The exploration of single‐cell 3D genome maps reveals that chromatin domains are indeed physical structures presenting in single cells, and domain boundaries vary from cell to cell. However, systematic analysis of the association between regulatory factor binding and elements and the formation of chromatin domains in single cells has not yet emerged. To this end, a hierarchical chromatin domain structure identification algorithm (named as HiCS) is first developed from individual single‐cell Hi‐C maps, with superior performance in both accuracy and efficiency. The results suggest that in addition to the known CTCF‐cohesin complex, Polycomb, TrxG, pluripotent protein families, and other multiple factors also contribute to shaping chromatin domain boundaries in single embryonic stem cells. Different cooperation patterns of these regulatory factors drive genomic position categories with differential preferences forming boundaries, and the most extensive six types of retrotransposons are differentially distributed in these genomic position categories with preferential localization. The above results suggest that these different retrotransposons within genomic regions interplay with regulatory factors navigating the preference of genomic positions forming boundaries, driving the formation of higher‐order chromatin structures, and thus regulating cell functions in single mouse embryonic stem cells. The authors first developed a hierarchical chromatin domain structure identification algorithm (HiCS) from single‐cell 3D genome maps. Further systematic analysis on single mouse embryonic stem cells suggests that different retrotransposons within genomic regions interplay with regulatory factors navigating the preference of genomic positions forming boundaries, driving the formation of chromatin landscapes, and thus regulating cell functions. These fibers further fold into chromatin domains, referred to as topologically associating domains (TADs) or sub‐TADs, which are associated with each other to generate chromosomal compartments. Each chromosome occupies a distinct volume or chromosome territory within the nucleus. Genome architecture is an integral part of the chromatin landscape that transcription factors (TFs) must navigate to exert their regulatory roles. Although most loci and chromosomes are characterized by a high degree of order and non‐randomness, the precise functional roles and formation mechanisms of these features remain obscure.Genome across a wide range of eukaryotic organisms is efficiently packaged and organized into hierarchical chromatin architecture via ubiquitous architectural features, which is critical to gene regulation and dynamical changes in development and disease.1 Thes Particularly, TAD boundaries are enriched with multiple factors including CTCF, cohesin, H3K4me3, H3K36me3, transcription start sites, and housekeeping genes, suggesting that CTCF binding, high levels of transcription activity, multiple histone modifications, and other regulatory factors may contribute to the formation of chromatin domains in mammals. Although TADs are highly conserved and stable across different cell types, single‐cell 3D genome analysis indicated that they display substantial cell‐to‐cell variation. Therefore, the bulk analysis only reflects properties of ensemble structures from millions of cells, which may mask chromatin features appearing in a few cells or a single cell.Some TFs, cofactors, and histone modifications that correlate with the chromatin structures have been identified to study features of chromatin organization.6 Part More surprisingly, single‐cell domain structures persist even after cohesin degradation. These results suggest other TFs or epigenetic factors may contribute to the formation of chromatin domains. Currently, mouse embryonic stem cells (mESCs) have served as a specific model to elucidate the mechanisms of 3D genome organization. Hundreds of TFs and epigenetic modification profiles have been identified in mESCs. The above observations support us to investigate the formation of chromatin domains and their relationship with functional elements in single mESCs systematically.A recent study reveals that domain structures often adopt globular conformation with spatially physical segregation, and domain boundaries are preferentially located at CTCF‐ and cohesin‐binding sites with a super‐resolution chromatin tracing method.8a MorHere, we first develop a hierarchical chromatin domain structure identification algorithm (named HiCS) from single‐cell Hi‐C maps, which shows superior performance in both accuracy and efficiency. We reorganize an atlas of ChIP‐seq for mESCs and reveal hundreds of regulatory factors are significantly either enriched or absented in domain boundaries of single cells, suggesting that, in addition to known CTCF‐cohesin complex, Polycomb, TrxG, pluripotent protein families, different types of histone modifications, and other multiple factors could promote the formation of chromatin domains in single mESCs. To further elaborate on genomic position categories with differential preference forming boundaries, we cluster 13 large genomic position categories (consisting of 29 sub‐categories) annotated by 7 different regulatory factor clusters (consisting of 27 sub‐clusters). The clear patterns provide a detailed view of the preference of these genomic position categories forming boundaries. Furthermore, we discover that these genomic position categories are enriched by different cooperation of retrotransposons with preferential localization. Last but not the least, we find that genomic positions enriched by Alu/B2/B4 retrotransposons have higher preference scores for forming boundaries in G1 and ES phases in comparison with MS and LS/G2 phases, whereas genomic positions enriched by L1/ERVK retrotransposons display opposite tendency. In summary, we reveal that multiple types of regulatory factors interplaying with each other in specific genomic positions could affect focal chromatin interactions, thereby changing the interaction density or insulation strength of these regions. This further navigates the preference of genomic position forming boundaries, shapes hierarchical chromatin domains, and thus regulates gene expression and cell functions, even cell identity in single embryonic stem cells.22.1Figureρ and the minimum distance between the bin and any other bin with higher strength δ, and controls the number of peaks to obtain the hierarchical chromatin domains by scaling parameter α  : 1) Preprocesses single‐cell Hi‐C data and calculates the two metrics to find the peaks at a given scale l Figure ; 2) dete Figures .2.2 and another is a recent single‐cell TAD detection method, deTOKI. We apply these methods to the preprocessed single‐cell Hi‐C data generated from mESCs. To compare their performances fairly, we adjust the scaling parameter of HiCS to obtain similar number and size of domains with IS and deTOKI, respectively ,9 and y Figure . Actually Figure . The runy Figure . Moreovey Figure , as well] Figure . Taken t2.3We adjust the optimal structural identification parameter to generate multiple‐scale chromatin domains at different genomic scales for 1315 single mESCs at 40 kb resolution. We clearly observe four peaks of domain size and insulation strength distribution with different scaling parameters of 0.2, 1, 4, and 8, and choose these scaling parameters for the downstream analysis Figure .FigureThe median size of chromatin domains increases and the median boundaries’ insulation strength enhances with the increase of genomic position level , and the expression of genes gradually increases along with the increasing levels of genomic positions , Nipbl, PolII, and TFIIB are all enriched in the boundaries of single cells, and as the level of boundaries increases, the degree of enrichment gradually increases  forms obvious single peaks at the domain boundaries, and the degree of enrichment gradually increases with the boundary level increasing  shows double peaks around the domain boundaries  are all enriched in domain boundaries, except for Mll3/4  and PRC2 just exhibit two different enrichment patterns at the domain boundaries of single cells. One type  controlling the pluripotent state do not prefer to appear in domain boundaries, which may be related to chromatin hubs occupied by super‐enhancers/enhancers  that were previously shown to occupy both typical enhancers and SEs do not prefer to appear in domain boundaries, while five factors  that were previously shown to occupy promoter‐proximal sites are enriched in domain boundaries. Among them, it is particularly interesting that Smad2/3, Stat3, and Tcf3 signaling pathways were considered key modulators controlling mESCs pluripotent state transition by modifying chromatin states and shaping chromatin domains.We find that the core TFs , which were annotated by seven different regulatory factor clusters (consisting of 27 sub‐clusters). The result helps to explain the preference of genomic position forming boundaries in single cells, and provides an increasingly complex view of the genomic structure‐function relationship , the clusters associated with highly active activators for SEs and enhancers (HASE&E), the clusters of highly transcribed activators (HTA), the clusters of transcriptional repressor factors (TRF), the cluster associated with heterochromatin factors (Hetero), and the clusters of architectural proteins (AP). There are also a few single factors (Other)  of cohesin occupy the same genomic locus and present similar enrichment patterns at the boundaries of single cells, suggesting that both Sa1 and Sa2 may also participate in the maintenance of domain boundaries  preferentially forming boundaries only are significantly occupied by AP1 factors  comparing with other types of factors Figures  and 5a. s Figure . ZC3H11AThe second major category of open chromatin positions preferentially forming boundaries  is associated with highly transcribed genes Figures  and 5a. th and 33rd classes, defined as TrxG‐PRC‐Positions, 1.5% from the 35th class, defined as