Plant senescence is the last stage of plant development and a type of programmed cell death, occurring at a predictable time and cell. It involves the functional conversion from nutrient assimilation to nutrient remobilization, which substantially impacts plant architecture and plant biomass, crop quality, and horticultural ornamental traits. In past two decades, DNA damage was believed to be a main reason for cell senescence. Increasing evidence suggests that the alteration of epigenetic information is a contributing factor to cell senescence in organisms. In this review, we summarize the current research progresses of epigenetic and epitranscriptional mechanism involved in cell senescence of plant, at the regulatory level of DNA methylation, histone methylation and acetylation, chromatin remodeling, non-coding RNAs and RNA methylation. Furthermore, we discuss their molecular genetic manipulation and potential application in agriculture for crop improvement. Finally we point out the prospects of future research topics. As known, cell senescence in plant can occur in predicable space and time during a plant’s lifespan, for example in root cap cell of root, treachery cell of vascular, aerenchym cell and leaf mesophyll cells, petal cell, tapetum cell of anther, macrospore cell, aleuronat layer cell of seed, as well as hypersensitive cell , where CG methylation is located in the gene promoter and the body, while non-CG methylation is at TEs for regulating gene expression, ensuring transposon silencing and maintaining genome stability . Moreove3DNA methylation at the fifth carbon of cytosine residues represents a conserved epigenetic mark in eukaryotes that has key function in regulating gene transcription, transposon silencing and genome stability . In plan3.1Arabidopsis shoots aging by using a methylation-sensitive DNA fragmentation (MS-AFLP) assay. They showed a global declining in DNA methylation levels during Arabidopsis shoots aging, simultaneously accompanying by a downregulation of CMT3 and MET1 methyltransferase genes, and an upregulation of demethylase genes including ROS1, DME, DML2, and DML3  promoter, led to global DNA hypomethylation and developmental defects including reduced fertility, failed flowering, or greatly delayed plant senescence (Arabidopsis leaves of demeter-like 3 (dml3) mutant that lost the DEMETER-like DNA demethylase, relative to wild-type at three developmental stages, namely NS (non-senescent), ES (early senescence)), and LS (late senescence). While a reduction of genome-wide methylation levels at CG context was observed at stage transition from NS to LS in wild-type, loss of DML3 led to significant increase of DNA methylation at the promoters of several senescence-associated genes, thereby suppressing their expression and delaying leaf senescence. These results demonstrated that DML3 activating DNA demethylation and expression of selected senescence-associated genes modulated leaf senescence  displayed a faster senescence progression and enhanced nitrogen remobilization from the leaves. Loss of methylation in CHG and CHH contexts at W-box element targeted by WRKYs as well as a core motif recognized by bZIP transcription factors (ACGTG) was observed in ddc. Differentially methylated regions at ROS1 promoter were related to down-regulation of ROS1 with the progression of senescence  displayed abnormal DNA methylation and developmental tissues like delayed or absent heading or growth defects  inhibited leaf growth, accelerated leaf and sepal senescence, and produced dark green fruit. Gene expression analysis revealed that the SlELP2L-deficient plants had up-regulated gene expression level of DNA methyltransferases, implying that SlELP2L may play a role in DNA methylation in tomato  gene, and the methylation status of NMR19-4 was associated with repressed PPH expression and delayed leaf senescence. Epi-allelic variations at key senescence associated genes have also been demonstrated to regulate leaf senescence in several other crop species. In maize, alterations in DNA methylation were observed during critical life event transitions when silencing the Mutator-Don Robertson transposable elements and were proposed to contribute to whole-plant senescence  as well as lysine 9 acetylation on histone 3 protein (H3K9ac) are active histone modifications associated with leaf senescence. Two silence marks, lysine 27 dimethylation and trimethylation , are also linked to leaf senescence .4.1Arabidopsis, that senescence-associated genes (SAGs) correlates with elevated levels of H3K4me3 and H3K9ac, while the inhibition of senescence-downregulated genes is related to H3K27me3 (The combination of chromatin immunoprecipitation sequencing (ChIP-seq) and RNA-seq methods has been employed to explore the genome-wide landscape of histone modifications and gene transcription during leaf senescence. H3K27me3 . Zhang e4.2WRKY53, a key regulator of leaf senescence, levels of H3K4me3 and H3K4me2 were observed significantly increased at the coding regions and 5’ end of WRKY53. The overexpression of SUVH2, a histone methyltransferase, however, suppressed the transcriptional initiation of WRKY53 and additional SAGs, thereby leading to a delay of leaf senescence , a H3K27me3 demethylase, directly accelerated the expression of ten specific SAGs, and loss-of-function of REF6 postponed leaf senescence by increasing H3K27me3 levels at all the target SAGs. Recently, in tomato plant overexpressing SlJMJ4, a histone H3K27 demethylase, resulted in premature senescence and enhanced dark-and ABA- induced leaf senescence , the Arabidopsis HDA6 mutant exhibited higher levels of acetylated H3 and prolonged leaf lifespan  by removing H3 acetylation marks, thereby promoting aging in Arabidopsis  of nutrient recycling-related genes , the D1 synthesizer attenuator PDIL1-2 of photosystem II protein, as well as WRKY53 and ELF4. This recruitment removes H3K9ac from their promoter region leading to the repressing of gene expression and the delaying both leaf senescence and flowering during the early stage of plant development  and deacetylases (HDACs). The earliest study exploring the relationship of histone deacetylases and leaf senescence is AtHD1 . Transgemalities . AtHDA6,lifespan . AtHDA9,bidopsis . AtHDA15bidopsis . Furtherelopment . In ricesistance . Howeverrichment .4.4HvS40, which encodes a potential regulator of leaf senescence, is significantly up-regulated. At the onset of leaf senescence, the regions of promoter and coding sequence of HvS40 enriched H3K9ac, but declined the enrichment of H3K9me2  and aging  in crop plants were identified in impact of histone modification enzymes. For example, during leaf senescence, the expression of the barley gene  H3K9me2 . Under dnescence . Conversnescence . In ricend aging . Zhong e . These complexes catalyze changes in nucleosome position, occupancy, or/and composition to regulate DNA and core histones interactions, thereby remodeling chromatin locally or globally to adopt or impede regulatory proteins. On the basis of ATPase/helicase like regions (Snf2 domain) characteristics, chromatin remodeling proteins have been briefly classified into six groups and 24 subfamilies, each with specific functional properties . Snf2 faectively . Roles oectively . This pa5.1Chromatin conformation is dynamically changed during cell senescence induced by age and environmental cues . Ay et a5.2ORE7/ore7-1D) with lower expression of the activated ORE7 genes was used in this study to analyze the senescent phenotypes. ORE7/ore7-1D mutant showed a delayed senescence phenotype as well as globally altered gene expression. Moreover, more reticular chromatin distribution and intensely nuclear bodies than wild-type plants were observed in ORE7/ore7-1D mutant. It is possible that ORE7 binds to AT-rich DNA sequences and modifies the chromatin architecture in the nucleus, then leading globally altered gene expression. In addition, a subset of hormones  related genes was down-regulated in ORE7/ore7-1D mutant, suggesting ORE7 repressed the signaling pathway of hormones during plant senescence  domain, showed delayed leaf senescence during dark-induced senescence and natural senescence. drd1-6 mutants exhibited higher photosynthetic parameters and lower expression levels of SAG12 and chlorophyll degradation-related genes than wile-type plants during dark-induced senescence. Moreover, the transcript levels of 180-bp centromeric (CEN) repeats and pericentromeric repeats termed transcriptionally silent information (TSI) showed slower and lesser increase in the drd1-6 mutant than in the wile-type plants. Similarly, ddm1-2 also exhibited a longer stayed green phenotype and higher photochemical efficiency than wild-type plants  and ATP-dependent DNA helicase DDM1 (DECREASED DNA METHYLATION 1), were identified as regulators of leaf senescence . drd1-6 e plants . TherefoNAC083, NAC032, NAC019, PIF4, WRKY6 etc. with BRM  and CHR19 (ETL1) were shown to be upregulated during senescence  is one member of the two catalytic ATPase subunits of the SWI2/SNF2 complex. It directly targets to a large amount of SAGs . The H3Kwith BRM , suggestwith BRM , while tnescence . Further6Arabidopsis and rice to retain chromatin and regulate the structure of chromatin and gene expression through interaction with H3K4me3-labeled gene sites , including miRNAs, siRNAs and piRNAs; medium-sized RNAs (50-300 nt), including snoRNAs and snRNAs; and long non-coding RNAs (lncRNAs) over 200 nt in length . miRNAs ne sites . LncRNAsne sites .6.1Caenorhabditis elegans, marked the beginning of miRNA research, and since then, researchers have identified thousands of miRNAs in both plants and animals. These miRNAs play diverse roles in regulating a wide range of biological processes  gene expression. It targets seven out of nine Arabidopsis GRFs  are short, non-coding RNAs that are highly conserved and typically range in length from 20 to 24 nucleotides. The discovery of lin-4, the first miRNA, in 1993 , in nemarocesses . miRNAs rocesses . Althougrocesses . These rrocesses . Only a  Populus . AtORE1 nescence ; MIR164 nescence . AtKIR1,nescence ; and thenescence . miR396 sis GRFs . The miRsis GRFs ; miR319 sis GRFs . A receneam SAGs . High-thnescence .Arabidopsis. MIM775 transgenic lines exhibited accelerated senescence during post-submergence recovery, while miR775-overexpresing (Oe1) and galt9 transgenic lines showed delayed senescence. The expression of SAG29, SAG12, and ORE1 decreased in miR775-Oe1 and galt9 transgenic lines, while their expression enhanced in MIM775-1 lines, suggesting that the miR775-GALT9 module regulated the expression of SAGs to control senescence during post-submergence recovery in Arabidopsis. In crop plants, very limited miRNA was reported their detail functions related to leaf senescence. A novel tomato miRNA known as SlymiR208 was identified. When it is overexpressed, the expression of SlIPT2 and SlIPT4 was significantly reduced, and endogenous CK concentrations in leaves was declined, leading to premature leaf senescence, which was in concurrence with the phenotype of Isopentenyltransferases 4 (IPT4)-silenced lines, showing early leaf aging  and the NAC family gene BGIOSGA016313 through osa-miR5809  were targeted by microRNAs. Notably, the majority of differently expressed target genes of DElncRNAs were related to the membrane component, with almost half of these genes involved in transporting organic material. These findings strongly suggest that DElncRNAs play a crucial role in substance transport across membranes during nodule senescence  signals, thereby regulating alternative splicing of NSRs-targeted pre-mRNA to influence root development and senescence  is a recently discovered class of epigenetic regulator involved in regulating gene expression during plant development and in response to stresses. Dynamic landscapes of long noncoding RNAs were observed in ng aging . Above, -miR5809 . Furthernescence . Anothernescence . In addif plants . Up to n7RNA modification, such as N6-methyladenosine (m6A) and 5-methylcytosine (m5C), are another kind of epigenetic