1-injected 4-cell embryos and two P300 siRNA #2-injected 4-cell embryos were collected for RNA-seq  in a humidified atmosphere at 37 °C with 6% CO2 in air. The 8-cell embryos with normal morphology were harvested at 3 days post fertilization. Two SAHA-treated 8-cell embryos and two control 8-cell embryos were collected for RNA-seq.For α-amanitin treatment, mouse zygotes (26 h post hCG administration) were cultured in EmbryoMax KSOM medium  containing 25 ng/μL α-amanitin  or 10 μM SAHA . The treated embryos were collected at the late 2-cell stage (48 h post hCG administration). The α-amanitin and SAHA-treated late 2-cell embryos were used for H3K27ac ChIP-seq assays  were washed twice with 150 μL ice-cold RIPA buffer  and resuspended in RIPA buffer to a final volume of 150 μL in a 200 μL tube. 1.5 μL antibody was added into the bead suspension, then incubated on a tube rotator for at least 3 h at 4 °C. The antibodies used in this study include anti-H3K27ac antibody , anti-H3K4me3 antibody , anti-H3K9ac antibody , anti-H3K18ac antibody  and anti-H3K9me3 antibody . The antibody-coated beads were washed twice in RIPA buffer, then suspended with 110 μL RIPA buffer for further use.g for 10 min at 4 °C, 12 μL of the supernatant was taken out for input. The left supernatant was transferred to the 200 μL tube containing 110 μL suspended antibody-coated Protein A beads, then incubated on a tube rotator overnight at 4 °C.The cross-linked samples were incubated in 120 μL lysis buffer  for 20 min on ice, then sonicated using Diagenode Bioruptor sonication device for 10 cycles by 30 s ON and 30 s OFF. After centrifugation at 12,000× For human 2-cell, 4-cell embryos and mouse zygote, the incubated Protein A beads were washed by RIPA buffer with 300 mM NaCl for twice, RIPA buffer with 500 mM NaCl for twice, and TE buffer  for one time. For embryos at other stages, the incubated Protein A beads were washed by RIPA buffer with 300 mM NaCl for four times, and TE buffer for one time. After that, the beads were transferred to a new 200 μL tube, then incubated in 100 μL ChIP elution buffer  containing 2.5 μL proteinase K  at 55 °C for 1 h, 65 °C for 6 h.Eluate was transferred to a 0.6 mL tube. A second elution with 20 μL ChIP elution buffer was performed for 5 min and pooled with the first eluate. ChIP DNA was purified by 1.8 volume SPRIselect beads , then dissolved in 50 μL TE buffer.NEBNext Ultra II DNA Library Prep Kit for Illumina  was used for library construction according to manufactory’s instruction. DNA was end repaired and A-tailed by adding 7 μL NEBNext Ultra II End Prep Reaction Buffer and 3 μL NEBNext Ultra II End Prep Enzyme Mix. Samples were incubated in a thermal cycler at 20 °C for 30 min, 65 °C for 30 min, and finally cooled to 4 °C. Adaptor ligation was performed by adding 30 μL NEBNext Ultra II Ligation Master Mix, 1 μL NEBNext Ligation Enhancer, 0.5 μL 200 mM ATP and 2.5 μL Y-shapped Illumina Multiplexing Adaptors (1.5 μM). Samples were thoroughly mixed and incubated at 20 °C for 25 min. After adaptor ligation, 5 ng circular carrier DNA (SP64 plasmid) was added, then 1.2 volume SPRIselect beads were used to purify DNA. PCR amplification was performed with NEBNext Ultra II Q5 Master Mix. In order to obtain adequate amount of DNA for sequencing, the cycle of PCR amplification was determined according to the amount of 1 μL amplified DNA, which was evaluated using FlashGelTM System . The volume of PCR product was adjusted to 150 μL by adding TE buffer. The 350–600 bp DNA fragments were selected with 0.5 volume plus 0.3 volume SPRIselect beads, then eluted in 15 μL water. The libraries were sequenced on Hiseq ×10 or NovaSeq with paired-end 150 bp (Illumina).For each RNA-seq library, only one 4-cell embryo, 8-cell embryo or morula was used. SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing  was used for RNA-seq library preparation according to manufacturer’s instructions. Briefly, the sample volume of siRNA-injected human embryo or SAHA-treated embryo was adjusted to 9.5 μL with nuclease-free water. After adding 1 μL 10× Reaction Buffer , samples were incubated at room temperature for 5 min, then placed on ice. 2 μL 3' SMART-Seq CDS Primer II A (12 μM) was added, followed by incubation at 72 °C for 3 min. Samples were placed immediately on ice for 2 min. cDNA synthesis reaction was prepared by adding 4 μL 5× Ultra Low First-Strand Buffer, 1 μL SMART-Seq v4 Oligonucleotide (48 μM), 0.5 μL RNase Inhibitor (40 U/μL) and 2 μL SMARTScribed Reverse Transcriptase. The reaction was performed in a thermal cycler with following program: 42 °C for 90 min, 70 °C for 10 min, 4 °C forever. After adding 25 μL 2× SeqAmp PCR Buffer, 1 μL PCR Primer II A (12 μM), 1 μL SeqAmp DNA Polymerase and 3 μL nuclease-free water, 16 rounds PCR amplification was performed with following program: 95 °C for 1 min; 98 °C for 10 s, 65 °C for 30 s and 68 °C for 3 min, repeat these 3 steps for 15 times; 72 °C for 10 min; 4 °C forever. The amplified cDNA was purified by SPRIselect beads, then fragmented to 200–400 bp by Covaris sonicator (Covaris). Sequencing libraries were prepared with NEBNext Ultra II DNA Library Prep Kit for Illumina  as described above. The libraries were sequenced on Hiseq ×10 or NovaSeq with paired-end 150 bp.After removal of zona pellucida, human and mouse oocytes and embryos were washed three times in PBS, and fixed in 4% paraformaldehyde, 0.04% Triton, 0.3% Tween and 0.2% sucrose in PBS for 20 min at room temperature. After washing three times in PBS for 5 min, the embryos were permeabilized in PBS containing 0.5% Triton for 20 min. Embryos were blocked in 3% BSA in PBST for 2 h at room temperature, then incubated with anti-H3K27ac or anti-H3K4me3 antibodies (1:200 dilution) for about 12 h at 4 °C. After washing 6 times in PBST, embryos were blocked in 3% BSA in PBST for 30 min and then incubated for 1 h at room temperature with Alexa Fluor 488-labeled goat anti-rabbit IgG . Embryos were washed in PBST for 3 times, then stained with DAPI  for 5 min. After washing in PBST for 3 times, embryos were mounted in Prolong gold antifade reagent . Confocal images were obtained by Zeiss LSM 710 confocal microscope using a 63× oil objective. All the staining assays were repeated independently at least twice. Images were processed and quantified by ImageJ software (version 1.53a). H3K27ac fluorescence intensity was measured by ImageJ.54. Paired reads were mapped to human genome hg19 or mouse genome mm10 by Bowtie2 v2.3.1 with parameters “-X 800 --no-mixed --no-discordant”55. Reads with low mapping quality (MAPQ < 10) and PCR duplicated reads were removed by Samtools v1.9 and Picard v2.18.25, respectively57. The fragment per kilobase per million mapped reads (FPKM) value for non-overlapping 5 kb window on entire genome was calculated as tag density in ChIP-seq data. The Pearson’s correlation coefficient (r) of tag densities between two replicates were used to evaluate the reproducibility. The alignment data of two or more replicates at each stage were merged for downstream analysis.The ChIP-seq raw reads were cropped to 100 bp, and the reads with low qualities were removed by Trimmomatic v0.3358. We used parameters “-p 1e-3 --broad --broad-cutoff 0.01 --max-gap 500” to call H3K27ac typical peaks or broad domains in human embryos. The H3K27ac peaks or domains with low H3K27ac signal were further filtered. The sum of FPKM values of consecutive 100 bp bins located in an H3K27ac peak or domain was referred as the signal of such peak or domain. The peaks or domains with top 75% of H3K27ac signal for human embryo at each stage were kept for downstream analysis. The remained peaks or domains within 5 kb were further merged as described7. For mouse ChIP-seq data analysis, 15 millions of PE reads were randomly selected to do peak calling for each sample collected after ZGA stage. The H3K27ac, H3K18ac and H3K9ac peaks were called by MACS2 with parameters “-p 1e-3 --broad --broad-cutoff 0.01 --max-gap 500”.To exclude the influences of sequencing depth on the numbers of H3K27ac peaks or domains in human early embryos, we randomly selected 13.5 millions of paired-end (PE) reads to call typical H3K27ac peaks for human embryos at each developmental stage  after ZGA, and 70 millions of paired-end reads to call broad H3K27ac domains for the 2-cell and 4-cell human embryos. Because the genomic regions covered by broad H3K27ac domains before ZGA were much larger than those covered by typical H3K27ac peaks after ZGA, we could not obtain good results by using as few as 13.5 millions of PE reads to do peak calling for embryos at the 2-cell and 4-cell stages. As a result, more paired-end reads were used to call H3K27ac broad domains at the 2-cell and 4-cell stages. Next, the peaks were called by MACS2 v2.1.07. With the assumption that the H3K27ac-marked regions with the highest signal represented fully H3K27ac modified regions and had similar H3K27ac signal levels among samples, we firstly divided genome into non-overlapping 100 bp bins, then calculated FPKM values for H3K27ac in each bin for human samples. The mean value of all the 100 bp bins located in a H3K27ac peak or broad domain was referred as the H3K27ac signal for such peak or domain. We supposed that the medium H3K27ac signal value of H3K27ac peaks or domains with top 25% signal should be equal between 8-cell embryo and 2-cell or 4-cell embryos. As a result, the scale factors for H3K27ac signal at the 2-cell and 4-cell stages were calculated.Because broadly distributed H3K27ac signal could result in lower FPKM values in human 2-cell and 4-cell embryos than the embryos at later stages, we further normalized the H3K27ac signal between typical peaks and broad domains by using similar strategy as described59 or UCSC Genome browser were firstly generated by callpeak command in MACS2 with parameters “-B --SPMR”, then the input signal was subtracted by bdgcmp command in MACS2 with parameter “-m subtract”.ChIP-seq signal tracks visualized in Integrative Genomics Viewer (IGV) v2.8.6To calculate H3K4me3 or H3K27ac signal at promoters, the ChIP-seq FPKM value of each promoter for sample was subtracted with the FPKM value of that promoter for input. If the subtracted FPKM value is < 0, the FPKM of the promoter for that sample will be assigned as 0. To plot H3K27ac ChIP-seq signal profile around H3K27ac peaks, the region from 2 kb upstream to 2 kb downstream of each H3K27ac peak center was segmented into non-overlapping 20-bp bins, which were labeled from bin 1 to bin 200. The ChIP-seq subtracted FPKM value for each bin was calculated as described above. The average FPKM values for bins with the same index of all the peaks were used to plot ChIP-seq profile. H3K4me3 or H3K27ac ChIP-seq signal profiles around TSS were plotted with similar method. The region of TSS ± 2.5 kb was divided into 100 non-overlapping 50-bp bins. To plot heat maps of H3K4me3 or H3K27ac ChIP-seq signal at promoter region (TSS ± 2.5 kb), the subtracted FPKM value for each non-overlapping 10-bp bin on entire genome was calculated. Each promoter region was divided into non-overlapping 50-bp bins. For each 50-bp bin, the average subtracted FPKM value for the genome-wide 10-bp bins within this region was assigned to it. Those FPKM values for all the 50-bp bins were used to draw heat maps by ggplots2 package.DiffBind v3.8.1 was used to identify downregulated or upregulated H3K27ac peaks in human morulae upon P300 KD. Only peaks with fold change > 2 and FDR < 0.05 were referred as differential peaks upon P300 KD. For P300 KD mouse embryos, only the H3K27ac peaks with fold change > 3 were identified as differential peaks.11. The H3K27ac ChIP-seq data of human sperm were from GSE5709560. The H3K27ac ChIP-seq data for mouse 8-cell embryos, the H3K4me3 ChIP-seq data for mouse oocytes and 2-cell embryos were downloaded from GSE727847. The H3K4me3 ChIP-seq data for mouse zygotes were downloaded from GSE714349. The H3K27ac ChIP-seq data for mouse sperm and oocyte were from GSE7923061 and GSE11283462, respectively. The H3K27ac ChIP-seq data for human heart and stomach were from GSE101345 and GSE10103463, respectively. The H3K9me3 ChIP-seq data for mouse zygotes were from GSE9777810. P300 ChIP-seq data for human embryonic stem cell were downloaded from GSE2961164. These ChIP-seq data were analyzed as described above.The published H3K4me3 ChIP-seq data for human early embryos were downloaded from GSE12471865. The H3K27ac peaks located outside promoter regions were defined as distal H3K27ac peaks. If distal H3K27ac peaks were located within TSS ± 300 kb of some genes, those genes are defined as genes with distal H3K27ac