TrxG‐AP1‐Positions) is associated with PRC, TrxG, and AP1 proteins  can provide an additional layer of regulation by changing chromatin structures, and balance the formation of domain boundaries by repressing and activating chromatin states, respectively.The third major category of open chromatin positions preferentially forming boundaries  almost encompasses more than 75% of SEs, which are strongly related to the HASE&E, CoreRF, TrxG, and Med‐PolII clusters. The second category  is mainly associated with enhancers occupied by CoreRF. The slight difference between the 21st and 9th classes is that the 9th one shows higher chromatin accessibility, while the 21st one shows a stronger DamID enrichment, which may be related to their locations in the nucleus. The third category  is related to enhancers enriched by H3K4me1. These regions taking SEs or enhancers associated with CoreRF and H3K4me1 as focal regions generate dense chromatin structures mediated by different regulatory factors in an orientation‐independent manner, and unfavorably form boundaries in single cells with weakened insulation strengths.The major categories of the open chromatin positions unfavorably forming boundaries are associated with highly active SEs or enhancers, which mainly contain three categories Figures  and 5a. trengths.21th, 28th, 20th, 31st, and 27th classes, respectively, defined as Hetero‐Positions) are associated with the Hetero cluster. The second category  maintains high enrichment of the DamID signal. The above observations suggest that the presence of heterochromatin factors reduce the probability of single‐cell domain boundary formation. Finally, we observe an interesting and specific category , which are associated with the AP2 (Yy1 and H2AZ) and H3K36me3 classes  The genomic positions occupied by architectural proteins (CTCF and cohesin), highly transcribed genes, and TrxG proteins preferentially form boundaries in the single cells analysis. 2) The genomic positions taking SEs or enhancers associated with CoreRF or marked by H3K4me1, heterochromatin factors, and repressing factors unfavorably form boundaries in single cells with weakened insulation strengths  with low accessibility and unfavorably forming boundaries are enriched by L1 elements, which tend to occupy gene‐poor, heterochromatic B compartments that interact with lamina‐associated domains in previous studies.23a Amth class of CoreRF‐Positions) are enriched by Alu/B2/B4, which may be related to euchromatin organization. HTG‐Positions, SEs‐Positions, as well as TrxG‐AP1‐Positions with higher Alu/B2 enrichment than others, suggest that the enrichment of Alu/B2 may indicate the transcription level of genes, and promote the formation of hierarchical chromatin structures by regulating gene transcription and SEs/Enhancer activation.We also observe that genomic regions . We first observe that the 21st class occurs a stronger heterochromatin factors (the Hetero cluster) enrichment compared to the 9th (CoreRF‐Positions), which may result in uncertain chromatin states in the 21st position  ERVK elements act as specific roles in heterochromatin formation, while Alu/B2 may promote high transcription of genes and high activation of SEs/Enhancers  L1‐Positions with low accessibility and unfavorably forming boundaries are associated with heterochromatin organization, and Alu/B2/B4‐Positions are associated with euchromatin chromatin, which are consistent with a recent study,23a wh2.7 Both complexes have been proven to modify local chromatin structure and regulate higher‐order chromatin organization. And functional groups (Hetero‐Positions and AP2‐Repressor‐Positions) associated with heterochromatin organization prefer to form boundaries in both MS and LS/G2 phases , mid‐S (MS), and late‐S/G2 (LS/G2). We first reveal that genomic positions enriched by SEs show a significant preference for forming boundaries in the G1 phase Figure . The obs25 Bots Figure . Both phth class of CoreRF‐Positions) enriched by Alu/B2/B4 retrotransposons have higher preference scores for forming boundaries of single cells in G1 and ES phases in comparison with MS and LS/G2 phases, whereas genomic positions  enriched by L1/ERVK retrotransposons display opposite tendency, following by high preference scores for forming boundaries in both MS and LS/G2 phases  to understand structure‐function relationships in complex tissues or diseases, particularly cancers. It will promote our understanding of how multiple types of regulatory factors interact with chromatin topological engines  to regulate the gene‐repression program, determine cell functions and identity, and further explain tissue complexity and disease development.The chromatin structures within the nucleus operate in an obvious dynamic process driven by both “loop extrusion” and an attractive process induced by regulatory factors . The process may condense or lose local chromatin landscapes in an overlapping and concerted manner accompanied by adjusting the insulation strength of chromatin position and generating chromatin loops, and then shaping gene expression programs during cell‐fate specification.25a Fu4 The top 1315 cells with > 250 000 contacts per cell were selected for downstream analysis. Among them, 317, 341, 303, and 354 cells belonged to G1, early‐S (ES), mid‐S (MS), and late‐S/G2 (LS/G2) phases labeled by fluorescence‐activated cell sorting (FACS) sort criterion, respectively.The single‐cell Hi‐C dataset used in this study consisted of 1992 diploid cells of mESCs grown in 2i media without feeder cells with a stringent quality control filter. This dataset involved a median number of 393 506 restriction fragments, and 127 233 distinct > 1 kb contacting pairs on average per cell.11 TheSmc1, Smc3, Rad21, Sa1, Sa2), mediators , codensin , PolII, TFIIB, polycomb repressive complex , trithorax protein family , the core regulatory factors of mESC , the regulatory factors of ESC occupied on enhancers or SEs , the regulatory factors of ESC occupied on promoter‐proximal sites or sites that border topological domains , and additional 45 TFs. 14 histone modification factors, including H3K4me1, H3K4me2, H3K4me3, H3K27me3, H3K36me3, H3K79me2, H3k9me3, H3K9ac, H3K122ac, H3K64ac, H3K27ac, H2AZ, ZC3H11A, and H2AK119ub1 were collected. In addition, two chromatin accessibility datasets (ATAC‐seq and Dnase‐seq) and the DamID‐seq dataset were also collected.An atlas of ChIP‐seq for hundreds of regulatory factors of mESCs was organized . PhastCons scores were downloaded from the UCSC Genome Browser via www.hgdownload.cse.ucsc.edu/goldenPath/mm9/phastCons30way/vertebrate. Mouse cell‐cycle annotated genes were obtained from the mouse genome informatics (MGI) (http://www.informatics.jax.org/), containing 891 genes relating to cell cycle process and regulation. The authors adopted Seurat to detect DEGs and the variable score of genes using FindAllMarkers and FindVariableFeatures functions based on single‐cell RNA‐seq data of ESCs, which consists of 182 cells labeled by FACS sort criterion, including 59, 58, and 65 cells belonging to “G1”, “S”, and “G2M” phases, respectively.Enhancers/SEs and gene expression datasets of mESCs were downloaded in GSE29278.29 Houhttp://genome.ucsc.edu/). The authors kept the top six categories of counts for the downstream analysis, including Alu, B2, B4, MaLR, L1, and ERVK.Retrotransposons built from RepeatMasker annotations were downloaded from the UCSC Table Browser  and the contact was contacted for each bin pair. Next, the authors modeled each chromosome as an unweighted network , and implemented a classic graph embedding method node2vec, which applied a biased random walks procedure, to compute the contact probability of edges by computing the cosine similarity of any two node embedding vectors, and obtained the preprocessed matrix A Figure .33]33A  of the ith genomic position is defined as the ratio of  and  using an 800 kb sliding window size:Inspired by a fast density‐based clustering method designed for grouping data points,34 thes Figure . SpecifiaI, bI, and cI, respectively, represent the summation of interaction frequencies for regions a, b, and c, and 2) minimum distance between the bin i and any other bin j with higher insulation strength is defined as δ(i):where i in the range of MAX = 500 (20 M genomic distance at 40 kb resolution). Next, the authors defined ρ′(i) = ρ′(i)/ρmax and δ′(i) = δ(i)/δmax such that both ρ′(i) and δ′(i) were within range . Then, the rank γ of all bins was generated for each chromosome by their η(i) = ρ′(i) × δ′(i) in the descending and the rank of each bin was normalized by γ′(i) = γ(i)/γmax. The optimal reflection point was defined with r=argmini=1:lenη(i)2+γ′(i)2, where len is defined as the number of bins for a specific chromosome. The boundaries of the optimal structure were assigned by bins with η(i) > α × η(r), (α = 1). The gap regions were defined by Ii,inter = 0 or had no contact with any other bins.The authors searched for higher insulation strength of bin η(i) > α × η(r) as a screening selection for different genomic levels, where the range of α was set as   Figure . In thiss Figure .The hierarchical clustering method was applied to group regulatory factors and chromatin positions, respectively Figure . The claThe hierarchical clustering method was further applied to merge the 27 classes into seven large clusters, based on the Pearson correlation of the normalization ratio of the mean counts for peaks of regulatory factor classes .All datasets analyzed in this study were published previously. The corresponding descriptions and preprocessing steps can be found in Supporting Information. The open‐source HiCS python package and tutorial are available at GitHub (The authors declare no conflict of interest.Y.Y. conceived the idea, implemented the algorithm, and performed the analyses. Y.Y. interpreted the results. S.Z. and L.G. provided scientific insights on the applications. Y.Y. wrote the manuscript with feedback from all other authors. All of the authors read and approved the final manuscript.Supporting InformationClick here for additional data file.