regulation. With the advances in technology of mass spectrometry, next-generation sequencing, and genome mapping, detection of mRNA modifications has become more precise. Up to now, more than 170 various RNA modifications were reported. The increasing reports are mostly focused on chemical modifications of specific mRNA nucleotides like m6A and m5C, which can enhance or reduce the binding activity of the regulatory factors, such as transcription factors, RNA binding proteins, and non-coding RNAs. Here, we try to summarize the chemical mRNA modifications, specifically m6A affect plant senescence .Arabidopsis, this complex contains two core methyltransferases and several accessory proteins. The former includes mRNA adenosine methylase MTA and mRNA adenosine methylase MTB, which are orthologue of METTL3 and METTL14, respectively.The latter includes FKBP12 INTERACTING PROTEIN 37KD (FIP37) and HAKAI. m6A methyltransferase deposits m6A on the region closed to stop codons of mRNAs and the region with a RRACH sequence in 3′UTRs, respectively, like in mammalian, responding for most of m6A enrichment of mRNA. Another m6A methyltransferase FIO1 that is orthologue of METTL16, can add m6A modifications on the regions with a YHAGA (Y = C/U) motif of coding sequence transcript around stop codons or with a RRACH in 3’UTRs, affecting all m6A levels in plant (N6-methyladenosine (m6A) is the most widespread, plentiful and conserved internal cotranscriptional modification in eukaryotic RNAs, especially within higher eukaryotic cells. m6A modification is modified by the m6A methyltransferases (writer) and removed by the demethylases (erasers). It is recognized by m6A-binding proteins (readers) . In planin plant , which iin plant . NeverthArabidopsis flowering (Recent research suggests that the specificity of m6A with its target could be developed through interaction of m6A writers and other writer-associated proteins such as RNA binding protein (RBP), named m6A recruiters with specific transcripts in plants. For examples, it has reported that the RBP FCA interacted with MTA, MTB, and FIP37 in the complex and acted m6A enrichment on the antisense transcript COOLAIR of noncoding RNA during lowering . The m6Alowering . Moreovelowering . Althouglowering . In darklowering  shows silowering .Arabidopsis, including roots, rosette leaves, and flowers showed that the fractions of transcripts apparently enriched m6A modifications in senescent tissues, and m6A enriched level is much higher than transcript altered level, suggesting that m6A may be more important regulator in organ differentiation and programed cell death process , alternative polyadenylation (APA), protein folding, translation, localization, RNA transport, and RNA decay . We summ-mutants  , MET1 and ROS1 were significantly regulated at the transcription level in dark-induced leaf senescence  are specifically modified SAGs? How do these writers and erasers determine their targets in distinct physiological procedure and various tissues and organs, at different developmental stage, or under multiple stresses? How do reader proteins recognize their targets (SAGs) and carry out their roles in following RNA metabolic processes? At the same time, when we are applying epigenetomic and epitranscriptome editing in crop biotechnology, numerous challenges must be faced. First, it is required to develop a novel techniques to analyze DNA modification, histone modification and RNA modification dynamics from the tissue levels to the cellular levels even at single-nucleotide resolution or at single-amino acid resolution. For example, DNA/RNA epigenetic modified sites at single-base resolution are largely unknown in crops, the techniques of TCTA-seq, miCLIP, MAZTER-seq, m6A- SAC-seq, and Nanopore DRS are necessary to be developed to accurately detect DNA/RNA methylation and interrogate modified sites at single-nucleotide resolution in crops. The biological actions of erasing/writing epigenomic marks at specific SAGs, histone epigenetic mark targeted to a specific SAGs locus are mostly unidentified in plants, especially, epitranscriptome mark at SAGs transcript is blank yet. Although many epigenetic enzymes and factors of DNA or RNA modifications or histone modification have been reported in several crops (Over the last decade, there have been significantly progressed in the mechanistic understanding of epigenetic modifications and epitranscriptome, including dynamics of H3K4me3 and H3K9ac, dynamics of non-coding RNAs, m6A dynamics during tissue and organ development and under stresses in the model plant al crops , their iYZ: Writing – original draft. DH: Writing – original draft. YM: Writing – original draft, Writing – review and editing.


Beta-like globin gene expression is developmentally regulated during life by transcription factors, chromatin looping and epigenome modifications of the β-globin locus. Epigenome modifications, such as histone methylation/demethylation and acetylation/deacetylation and DNA methylation, are associated with up- or down-regulation of gene expression. The understanding of these mechanisms and their outcome in gene expression has paved the way to the development of new therapeutic strategies for treating various diseases, such as β-hemoglobinopathies. Histone deacetylase and DNA methyl-transferase inhibitors are currently being tested in clinical trials for hemoglobinopathies patients. However, these approaches are often uncertain, non-specific and their global effect poses serious safety concerns. Epigenome editing is a recently developed and promising tool that consists of a DNA recognition domain  fused to the catalytic domain of a chromatin-modifying enzyme. It offers a more specific targeting of disease-related genes  and it facilitates the development of scarless gene therapy approaches. Here, we summarize the mechanisms of epigenome regulation of the β-globin locus, and we discuss the application of epigenome editing for the treatment of hemoglobinopathies. Embryonic Hb  and fetal Hb  exhibit a higher O2 affinity than the adult one  [Hemoglobin (Hb), the main component of mature red blood cells (RBCs), is a tetrameric metalloprotein composed of four polypeptide globin helicoid chains (two α-like and two β-like globins), a heme molecule and a ferrous ion (Fe). Different variants of Hb, with a different composition of α- and β-like chains, are produced throughout life in a stage-specific manner  with twols (~1%) ,4  ,6.Human globin genes are organized in two different clusters, the α- and the β-globin loci, located in chromosomes 16 and 11, respectively. The human α- and β-globin locus genes are arranged in a 5′ to 3′ direction according to their sequential expression during development . Human gThe fetal-to-adult Hb switch results in the silencing of γ-globin genes and the activation of the β-globin gene. The specific binding of TFs to the β-like globin genes and the chromatin remodeling through LCR-mediated looping formation is required for the regulated expression of β-like globin genes. The chromatin looping drives the γ- or β-globin gene expression in fetal and adult erythroblasts, respectively. The looping is mediated by a protein complex and facilitates the juxtaposition of the LCR to the γ- or β-globin gene promoter, thus promoting their expression ,34,35,36HBG genes  are expressed in adult cells, whereas shorter forms (S and XS) are expressed in fetal cells, indicating the stage-specific repressive action of larger BCL11A isoforms . BCL11A +) or abolish (β0) the production of adult β-globin chains composing the HbA tetramer. This leads to the precipitation of uncoupled α-globin chains, ineffective erythropoiesis, erythroid cell death by apoptosis and anemia [HBB) causes the substitution of valine for glutamic acid at position 6 of the β-globin chain (βS) that is responsible for deoxygenation-induced polymerization of the sickle hemoglobin (HbS). This primary event drives ineffective erythropoiesis, apoptosis of erythroid precursors, red blood cell (RBC) sickling, hemolysis, vaso-occlusive crises and multi-organ damage, often associated with severely reduced life expectancy [Beta-hemoglobinopathies are monogenic diseases caused by mutations in the β-globin locus that affect the synthesis, structure or properties of HbA. β-thalassemia is caused by mutations in the β-globin locus that reduce . In β-thalassemia, elevated fetal γ-globin levels lower the imbalance between α- and non-α globin chains, reduce the formation of α-globin precipitates and thus improve the clinical phenotype. In SCD, fetal γ-globin exerts a potent anti-sickling effect by replacing the sickle β-globin in the Hb tetramers . TherefoEpigenetic modifications, which are defined as chromatin alterations that result in a determined expression pattern without changing the genetic sequence, have been shown to occur during the fetal-to-adult hemoglobin switching . These pThe promoters of many human genes (including globin genes) contain Cytosine-phosphate Guanine (CpG) sites, which occur at a high frequency in regions known as “CpG islands”, and are amenable to epigenetic modifications. DNA methyltransferases (DNMT) deposit methyl groups in the fifth carbon atom of the cytosine of a CpG site. DNMT3A and DNMT3B mediate the de novo deposition of methyl groups in a non-methylated 5′-CpG-3′ and symmetrically to the non-methylated 3′-GpC-5′ of the complementary strand . DNMT1 iDNMT1 gene found in patients with HbE/β-thalassemia, leads to lower stability and enzymatic activity loss of DNMT1. This was associated with the reactivation of HbF due to the diminished recruitment of DNMT1 to the γ-globin promoters and the reduced methylation levels in CpG sites in the γ-globin promoters  of erythroid cells differentiated from patient HSPCs [In the γ-globin promoters, CpG sites span a 500 nt region centered on the transcription start site  ,56,57 F. Studiesnt HSPCs .The DNA of eukaryotes is wrapped around histone octamers, i.e., nucleosomes, forming the chromatin. A 147 bp-long DNA fragment is wrapped around a histone octamer, composed of two copies of histone (H) 2A, H2B, H3 and H4, while histone H1 binds to the linker DNA between two nucleosomes. A variety of post-translational modifications  in different amino acid residues of the histones have been identified and associated with distinct regulatory functions ,61,62,63The transfer of methyl groups to histone amino acid residues by methyl-transferase proteins (lysine methyltransferase (KMT) and protein arginine methyltransferase (PRMT)) is associated either with an open chromatin state and gene activation, or to a compact chromatin state and gene repression. For instance, the H3 lysine 4 monomethylation, dimethylation or trimethylation  and the asymmetric H4 arginine 3 dimethylation (H4R3me2a) mark transcriptionally active genomic regions . On the other hand, the deposition of methyl, dimethyl or trimethyl groups on H3 lysine 9 or lysine 27  or symmetric H4 arginine 3 (H4R3me2s) or H4 lysine 20 (H4K20me3) is associated with transcription inactivation . InteresThe role of histone methylation in the regulation of the β-globin genes has been studied for decades . The devEHMT1/2 as well as the global H3K9me2 levels decrease along the erythroid differentiation in cells derived from adult HSPCs [EHMT1/2 led to decreased levels of H3K9me2 and, consequently, HbF reactivation [HBG1/2 promoters, which is typically associated with gene activation (see paragraph “Histone acetylations”) [Additional proteins are involved in the determination of the histone methylation status of the β-globin locus. In particular, the expression of lt HSPCs . Pharmactivation ,71. Howeations”) . Promisiations”) . FTX-605Methylation of arginine 3 on H4 also plays an important role in globin gene regulation. In human erythroid cell lines, PRMT5 induces symmetric H4R3me2 in the γ-globin promoters . The dev. Furthermore, in human erythroid cell lines, PRMT5 also induces the deposition of additional repressive histone methylation marks in the γ-globin promoters, such as H4K20me3, H3K9me3 and H3K27me3, likely by