peaks. To check whether the H3K27ac peaks in human embryos were experimentally validated enhancers, the experimentally validated human enhancer data (1792 enhancers) were downloaded from VISTA database32.The hg19 refGene files from UCSC Table Browser were used for genome annotations. Only protein-coding genes were selected for analysis. TSS ± 2.5 kb was defined as promoter region. Transcription terminate site (TTS) ± 2.5 kb was defined as transcription end site (TES). The genomic regions were segmented into five groups of genomic elements including promoter, TES, exon, intron and intergenic region. The ChIP-seq peak/domain was assigned to a genomic group if there was at least 150 bp overlapping between the peak/domain and any genomic element in the group. The priority ranking of genomic assignment was promoter, TES, exon, intron and intergenic region. The Bedtools v2.26.0 was used for the analysisThe enrichment of histone modification peaks in retrotransposons was calculated using observed versus expected probability. The observed probability was calculated using the length of the histone modification peaks that cover the related transposon regions versus the length of the total histone modification peaks, and the expected probability was calculated using the length of the total related transposon regions versus the length of the human genome. The annotation files for human and mouse transposons were downloaded from UCSC Table Browser.66. For RNA-seq data obtained by using SMART-Seq kit, only Reads 1 from paired-end sequencing were used for mapping. The FPKM value of each gene was calculated by Cufflinks v2.2.167 with parameter “-u”. The reference transcript annotation of protein-coding genes was downloaded from refGene in UCSC Table Browser. For protein-coding genes with multiple transcripts, only the longest transcripts were kept. The stage-specific expressed genes were identified based on Shannon entropy score as described in the section “Identification of stage-specific putative enhancers”. The ZGA genes were defined as those significantly upregulated in human 8-cell embryo (FPKM > 1 and fold change > 3) comparing with 2-cell embryo. DESeq2 package was used for gene differential expression analysis68. FDR < 0.05 and fold change > 2 were used as cutoff for differentially expressed genes. RNA-seq hierarchical clustering was also carried out by DESeq2 package. RNA-seq tracks visualized in IGV were generated by bamCoverage program in Deeptools v2.5.769 with parameter “--noralizeUsingRPKM”.For human and mouse RNA-seq data, low quality reads and adaptor sequences were removed by Trimmomatic v0.33. Then the trimmed reads were aligned to human genome hg19 or mouse genome mm10 by Hisat v2.0.4 with parameter “--dta-cufflinks”38. RNA-seq data for human 6-week embryos were downloaded from CRA001056 in PRJCA0002485. RNA-seq data for mouse early embryos were downloaded from GSE4418370. RNA-seq data for mouse blastocyst were downloaded from GSE6658242.RNA-seq raw data for human preimplantation embryos were downloaded from GSE3655271; total RNA-seq data for human heart and stomach were from GSE8851063. The RNA-seq data for mouse early embryos from GSE44183, and the RNA-seq data for mouse blastocyst from GSE66582 were also used for transposon analysis. The RNA-seq data were analyzed as described above. To exclude the influence of the transcription of protein-coding genes on the expression analysis of transposons, the transposons located in exons were exclude for further analysis. The analyzeRepeats.pl script in HOMER software (v4.10) was used to calculate the reads counts in transposons. The reads counts were normalized by the library sizes, then further normalized by z-score. To evaluate expression level for each transposon, the FPKM value was calculated by Cufflinks. Only the expressed transposons (FPKM > 0) were used for further analysis. To find out the differentially expressed transposons in mouse P300 KD embryos, the read counts in different transposon families were analyzed by DESeq2. The transposon families with fold change > 2 and FDR value < 0.05 were referred as significantly downregulated transposon families.To calculate transposon expression levels, RNA-seq raw data for human preimplantation embryos were downloaded from GSE71318To obtain lncRNA annotation, de novo transcriptome assembly was performed by Cufflinks using human early embryo RNA-seq data from GSE71318. The transcripts with single exon and those overlapping with RefSeq genes were removed. The de novo gtf file was compared to annotation file from Ensembl, 16,869 potential novel transcripts from 7813 loci were obtained. The coding potential for all new lncRNA transcripts was determined by Coding Potential Assessment Tool (CPAT). More than 99% transcripts show no protein coding potentiality. These new transcripts were then combined with lincRNA transcripts from Ensembl and Gencode V5. Finally, 30,773 lncRNA transcripts from 16,171 loci were obtained. The RNA expression levels of those lncRNA were calculated by Cufflinks.To identify eRNAs, the H3K27ac peaks without H3K4me3 signal in human early embryos from 8-cell stage to 6-week stage were merged. The expression levels (FPKM) of these merged H3K27ac peaks were calculated by Cufflinks as described above based on the total RNA-seq data of human early embryos from GSE71318. To exclude the influence of the transcription of protein-coding genes, the H3K27ac peaks located in the promoters and exons were excluded for further analysis. The H3K27ac peaks with RNA FPKM > 0.5 at a developmental stage were defined as eRNAs at the corresponding stage.13, which had been deposited in the Genome Sequence Archive (GSA). To plot DNase-seq profile around blastocyst H3K27ac peaks, the region from 2 kb upstream to 2 kb downstream of each H3K27ac peak center was segmented into 200 non-overlapping 20-bp bins, which were labeled from bin 1 to bin 200. DNase-seq FPKM value for each bin was calculated. The average FPKM values for bins with the same index of all the peaks were used to plot DNase-seq profile. The promoters overlapping with DHSs were defined as promoters with DHSs. If DHSs were located within 500 bp upstream or 500 bp downstream of a H3K27ac peak, the H3K27ac peak was referred as H3K27ac peak with DHSs, or putative enhancer.The processed DNase-seq