DEAD-box decapping enzyme 20 (DDX20) is a putative RNA-decapping enzyme that can be identified by the conserved motif Asp–Glu–Ala–Asp (DEAD). Cellular processes involve numerous RNA secondary structure alterations, including translation initiation, nuclear and mitochondrial splicing, and assembly of ribosomes and spliceosomes. DDX20 reportedly plays an important role in cellular transcription and post-transcriptional modifications. On the one hand, DDX20 can interact with various transcription factors and repress the transcriptional process. On the other hand, DDX20 forms the survival motor neuron complex and participates in the assembly of snRNP, ultimately affecting the RNA splicing process. Finally, DDX20 can potentially rely on its RNA-unwinding enzyme function to participate in microRNA (miRNA) maturation and act as a component of the RNA-induced silencing complex. In addition, although DDX20 is not a key component in the innate immune system signaling pathway, it can affect the nuclear factor kappa B (NF-κB) and p53 signaling pathways. In particular, DDX20 plays different roles in tumorigenesis development through the NF-κB signaling pathway. This process is regulated by various factors such as miRNA. DDX20 can influence processes such as viral replication in cells by interacting with two proteins in Epstein–Barr virus and can regulate the replication process of several viruses through the innate immune system, indicating that DDX20 plays an important role in the innate immune system. Herein, we review the effects of DDX20 on the innate immune system and its role in transcriptional and post-transcriptional modification processes, based on which we provide an outlook on the future of DDX20 research in innate immunity and viral infections. DDX20 is also known as Gemin3 or DP103 and is among the most extensively researched genes . AdditioAs a novel human member of the DEAD-box family with ATP-dependent RNA-unwinding enzymes, DDX20 was detectable at the molecular level in mammalian cells with monoclonal antibodies against DDX20 elevated to 103 kDa in size . SubsequThe DEAD-box RNA-decapping enzyme family features a unique Asp–Glu–Ala–Asp (D-E-A-D) sequence motif . All DEADDX20 participates in a complex biological process regarding its subcellular localization. As a member of the SMN complex, DDX20 plays a role in the assembly of snRNP . The assUpon assembly and capping at the 5′ end, snRNP enters the nucleus aided by the SMN complex and the import factor importin β, with DDX20 associated with snRNP following suit ,25. In tThe localization of DDX20 in the cytoplasm and nucleus is subject to several influencing factors. First, the SMN complex undergoes extensive phosphorylation modifications, and dephosphorylation modifications of several proteins within the SMN complex, including DDX20, obstructs the movement of the SMN complex into the nucleus and its accumulation in the CBs . Second,Existing research indicates that the motor neuron protein SMN, along with Gemin2–8 proteins, including DDX20/Gemin3 and Unrip, form a stable SMN complex, with DDX20/Gemin3 playing a vital role in the assembly of this complex ,33,34. TAs a molecular companion, the SMN complex facilitates the assembly and function of spliceosomal small ribonucleoprotein (snRNP) . snRNP cInitially, snRNAs (excluding U6) are transcribed in the nucleus by RNA polymerase II as precursor RNAs, containing an additional stem-loop structure at the 3′ end and a monomethylated m7GpppG (m7G) cap structure at the 5′ end . This prIn the cytoplasm, Sm and Sm-like proteins first bind to the chloride conductance regulatory protein (pICln) and protein arginine methyltransferase 5 (PRMT5) complexes. These proteins are then prearranged into spatial positions and separated upon the addition of the SMN complex, which assembles the Sm and Sm-like proteins into protein binding sites on snRNAs, forming the complete snRNP with a seven-membered loop structure ,40.Finally, the snRNA with the Sm core undergoes methylation at its 5′ end, catalyzed by trimethylguanosine synthase 1 (TGS1), to form an m3G cap structure. This process is mediated by the SMN complex and importin β, facilitating the performance of splicing functions in the nucleus ,41. ThisFirst and foremost, the SMN complex serves a critical, nonreplaceable role in snRNP assembly, and DDX20/Gemin3 is indispensable for the stabilization of the complex. Within the SMN complex, DDX20/Gemin3 engages with SMN proteins, along with Gemin2, 4, and 5. This interaction recruits Gemin3 as a core component of the SMN complex, promoting its stability ,43,44. AThe constituent proteins of the SMN complex mutually interact; thus, changes in one protein affect the stability and abundance of other proteins. For instance, a reduction in SMN protein results in the disappearance of Gems and a decrease in several Gemin levels, ultimately affecting snRNP assembly  and therSecond, the SMN complex is extensively regulated by phosphorylation. DDX20/Gemin3, containing 13 phosphorylation sites, is among the most highly phosphorylated proteins in the complex. The phosphorylation of DDX20/Gemin3 and other complex proteins influences the exchange of the SMN complex between the cytoplasm and nucleus as well as the assembly of snRNP . The effExisting studies have reported that Gemin3 significantly impacts snRNP assembly. Notably, the disruption of snRNP assembly following Gemin3 knockdown via RNAi provides the most direct evidence ,48. The Further evidence regarding the role of Gemin3 in snRNP assembly is provided by Almstead et al., who found that Gemin3′s specific cleavage by the poliovirus-encoded protease 2Apro results in reduced Gemin3 protein levels, which led to specific rearrangement of Sm proteins from the nucleus to the cytoplasm and a reduction in snRNP assembly . In addiBased on the outlined role of DDX20/Gemin3, we understand that it contributes to RNA splicing not through direct interaction with the pre-RNA but by playing a critical part in the assembly of the spliceosomal snRNP as a component of the SMN complex. This process facilitates the subsequent modification and splicing of the pre-RNA by the snRNP within the nucleus. In this intricate and indirect manner, DDX20/Gemin3 is instrumental in the RNA splicing process.In recent years, the role of DDX20 in transcription has been increasingly reported and considerable evidence regarding its role as a transcription regulator has been reported. DDX20 primarily suppresses transcription by interacting with the nuclear receptor steroidogenic factor-1 (SF-1) . SF-1, aSF-1 has a homologous gene in arthropods, known as FTZ-F1 (fushi tarazu factor-1) . A studyThe Forkhead transcription factor (FOXL) 2, a transcription factor closely related to FTZ-F1 and DDX20, also plays a significant role in regulating ovarian development. In the ovary, FOXL2 is involved in regulating cholesterol and steroid metabolism, apoptosis, reactive oxygen species detoxification, and cell proliferation . InitialIn addition to F-1, DDX20 has been discovered to interact with two other transcription factors to inhibit their activity by different mechanisms compared with that used with SF-1. First, DDX20 can engage with the mitotic Ets transcription inhibitor (PE-1/METS) via its C-terminal domain. Ets is a transcription factor that serves as a nuclear target to activate the Ras–MAPK signaling pathway, while PE-1/METS, another member of the Ets family, acts as an Ets inhibitor to repress the Ras-dependent proliferation of Ets target genes, thus causing macrophage growth arrest .DDX20 interacts with the N-terminal domain of PE-1/METS via its C-terminal domain, which has an inhibitory effect. In this process, DDX20 also recruits factors such as histone deacetylase HDAC-2 and HDAC5 . HoweverThe C-segment domain of DDX20 also interacts with another transcription factor known as early growth reaction 2 (Egr2/Krox-20). All proteins interacting with the C-terminal of DDX20 are summarized in Thus, DDX20 does not undergo sumoacylation as DDX5 and its transcription regulation activity do not solely rely on a single intrinsic function but involve multiple mechanisms, many of which depend on its unique noncore C-terminal domain. This multifaceted approach to transcriptional regulation reinforces the complexity of the function of DDX20 in this essential cellular process.DDX20/Gemin3 also contributes to the maturation of miRNA. The biogenesis of miRNA begins with the transcription