recruiting other repressor complexes such as KMT5B , a lysine methyltransferase that deposits the repressive H4K20me3 mark. The H4K20me3 mark was higher at the embryonic/fetal ε- and γ-globin promoters in human bone marrow-derived erythroblasts as compared to cord blood-derived erythroid cells. Accordingly, shRNA-mediated downregulation of SUV4-20h1 led to γ-globin gene reactivation in adult cells [An interplay between the different histone methylations and between histone methylations and DNA methylation has been reported at the β-like globin promoters. In human erythroid cell lines, PRMT5-induced symmetric H4R3me2 in the γ-globin promoters is associated with the recruitment of DNMT3A that methylates close CpG sites, highlighting the interplay between histone and DNA methylation . Furtherlt cells .The deposition of acetyl groups on histone lysine residues loosens the interactions between histones and DNA due to a neutralization of the positive histone charge ,76. As aHistone acetylation plays a dynamic role in the developmental-stage specific regulation of β-like globin gene expression . A positHBB expression levels [Studies using human fetal and adult erythroid cells have shown that the levels of H3 acetylation in the LCR are stable during development, while the same marker in the β-globin coding regions positively correlates with n levels ,80. In gn levels ,66,67,80Interestingly, histone deacetylase activity is not always correlated with γ-globin repression. In fact, SIRT1, an histone deacetylase has been positively associated with increased γ-globin expression, and SIRT1 activators enhance the expression of γ-globin in primary human erythroid cells . In fetaThe interplay between different types of histone modifications also characterizes the β-globin locus regulation and has also been investigated at the level of enhancers  in human erythroid cell lines. In the β-globin LCR, H3K4me1 was correlated with histone acetylation, namely, H3K27ac. Interestingly, the loss of H3K4me1 led to a concomitant reduction in H3K27ac, but not vice versa, indicating a hierarchy that places methylation upstream of acetylation in the β-globin locus regulation .Histone acetylations also cooperate with DNA methylation in regulating globin gene expression. In human cells, H3 and H4 acetylation regulates the β-globin genes in concert with other histone modifications  and DNA methylation. Along the human erythroid differentiation, the activating H3K9ac marks in the β-globin locus overlap with histone methylation marks (H3K4me1 and H3K4me3), and 5-hmC , highligThe fine-tuning of globin gene expression is mediated by the direct or indirect repressive or activating effects of TFs that bind to the β-globin locus, and by an interplay between these TFs and DNA or histone modifiers that alter the epigenetic profile of the locus and, thus, its chromatin accessibility. By way of example, the GATA1 TF interacts with the CBP/p300 acetyltransferase during erythropoiesis . With reFinally, the specific binding of TFs to the β-like globin genes and the chromatin remodeling through LCR-mediated looping formation is required for the regulated expression of β-like globin genes. The chromatin looping drives the γ-globin or β-globin gene expression in fetal and adult erythroblasts, respectively. The looping is mediated by a protein complex containing LDB1, LMO2, GATA1, TAL1 and E2A and facilitates the juxtaposition of the LCR to the γ- or the β-globin gene promoter, thus promoting their expression ,34,35,36Growing evidence of the crucial role of the epigenome in gene regulation, and the reversible nature of epigenetic modifications has led to the development of “epidrugs” as a clinical treatment for different diseases ,91. HoweIn order to mitigate this effect, engineered epigenetic effectors that are capable of performing targeted epigenome editing have been developed. Targeted epigenome modifications can be generated by fusing an effector domain (that either has intrinsic epigenetic enzymatic activity or recruits epigenome modifiers in the case of TFs) to a sequence-specific DNA-binding domain (DBD) to ensure that the effector domain acts only on a specific target .Zinc-finger proteins (ZFs) have been the DBD of choice for the first epigenetic applications. Those proteins are composed of repetitive Cys2His2 domains, consisting of 28–30 amino acids, which allow binding to double-stranded DNA through a zinc ion, coordinated by four conserved residues . MultiplInitially, ZFs were fused to TFs or epigenetic modifiers. They were coupled with TFs such as p65, viral activation domains such as VP16, or transcriptional activators composed of several tandem copies of VP16 to achieve activation of specific endogenous genes ,97,98,99To overcome this limitation, a wide range of effector domains with intrinsic epigenetic activity have been employed in conjunction with ZF DBDs to either increase or repress gene expression ,102,103.Although different platforms using ZFs as DBDs have been developed and have reached clinical application, the low specificity of the tools remains an important barrier for their use in the epigenome editing field. ChIP-seq analysis has showed that although ZFs preferentially bind to the target genomic region, there is also widespread binding at potentially off-target sites ,101, mosLessons learned from ZF-based epigenome editors encouraged a faster development of other epigenome editing platforms with higher DNA-recognition capacities, namely, transcription activator-like effectors (TALE) and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9.An alternative DBD option for epigenome editing applications arose from the study of TALEs in bacterial plant pathogens . These pTALEs have been fused to various effector domains to either up- or down-regulate gene expression. TFs such as VP16 and VP64 were fused to TALEs with the aim of reactivating the pluripotency in somatic stem cells, by promoting the expression of SOX2, KLF4 and OCT4 ,115,116.SOX2 gene [Pu.1 genes [TCF3, involved in the suppression of pluripotency and self-renewal pathways, thus providing a promising option for obtaining a sufficient number of HSCs for transplantation [Other artificial transcriptional repressors have been developed by combining TALEs with the catalytic domain of KRAB to silence endogenous gene expression. The KRAB domain has a transcriptional repressive activity and operates through interactions with different corepressors and chromatin modifiers that remove histone H3 acetylation and increase H3K9me3 . TALE:KROX2 gene , as well.1 genes . Moreoveantation .CACNA1C gene) [Grm2 gene, encoding one of the major excitatory neurotransmitters) [With the same intent of gene silencing, TALEs have also been tested in combination with other repressive domains. For instance, TALEs have been tethered with the mSin interaction domain (SID), which acts by reducing H3K9 acetylation, thus inducing transcriptional repression in cell lines   and primmitters) .KLF4 in the K562 cell line and reactivate its expression [Several TALE-based tools have been developed to directly modulate DNA methylation or deposit or remove histone modifications. TALE-based epigenome editors have been used to remove aberrant DNA methylation, which is associated with several diseases. By way of example, TALE DBDs have been fused with TET1 to remove critical CpG methylations at the endogenous promoter of pression . Howeverpression . Therefopression .CDKN2A, encoding the p16 protein that participates in cell senescence, with the aim of increasing the replication of primary human fibroblasts [Other strategies with a focus on altering disease-associated epigenetic marks include TALE DBDs tethered with DNMTs to promote DNA methylation at specific loci. TALE:DNMT fusions have been used to silence the expression of roblasts .SCL gene , and to significantly reduce histone methylation and acetylation, and as a consequence, SCL expression [Grm2 promoter in primary neurons and consequently downregulate Grm2. However, when tested on other genes in different cell types, TALE:HDAC8 showed variable effects on gene repression, suggesting that the chromatin state influences the outcome [TALEs have also been fused with histone modifiers, such as KDM1A, which demethylates H3K4me2 and promotes the interaction with HDAC. When tested in a leukemia cell line, TALE:KDM1A was able to target an active H3K4me2+ and H3K27ac+ enhancer of the pression . Further outcome .B2M expression in a leukemia cell line, obtaining no or transient effects. However, co-delivery of these three components significantly reduced B2M expression, and the effect was stable after numerous cell divisions. Moreover, the same platform was tested for silencing other endogenous genes, namely, interferon alpha/beta/omega receptor 1 (IFNAR1) and vascular endothelial growth factor A (VEGFA), confirming the crucial role of the interaction between epigenetic modifications in gene regulation [Of note, several studies reported how the interaction between different epigenetic modifications play a crucial role in stable regulation of gene expression over time. In this regard, several studies combined different tools simultaneously introducing multiple epigenetic modifications. TALEs fused with KRAB, DNMT3A and DNMT3L have been individually tested to silence gulation .+ T cells to efficiently silence CCR5 and CXCR4. Interestingly, when splitting the KRAB and the DNMT activity into two distinct molecules, they failed to promote gene silencing [Following these observations, other tools have been developed to perform combinatorial epigenetic modifications. Designer epigenome modifiers combining a single TALE with KRAB, DNMT3A and DNMT3L have been tested in primary human CD4ilencing .Similarly to ZF DBDs, the off-target activity is one of the major concerns regarding TALE-based technology . A numbeAnother significant limitation of TALEs is their large number of tandem repeats, which makes TALE-expressing plasmid cloning and delivery difficult. However, after optimization and minimization of the number of tandem repeats, delivery as plasmid DNA or in vitro transcribed mRNA has been successful , as wellThe CRISPR/Cas9 technology is commonly used for epigenome editing applications, thanks to its easier design and flexibility. The CRISPR/Cas9 system is based on a guide RNA (gRNA) which drives the Cas9 nuclease to the protospacer or target site, which has to be located upstream of a protospacer adjacent motif (PAM). The recognition of the target site is directed by the complementarity between the gRNA spacer and the DNA protospacer and allows the Cas9 nuclease to create a double-strand break (DSB) at the target site. Adaptations of Cas9 have been developed to create nuclease-deficient variants. For instance, the catalytically inactive dead Cas9 nuclease (dCas9 that does not generate DSBs but retains binding activity) has been fused to TFs or to epigenetic modifiers to modulate gene expression.