data for human early embryos were obtained from CRA00029772, which had been deposited in the GSA. Low quality reads were removed by Trimmomatic v0.33. The filtered reads were aligned to reference genomes by Bismark (v0.16.3) with default parameters73. PCR duplicates and the overlapping regions of paired-end reads were trimmed by Bismark. The methylation level of each CpG site was calculated by a custom script. The processed DNA methylation data for human 2-cell and 4-cell embryos were downloaded from GSE8123374. The single-cell DNA methylome data for the embryos at the same developmental stage were merged. The CpG number was counted in each assigned region by a custom script. The CpG density was defined as the average number of CpG sites per 100 bp for this region. The DNA methylation level of each promoter (TSS ± 2.5 kb) or other specified region was measured as the average DNA methylation level of all CpG sites in the region.The DNA methylation data for human 8-cell, blastocyst and 6-week embryos were downloaded from CRA000114 in PRJCA000248To identify PMDs in early embryos, we calculated the DNA methylation level and the number of CpGs covered for each 10-kb window on entire genome. The regions with DNA methylation levels < 0.5 and > 20 CpGs covered were selected and merged into PMDs.33. For each putative enhancer, its relative H3K27ac ChIP-seq FPKM value at developmental stage i was defined as Ri = Ei / ∑ E, where Ei is the FPKM value of putative enhancers at stage i; ∑ E is the sum of FPKM values in all stages; N is the total number of developmental stages. Then the entropy score for this putative enhancer across developmental stages can be defined as H = −1* ∑ (Ri * log2Ri), where the value of H ranges between 0 and log2(N). An entropy score close to zero indicates the putative enhancer is highly stage-specific, while an entropy score close to log2(N) indicates that this putative enhancer is ubiquitous. The putative enhancers with H < 0.5 were selected as stage-specific enhancers.Because enhancers are the binding sites of transcription factors, most of enhancers are narrow regions in genome. To identify putative enhancers in genome, we applied stringent criteria to call narrow H3K27ac peaks with parameters “--keep-dup all -p 1e-5”. H3K27ac peaks for human 8-cell, morula, blastocyst and 6-week embryos were concatenated to get H3K27ac peak union. The distal H3K27ac peaks with nearby DHSs in the union are considered as putative enhancers. A Shannon entropy-based method was used to identify stage-specific putative enhancers as previously described75 was used to analyze transcription factors binding motifs enrichment in stage-specific putative enhancers.The script findMotifsGenome.pl with default parameters in HOMER software (v4.10)P-values were adjusted by “Benjamini-Hochberg” method. The H3K27ac peaks with adjusted P-values < 0.001 and the ratio of paternal reads to maternal reads or the ratio of maternal reads to paternal reads > 5 were referred as allelic H3K27ac peaks. Similarly, RNA-seq data from GSE71434 were used to analyze allelic RNA expression in mouse early embryos9.To identify allelic reads in mouse H3K27ac ChIP-seq data, the sequencing data were aligned to SNP masked mm10 genome sequences. The SNP information between C57BL/6N and PWK/PhJ strains was downloaded from Mouse Genomes Project. SNPsplit v0.3.2 was used to distinguish paternal and maternal reads in ChIP-seq data. The H3K27ac peaks with at least 10 reads with distinguishable SNPs per kb were selected to identify allelic peaks. The significance of allele bias was evaluated by Chi-square test. The total paternal and maternal reads in mouse morula and blastocyst were used as background for Chi-square test. The 76. For protein-coding genes, DAVID v6.8 was used for GO analysis77. For genes with allelic H3K27ac signal or different types of histone acetylations, clusterProfiler v3.10.1 was used for GO analysis78.For distal putative active enhancers, the nearest genes within 300 kb were chosen for function enrichment analysis by GREAT program (version 3)t-test was used for RNA differential expression analysis between siRNA KD embryos and control siRNA-injected embryos. t-test was also used for H3K27ac fluorescence intensity comparison. No statistical methods were used to predetermine sample size.R (version 3.6.0) was used for statistics analysis. The statistical methods used for analysis were described in the figure legends. In summary, Pearson’s correlation coefficient was used to evaluate reproducibility of replicates. Wilcoxon rank sum test was used to examine the significance for the comparisons of RNA expression, ChIP-seq signal, peak length, DNA methylation and CpG density. Wilcoxon rank sum test was carried out by the wilcox.test function in R. The unpaired two-samples Supplementary figures and tablesTable S5


The prevalent one-dimensional alignment of genomic signals to a reference landmark is a cornerstone of current methods to study transcription and its DNA-dependent processes but it is prone to mask potential relations among multiple DNA elements. We developed a systematic approach to align genomic signals to multiple locations simultaneously by expanding the dimensionality of the genomic-coordinate space. We analyzed transcription in human and uncovered a complex dependence on the relative position of neighboring transcription start sites (TSSs) that is consistently conserved among cell types. The dependence ranges from enhancement to suppression of transcription depending on the relative distances to the TSSs, their intragenic position, and the transcriptional activity of the gene. Our results reveal a conserved hierarchy of alternative TSS usage within a previously unrecognized level of genomic organization and provide a general methodology to analyze complex functional relationships among multiple types of DNA elements. At the core of interpreting this information, there are specific genomic locations, or genomic landmarks, such as TSSs, transcription factor binding sites, RNA splice junctions, or the midpoint of a DNA extended region5. These landmarks provide anchoring points to summarize general trends and characterize different types of DNA regions.Genomic signals encapsulate highly detailed quantitative information up to the nucleotide level7; how the