of miRNA genes into primary miRNA (pri-miRNA) by RNA polymerase II. The pri-miRNA is subsequently processed into a precursor miRNA (pre-miRNA) of ~70 nucleotides by a complex containing the RNase III endonuclease Drosha and a protein containing a double-stranded RNA-binding structural domain (dsRBD) named Pasha (DGCR8) ,78.Subsequently, the stem–loop-structured pre-miRNAs are transported into the cytoplasm, which is dependent on the GTP-dependent transport protein Exportin-5 (Exp5) . In the Although most Gemin3 and Gemin4 are components of the SMN complex, the complexes of Gemin3 and Gemin4 isolated from HeLa and neuronal cells are not part of the SMN complex. Instead, these complexes coprecipitate with polyribosomes ,81,82. NMiRNAs can inhibit the translation of partially complementary target messenger RNAs by directing the sequence-specific degradation of both fully and partially complementary target mRNAs ,86. HoweRISC was not previously identified as having decapping components for siRNAs and miRNAs within the complex. Consequently, researchers posit that Gemin3 serves as a decapping enzyme within the RISC complex and plays a role in RNA decapping or recombination during miRNA maturation as well as in target RNA recognition .The NF-κB family comprises six distinct components. When activated, they produce various proinflammatory factors that regulate inflammation and significantly contribute to innate immunity ,88. The While the NF-κB signaling pathway has an antiapoptotic role, its dysregulation has been implicated in the pathogenesis of most human malignancies . TherefoDDX20 can have two distinct effects on the NF-κB signaling pathway. Numerous studies have reported that DDX20 does not directly affect NF-κB activity but rather acts through a naturally occurring small non-coding RNA (miRNA) intermediate. The present study demonstrated that DDX20 can modulate the signaling of the NF-κB pathway through miRNA-22, miRNA-140-3p, and miR-222 ,95. AddiAnother way DDX20 can inhibit the NF-κB signaling pathway is through miRNA-140 dysfunction, which increases expression of its downstream target gene Dnmt1 and the methylation of CpG islands in the promoter region of metallothionein (MTs) and decreases MT expression, leading to enhanced NF-κB activity . Second,The TP53 gene encodes the key p53 transcription factor and evolutionarily conserved tumor suppressor involved in maintaining genomic stability . To accoIn fact, the effect of DDX20 on the p53 signaling pathway can be realized by directly affecting the pivotal p53 protein. Changes in DDX20 expression can consequently impact the organism’s state through the p53 signaling pathway. Reportedly, DDX20 interacts with p53 protein through its C-terminal structural domain . The norIn this context, several cytokines, such as oligodendrocyte transcription factor 2 (Olig2) and Epstein–Barr (EB) nuclear antigen 3C (EBNA3C), can directly interact with DDX20 to stabilize its expression. This interaction inhibits transcription and apoptosis caused by the p53 signaling pathway and its downstream genes within the organism ,67.DDX20 not only plays a role in the invasion of multiple pathogens but also plays an active role in the case of multiple tumorigenesis. We have previously mentioned that DDX20 is inconsistently expressed in different cancerous tissues and studies have reported that DDX20 exhibits contradictory effects in cancer. For example, it plays an oncogenic role in breast, prostate, and liver cancers while acting as a tumor suppressor ,104,105.In breast cancer, DDX20 is involved in cell signaling pathway activity, which is a key factor in tumorigenesis. The NF-κB signaling pathway is more closely linked to tumor development, and, reportedly, it can improve cancer cell survival, promote cancer cell angiogenesis and migration, and has other characteristics alongside being associated with the poor prognosis of cancer diseases . It is bIn contrast to its role in prostate and breast cancers, DDX20 acts as a tumor suppressor in liver cancer and suppresses tumorigenesis. As reported, tumor genomics of RNAi performed in a mouse model of hepatocellular carcinoma has identified DDX20 as a tumor suppressor . MiRNAs Thus, DDX20 acts either as an oncogenic factor promoting tumor development or as a tumor suppressor inhibiting tumor progression. In conclusion, DDX20 plays a role in the regulation of hepatocellular carcinoma development through various miRNAs, including miRNA-140-3P and miRNA-22, as well as in breast cancer development through miRNA-222. DDX20 primarily functions by influencing the NF-κB signaling pathway. Beyond its roles in breast cancer, prostate cancer, and hepatocellular carcinoma, DDX20 has been implicated in several other tumors . Table 2Early research identified the capacity of DDX20 to interact with two vitally encoded nuclear antigens of the EB virus (EBV), EBNA2 and EBNA3C, and modulate the transcription of viral and cellular genes . EBV is According to the report, EBNA2 can serve as a transcriptional activator of transformative viral and cellular genes by regulating two EBNA2-regulated viral promoters (TP1 and LMP/TP2 promoter) following its binding to the homologous promoter element RBPJkappa ,122. ThiLater studies revealed that DDX20 can bind EBNA2 and survival motor neuron (SMN) proteins via its C-terminal structural domain, reporting that EBNA2 targets the spliceosome complex to release the SMN protein after binding DDX20, which ultimately acts as a coactivator in RNA polymerase II transcriptional complexes on the LMP1 promoter . An addiConversely, EB nuclear antigen 3C (EBNA3C) is an important latent antigen that induces B lymphocyte immortalization in EBV through its contribution to viral pathogenicity. This is achieved via the putative bZIP structural domain at the N terminus of the protein and its interaction with cellular transcription factors RBPJkappa and HDAC1 to regulate transcriptional activation ,127. AddSimilarly, DDX20 interacts with EBNA3C via its C-terminal structural domain. DDX20 can be stabilized by EBNA3C to form a complex with p53, which subsequently blocks p53-mediated transcriptional activity and apoptosis . MoreoveIn addition to EBV, DDX20 has significant connections with other viruses. DDX20 expression has previously been identified to vary in tumor tissues. In terms of changes in its expression upon viral infection, researchers reported that DDX20 is differentially expressed at distinct stages of human immunodeficiency virus type 1 (HIV-1) infection and found that DDX20 interacts with the HIV-1 coprotein Vpr as early as 2012 .Further analysis of global miRNA and mRNA expression through microarray and quantitative reverse transcription polymerase chain reaction in well-characterized HIV-1 latently and actively infected cells revealed that DDX20 was significantly upregulated in the former . ConversDDX20 can enhance Interferon regulatory factor 3 (IRF3) phosphorylation levels by promoting the interaction between TBK1 and IRF3, which further promotes IFN-β expression, ultimately inhibiting the replication of VSV and HSV-1 through INF-stimulated genes (ISG) [Since its identification as a member of the DEAD family of RNA-unwinding enzymes, the diverse functions of DDX20 have been gradually unveiled. Early discoveries indicated that DDX20 could modulate transcriptional processes by influencing transcription factors such as SF-1, PE-1/METS, and Egr2/Krox-20 ,63,70. HFurthermore, early investigations revealed the involvement of DDX20 in the transcription of viral genes influenced by EBV nuclear antigens . Recent Given the close relationship between the functions of DDX20 and its structural attributes, a comprehensive understanding of the structure of DDX20 is crucial. Whether it interacts with transcription factors to regulate transcriptional processes or acts as a target protein in tumorigenesis, the C-terminal domain of DDX20 plays a vital role. Therefore, gaining better insights into the structure of DDX20 is imperative. Moreover, it is foreseeable that DDX20 can be developed as a potential drug target for antitumor treatments in oncological diseases. Furthermore, the specific mechanism through which DDX20 exerts its antiviral effect via innate immunity remains unclear, emphasizing the need for further research to unveil the mechanisms underlying virus replication. Such investigations can also establish a theoretical foundation for the development of relevant antiviral products.