+ reporter gene in HEK293 cells [VEGFA and NFT3, increasing their expression in HEK293 cells [+ reporter gene [Nrl gene, a master regulator of rod photoreceptor determination, which mediates the in situ reprogramming of rod cells into cone-like cells that are resistant to retinitis pigmentosa-specific mutations [PTEN (encoding a phosphatase that limits cell growth), in human cell lines and neural cells derived from human-induced pluripotent stem cells (iPSCs). Thus, suppression of PTEN resulted in a robust central nervous system (CNS) axon regrowth, providing a new option for promoting axon regeneration and functional recovery after CNS trauma [dCas9 was initially fused with VP64 and p65 (creating dCas9:VP64 and dCas9:p65) which were first used to target and activate GFP93 cells . The sam93 cells . KRAB doter gene . More reutations . MoreoveS trauma .Of note, experiments targeting both gene enhancers and promoters revealed that the design of the tools is critical for their activity. By way of example, the position of the fusion site between dCas9 and the TF, and the orientation of dCas9 and the effector domain can strongly impact the editing efficiency ,136. MorCaenorhabditis elegans, and in murine and human cells [The first studies aimed at optimizing the tools by increasing the number of transcriptional domains fused to the dCas9, leading to the generation of activators such as dCas9:VP160, which contains ten repeats of VP16 domain , or dCasan cells .SNCA in iPSCs from Parkinson’s disease patients, who typically present high SNCA levels [CXCR4 and TFRC leading to gene repression in SKOV-3 and HEK293 cell lines [VEGFA and IFNAR1 [However, a more stable epigenetic modification has been obtained by fusing dCas9 with domains of endogenous epigenome modifiers. dCas9 was initially fused to DNMT3A alone or in tandem with its cofactor DNMT3L, to obtain efficient site-specific DNA methylation and consequently transcriptional repression at several human genes. dCas9:DNMT3A, for instance, led to a therapeutic downregulation of A levels . Similarll lines . To incrd IFNAR1 . More red IFNAR1 .BDNF in post-mitotic neurons, a gene that encodes a protein involved in neuronal survival and differentiation, leading to reduced DNA methylation and BDNF re-expression [FMR1 gene associated with hypermethylation of the CGG expanded repeats in the 5′UTR of FMR1. dCas9:TET1 led to efficient demethylation of the CGG repeats and to a consequent FMR1 reactivation in vivo in mice [BRCA1, which is often hypermethylated in breast cancer cells. In this case, dCas9:TET1 increased BRCA1 expression and inhibited the proliferation of a cancer cell line, highlighting the therapeutic potential of epigenome editing to reduce hypermethylation in promoters of tumor suppressor genes, which is one of the hallmarks of cancer [Foxp3 enhancer alone was insufficient to stabilize the Foxp3 expression and modulation of histone modifications was also necessary to activate the gene [Hand1 (encoding a factor that plays a key role in cardiac development) using dCas9:TET1 up-regulated Hand1 expression, it was shown that TET1 and DNA methylation play a crucial role in regulating gene expression during cardiac differentiation [To reactivate gene expression, CRISPR/Cas9-based epigenome editors capable of demethylating DNA have been developed. To this end, TET1 has been tethered to dCas9 targeting the promoter of pression . The sam in mice . dCas9:Tf cancer . dCas9:Tf cancer . Howeverthe gene . Finallythe gene . Targetintiation .IL1RN, MYOD and OCT4 gene promoters and HS2 enhancer of the β-globin locus) and activate gene expression in HEK293T cells [CFTR gene (affected in this disease) with a consequent restoration of the normal phenotype in patient-derived human bronchial epithelial cells when combined with a pharmaceutical treatment [CRISPR/Cas9-based epigenome editors able to modulate histone modifications have been generated by fusing dCas9 with different histone modifiers. For instance, the catalytic core domain of the human HAT p300 protein was fused to dCas9 to deposit H3K27 acetylation at both promoters and enhancers ; this led to the loss of expression of these genes and consequent phenotypic changes [Deactivation of enhancers and gene repression can likewise be achieved by using dCas9 fused to the histone demethylase KDM1A, which eliminates H3K4me2, a histone mark typically found at active promoters and enhancers. A first attempt to use dCas9:KDM1A was performed in murine ESCs, targeting the enhancers of genes that encode factors critical for maintaining the stem cell state  and EZH2, respectively. However, these studies showed that a repressive histone modification alone is not sufficient for gene silencing . Indeed,Overall, CRISPR/Cas9-based epigenome editors present several advantages compared to ZFs and TALEs. The main advantage of the CRISPR/Cas9 system is the ease of designing a single guide RNA (sgRNA) targeting a specific region compared to the protein design required for TALE- and ZF-based epigenome editors. Off-target binding can also occur with the CRISPR/Cas9 system . HoweverCRISPR/Cas9-based epigenome editors have been successfully delivered into cells using plasmid , RNA 12, ribonucThe importance of epigenetic regulations on globin gene expression has fueled the identification of novel medications capable of directly altering the epigenome for therapeutic purposes. Epigenetic regulation can act through multiple mechanisms that eventually lead to chromatin alterations that either permit or inhibit transcription . Both DNHBG promoters negatively correlates with γ-globin gene expression in primary erythroblasts [HBG promoters are hypomethylated in individuals harboring HPFH mutations [For example, DNA methylation of the CpG dinucleotides at the roblasts ,57. Furtutations . As a coutations  and SCD utations  patientsutations .HBG promoters in fetal but not in adult erythroblasts. HDAC inhibitors, such as vorinostat, have been used in clinical trials with the aim of reactivating HbF [Activating histone modifications, such as H3K27ac or H3K4me3, mark active ting HbF . Vorinosting HbF , and it In addition, other drugs interfering with the activity of epigenetic modifiers modulating histone methylations have been used to reactivate HbF and will likely be further developed, such as RN-1, an inhibitor of KDM1A ,170,171,However, the mode of action of these pharmacological inhibitors are often uncertain as they are non-specific, and their global effect poses serious safety concerns. For instance, down-regulation of components of chromatin-remodeling and modifier complexes  via shRNA or CRISPR/Cas9 is associated with HbF upregulation ,173. HowSeveral gene therapy strategies for reactivating the expression of the endogenous fetal γ-globin have been successfully developed. However, although they offer the opportunity to achieve permanent therapeutic changes in the genome, these approaches still rely on the intermediate formation of DSBs. This in turn poses serious safety concerns as the introduction of DSBs might result in unpredictable genotoxic effects . Thus, aHBG promoters successfully increased HbF expression in primary human erythroid cells [HBB gene through targeted DNA demethylation in an HEK293 cell line [Artificial transcription factors and epigenome editors have been used to directly reactivate human γ-globin or β-globin expression and to study the mechanisms of β-like globin gene regulation. The use of an artificial ZF transcriptional activator ZF:VP64 targeting the id cells  and an iid cells . Nonetheid cells . Fusion ell line .Both dCas9:p300  and dCasHowever, since epigenetic regulation at the globin loci is complex, it is reasonable to believe that the fine-tuned expression of the different genes requires the interplay of different epigenetic modifications in parallel to the recruitment of TFs. The first attempt to both deposit histone modifications and recruit TFs was performed by taking advantage of the natural interaction that occurs between the phage MS2 hairpins and MS2 bacteriophage coat protein (MCP). In particular, dCas9:p300 was fused to MS2-sgRNA sequence which recruits the VP64 activator domains fused with MCP and targets the HS2 enhancer. This allowed the concomitant introduction of H3K27ac, the recruitment of a transcriptional activator and, as a consequence, the potent activation of β-like globin genes in HEK293T cells .The regulation of β-like globin gene expression has been extensively studied, which has led the identification of different layers of regulation, involving transcription factors, chromatin looping and epigenome modifications of the β-globin locus. The interplay between TFs, DNA methylation and histone modifications play a crucial role in determining gene activation or repression.HBG genes) in a scarless manner, i.e., without affecting genome integrity. Moreover, epigenome editing is more targeted as compared to drugs able to change the epigenetic profile of the β-globin locus (such as HDAC and DNMT inhibitors).The availability of genome editing tools has allowed the development of therapeutic approaches for β-hemoglobinopathies through the direct genetic modification of HSCs that either correct the disease-causing mutation or reactivate fetal hemoglobin expression by altering transcription factor occupancy. However, genome editing tools can cause a DNA damage response and unwanted genomic alterations. By contrast, epigenome editing would allow the modulation of gene expression  needs to be evaluated in long-term repopulating of patient HSCs. Furthermore, an extensive assessment of the off-target activity of epigenome modifiers is crucial, and last but not least, it is vital that clinically relevant protocols are developed for the delivery of this system.


Homo sapiens and Mus musculus genomes. Structural analysis of nucleotide sequences in positions −50, +30 bp in relation to the TSS have shown the extremely heterogeneous 3D structure that includes two singular regions - hexanucleotide “INR” around the TSS and octanucleotide “TATA-box” at around ~−28 bp upstream. Thus, the 3D structure of core promoters of lncRNA resembles the architecture of the core promoters of mRNAs; however, textual analysis revealed differences between promoters of lncRNAs and promoters of mRNAs, which lies in their textual characteristics; namely, the informational entropy at each position of the nucleotide text of lncRNA core promoters (by the exception of singular regions) is significantly higher than that of the mRNA core promoters. Another distinguishing feature of lncRNA is the extremely rare occurrence in the TATA box of octanucleotides with the consensus sequence. These textual differences can significantly affect the efficiency of the transcription of lncRNAs.RNA polymerase II (POL II) is responsible for the transcription of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). Previously, we have shown the evolutionary invariance of the structural features of DNA in the POL II core promoters of the precursors of mRNAs. In this work, we have analyzed the POL II core promoters of the precursors of lncRNAs in  An analysis of the genome-wide catalog of human transcripts obtained within the ENCODE project  led to tNcRNAs vary considerably in length, shape, and functionality. The lncRNAs POL II transcribes at various elements of the eukaryotic genome, including promoters, enhancers, and intergenic regions. They can be considered as precursors of key regulators of the gene expression network . AdditioThe editing of primary RNAs transcripts, including ncRNAs, plays relevant roles in the molecular processes in living cells. Disturbances in editing may be the cause of pathogenic processes. For example, “change of the binding capacity of 359 miRNAs towards 9654 target genes” as the result of oxidative stress is found to be coupled with retinal degeneration , which are taken from DiPro DB or Table 1 in [ble 1 in , and theble 1 in , and theble 1 in , and theWe have written the programs in Python 3.10 for profiles construction.The organization of the 3D structure of the naked DNA in POL II core promoter regions of lincRNAs and asRNAs is, in many respects, similar to the organization of the naked DNA in mRNA core promoters. Namely, their 3D structure contains two regions with singular mechanical and structural properties: TATA box and INR.H. sapiens as for M. musculus, and the sets of mRNAs core promoters of these organisms. These defferences are following: (a) the informational entropy in each position of the nucleotide sequences of pooled samples of lincRNAs and asRNAs significantly exceeds the informational entropy of mRNA core promoter sequences; (b) among octanucleotides that represent the TATA box in the sets of core promoters of ncRNAs, the percentage of sequences that satisfy the TATAWAAR consensus is markedly lower than in the sets mRNA core promoters.Nevertheless, a number of differences were identified in the text characteristics between the core promoters of pooled sets of lincRNAs and asRNAs as for By our opinion these textual differences in the core promoter regions may lead to the spontaneity and lower level of their transcription compared to mRNA transcription.