directionality of promoters reflects on the asymmetry of DNA accessibility and histone methylation signals around TSSs8; and the enrichment or depletion of single nucleotide variation occurrence around multiple landmarks in the genomes of human populations9. To capture the inherent heterogeneity, aligned signals are often structured into heatmaps10, which can be sorted and clustered according to specific parameters11 and can be incorporated into automated machine-learning pipelines12. This type of one-dimensional alignments is also the usual approach to link genomic signals with the results of methodologies, such as chromosome conformation capture techniques14, that map the three-dimensional DNA looping16 interactions between distal DNA elements.The prototypical approaches to analyze these data start with the alignment of the signals to a landmark along a one-dimensional coordinate for subsequent processing. In mathematical terms, the alignment of a genomic signal 18. The presence of a landmark, such as a TSS, can often affect the functioning of another one and, in general, multiple landmarks can affect each other's function. To analyze functional relationships among multiple types of DNA elements, we develop a method to consider multiple landmarks at the same level , on pairs of consecutive TSSs. Specifically, we focus on how transcription in human at a given genomic location depends on the relative positions of two TSSs, including how the presence of a TSS correlates with transcription at another TSS. The transcription of mammalian genomesTSSs are ordered according to their genomic position, starting the enumeration from the most upstream TSS. The two-dimensional RNA-seq signal density As a representative case, we consider explicitly K562 human myeloid leukemia cells for the first and second TSSs Figs. A and secMany key features, however, are strongly dependent on the intragenic position of the TSSs and the transcriptional activity of the gene, which we have stratified as high, medium–high, medium, medium–low, low, and zero Figure . WithoutTo accurately characterize the observed dependence patterns, we consider seven regions of the two-dimensional signal density Fig. C. Five oupstream effects of the 2nd annotated TSS extend up to ~ 1kbp distances. In contrast, transcription in region D is higher than in region E, which shows that the downstream effects of the 1st annotated TSS statistically enhance transcription at the 2nd TSS. This effect is even more marked when comparing transcription in region C with transcription in region E, which we have termed proximal cooperativity, indicating that on average there is more transcription at the 2nd TSS the closer it is to the 1st TSS. To compare transcription when the two TSSs are far from each other, we consider regions A and E. For highly transcribed genes, transcription is much more prominent at the 2nd than at the 1st TSS. This distal positional dominance of the downstream TSS shifts to the upstream TSS as the transcriptional activity of the gene decreases.Explicitly, comparing RNA-seq densities in region B with those of region A indicates that the proximity of the 2nd TSS strongly correlates with reduced transcription at the 1st TSS. These The statistical interdependence of the RNA-seq signal at the first pair of annotated TSSs is also present to a large extent at the second and third TSSs Fig. B. Proxim24. The persistence of the RNA-seq signal is strongly influenced by the position of the downstream TSS  is substantially higher than persistence with a distal downstream TSS , especially for low and medium values of the transcriptional activity. Therefore, the presence of a nearby downstream TSS correlates with lower transcription initiation but, at the same time, with more persistent RNA-seq signals. Any dependence on the TSS arrangement of the processes that lead to loss of the average RNA-seq signal could affect persistence. For instance, a downstream TSS located nearby could favor a lower intron-to-exon RNA ratio between the two TSSs, thus promoting more persistent RNA-seq signals. The presence of a downstream TSS could also lead to an increase in DNA accessibility between the two TSSs, positively correlating with higher transcriptional progression and lower abortion rates. Regarding the absolute value of the average RNA-seq signal between TSSs, it tends to be higher as the downstream TSS gets closer to the upstream TSS , which we refer to as signal dominance  dataata Fig. . This teWe investigated how the interdependence of the RNA-seq signal on consecutive pairs of TSSs is associated with known transcriptional regulation features. Explicitly, we considered three types of data: chromatin immunoprecipitation followed by sequencing (ChIP-seq) data for POLR2A as a reporter of RNA polymerase II (Pol II) occupancy Figs. A and S3AExplicitly, the presence of a downstream TSS negatively impacts both Pol II occupancy and DNA accessibility around TSSs positioned upstream at intermediate distances. As in the case of transcription, these effects are much more marked for the first pair Fig. A and B t26. Similarly, the active chromatin marker H3K4me3 . These included 65 experiments with 56 different cell types, covering all biosample types. The results show that the main trends observed for the transcription initiation in K562 are conserved across human cell types Figs. , and S10Positional dominance for RAMPAGE data across multiple cell types closely mimics the results for RNA-seq data, indicating that transcription initiation as well as transcription generally shift from the upstream to the downstream TSS of the distal TSS pair as the transcriptional activity of the gene increases. Upstream effects are also remarkably similar for both processes, except for the first pair of TSSs with low transcriptional activity of the gene. Downstream effects and proximal cooperativity are positive in both RNA-seq and RAMPAGE data but are much more marked in the latter. In general, it is observed that these effects become more pronounced in RAMPAGE data as the positional order of the TSS pair in the gene increases. The fact that these marked transcription initiation effects are reduced to a large extent in transcription as the positional order of the TSS pair increases is consistent with transcription at a given position accounting