Escherichia coli transcription factor PhoB, a response regulator that controls transcription of genes involved in phosphate homeostasis. Strikingly, the majority of PhoB binding sites are located within genes, but these intragenic sites are not associated with detectable transcription regulation and are not evolutionarily conserved. Many intragenic PhoB sites are located in regions bound by H-NS, likely due to shared sequence preferences of PhoB and H-NS. However, these PhoB binding sites are not associated with transcription regulation even in the absence of H-NS. We propose that for many transcription factors, including PhoB, binding sites not associated with promoter sequences are transcriptionally inert and hence are tolerated as genomic “noise.”Genome-scale analyses have revealed many transcription factor binding sites within, rather than upstream of, genes, raising questions as to the function of these binding sites. Here, we use complementary approaches to map the regulon of the  Bacteria encode numerous transcription factors (TFs) that regulate transcription initiation by binding DNA near promoters and modulating the ability of RNA polymerase (RNAP) holoenzyme to bind promoter DNA or to isomerize to an actively transcribing conformation . TF func3–Regulatory activity has been described for few intragenic TF binding sites and can be classified into the following distinct classes based on the regulatory target and mechanism of action: (i) canonical regulation of transcription initiation of the downstream gene, generating an RNA with an extended 5′ untranslated region (UTR) that overlaps a gene –10; (ii)–15–22–i) levels are low, PhoR autophosphorylates and then phosphorylates PhoB , a phosphonate transport complex (phn), and the glycerol-3-phosphate transporter (ugp) and other genes related to phosphate homeostasis  and transcriptome sequencing (RNA-seq). We refine and expand the set of known pho regulon genes and identify many intragenic PhoB binding sites. We show that the large majority of intragenic PhoB binding sites are not conserved and are not associated with detectable regulatory function. Thus, our data suggest that individual intragenic PhoB sites are nonfunctional and that TFs can bind many intragenic sites with little or no impact on local transcription.Here, we describe a high-resolution, genome-wide mapping of the E. coli PhoB, we introduced C-terminal FLAG tags at the native phoB locus. We used quantitative reverse transcriptase PCR (qRT-PCR) to measure the expression of pstS, a PhoB-activated gene, in wild-type cells, ΔphoB cells, and cells expressing phoB-FLAG3. Cells were grown in minimal medium with low phosphate levels to induce the kinase activity of PhoR. As expected, we observed a large decrease (~900-fold) in pstS levels in ΔphoB cells relative to those of wild-type cells  in pstS levels relative to those of wild-type cells  (pho box consensus sequence (−12). The presence and central enrichment of this motif at ChIP-seq peaks further support the veracity of PhoB-bound regions and confirm the high spatial resolution of the ChIP-seq data.We used ChIP-seq to map the genome-wide binding of PhoB-FLAG regions ; Table 1data set . We concoB sites . We idenes (−72) . This mosequence . Furthersequence   and andpho r O157:H7 , 42. We de genes ; Table 1de genes ; Table 1waaH, ytfK, or psiE. Of the 43 ChIP-chip peaks identified by Yang et al. (de novo motif identification for three sets of peaks: (i) shared, (ii) unique to the current study, and (iii) unique to the study of Yang et al. (Our PhoB ChIP-seq data show only modest agreement with an earlier study that identified many putative PhoB binding sites using chromatin immunoprecipitation with microarray technology (ChIP-chip) , althougg et al.  and B. FpstS  . We also observed significant positive regulation of amn and yoaI; ChIP-seq identified PhoB binding upstream of these genes, and although they have not generally been considered part of the pho regulon, they have been previously reported as being direct PhoB targets  were differentially expressed >2-fold between wild-type and ΔphoB cells, all but yahA were more highly expressed in ΔphoB cells than wild-type cells. We hypothesized that PhoB represses transcription of these genes by acting as a roadblock for RNAP. To test this hypothesis, we grew wild-type and ΔphoB cells under phosphate-limiting conditions and measured RNAP (β subunit) occupancy upstream and downstream of the PhoB sites within the gloC, pnp, and evgS genes by using ChIP-quantitative PCR (ChIP-qPCR). For controls, we measured RNAP occupancy within the pstS and ugpB genes, confirmed members of the pho regulon. We also measured RNAP occupancy within the yoaI and amn genes that the combined RNA-seq and ChIP-seq data suggested are members of the pho regulon. As expected, we observed substantially higher RNAP occupancy within pstS and ugpB in wild-type cells than in ΔphoB cells , suggesting that PhoB regulation of these genes is indirect.To identify novel oB cells . MoreoveoB cells , supportee genes   the binding sites are nonregulatory, (ii) regulation is condition specific and/or requires additional factors, or (iii) PhoB regulates transcription of short, unstable, noncoding RNAs that are not detectable by conventional RNA-seq. To test the latter possibility, we used ChIP-seq to measure the association of σongation ; thus, σble RNAs .70 in wild-type and ΔphoB strains grown in low-phosphate medium. Normalized σ70 occupancy was calculated for 400-bp windows surrounding each PhoB binding site to systematically assess σ70 binding at these sites . Since H-NS is known to silence transcription  in σ70 occupancy were associated with the PhoB sites at phoB and pstS. Interestingly, we did not observe differences of >1.5-fold in σ70 occupancy at any other sites, including the sites upstream of yoaI, phoA, mglB, phnC, and phoH. However, the differences in σ70 occupancy that we observed for phoB and pstS sites were between 2- and 4-fold lower than the differences observed in the hns+ strains at the same sites. Hence, the more subtle differences in σ70 occupancy observed at the sites upstream of yoaI, phoA, phnC, and phoH in the hns+ strains may have escaped detection in the Δhns strains. The lower effect of PhoB on σ70 occupancy in the Δhns strain background may be due to the large-scale redeployment of RNAP that occurs in the absence of H-NS (We noted that 18 PhoB sites (12 intragenic and 6 intergenic), representing 28% of all sites identified by ChIP-seq, are in regions bound by the nucleoid-associated protein H-NS . Thus, Pcription , we hypotragenic . Indeed,ccupancy  49). Th. ThP = 0ccupancy . We next strains . As for  of H-NS .While PhoB sites are enriched within H-NS-bound regions, H-NS does not appear to modulate PhoB activity at any site. We hypothesized that the enrichment of PhoB binding within H-NS-bound regions is due simply to the nucleotide content of the PhoB binding site; like H-NS-bound regions, the PhoB binding site has a higher A/T content than the genome as a whole. To test this hypothesis, we scrambled the sequence of every PhoB binding site identified by ChIP-seq. We then derived a position weight matrix (PWM) from these scrambled sites and scored every genomic sequence for a match to this PWM. Strikingly, 36% of the top 1,000 scoring positions are within regions bound by H-NS . We concE. coli PhoB sites identified by ChIP-seq for which we could identify an instance of the PhoB binding motif. Specifically, we scored homologous regions from 29 diverse gammaproteobacterial species for matches to the PhoB binding site motif  show no difference in σ70 occupancy upon deletion of phoB. Consistent with this idea, structural models of the PhoB-RNAP-DNA complex formed at PhoB-activated promoters support strict spacing requirements between the pho box and core promoter elements . A previous study showed that the predicted number of intragenic binding sites for many bacterial TFs is the same in actual genome sequences as it is in randomized genome sequences, suggesting that intragenic TF binding sites are common and arise largely due to genetic drift  and regulatory information  is required to accurately define the regulons of most TFs.We have comprehensively mapped the PhoB regulon by assessing PhoB binding, PhoB-dependent transcriptome changes, and PhoB-dependent RNAP recruitment. We identified novel E. coli MG1655 and its derivatives were used for this study. The strains and plasmid used are listed in phoB locus by use of FRUIT recombineering (phoB (DMF84) and Δhns (AMD565a) strains were constructed by P1 transduction from Keio collection strains  was constructed by use of FRUIT /pBAD24, or DMF34/pBAD24 cells were grown at 37°C with aeration in MOPS (morpholinepropanesulfonic acid) minimal medium with 0.2 mM K2PO4, 0.4% glucose, and 100 μg/mL ampicillin to an optical density at 600 nm (OD600) of 0.5 to 0.6. Arabinose was added to a final concentration of 0.2% for 7 min or 20 min, with one or two of the three replicate samples for each strain receiving arabinose for 7 min. Thus, the replicate