Control of gene expression, including transcription, is central in dictating the outcome of viral infection. One of the profound alterations induced by viruses is modification to the integrity and function of eukaryotic RNA polymerase II (Pol II). Here, we discuss how infection perturbs the Pol II complex by altering subunit phosphorylation and turnover, as well as how cellular genotoxic stress (e.g. DNA damage) elicits similar outcomes. By highlighting emerging parallels and differences in Pol II control during viral infection and abiotic stress, we hope to bolster identification of pathways that target Pol II and regulate the transcriptome. The battle between a virus and its infected cell is one that centers on the control of gene expression. On the cellular side, these are the responses that drive antiviral signaling, while the ability of a virus to block those signals and simultaneously redirect gene expression machinery toward viral genes can define whether an infection will amplify or be restricted. Viruses thus frequently manipulate transcriptional regulators such as activators and repressors, as well as chromatin structure . HoweverPol II is a 12-subunit complex that is responsible for mRNA transcription in eukaryotic cells. Its activity relies on accessory proteins such as general transcription factors (GTFs), TATA-associated factors, and the mediator complex; the ensemble of this machinery is collectively referred to as the Pol II holoenzyme  –6. ViralDespite the prevalence of stress-linked Pol II abundance changes, much remains to be discovered about the mechanistic basis of these phenotypes, particularly in the case of infection. However, emerging evidence suggests that there is at least some conservation in the pathways that control Pol II during viral and nonviral stress, which we explore herein to create a framework to guide future studies on how stress modulates the cellular transcriptional landscape.I), composed mainly of serine 5-phosphorylated (Ser5-P), a modification that is usually associated with transcription initiation and splicing [I and hypophosphorylated Pol II are present in viral replication compartments following recruitment by the IE protein ICP27, while Ser2-P Pol II is specifically excluded or degraded [Regulation of the Pol II holoenzyme occurs through post-translational modifications that influence the stages of transcription . Rpb1 . For RNA viruses that encode their own RNA-dependent RNA polymerase (RdRp), targeting Pol II enables selective dampening of host gene expression because viral genes are transcribed by the RdRp. Old World alphaviruses, including Sindbis, Chikungunya, and Semliki Forest virus, potentiate a rapid disappearance of vertebrate Rpb1 that involves proteasomal degradation. This is linked to viral nonstructural protein 2 (nsP2), as viruses unable to cleave and release nsP2 do not elicit Rpb1 degradation, and nsP2 expressed from a noncytopathic replication vector is sufficient to reduce Rpb1 levels . Bunyavinduction ,22••, annduction ,24, whilnduction .Some DNA viruses achieve similar outcomes, despite many of these viruses relying on host Pol II for viral transcription. This paradox is presumably resolved by recruitment of the remaining Pol II to viral lytic replication compartments by the open, comparatively nucleosome-depleted chromatin environment of the viral genome ,27. As mRemoval of Pol II occurs with many stressors, often resulting in proteasomal degradation of Rpb1 . Here, wThe best-defined mechanism of Pol II subunit turnover occurs in the context of the DNA damage response (DDR) in uninfected cells . More unbiased screening approaches to determine the contributions of different E3s or ubiquitylation residues are needed to reveal links to DDR pathways as well as identify alternate or new pathways. If these mechanisms were inhibited in the presence of virus, this could also permit assessment of the functional implications of Pol II destruction for both virus and host. Furthermore, given that proteasome inhibition yields incomplete rescue of Pol II abundance during viral infection, regulation of Pol II abundance likely relies on the convergence of multiple processes.Rpb1 turnover during UV DNA damage does not result in co-depletion of other complex subunits in yeast, suggesting that individual subunits may be independently rather than interdependently regulated ,33. ThatOther possible mechanisms are that MHV68 targets multiple subunits independently for protein turnover, or that select protein subunit(s) are targeted directly while others deplete due to a failure to replenish the protein pool upon degradation of their mRNA. In this regard, widespread mRNA degradation is a well-established feature of lytic gammaherpesvirus infection and Pol II subunit levels are not reduced upon infection with an MHV68 mutant that is deficient for mRNA decay ,45,46. VMHV68-induced mRNA decay also prompts the redistribution of RNA binding proteins to the nucleus, which is both necessary and sufficient to induce host transcriptional repression . While tThe Pol II holoenzyme is critical for maintaining and regulating the cellular transcriptome. The consequences of shifting Pol II activity and abundance are particularly important for stress responses; thus, understanding how this occurs in the context of viral infection is crucial for predicting host–virus interactions. Data suggest that some protein-level Pol II adjustments occur via conserved pathways during infection and genotoxic conditions, but there are major gaps in our knowledge of how these are activated, as well as the functional consequences.While many viral Pol II depletion strategies seem to be at least partially dependent on proteasomal degradation, in general, the E3-mediated pathways leading to this outcome have not been identified. Regulation of Pol II abundance likely results from the combined effects of converging pathways — individually testing the few E3s implicated in other stress responses probably cannot provide a complete picture. Moreover, not all Pol II turnover occurs via the proteasome. UV radiation is reported to induce a less prominent, caspase-mediated cleavage event near the Rpb1 CTD , and anoScreening approaches are needed to flesh out the pathways involved in infection-induced changes to Pol II. Identification of these pathway components could additionally provide more targeted tools beyond inhibitors of the ubiquitin–proteasome system or transcription (which are often toxic and have pleotropic effects) to interrogate the impact of Pol II depletion or modification on the viral lifecycle. In this way, we could identify new players similarly involved in Pol II regulation during abiotic stress. While much remains to be discovered about the dynamics of Pol II regulation, viral research has and will continue to provide critical insight into this fundamental cellular process.


Analyzing the overlap between two sets of genomic intervals is a frequent task in the field of bioinformatics. Typically, this is accomplished by counting the number (or proportion) of overlapped regions, which applies an arbitrary threshold to determine if two genomic intervals are overlapped. By making binary calls but disregarding the magnitude of the overlap, such an approach often leads to biased, non-reproducible, and incomparable results.We developed the cobind package, which incorporates six statistical measures: the Jaccard coefficient, Sørensen–Dice coefficient, Szymkiewicz–Simpson coefficient, collocation coefficient, pointwise mutual information (PMI), and normalized PMI. These measures allow for a quantitative assessment of the collocation strength between two sets of genomic intervals. To demonstrate the effectiveness of these methods, we applied them to analyze CTCF’s binding sites identified from ChIP-seq, cancer-specific open-chromatin regions (OCRs) identified from ATAC-seq of 17 cancer types, and oligodendrocytes-specific OCRs identified from scATAC-seq. Our results indicated that these new approaches effectively re-discover CTCF’s cofactors, as well as cancer-specific and oligodendrocytes-specific master regulators implicated in disease and cell type development.https://cobind.readthedocs.io/en/latest/.The cobind package is implemented in Python and freely available at  Collocated genomic intervals indicate biological or functional connections. For example, a previous study found that over 50% of ERα-binding sites occur on FoxA1-occupied sites, and a further mechanistic study confirmed the direct collaboration between ERα and FoxA1 to control the transcription of estrogen-responsive genes  (https://gdc.cancer.gov/about-data/publications/ATACseq-AWG)  using the 10× Chromium Single Cell 3' Reagent Kit v3. Gene expression of count per million values was quantified using the default 10× Cell Ranger v3 pipeline. The University of California, Santa Cruz (UCSC) genome browser track hub to visualize these data is available at https://human-m1-rna-hub.s3-us-west-2.amazonaws.com/HumanM1RNAHub.html. Oligodendrocyte-specific single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) peaks were downloaded from this study (n = 28 109) were used for collocation analysis to identify the master regulators since peaks identified from scATAC-seq are highly enriched in distal/intron regions but depleted in promoter regions.Curated binding sites of 1208 transcription factors (TFs) were downloaded from the ReMap 2022 database (seq-AWG) . Single A and B represent two sets of genomic intervals. |A| represents the cardinality of A . |B| represents the cardinality of B. |A ∩ B| represents all the non-redundant bases covered by both A and B. The collocation coefficient (C) is calculated as the size of the intersection divided by the geometric mean of the two sets. C is defined as 0 when either of |A| or |B| is 0.J) calculated as the size of the intersection divided by the union of the two sets. J is defined as 0 when either of |A| or |B| is 0.The Jaccard coefficient is (A| or |B| is 0.The Sørensen–Dice (SD) coefficient is calculated as the size of the intersection divided by the arithmetic mean of the two sets. SD is defined as 0 when either of |J) can be converted from SD with J = SD/(2−SD) and vice versa with SD = 2J/(1+J).The Jaccard coefficient (The Szymkiewicz–Simpson (SS) coefficient is defined as the size of the intersection divided by the smaller size of the two sets.G represents the size of the genome (A ⊂ G and B ⊂ G). Pointwise mutual information (PMI) is calculated asp(A), p(B), and p(A∩B) are defined aswhere p(A ∩ B)), the lower bound and upper bound are normalized to −1 and 1, respectively. Normalized PMI (NPMI) is calculated asWhen dividing PMI by −log. ChIP-seq peaks from 1208 TFs were downloaded from the ReMap database (https://remap.univ-amu.fr/). The threshold-and-count approach consists of two steps: first, an arbitrary threshold (minimum number of nucleotides overlapped) was used to determine the overlapping of two peaks, and then each CTCF peak was labeled by “0” or “1”, representing non-overlap or overlap with peaks of the TF compared, respectively; second, the percentage of overlapped CTCF peaks was calculated as the measure of collocation strength. This approach was evaluated using six different thresholds: 1-nucleotide (the most relaxed), 10%, 30%, 50%, 80%, and 100% (the most stringent). Based on the percentage of overlapped CTCF peaks, the 1207 TFs (CTCF itself was excluded) were ranked in descending order under each criterion.Pre-analyzed, IDR (Irreproducible Discovery Rate) filtered CTCF (CCCTC-binding factor) ChIP-seq peaks (GRCh38) were downloaded from the ENCODE project /σ, where μ and σ are the average and standard deviation of the score, and i ∈ {C, J, SD, SS, PMI, NPMI}. The combined Z-score is defined as:We used the Gene Set Variation Analysis package .TFs with C), the Jaccard coefficient (J), the Sørensen–Dice coefficient (SD), and the Szymkiewicz–Simpson coefficient (SS) are intersection-based metrics and conceptually similar: they normalize the size of the intersection between A and B by the “geometric mean,” “union,” “arithmetic mean,” and “min” of A and B , respectively. As can be seen from Section 2 and J and SD scores are prone to be biased by the larger interval set due to their use of “union” and “arithmetic mean” as denominators, respectively. Conversely, SS is susceptible to the smaller interval set as it uses “min” as the denominator. However, the novel metric C developed in this study utilizes the geometric mean that is less sensitive to highly skewed data, thus could alleviate the observed bias in J, SD, and SS . PMI measures how much the observed overlap differs from what we expected. PMI =ectively .https://remap.univ-amu.fr/). We also calculated Z-scores combining all six metrics as an overall measure  to regulate the 3D chromatin architecture is well-known and has been independently validated by numerous studies (ρ) ranging from 0.60 to 0.99  and Pearson’s r ranging from 0.57 to 0.99  (The results indicated that the six metrics exhibited high pairwise concordance with Spearman’s correlation coefficient ( = 0.78) . All six = 0.78) . Conside = 0.78) . This su = 0.78) . In summAs a comparison, the conventional threshold-and-count approach missed the majority of cohesin proteins from the top 10 rankings, regardless of the thresholds applied . We founZ-scores are listed in OCRs refer to exposed DNA accessible to TFs and other regulatory proteins. Collocation analysis of TF binding sites (TFBS) with cancer-specific OCRs offers the opportunity to identify master regulators implicated in modulating cancer-specific transcription programs. To demonstrate the utility of our approach, we applied the algorithms implemented in the cobind package to analyze the collocation between the TFBS of 1208 TFs  and OCRsOur collocation analyses revealed many well-known, cancer-specific master regulators . For exaIn a study profiling the chromatin accessibility of more than 70 000 human adult brain cells, cis-regulatory regions . Indeed, we observed that SOX10 and OLIG2 were expressed specifically in oligodendrocytes  reported by database . snRNA-sdatabase . In contdatabase  and has The metrics implemented in the cobind package (except the collocation coefficient) have been widely utilized in the Natural Language Processing field to determine if two words occur more frequently than expected by chance. However, in the genomics field, only the Jaccard coefficient has been adopted by BEDTools to measure the overlaps between two BED files. It is worth noting that there is a slight difference in the calculation of the Jaccard coefficient between cobind and BEDTools. Cobind considers every input genomic interval, regardless of overlap, while BEDTools only consider intervals that exhibit overlap.cis-regulatory sites. Fortunately, public consortia such as ENCODE, modENCODE, Roadmap, and functional genomic repositories like GEO (https://www.ncbi.nlm.nih.gov/geo/) offer rich resources encompassing a wide range of TFs from numerous tissues and cell lines.Motif analysis, a computational approach to identify overrepresented short-nucleotide patterns, has been extensively applied to identify common regulators from a given set of genomic intervals. Although DNA motif analysis can provide valuable insights, it is prone to false positives and false negatives since most TFs only bind less than a few percent of their consensus target sites in the genome . In our The cobind package is the first bioinformatics tool specifically designed for quantitative collocation analysis of genomic intervals. It is efficient in terms of speed and memory usage . This stvbad104_Supplementary_DataClick here for additional data file.


Twist2 as a previously unappreciated effector of adipocyte differentiation. We find that TWIST2 acts as a negative regulator of 3T3-L1 and primary preadipocyte differentiation. We confirm that Twist2 knockout mice have compromised lipid storage within subcutaneous and brown adipose tissue. Previous phenotyping of Twist2 knockout mice and Setleis syndrome Twist2/−− patients noted deficiencies in subcutaneous adipose tissue. This network inference framework is a powerful and general approach for interpreting complex biological phenomena and can be applied to a wide range of cellular processes.Adipocytes contribute to metabolic disorders such as obesity, diabetes, and atherosclerosis. Prior characterizations of the transcriptional network driving adipogenesis have overlooked transiently acting transcription factors (TFs), genes, and regulatory elements that are essential for proper differentiation. Moreover, traditional gene regulatory networks provide neither mechanistic details about individual regulatory element–gene relationships nor temporal information needed to define a regulatory hierarchy that prioritizes key regulatory factors. To address these shortcomings, we integrate kinetic chromatin accessibility (ATAC-seq) and nascent transcription (PRO-seq) data to generate temporally resolved networks that describe TF binding events and resultant effects on target gene expression. Our data indicate which TF families cooperate with and antagonize each other to regulate adipogenesis. Compartment modeling of RNA polymerase density quantifies how individual TFs mechanistically contribute to distinct steps in transcription. The glucocorticoid receptor activates transcription by inducing RNA polymerase pause release, whereas SP and AP-1 factors affect RNA polymerase initiation. We identify  Mature adipocytes contribute to a multitude of metabolic processes by regulating energy balance, producing hormones, and providing structural and mechanical support . AdipocyMolecular genomics assays can query transcriptional events with extremely high temporal resolution. Although each assay delivers a tremendous amount of information, each is limited in the biology that it measures. ChIP-seq directly quantifies chromatin occupancy of proteins, but the assay is dependent upon the availability of antibodies and is limited to a single factor at a time. ATAC-seq and DNase-seq assays quantify chromatin accessibility, which is an indirect measure of regulatory element (RE) activity . CombiniDifferential TF activity defines cell identity and drives cellular responses to environmental stimuli by enforcing gene regulatory programs . SequencTranscriptional networks consist of multiple rapid waves of signaling through time with potential regulatory feedback and signal propagation through activation and repression of regulatory factors. These complex regulatory cascades are not captured in traditional gene regulatory networks. Differentiating one wave from the next requires observations at multiple, closely spaced time points. In this study, we perform ATAC-seq and PRO-seq on 3T3-L1 cells at seven time points within the first four hours of adipogenesis. We incorporate accessibility and transcription changes into a multiwave signaling network and identify TF families driving the regulatory cascade.Supplemental Fig. S1A). To address whether the sequencing libraries are saturated, we called peaks on subsets of the total reads and found that the number of called peaks had not reached saturation . The fraction of reads in peaks (FRiP) varied between 0.2 and 0.3 for most of our ATAC-seq libraries . However, we note that this score is depressed because we use our total peak set, which includes dynamically accessible peaks that may not be accessible at all time points. Approximately 13% of all peaks change significantly over the time course . We clustered dynamic peaks based on kinetic profiles , which resulted in five general response classes . TF families comprise multiple proteins containing paralogous DNA-binding domains that recognize very similar sequence motifs . Therefore, we refer to the steroid hormone receptor binding consensus sequence as the GR motif. We identified AP-1, CEBP, and GR, which are known positive effectors of adipogenesis .TFs bind promoters and enhancers to modify chromatin structure and influence transcription of nearby genes. To identify dynamic REs and potential TFs that regulate adipogenic differentiation, we induced adipogenesis in 3T3-L1 mouse preadipocytes (see Methods), harvested samples at eight time points, and performed genome-wide chromatin accessibility assays (ATAC-seq) A. Chroma classes B. To ide classes . This ape motifs C. For exogenesis . Membersogenesis  and antiogenesis . The TWIogenesis . MembersSupplemental Fig. S1H). AP-1, GR, and CEBP motifs are strongly enriched around summits of increased peaks, whereas TWIST and SP motifs are enriched around summits of decreased peaks. SP and KLF families have paralogous DNA-binding domains and recognize similar motif sequences; however, we confidently associate chromatin decondensation to KLF factors and chromatin condensation to SP factors . The SP family is composed of canonical activators . ATAC-seq and PRO-seq measure distinct but related biological phenomenon; therefore, they identify different but overlapping sets of REs. Approximately 22% of dynamic dREG peaks overlap with dynamic ATAC-seq peaks compared with 20% of dynamic ATAC-seq peaks in the inverse comparison. To further analyze the two classes of REs, we separated the dynamic dREG and ATAC-seq peaks into intragenic, intergenic, and promoter regions . We find PRO-seq more sensitively detects intragenic REs relative to the other categories, whereas ATAC-seq efficiently detects intergenic REs. We closely evaluated the overlap between ATAC and dREG peaks by plotting PRO-seq signal at ATAC peaks and vice versa . We observe the distinctive bidirectional transcription signature at ATAC peaks irrespective of whether or not the ATAC peaks intersect dREG peaks. The signature is less intense at ATAC-seq peaks that do not overlap dREG peaks. Likewise, ATAC-seq signal is enriched at dREG-peaks that do not overlap ATAC-seq peaks . Moreover, dREG peaks within intergenic, intragenic, or promoter regions that do not overlap with ATAC-seq peaks have less bidirectional transcription . Although we find that bidirectional transcription and accessibility do not perfectly correlate, we are likely underestimating the extent of accessibility and bidirectional transcription overlap.Coordinate TF binding ultimately results in the recruitment of RNA polymerases and initiation of transcription. In mammals, core promoters and enhancers often lack sequence information that consistently orients initiating RNA polymerases . Therefo regions C. Both mSupplemental Fig. S2F). Of the motifs found de novo in dREG peaks, only E2F6, NRF, and SP were more enriched in dynamic dREG versus dynamic ATAC-seq peaks. We hypothesize that these three factor families regulate bidirectional transcription in adipogenesis. The SP motif is found in >25% of human and mouse promoters, making the SP motif the most enriched cis-RE within promoters , implying that these factors and SP preferentially regulate divergent transcription at promoters. Next, we wanted to determine whether SP, NRF, and E2F6 motifs within the promoters associate with increasing or decreasing divergent transcription. We find that bidirectional transcription tends to decrease in REs with dREG-enriched motifs as opposed to those without dREG-enriched motifs . We find distal REs are more likely to show accessibility changes, whereas promoters are more likely to show bidirectional transcription changes.We sought to identify TFs that drive bidirectional transcription by further characterizing PRO-seq-defined REs. We hypothesized that different sets of TF motifs are enriched within REs defined by ATAC-seq and PRO-seq. For instance, the cognate motifs of TFs that recruit initiation machinery may be preferentially enriched at dREG-defined REs. We performed de novo motif analysis on dynamic dREG peaks and found enrichment of the AP-1, SP, KLF, NRF, and E2F6 motifs D. Of theromoters . To deteromoters F, top. Iromoters F, bottomd motifs G. This ftrans-edges in our networks. For simplicity, we refer to trans-edges as factor binding or dissociation events.We determined candidate functional TFs within the set of REs by searching for overrepresented sequence motifs and determining the expression levels of TF