for the cumulative effects of transcription initiated at the upstream TSSs.persistence dominance. In absolute terms, the average RNA-seq signal between TSSs does not depend on the downstream TSS distance for the first pair of TSSs of the gene, but tends to be higher in the presence of a non-distal downstream TSS for subsequent TSS pairs in the gene  has been a fundamental element of our approach to uncover the existence of regulated interdependence patterns of gene expression at alternative TSSs and between TSSs across human cell lines, primary cells, in vitro differentiated cells, and tissues. This interdependence comprises proximal cooperativity, upstream and downstream interactions, positional dominance, enhancement of transcription persistence, and attenuation of the transcriptional signal. In general, these effects are highly dependent on the intragenic position of the TSSs, the transcriptional activity of the gene, and the precise distances between TSSs, but at the same time, they are consistently conserved across human cell types, irrespective of their specific origin or function within the organism.Among the most salient phenomena, there are proximal cooperativity and downstream effects, which encompass higher transcription downstream a TSS the closer it is to an upstream TSSs within the gene. This type of enhancement observed in transcription is also present, even more prominently, in transcription initiation. On the opposite side, our results show the presence of marked upstream effects, namely, the attenuation of the transcriptional signal and transcription initiation at an upstream TSS by the presence of a nearby downstream TSS. Simultaneously with the negative effects on the absolute levels of transcription, a downstream TSS positively enhances the persistence of transcription after its initiation. Concomitantly, DNA accessibility and Pol II densities show lower but more sustained profiles for a non-distal than for a distal downstream TSS. These results can be understood mechanistically considering that the assembly of the transcription initiation complex upstream a TSS interferes with transcription initiation and that the overall transcription process promotes upstream and downstream DNA accessibility.37. At the molecular level, fundamental biochemical principles dictate that non-specific effects, as quantified and validated in simpler gene expression prokaryotic systems, cannot generally be suppressed completely and that they are affected by regulatory processes in the usual way38. In this context, the existence of general patterns within multiple levels of variability we have identified shows both a consistent signal across cell types determined by the genomic context and a random-like component dependent on the cell type and conditions, akin to non-specific transcription initiation.Recent genome-wide analyses have concluded that multiple alternative transcription initiation is largely nonadaptive and resulting predominantly from imprecise eventsOur analysis has also shown that there are clear positional dominance effects when the two TSSs are far from each other, resulting in most of transcription and transcription initiation shifting from the upstream to the downstream TSS of the distal TSS pair as the transcriptional activity of the gene increases. This effect is extremely marked for the 1st and 2nd annotated TSS of a gene and it is generally more pronounced for transcription initiation than for transcription. These types of results have also a practical side as they can be used to refine and complement TSS annotations. Explicitly, the case of positional dominance implies that the 1st annotated TSS of a gene is essentially not active, or it is not an actual TSS, if the transcriptional activity of the gene is high.The most remarkable finding of our work is therefore the discovery of the existence of general regulated interdependence patterns of gene expression at and between alternative TSSs of protein-coding genes. We showed that these effects are conserved across cell types through a comprehensive analysis of the hundreds of human transcription and transcription initiation experiments of the ENCODE project. Compounded with these general patterns, there are multiple levels of variability, such as replicate noise, cell-type-specific TSS usage, and adaptation to different conditions. The identification of these general patterns in the alternating structure of transcription has important implications for gene expression as they determine the variability of the repertoire of isoform proteins.On the methodological side, our approach can generally be applied to virtually any combination of landmarks and any genomic signal in the same way as we have applied them to TSSs and RNA-seq, RAMPAGE, DNase-seq, and ChIP-seq signals. Therefore, our results open an avenue to find novel distance-dependent functional relationships among multiple DNA elements in a wide variety of systems.The average of the signal To compute the region average from the previous expression efficiently, we take into account that To compute the signal densities, we use a moving window defined by a rectangular domain centered at The average transcription in a region https://www.gencodegenes.org/human/release_19.html).TSSs were obtained from the comprehensive gene annotation on the reference chromosomes of Gencode V19  as bigWig files for the hg19 mapping assembly/V19 genome annotation. Gene quantifications for the corresponding RNA-seq signals were downloaded as tsv files. Signals were normalized by their average value over the whole genome before analysis.RNA-seq, RAMPAGE, DNase-seq, and ChIP-seq genomic signals were downloaded from the Encyclopedia of DNA Elements (ENCODE) consortium repository , which resulted in 191 experiments.RAMPAGE experiments were selected in two steps. First, we considered all the experiments that matched the search criteria "hg19" for assembly and "RAMPAGE" for assay title, which produced 155 results. Subsequently, we selected RAMPAGE experiments that included plus and minus strand signal of unique reads and that had high replicate concordance (Spearman correlation > 0.8 between gene quantifications of the replicates), which resulted in 65 experiments.The data analysis was performed using custom Python 3.8 scripts implemented in Jupyter Notebooks available in the supplemental information.