samples were not always consistent with respect to the extent of growth after arabinose addition. However, since arabinose is not expected to affect pstS expression, all samples for a single strain were treated as replicates regardless of whether the cells were grown for 7 min or 20 min after arabinose addition. RNA was prepared as described for RNA-seq. RNA was reverse transcribed using SuperScript III reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. A control reaction omitting reverse transcriptase was performed. One percent of the cDNA (or negative control) was used as a template in a quantitative real-time PCR using an Applied Biosystems 7500 fast real-time PCR machine, with primers JW156 and JW157 for amplifying the minD control gene and primers JW7802 and JW7803 for amplifying pstS. Relative expression of pstS was determined by the ΔTC method, with normalization to minD expression.qRT-PCR was performed based on a previous study . MG1655/2HPO4 and 0.4% glucose, as previously described (2HPO4 and 0.4% glucose. Subcultures were inoculated 1:100 and grown at 37°C with aeration to an OD600 of 0.5 to 0.7. ChIP-seq libraries were prepared as previously described (phoB-FLAG3) or MG1655, PhoB-FLAG3 (or the untagged control) was immunoprecipitated with 2 μL of anti-FLAG M2 monoclonal antibody (Sigma). For MG1655, DMF84 (ΔphoB), AMD565a (MG1655 Δhns), and DMF85 (MG1655 ΔphoB Δhns), σ70 was immunoprecipitated with 1 μL of anti-σ70 antibody (Neoclone). Libraries were sequenced on a HiSeq 2000 (Illumina) by the University at Buffalo Next-Generation Sequencing Core Facility or on a NextSeq (Illumina) by the Wadsworth Center Applied Genomic Technologies Core Facility.For low-phosphate growth experiments, cells were grown at 37°C with aeration in MOPS minimal medium with 0.2 mM Kescribed . For higescribed . LibrariphoB-FLAG3 (DMF34) grown in low-phosphate conditions, (ii) untagged MG1655 grown in low-phosphate conditions, and (iii) MG1655 phoB-FLAG3 (DMF34) grown in high-phosphate conditions were aligned to the E. coli MG1655 genome (GenBank accession no. NC_000913.3) using CLC Genomics Workbench (version 8). ChIP-seq peaks were called using a previously described analysis pipeline (Duplicate ChIP-seq data for (i) MG1655  version . ChIP-sephoB (CDS091) cells were grown at 37°C with aeration to an OD600 of 0.6 to 0.7 in MOPS minimal medium with 0.2 mM K2HPO4 and 0.4% glucose, as previously described , JW10937 + JW10938 (yoaI), JW10939 + JW10940 (amn), JW10941 + JW10942 (pstS), JW10943 + JW10944 (ugpB), JW10945 + JW10946 (mepK), JW10947 + JW10948 (gloC), JW10949 + JW10950 (evgA), JW10951 + JW10952 (evgS), JW10953 + JW10954 (pnp upstream), and JW10955 + JW10956 (pnp downstream). For statistical analysis of relative changes in RNAP occupancy upstream and downstream of intragenic PhoB sites, we used the mean and standard deviation values for ChIP-qPCR occupancy to generate 1,000 simulations for occupancy at each region tested. We then determined how frequently the ratio of predicted RNAP occupancy downstream to predicted RNAP occupancy upstream of an intragenic PhoB site was higher in ΔphoB cells than wild-type cells. We repeated this simulation 10 times to estimate a P value for each of the three intragenic PhoB sites tested.Wild-type MG1655 and MG1655 Δescribed . ChIP waescribed  using 1 70 from wild-type MG1655, MG1655 ΔphoB (DMF84), MG1655 Δhns (AMD565a), and MG1655 Δhns ΔphoB (DMF85) were aligned to the E. coli MG1655 genome (GenBank accession no. NC_000913.3) using Rockhopper  (70 ChIP-seq data set in 400-bp windows centered on each PhoB ChIP-seq peak (coordinates listed in Duplicate ChIP-seq data for σameters) , which a70 for wild-type MG1655 or MG1655 Δhns (AMD565a) were aligned to the E. coli MG1655 genome (GenBank accession no. NC_000913.3) using CLC Genomics Workbench (version 8). ChIP-seq peaks were called using a previously described analysis pipeline (70 ChIP-seq data set in 50-bp windows centered on each σ70 ChIP-seq peak from MG1655 Δhns (AMD565a).Duplicate ChIP-seq data for σ version . ChIP-se70 ChIP-seq peaks were converted from NCBI genome sequence version U00096.3 to U00096.2 at https://biocyc.org/ECOLI/map-seq-coords-form?chromosome=COLI-K12. H-NS occupancy was determined as the average from four normalized sequence read coverage files available from EBI ArrayExpress under accession number E-MTAB-332.H-NS ChIP-seq occupancy was determined from published data . SpecifiphoB)/pBAD24 cells were grown at 37°C with aeration in MOPS minimal medium with 0.2 mM K2PO4, 0.4% glucose, and 100 μg/mL ampicillin to an OD600 of 0.5 to 0.6. Arabinose was added to a final concentration of 0.2% for 7 min. Note that addition of arabinose is not expected to impact the expression of PhoB-regulated genes. RNA was isolated using a modified hot-phenol method, as previously described (E. coli MG1655 genome (GenBank accession no. NC_000913.3) using BWA for Illumina (v0.5.9-r16)  (MG1655/pBAD24 or CDS091 (MG1655 Δescribed . Samplesescribed . Librari5.9-r16)  on Galaxaxy.org) . Read coaxy.org)  and DEseaxy.org) .Sequences of 100 bp surrounding PhoB ChIP-seq peaks were extracted and analyzed using MEME  , 83. TheE. coli MG1655 genome (GenBank accession no. NC_000913.3) for the top 1,000 matches to this PWM using FIMO   .Arsenophonus nasoniae DSM 15247, Brenneria sp. EniD312, Cedecea davisae DSM 4568, Citrobacter rodentium ICC168, Cronobacter sakazakii ATCC BAA-894, Dickeya dadantii 3937, Edwardsiella tarda EIB202, Enterobacter cloacae subsp. cloacae ATCC 13047, Erwinia amylovora ATCC 49946, Escherichia coli strain K-12 substrain MG1655, Hafnia alvei ATCC 51873, Klebsiella pneumoniae KCTC 2242, Leminorella grimontii ATCC 33999 = DSM 5078, Morganella morganii subsp. morganii KT, Pantoea agglomerans 299R, Pectobacterium atrosepticum SCRI1043, Photorhabdus asymbiotica, Plesiomonas shigelloides 302-73, Proteus mirabilis HI4320, Providencia stuartii MRSN 2154, Pseudomonas aeruginosa PAO1, Rahnella sp. Y9602, Raoultella ornithinolytica B6, Salmonella enterica strain P125109, Serratia marcescens FGI94, Vibrio cholerae M66-2, Xenorhabdus bovienii SS-2004, Yersinia pestis KIM 10, and Yokenella regensburgei ATCC 43003.Binding site conservation analysis was performed as described previously . ProteinE-MTAB-9293. RNA-seq data are available at EBI ArrayExpress under accession number E-MTAB-9591.ChIP-seq data are available at EBI ArrayExpress under accession number


Transcription-factor binding to cis-regulatory regions regulates the gene expression program of a cell, but occupancy is often a poor predictor of the gene response. Here, we show that glucocorticoid stimulation led to the reorganization of transcriptional coregulators MED1 and BRD4 within topologically associating domains (TADs), resulting in active or repressive gene environments. Indeed, we observed a bias toward the activation or repression of a TAD when their activities were defined by the number of regions gaining and losing MED1 and BRD4 following dexamethasone (Dex) stimulation. Variations in Dex-responsive genes at the RNA levels were consistent with the redistribution of MED1 and BRD4 at the associated cis-regulatory regions. Interestingly, Dex-responsive genes without the differential recruitment of MED1 and BRD4 or binding by the glucocorticoid receptor were found within TADs, which gained or lost MED1 and BRD4, suggesting a role of the surrounding environment in gene regulation. However, the amplitude of the response of Dex-regulated genes was higher when the differential recruitment of the glucocorticoid receptor and transcriptional coregulators was observed, reaffirming the role of transcription factor-driven gene regulation and attributing a lesser role to the TAD environment. These results support a model where a signal-induced transcription factor induces a regionalized effect throughout the TAD, redefining the notion of direct and indirect effects of transcription factors on target genes. Gene regulation is controlled by the recruitment of transcriptional regulators to cis-regulatory regions and implicates an intricate interplay with the chromatin environment and the chromosome architecture . In factIn higher eukaryotes, genes are clustered not only in the linear genome but also in the three-dimensional genome . Indeed,Transcription factors modify the transcriptional program in response to external signals by converging toward sequence-specific DNA motifs within cis-regulatory regions. Accordingly, they play major roles in normal and disease development by establishing and maintaining cell states . Across Signal-induced transcription factor stimulations, including steroid nuclear receptors, are associated with a global redistribution of transcriptional coregulators, leading to the activation or repression of specific genes . Among tHere, we show that glucocorticoids, through the GR, induce a reorganization of transcriptional coregulators MED1 and BRD4. These regionalized changes in gene regulation are secluded within TADs, showing gains and losses in MED1 and BRD4. The presence of a gene within a changing TAD is an important determinant of the gene response. Our model proposes that regionalized gene regulation within TADs is a direct consequence of the modulation of the levels of transcriptional coregulators in response to glucocorticoids.A549  cells were grown in F12K medium . The culture medium was supplemented with 10% fetal bovine serum , 100 μM MEM non-essential amino acids , 2 mM L-glutamine , 100 U/mL penicillin, and 100 μg/mL streptomycin . For Dex  stimulations, cells were kept for 3 days in phenol red-free DMEM  supplemented with 5% charcoal/dextran-stripped fetal bovine serum , 100 μM MEM non-essential amino acids, 2 mM L-glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin. A Dex concentration of 100 nM was used for 60 min.http://broadinstitute.github.io/picard/). Narrow peaks were called using MACS2  < 5%. The volcano plot was generated using the R package EnhancedVolcano. Heatmaps were generated using computeMatrix and plotHeatmap from deepTools 3.5.1. To generate genomic visualizations, reads from BAM files were normalized using reads per kilobase per million mapped reads (RPKM), extended to 225 bp using the bamCoverage function from deepTools 3.5.1 . Gene counts were determined using featureCounts (version 2.0.1)   with then 2.0.1)  and callp < 2.2e-16) and Dex-stimulated (Pearson’s r = 0.92 and p < 2.2e-16) cells , showed increased and decreased levels of BRD4 and MED1 at regions bound by the GR  and the mediator complex subunit MED1, which are functional coregulators of the GR  and are 6) cells . Concurry the GR . Overally the GR . These ry the GR . IncreasTo validate that the GR was directly implicated in the rearrangement of transcriptional coregulators, regions gaining and losing BRD4 and MED1 were overlaid with the GR genome-wide occupancy using the available data . A largeMaterials and Methods). As expected, gains in MED1 and BRD4 were enriched in Dex-activated genes . On the other hand, losses in transcriptional coregulators were enriched in Dex-repressed genes . To determine if the differential recruitment of MED1 and BRD4 was associated with the amplitude of RNA-level variations, we compared fold changes. Variations in MED1 and BRD4 were correlated with variations at the RNA levels at all Dex stimulation time points , highlighting a bias toward homogeneity for the differential recruitment of transcriptional coregulators  (p = 7.56e-113) and down . If the TAD environment surrounding a gene is an important determinant in the response to Dex, differentially expressed genes should be found within up and down TADs, matching the changes independently from the differential recruitment of transcriptional coregulators. Globally, 39.8% of Dex-regulated genes assigned to a responsive TAD were associated with a region gaining or losing MED1 and BRD4, compared to 60.2% that were not (2 = 214.42 and p = 2.98e-45) or not (χ2 = 81.58 and p = 8.07e-17) by regions gaining or losing MED1 and BRD4 (2 = 163.69 and p = 2.36e-34) or not (χ2 = 71.24 and p = 1.24e-14), were confirmed  were enriched within a TAD, gaining or losing MED1 and BRD4  . As expewere not . Intereswere not . Indeed,and BRD4 . Analysiand BRD4 . To valionfirmed . These rOur results support that the differential amount of MED1 and BRD4 is associated with changes at the gene level . If trueOur results establish that following ligand activation, the GR elicits regionalized gains and losses in transcriptional coregulators MED1 and BRD4, creating favorable or unfavorable environments for transcriptional regulation. The primary consequence of the GR recruitment is to modulate levels of transcriptional coregulators, which not only leads directly to the activation or repression of the bound target genes but also influences other genes in the vicinity , 4. WhilThe proposed model represents an important shift in the conception of direct and indirect effects elicited by transcription factors. Current methods to determine the primary effects of a transcription factor are based on the integration of multi-omics data. For example, chromosome architecture data are coupled to genome-wide localization of the transcription factor to determine transcription factor-bound regions and assign them to genes. Following the integration of gene expression datasets, differentially expressed genes that are unbound are typically discarded and labeled as indirect effects. Here, we are proposing that recruitment of a transcription factor and its associated coregulators has two types of primary effects on transcription: 1) a direct effect through DNA binding of the transcription factor at cis-regulatory regions and 2) a domain-dependent effect based on the position in a specific environment. It is important to note that what we are describing as domain-dependent could represent highly dynamic transcription factors or physical contacts with cis-regulatory regions making detection of enhancer–promoter contacts more difficult. The distinction between these models and the precise definition of the boundaries in which they are acting will require further investigations. Nonetheless, our interpretation is an important distinction from the current definition of indirect effects. Based on our assessment, inducing a transcription factor will redistribute transcriptional coregulators, leading to primary direct and indirect effects. With this nomenclature, gene expression changes independent of direct DNA binding or position effects would be considered as secondary.Transcription factors activate and repress genes by modulating recruitment of coregulators . Our resThe influence of the genomic microenvironment has been associated with the gene position effect in mammalian cells. We are proposing that TADs are being modulated by the redistribution of transcriptional coregulators. Therefore, when a transgene is added to different genomic contexts, access to mobile transcriptional regulators or dynamic cis-regulatory regions will differ. Furthermore, this model explains the fast kinetics of differential expression observed for genes not bound by an induced transcription factor but responding to stimulation. It remains to be determined if, during the biological response of a cell, activation and repression mechanisms are molecularly linked.


The film-forming electrolyte additive/co-solvent fluoroethylenecarbonate (FEC) can play a crucial role in enabling high-energy-densitylithium metal batteries (LMBs). Its beneficial impact on homogeneousand compact lithium (Li) deposition morphology leads to improved Coulombicefficiency (CE) of the resulting cell chemistry during galvanostaticcycling and consequently an extended cell lifetime. Herein, the impactof this promising additive/co-solvent on selected properties of LMBsis systematically investigated by utilizing an in-house developedlithium pretreatment method. The results reveal that as long as FECis present in the organic carbonate-based electrolyte, a dense mosaic-likelithium morphology of Li deposits with a reduced polarization of only20 mV combined with a prolonged cycle life is achieved. When the pretreatedLi electrodes with an FEC-derived preformed SEI (pSEI) are galvanostaticallycycled with the FEC-free electrolyte, the described benefits inducedby the additive are not observable. These results underline that thefavorable properties of the FEC-derived SEI are beneficial only ifthere is unreacted FEC in the electrolyte formulation left to constantlyreform the interphase layer, which is especially important for anodeswith high-volume changes and dynamic surfaces like lithium metal andlithiated silicon. Analogousto the electrochemically derived SEI on lithium, the presence of FECduring the pretreatment also induces a LiF-rich passivation layer.The positive effects associated with this additive/co-solvent, includinga dense mosaic-like Li deposition morphology, less resistive interphase,and prolonged lifetime of lithium metal-based cells, were furtherenhanced by employing a pretreated lithium electrode. However, theabsence of FEC in the electrolyte diminishes the positive effectsof the LiF-rich pSEI and resulting cells have shown similar characteristicsas cells without the FEC-derived pSEI. Especially during galvanostaticcycling of symmetric Li||Li and NMC811||Li full-cells, the importanceof FEC present during cycling became evident as the cycle life ofthe cell correlated with the amount of FEC in the considered electrolyteformulation.In this study, the pretreatmentmethod was utilized to investigatethe impact of various FEC concentrations in the resulting electrolyteformulation and during the pretreatment of the lithium electrodeson the properties of the as-defined pSEI and its implications on thegalvanostatic cycling performance in symmetric Li||Li as well as ina full-cell setup with a state-of-the-art LiNi22.1–2 were dried under vacuum at 120°C for 24 h before use. The considered electrolyte formulations(E-Lyte Innovation) are listed in The lithium metal rods(Ø13 mm) used to prepare the Li metal electrodes were purchasedfrom Sigma-Aldrich and used without purification. The NMC811 electrodes(CUSTOMCELLS) used for the full-cell setup with an aerial capacityof 1.0 mA h cm2 < 0.1 ppm, H2O <0.1 ppm, O2 < 0.1 ppm, and N2 < 5 ppm)with a built-in nitrogen removal system.All considered materials were stored in an argon-filledglovebox were generated using thesame lithium rod without an electrolyte present during the preparationof the electrodes. Furthermore, Mylar foil was used for the pressingprocess instead of copper sheets.The Li electrodes (thickness: 200 μm) were prepared within-house built cutting and pressing devices according to our previouspublication.2.3SEM measurementswere performed in a Carl Zeiss Auriga Modular Crossbeam workstationwith a Schottky field emission gun and a Gemini column as the electronsource. The images were taken at an accelerating voltage of 3 kV anda working distance of 3.0 mm.post mortem surface morphology analysis, the electrodes were disassembled fromthe coin cell setup, gently washed with 1 mL of EMC, and dried ina vacuum for 30 min.For the hν = 1486.6 eV) and a chamberpressure of 2 × 10–9 mbar.For the XPS measurements, a photoelectronspectrometer of the type“K-Alpha” by Thermo VG Scientific was used with monochromaticAl Kα X-rays (2.4–2 and 1 h stripping/platingsteps (0.5 mA h cm–2). The impedance measurementswere carried out using a Bio-Logic VMP3 workstation with the samecycling parameters. After every 5 charge/discharge cycles, the impedancewas recorded over a frequency range of 1 mHz to 100 MHz and fittedusing an equivalent circuit selection of REl + RSEI/CSEI + RCT/CCT. For the galvanostatic cycling experiments in the full-cell setup,NMC811 electrodes (1.0 mA h cm–2) were used andthe corresponding cells galvanostatically cycled at the constant currentof 0.5 mA cm–2  in thevoltage range from 4.2 to 3.0 V.Allelectrochemical measurements were performed in a two-electrode configuration.Lithium stripping/plating was studied in a symmetric coin cell setup(type CR2032). For this purpose, two freshly prepared Li metal electrodeswere combined with one Celgard 2500 separator soaked with 30 μLelectrolyte. Galvanostatic cycling was conducted with a MACCOR batterycycler (MACCOR Series 4000) at 20 °C with a constant currentdensity of 0.5 mA cm33.140 In contrast to the assumption,the fluorine content does not increase with the FEC concentrationduring pretreatment as 3FEC induced a higher fluorine content in thepSEI with 42% compared to 6FEC (25%) and 9FEC (38%). The higher fluorineconcentrations of the FEC-induced pSEIs are accompanied by a substantialdecrease in both the oxygen and carbon content and according to thedecreased CO3– (290 eV) and C=O–resonance (523 eV), a formation of Li2CO3 istherefore less pronounced in the presence of FEC.40 Additionally, an abundance of alcoholates  and lowamounts of Li2O (528 eV) are present in the FEC-derivedpSEIs, which were also suggested as FEC decomposition intermediatesby complementary density functional theory (DFT) calculations andexperimental results .The chemicalcomposition of the lithium anode surfaces was characterized and analyzedvia X-ray photoelectron spectroscopy (XPS) to gain insights into thepSEI obtained from considered electrolyte formulations 1. The pr results 1c–f.26,427 In addition, a four times faster SEI formationwith a twice as fast Li+ ion exchange is achieved whenFEC is present in an organic carbonate-based electrolyte, reducingstress-build up in the SEI during galvanostatic cycling.32The surface analysis via SEM after 50 stripping/platingcyclesrevealed that with increasing FEC concentrations in the electrolyte,a dense surface morphology of the deposited lithium can be achievedon the pristine lithium electrodes (pLi) with pLi × 12FEC havinga mosaic-like surface structure in contrast to the more mossy-likelithium deposits observed with pLi × BE 2. The geHowever, considerable parts of the Li metal surfaceseem to remainin a pristine state when cycled with FEC, as the main stripping/platingtakes place on the edges of the electrode to avoid the stack pressureof the cell 2b,c. In Figure S3). This indicates that unreacted FECmust be present in the electrolyte during cycling to achieve the mentioneddense mosaic-like lithium deposition structure.Galvanostatic cycling of the cells containing the pretreatedelectrodeswith the FEC-free BE electrolyte revealed a partial detachment ofthe pSEI taking place, with highly porous Li deposits emerging regardlessof the presence and concentration of FEC during pretreatment , caused by the presence of the NPL.The positiveimpact of FEC in the resulting electrolyte formulationand the replacement of the NPL with a pSEI were further validatedby EIS measurements 3. High iRInt further decreases and converges to ∼40Ω, with pLi × 12FEC and pLi × 9FEC displaying thelowest resistance values. This explains why the Li stripping/platingonly occurs on small areas of the lithium electrode cycled with FECbecause as soon as parts of the very resistive NPL are replaced bya highly ion-conductive FEC-induced SEI, lithium release and depositionon these areas are strongly favored.Galvanostatic cycling of these cells led to a considerabledecreaseof the interphasial resistance after 10 h, most drastic on the pLi× BE probably caused by the expanded stripping/plating on thewhole electrode and the resulting increased surface area observedin the SEM images of pLi × BE 3b. AfterFigure S5). The presence of FECduring electrode pretreatment leads to a further decrease in the interphasialresistance at first with no correlation of the FEC concentration . This positive effect of the FEC-derivedpSEI on the interphase resistance diminishes after 50 h of cyclingwith BE, as no distinct difference in RInt between BE@Li × BE and 3/6/9/12FEC@Li × BE is visible.By adding FEC to the electrolyte, even lower RInt down to 10 Ω can be achieved for 12FEC@Li ×12FEC . Only with 12FEC@Li, a prolonged cycle life can be achieved,which is, however, accompanied by an inhomogeneous overvoltage behavior.With the introduction of a pSEI, a further enhancementof the celllifetime by 85% for BE and ∼100% for achieved4b. For 926 As a consequence, 12FEC (black) can achieve the most extended celllifetime with 458 cycles, followed by 9FEC (dark blue), 6FEC (blue),and 3FEC (light blue) with 388, 272, and 211 cycles, respectively.Nonetheless, in the first 61 cycles, the FEC-free setup actually hasa higher specific discharge capacity as the specific capacity of thecell slightly increases until the 21st cycle before a rapid capacitydecline begins. This anomaly is assumed to be caused by an initial,thinner but less effective SEI, formed in the presence of the BE electrolyte,which causes less polarization in the beginning and thus leads tohigher capacities during the first cycles compared to the FEC-containingcell setups.24 Based on the EIS data, whichshowed a less resistive interphasial layer with BE after the firstcycles compared to the FEC-containing cell setups, a similar assumptioncan be made for the NMC811||Li cells.The positive impact of FEC in the electrolyte further translatesto the NMC811||Li full-cell setup. The FEC-free cell setup pLi ×BE experiences the fastest capacity decline and reaches the 80% State-of-Health(SOH) threshold, which is marked with a star, after 150 cycles 5a, red. Figure S8). Only12FEC@Li × BE shows a slight increase in the cycle life, whichmight be due to FEC residues in or on the pSEI which is underlinedby the FEC-specific capacity drop.Coupling the pretreated electrodes with the NMC811electrode, afurther delay of the capacity drop with the FEC-containing electrolytescan be achieved, probably due to the decelerated FEC consumption observablein the corresponding symmetric Li||Li cells 5b. This 4In this work, an in-house-designedand developed lithium pretreatmentmethod has been utilized to systematically evaluate the effect ofvarying FEC concentrations on the interphase properties and its impacton the galvanostatic cycling performance of lithium–metal batteries.The presence of FEC as a functional additive/co-solvent in the organiccarbonate-based electrolyte leads to a dense mosaic-like lithium depositionmorphology with a reduced polarization of down to 20 mV. In addition,the cycle life of symmetric Li||Li and NMC811||Li cells could alsobe successfully prolonged with increasing concentrations of FEC inthe considered electrolyte formulation. If the FEC is solely presentin the formation of the pSEI, the observed benefits of the additivewith regard to the interphase properties and the cycling performancecould not be achieved even though the surface analysis by XPS revealeda LiF-rich decomposition layer on the lithium electrode. By combiningthe FEC-containing electrolyte with the FEC-derived pSEI, the positiveeffects of the FEC could be even more enhanced in comparison to thoseof the cells with the pristine lithium electrodes.This systematicstudy has revealed the importance of FEC in theelectrolyte during galvanostatic cycling to achieve the benefits associatedwith the enhancement of the LMB performance. The mere existence ofa LiF-rich FEC-derived SEI is not advantageous if no FEC is presentto maintain the interphase during galvanostatic cycling.