family members. However, inferring TF binding from accessibility, motif, and expression data at any individual site remains a challenge . In additrans-edge inference: (1) The RE must first be defined as an ATAC-seq peak at any time point; (2) the binding motif of the upstream TF must be present in RE; (3) chromatin accessibility must change significantly between two time points to infer binding or dissociation; (4) the direction of accessibility changes must match with the molecular function of the TF as defined in trans-edges from their genic node to an RE node. Mechanistically, TFs have short residency times on DNA, and they are continually binding and dissociating from their sites in vivo . We suggest that the rapid transcriptional repression of Nr3c1 is the reason GR-associated increases in accessibility are transient. Therefore, we restrict binding edges attributed to GR to the first 40 min of the time course. We attribute any significant decreases in accessibility at inferred GR binding REs observed at later time points to dissociation of GR. We label these edges with a dissociation attribute. In the case of SP, we find that Sp1, Sp3, and Sp4 are all repressed early in the time course . We hypothesize that the delayed accessibility decrease associated with the SP motifs is owing to transcriptional repression and natural turnover of the SP pool, which result in overall dissociation of SP on chromatin . Therefore, we predict that TWIST-associated repression is a result of increased TWIST binding and recruitment of negative cofactors. Twist2 expression levels have returned to baseline in mature adipocytes , suggesting that TWIST's effects are transient and can only be captured with an early, high-resolution time course. By focusing only on the REs that change accessibility and integrating with transcription data, we infer TF binding and dissociation events that drive adipogenesis.The following examples highlight implementations of these rules. The hromatin F. We restrans-edge inference. Specifically, we incorporated ChIP-seq profiling of AP-1 (cJun and JunB), KLF (KLF4 and KLF5), CEBPB, and GR . The one exception was for GR, which showed a much lower degree of overlap (35%). This is consistent with our network, which suggests that GR binds and dissociates rapidly from the chromatin at many sites and would not be expected to be bound at the 4-h ChIP-seq time point. Furthermore, CEBP, GR, and KLF REs that show sustained high accessibility  displayed a higher degree of overlap with ChIP-seq peaks than those that did not, suggesting that our ATAC-seq dynamics captured fluctuations in factor binding. In addition, we plotted composite ChIP signal at our predicted binding sites and found a strong enrichment in signal at REs that overlap with ChIP-seq peaks . We also observed a weaker enrichment of ChIP signal around inferred binding events that do not overlap with ChIP-seq peaks, suggesting weaker binding events at these locations were overlooked in ChIP-seq peak calling. However, it is also possible that the enrichment in ChIP-seq signal at these regions reflects underlying accessibility rather than actual factor binding. We note that ChIP assays are limited by antibody specificity, control data sets, and peak thresholding. Regardless, these ChIP-seq data validate the predictive power of using dynamic ATAC signal and the presence of sequence motifs to infer factor binding.We integrated publicly available TF ChIP-seq data sets to assess the performance of , and GR , 2014. A, and GR . Both thSupplemental Fig. S3G–I). The closer the peak and the gene, the more likely gene transcription and peak accessibility correlate in the same direction. This result indicates that proximal REs have a greater impact on gene expression than distal elements. Moreover, we plotted change in gene transcription against distance-scaled local accessibility changes and observed the expected positive correlation between transcription and accessibility at both the early and late phases of the time course . These findings indicate that both accessibility dynamics and distance are important factors when considering the relationship between REs and genes.Chromatin accessibility positively correlates with local gene transcription. We confirmed this assertion by quantifying transcription of genes within 10 kb of dynamic ATAC-seq peak sets that exclusively increase or decrease accessibility A. The macis-edges, in our networks. We define cis-edges as predicted regulatory relationships between REs and genes. For example, if GR binds a RE within a gene's promoter and induces gene activation, we draw a cis-edge between the RE and the gene. Within the network, we assign GR as an attribute to the edge. To confidently infer and annotate cis-edges, we must assess whether a class of TFs is associated with increasing or decreasing transcription. Because the distance between a RE and a gene influences the likelihood that accessibility and transcription will covary, we classified the function of a TF class within the context of adipogenesis by determining if peaks with a cognate TF motif are closer to activated or repressed gene classes. We expected that factor families associated with decreases in accessibility, like SP, would be closer to repressed genes on average. To test this hypothesis, we first categorized genes as significantly activated, repressed, or unchanged for each pairwise comparison within the time course. For example, Klf5  that regulate downstream changes in transcription (cis-edges). Genes and REs can be regulated or bound by either one or a combination of TFs. For example, we constructed a constrained network with RE and gene nodes downstream from individual TFs, including AP-1. In this network, 1224 genes are solely activated by AP-1, and 1847 genes are activated by AP-1 and at least one other factor , KLF , and TWIST . These networks illustrate the interconnectivity of gene regulation while simultaneously identifying genes that are predominantly regulated by individual factors.We constructed a bipartite network inferring changes in TF binding . The resulting traces show the characteristic pause peak centered around zero followed by release of the RNA polymerase into the gene body. Examining RNA polymerase density traces of genes only activated by AP-1 at 60 and 40 min shows an increase in density in the pause region, suggesting increased RNA polymerase recruitment to AP-1-activated genes . Isolated GR genes show increases in PI later in the time course, likely owing to GR dissociation from the genome after the early phase of the time course and an associated decrease in pause release rate . As for repressed genes, we find a decrease in pause peak and gene body intensity in predicted SP and TWIST target genes . We find that SP target genes show a more symmetrical distribution of PIs , premature termination (kpre), pause release (krel), and elongation (kelong). The values of the rate constants determine the predicted density within the two compartments. We vary the rate constants for kinit, kpre, and krel over two orders of magnitude and vary the kelong rate from 600 to 6000 bases per minute to determine the effect on pause and gene body density and how the model compares to observed changes. We make the assumption that kelong remains constant between time points. Because krel and kinit are opposing rates in the model, we cannot distinguish an increase in one rate from a decrease in another. To simplify the model, we keep kpre constant between time points. We determine how changes in kinit combined with krel changes can account for the average density changes for the 174 isolated GR-regulated genes from kinit and krel for AP-1 targets do result in gene activation, but it was unexpected that the profiles are explained by a decrease in krel. Because composite profiles represent the average of all included genes, it is possible that the composite represents a diverse set of genes that are regulated by different AP-1 family members. We speculate that we could gain a more clear insight if we were able to deconstruct the AP-1 targets and identify gene targets of specific AP-1 factors. The above analyses indicate that we can deconvolve complex transcriptional networks to identify gene targets of individual TFs and determine which steps in the transcription cycle each TF preferentially regulates.We developed a mathematical model to further characterize how TFs target specific steps in the transcription cycle. Our model breaks up the gene unit into two compartments: a pause region and gene body region. PRO-seq directly measures RNA polymerase density within these regions for each gene. We define a series of differential equations to model polymerase density as measured by PRO-seq within the two compartments A. We estSupplemental Fig. S5A). Compartment modeling of these genes showed that an approximately 1.33-fold increase in pause release rate explained the observed changes in Pol II density at the activated genes . These validation results support the role of GR regulating pause release and highlight the power of predicting the molecular function of TFs within complicated regulatory cascade networks generated from kinetic PRO and ATAC data.We applied this model and approach to a separate PRO-seq data set of the C7 B cell line treated with dexamethasone to determine whether GR regulates pause release within a different system in which GR is specifically activated. We identified 70 genes activated by dexamethasone treatment. The PI of 80% of these genes decreases between 0 and 60 min . The largest population of RE classes is isolated AP-1 peaks with 8794, whereas peaks bound by all activating factors are the smallest category with 74. The distribution of gene classes generally mirrors the distribution of RE classes, with isolated AP-1 genes being the largest class. As discussed above, all factors activate more genes in combination than in isolation. There are comparatively few combinatorially-regulated genes without AP-1 contribution (1924 with AP-1 vs. 149 without). This finding, along with the high number of genes regulated by AP-1, underscores the importance of the AP-1 family in the network. Although the bulk of negatively regulated genes are downstream from either SP or TWIST, ∼20% are affected by both TWIST-mediated repression and SP-mediated attenuation .AP-1, CEBP, GR, and KLF bind REs either individually or in combination in order to activate expression. We classify REs based on the combination of factors that bind and drive accessibility changes. Likewise, we classify genes based on which TFs are immediately upstream in the network. Genes activated by the same combination of factors can be downstream from different classes of REs. We use genes activated by all four of AP-1, CEBP, GR, and KLF to illustrate potential regulatory scenarios. These genes may be downstream from a single RE that binds all factors A, orangeSupplemental Fig. S6C). We found that the relative change in transcription positively correlates with the number of immediate upstream activators in the network . Normalizing transcriptional change by local accessibility change eliminates the observed correlation between transcription and number of regulatory factors . We confirmed this observation by plotting the transcription of all predicted target genes against the total local accessibility stratified by the number of regulatory factors and peaks . We find that more local regulatory peaks, which corresponds to greater total local accessibility, correlate with a greater magnitude of transcription. However, the number of regulatory factors largely does not affect transcription. Therefore, we find that transcription is positively correlated with total local accessibility change, regardless of the number of factors effecting that change. We conclude that if a gene is regulated in the network, the magnitude of expression change is independent from the number of upstream TFs.We did not observe a significant relationship between magnitude of RE accessibility change and number of regulatory factors . We find a lower proportion of repressed