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Spermatogonial differentiation is a process that commits germ cells to the complex process of spermatogenesis. Spermatogonial differentiation is mediated by the action of retinoic acid, which triggers major morphological and transcriptional changes. While these transcriptional changes have been well explored, there has been little effort devoted to epigenetic regulation surrounding spermatogonial development. This study aimed to uncover the timing and dynamics of chromatin organization during spermatogonial development within the context of these transcriptional changes. Using germ cell synchrony and the assay for transposase accessible chromatin and next generation sequencing (ATAC-seq) to isolate subpopulations of developing spermatogonia and identify accessible regions within their genome, we found that 50% of accessible regions in undifferentiated spermatogonia were condensed following retinoic acid action within 18 h. Surprisingly, genes with known functional relevance during spermatogonial development were accessible at all times, indicating that chromatin state does not impact transcription at these sites. While there was an overall decrease in gene accessibility during spermatogonial development, we found that transcriptionally active regions were not predictive of chromatin state. Spermatogenesis is a complex process whereby relatively undifferentiated germ cells undergo morphological, transcriptional, and epigenetic changes as they mature into sperm. These critical changes not only underlie the functionality of cells in each stage of development but also allow for the identification of cell types based on these characteristics. Gross morphological traits can be used to identify the four main types of male germ cells: spermatogonia, spermatocytes, round spermatids, and elongating spermatids . The assundiff) and differentiating A1 spermatogonia (the transition between these two cell types is termed the A-to-A1 transition) [Multiple studies have attempted to uncover the transcriptional changes during male germ cell development ,6,7,8. Snsition) ,12. Whilnsition) . When taPrevious studies have examined chromatin state and possible chromatin modifiers in germ cells using the assay for transposase-accessible chromatin with sequencing (ATAC-seq) and chromatin immunoprecipitation with sequencing (ChIP-seq) ,14. HoweIn this study, we characterized where in the genome chromatin compaction was occurring throughout spermatogonial development. Further, we correlated these changes in chromatin compaction with RNA-seq data to determine whether these changes were broad throughout the genome, or if they correlated with transcriptome changes seen during spermatogonial development. We synchronized mouse spermatogenesis to isolate purified spermatogonial subpopulations at several points during spermatogonial differentiation and then performed ATAC-seq to characterize the accessibility of chromatin in each of the spermatogonial subpopulations.Piwil2) promoter, and expression is seen prenatally through early spermatids; these mice were produced from a C57BL/6 x SJL background [All animal experiments were approved by the Washington State University Animal Care and Use Committees and were conducted in accordance with the principles for the care and use of research animals of the National Institutes of Health. Tg(Piwil2/EGFP)1GHan/J mice (denoted here as PIWIL2-eGFP) were obtained from the Jackson Laboratories (stock no. 012276) . These mckground . AnimalsTail clips were taken from neonatal mice to identify genotypes prior to euthanasia. These tail clips were lysed in 75 µL of 25 mM NaOH, 200 µM EDTA (pH 12) in a 95 °C heat block for 1 h, with manual dissociation at 30 min. A total of 75 µL of neutralization buffer  was then added, before DNA was used for genotyping reactions. Mice were assayed for the presence of the PIWIL2-eGFP transgene using primers specific to eGFP . The presence of a band at ~500 bp signified the presence of the transgene.Spermatogenesis synchronization was performed as previously described ,19. Brieg for 7 min at 4 °C, then resuspended in 12.5% DNase solution diluted in Dulbecco’s phosphate-buffered saline (DPBS)  at 4 million cells/mL. Fluorescence-activated cell sorting (FACS) was performed with an SH800 machine  to distinguish eGFP-positive and eGFP-negative populations. eGFP-positive cells were collected into a tube containing 0.5 mL DPBS. As previously described in Gewiss et al. (2021), eGFP-positive cells were collected at a rate >95% [Testes to be used for cell sorting were collected and the tunica removed. Tubules were placed in 1X Hank’s Balanced Salt Solution  until transfer to a Petri dish with 5 mL trypsin/EDTA , and 0.5 mL DNase . Tubules were manually dissociated via pipetting and then incubated for 5 min at 37 °C. A total of 1 mL of DNase was then added to the dish, and tubules were further dissociated. The cells were incubated for another 5 min at 37 °C, and these steps were repeated twice more. A total of 850 µL of fetal bovine serum (FBS) was then added to inactivate the trypsin, and the suspension was strained through a 30 µm filter. The Petri dish was rinsed with 4 mL of HBSS to remove any remaining cells which were then also filtered, followed by a final 1 mL HBSS wash through the filter. The cell number in the suspension was estimated using a hemocytometer. Cells were spun down at 600× g for 7 min at 4 °C. Cells were then resuspended in 500 µL cold solution comprised of 50% FBS, 40% DPBS, 10% DMSO. Cells were cryopreserved at -80 °C overnight. Prior to the ATAC-seq assay, cells were thawed in a 37 °C water bath, pelleted, washed with cold PBS, and tagmented as described in Buenrostro et al. (2013) with modifications [A modified version of the Omni-ATAC protocol was used to generate ATAC-seq libraries ,21. Afteo et al. 13 with mp-value 1 × 10−7, without a control file, and with the –nomodel option. Peaks that were on the ENCODE blacklist of known false ChIP-Seq peaks were removed. Signal maps and peak locations were used as input data to Active Motif’s  proprietary analysis program, which creates Excel tables containing detailed information on sample comparison, peak metrics, peak locations, and gene annotations. Sequencing metrics are provided in the ATAC-seq analysis used the mouse mm10 genome and read alignment was performed using the BWA algorithm  . DuplicaWe isolated spermatogonial subpopulations with FACS using a PIWIL2-eGFP mouse line. The PIWIL2-eGFP transgene is expressed prenatally in all germ cells and in adults through early spermatids, which allowed us to use fluorescence to isolate germ cells from other testicular cell types. Further, we used a previously characterized WIN 18,446/RA spermatogenesis synchrony protocol to produce testes highly enriched for given types of germ cells . We haveAdditionally, this synchrony remains tightly coordinated for over the six weeks following RA administration . This enundiff, A1/A2, A3, and B spermatogonial populations, respectively. These timepoints allowed us to see the impact on the chromatin state of RA-induced spermatogonial differentiation. The 0 h samples represent the Aundiff spermatogonial population prior to RA action. Previous transcript analysis has shown that for many transiently induced RA-influenced genes, transcript levels increase through 18 h post-RA and then return to near-basal levels by 48 h [We collected two ATAC-seq replicates following synchronization at 0, 18, 48, and 120 h post-RA, which correspond to the A by 48 h ,29,30. TWe first analyzed the number of ATAC-seq peaks in each sample, representing the areas of open chromatin. undiff population.A breakdown of the genomic location of these peaks is shown in To characterize global trends for peaks around genes, we generated heatmaps for genes which are either down- or upregulated by 120 h post-RA administration  Figure . For botundiff spermatogonia display relatively high transcript levels of characteristic markers, including neurogenin 3 (Neurog3), SRY-box transcription factor 3 (Sox3), zinc finger and BTB domain containing 16 (Zbtb16), lin-28 homolog A (Lin28a), nanos C2HC-type zinc finger 2 (Nanos2), and POU class 5 homeobox 1  [Pou5f1 and Zbtb16 showed fewer peaks in timepoints following RA-triggered differentiation. Several of these genes also showed decreases in peak amplitude, seen notably in Peak 1 of Neurog3 and Peak 1 of Sox3.We next examined some classical marker transcripts for different spermatogonial developmental stages. Prior to the A-to-A1 transition, A Oct3/4) ,35,36,37undiff spermatogonia and significantly decreased expression following RA action that were previously identified as potential novel Aundiff markers [undiff population were not exclusively accessible at 0 h but rather showed some accessibility at all timepoints. However, all these genes displayed additional examples of a reduction in accessibility, seen clearly as a reduction in peak count following RA action in Enc1 peaks 1, 3, and 5  and the KIT proto-oncogene, receptor tyrosine kinase (Kit) [Stra8, even in Aundiff spermatogonia, when transcript levels are minimal  ,39,40,41 minimal A. Furthenjection B.As previously discussed, the majority of accessible genes were accessible at all timepoints. We thus took a particular interest in the subset of genes with accessible regions only in undifferentiated spermatogonia (0 h), which made up ~13% of the total number of genes. For these 2666 genes exclusively accessible in undifferentiated spermatogonia, we looked for correlations in transcription from our previous dataset. If chromatin compaction was affecting the transcription of these genes, there should be fewer transcripts at assayed timepoints following RA action. We cross-referenced these genes with genes showing significantly lower transcript levels following RA treatment from our previously published data, resulting in 111 genes of interest . These 1Our results support the general knowledge that has been ascertained via TEM—that there is a relatively large amount of accessible chromatin prior to the A-to-A1 transition of spermatogonial differentiation, and this amount declines as spermatogonial development progresses ,16. Our undiff population. Genes from both the down- and upregulated categories showed relatively little change in the number of peaks called during the time course. Interestingly, there was a small subsection of upregulated genes which showed a greater number of peaks prior to RA administration, then showed a decline in peak number following RA action (While chromatin compaction increased overall during the time of spermatogonial development, we assessed how this may impact transcriptional regulation. Globally, we examined genes which are known to possess significantly decreased A or incrConversely, we examined transcriptional patterns for genes with characterized accessibility patterns, focusing on those genes accessible at 0 h only or accessible at all times as these represented the majority of genes with accessible regions . These gNanos2, Lin28a, Neurog3, Pou5f1, Sox3, Zbtb16, Enc1, Onecut2, Ptch1, Sdc4, and Zfp462, there was accessible chromatin at all times, but these genes showed the most accessibility in undifferentiated spermatogonia where there were more peaks and/or a greater peak amplitude (While global trends did not indicate a functional link between chromatin accessibility and transcription, we examined specific genes with known transcriptional patterns during spermatogonial development. We found that for genes that have decreased transcript abundance following RA action, including mplitude . HoweverStra8 and Kit, were also accessible at all times (Kit have suggested it may be epigenetically controlled at spermatogonial differentiation [Genes with increased expression, including biologically relevant markers of differentiating spermatogonia such as ll times . STRA8 hntiation . Our datWe found that ~73% of genes are not only accessible over all four timepoints , but theRetinoic acid triggers complex changes at both the genetic and morphological levels. These data build on previous chromatin characterization in spermatogonia to show where in the genome chromatin compaction occurs. On a global scale, there does not appear to be a functional, predictive link between chromatin compaction and transcription in the examined spermatogonial populations. Future work on potential transcription factors acting in areas of open chromatin may help to further elucidate the complex network of transcriptional changes induced by RA.