genes proximal to SP motifs in both the presence and absence of TWIST . These results highlight the complexity of gene regulatory control and how kinetic networks reveal coordinate and independent relationships between TFs.Because the degrees of activation and repression are unrelated to the number of upstream factors, we asked if having multiple TFs upstream in the network influences whether a gene is dynamic. To determine whether two TFs cooperate with one another, we considered genes close to dynamic peaks with either a single TF motif or both TF motifs. We determine if the fraction of dynamic and nondynamic genes proximal to a TF is influenced by the presence of another TF. We define TF-proximal genes as genes that are close to dynamic ATAC peaks containing the TF motif. We find that there is no difference between the fraction of GR-proximal activated genes in the absence of AP-1. However, there is an increase in the fraction of activated genes proximal to GR in the presence of AP-1 C. The reof TWIST F. We speTwist2 gene . About 63% of AP-1, 74% of CEBP, and 80% of GR cis-edges are transient. Only 27% of KLF cis-edges are attenuated, suggesting that KLF-mediated activation is less transient and less dependent on the extracellular stimuli found in the adipogenic cocktail. Similarly, a minority of TWIST cis-edges and no SP cis-edges are attenuated, indicating that SP and TWIST factors mediate sustained repression. A much smaller proportion of trans-edges are attenuated, implying that accessibility changes downstream from factor binding and dissociation are more stable  than are changes in nascent transcription.Many genes activated in the early phase of the time course are repressed later on, through either active repression or factor dissociation. We detect these negative feedback loops for each activating TF . We begin to detect changes in accessibility at KLF- and TWIST-bound REs as early as 20 min ; however, these presumptive binding events do not manifest as detectable changes in nascent transcription until 40 min , but this kinetic network implicates TWIST2 as a critical intermediary in the adipogenesis cascade.In addition to the TFs whose activity is stimulated by the adipogenesis cocktail, we identify transcriptionally regulated TF genes that are highly connected nodes within the network. The al genes F. TWIST2Twist2 depletion. We used two different shRNA sequences (v1 and v2) to knockdown Twist2 and harvested RNA for RNA-seq at 0, 1, 2, and 4 h after switching cells into differentiation media. We observed a ∼50%–75% reduction of Twist2 expression before differentiation . Unlike PRO-seq, RNA-seq requires mature mRNA accumulation above the baseline signal to detect activation, and RNA degradation to detect repression. Therefore, many observed transcriptional changes at the nascent RNA level take much longer to be detected at the mature RNA level. To this point, we find 32,094 total genes expressed in the control RNA-seq data set compared with 13,122 in the PRO-seq data set. This suggests that of all the genes detected by RNA-seq, only ∼40% are actively transcribed. Furthermore, we identify 285 dynamic genes over the first hour of differentiation by RNA-seq versus 9201 by PRO-seq, underscoring the sensitivity of PRO-seq for detecting transcription changes over short time intervals. For RNA-seq analysis, we only focused on the 520 predicted TWIST2 targets that are significantly repressed in both the PRO-seq and RNA-seq time courses. The other 547 predicted TWIST2 target genes from the network are not detected as repressed by conventional RNA-seq, so we would have no power to detect derepression upon Twist2 depletion. Approximately 66% of the examined genes were expressed at higher levels at the baseline in the Twist2 knockdown compared with the control, supporting our hypothesis that TWIST2 directly represses the majority of our predicted targets . This result is likely an underestimate of the specificity of our network, because the cells can compensate for chronic RNAi-mediated depletion of Twist2.We tested whether inferred TWIST2-repressed target genes from the network increase expression upon Twist2 depletion on differentiation-induced transcription. Twist2 was activated in both the control and the knockdown samples, supporting our PRO-seq results . Twist2 was more strongly activated in the knockdown sample than in the control (approximately 3.1-fold to approximately twofold). This is likely because the RNAi machinery cannot keep up with the dynamic accumulation of Twist2 transcripts in the hours following treatment with the differentiation cocktail. Because we inferred that TWIST2 represses target genes in this system, we would expect a greater degree of repression over the time course in the knockdown samples owing to the relatively greater accumulation of TWIST2 protein. This analysis more directly tests the accuracy of predicting TWIST2-target genes compared with chronic knockdown. We find that ∼75% of the predicted targets are repressed to a greater magnitude in the shTwist2 samples compared with the control knockdown . The above findings support the conclusions from the network and indicate that TWIST2 is a transcriptional repressor of predicted target genes in 3T3-L1 differentiation.We then measured the effect of chronic nockdown C. We obsTwist2 construct into 3T3-L1 cells . After 6 d of differentiation, 3T3-L1s overexpressing Twist2 showed decreased Oil Red O staining , supporting our previous finding that TWIST2 expression reduces 3T3-L1 differentiation.To test TWIST2's effect on differentiation of preadipocytes, we depleted TWIST2, induced differentiation of 3T3-L1s, and measured lipid uptake after 6 d of differentiation. We stained differentiated adipocytes with Oil Red O and measured absorbance at 540 nm to quantify lipid uptake. Lipid accumulation is a cellular phenotype that acts as a proxy measurement for adipogenesis. Lipid uptake increased with shTwist2 treatment compared with shControl within each experiment, suggesting that TWIST2 expression negatively regulates differentiation in the 3T3-L1 system D. As an Twist2+/− pups. We induced differentiation in the primary preadipocytes and found that preadipocytes derived from heterozygous mice differentiated to a greater extent than those derived from wild-type mice . The 3T3-L1 and primary cultured preadipocyte results indicate that TWIST2 opposes induced differentiation in both in vitro and ex vivo contexts.Next, we extracted preadipocytes from inguinal white adipose tissue (WAT) of 3 d old Twist2+/− mice have a deficiency of dermal and subcutaneous WAT in the skin  deposits , then we can confidently infer regulatory interactions between TF binding at REs and changes in transcription of the nearby gene. Our bipartite-directed graph networks are unique in the gene regulation field because each edge represents a functional interaction as opposed to an abstract relationship between linked nodes. Trans-edges represent binding of TF proteins to cognate DNA elements, and cis-edges describe regulatory interactions between REs and target genes. These networks can define gene sets that are predominantly regulated by a single TF and identify the target regulatory steps of the TF. Highly connected nodes in the network are candidate key regulatory hubs in the differentiation cascade. Moreover, these networks ascribe time attributes to each edge, so subgraphs that respect the flow of time are easily extracted from the larger graph. This integrative genomics approach to network construction can be applied to a multitude of cellular responses and transitions to uncover novel biology and new hypotheses.We confidently differentiate primary, secondary, and tertiary transcriptional changes by examining multiple, closely spaced time points upon induced adipogenesis. ATAC-seq, ChIP-seq, or chromatin conformation assays alone can only suggest functional relationships between REs and genes . Similar3T3-L1 cells were provided by Thurl Harris. 3T3-L1 cells were cultured in high-glucose DMEM (Gibco) supplemented with 10% newborn calf serum, 1% fetal bovine serum (FBS), 100 U/mL penicillin G, and 100 µg/mL streptomycin. We induced adipogenesis ∼3 d after cells reached confluency by switching cells into high-glucose DMEM supplemented with 0.25 µM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, 2.5 U/mL insulin, 10% FBS, 100 U/mL penicillin G, and 100 µg/mL streptomycin . We coll4 cells per replicate and transferred them to 1.5-mL tubes. We centrifuged cells at 500g for 5 min at 4°C and resuspended the pellet in 50 µL cold lysis buffer  and incubated on ice for 3 min. We washed the samples with 1 mL cold wash buffer . We centrifuged at 500g for 10 min at 4°C, resuspended cells in the transposition reaction mix (25 µL 2× TD buffer (Illumina), 2.5 µL TDE1 Tn5 transposase (Illumina), 16.5 µL PBS, 0.5 µL 1% digitonin, 0.5 µL 10% Tween-20, 5 µL nuclease-free water), and incubated for 30 min at 37°C. We extracted DNA with the MinElute kit (Qiagen). We attached sequencing adapters to the transposed DNA fragments using the Nextera XT index kit (Illumina) and amplified libraries with six cycles of PCR. We performed PEG-mediated size fractionation  and identified differentially accessible REs with DESeq2 . We performed de novo motif extraction on dynamic REs with MEME  and used TOMTOM  to match motifs to the HOMER, JASPAR, and UniPROBE TF binding motif databases .We aligned reads to the mm10 mouse genome assembly with Bowtie 2, sorted output BAM files with SAMtools, and converted files to bigWig format with seqOutBias . We callh DESeq2 . We clusatabases . We use atabases . We geneatabases . We use 2, 0.5 mM CaCl2, 0.5 mM DTT, 0.004 U/mL SUPERaseIN RNase inhibitor [Invitrogen], protease inhibitors ). We permeabilized cells by incubating with buffer P  for 3 min. We washed cells with 10 mL buffer W before transferring into 1.5-mL tubes using wide-bore pipette tips. Finally, we resuspended cells in 500 µL buffer F . After counting nuclei, we separated cells into 50 µL aliquots with approximately 3 × 105 to 5 × 105 cells each. We snap-froze our aliquots in liquid nitrogen and stored them at −80°C. We prepared PRO-seq libraries as previously described (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE133147.We performed cell permeabilization as previously described . We trypescribed . We inclhttps://github.com/guertinlab/fqdedup, v1.1.0). Next we removed UMIs and converted reads to their reverse complement with the FASTX-Toolkit . As with the ATAC-seq samples, we used Bowtie 2, SAMtools, and seqOutBias to align, sort, and convert reads to bigWig files, respectively  and UCSC Genome Browser Utilities . We define the summit of the pause peak for genes by first identifying the point of maximum density within 1 kb of the TSS. We define the pause region as the 50-bp window around the summit.First we used cutadapt to remove adapters from our reads . We usedectively . We usedectively . We quertilities . We identilities . We usedtilities . As withtilities . We evaltrans-edges from TF families to individual REs is that the RE must contain the cognate motif for the TF family. The second is that the peak must be dynamically accessible over some part of the time course. The third is that at least one gene encoding a member of the TF family must be expressed and activated  over the same time range. We restrict trans-edges attributed to GR to the first 40 min of the time course for reasons discussed in the text. Similarly, we do not draw edges from SP before 40 min. Next, we drew cis-edges between REs and proximal genes based on a different rule set. First, REs need to be within 10 kb of gene bodies as defined by primary transcript annotation of our PRO-seq data. We used BEDTools to find gene–RE pairs that satisfied this rule  under accession numbers GSE150492 (3T3-L1 ATAC-seq), GSE219041 (C7 cells + dexamethasone PRO-seq), and GSE219051 (shTwist2 3T3-L1 RNA-seq). All code used for data analysis can be found at GitHub (https://github.com/guertinlab/adipogenesis) and as Supplemental Code.All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO;