S. pombe has provided an important model system for dissection of chromatin biology due to the relative ease of genetic manipulation and strong conservation of important regulatory proteins with higher eukaryotes. Telomeres and the telomere-binding shelterin complex are highly conserved with mammals, as is the assembly of constitutive heterochromatin at subtelomeres. In this review, we seek to summarize recent work detailing the assembly of distinct chromatin structures within subtelomeric domains in fission yeast. These include the heterochromatic SH subtelomeric domains, the telomere-associated sequences (TAS), and ST chromatin domains that assemble highly condensed chromatin clusters called knobs. Specifically, we review new insights into the sequence of subtelomeric domains, the distinct types of chromatin that assemble on these sequences and how histone H3 K36 modifications influence these chromatin structures. We address the interplay between the subdomains of chromatin structure and how subtelomeric chromatin is influenced by both the telomere-bound shelterin complexes and by euchromatic chromatin regulators internal to the subtelomeric domain. Finally, we demonstrate that telomere clustering, which is mediated via the condensed ST chromatin knob domains, does not depend on knob assembly within these domains but on Set2, which mediates H3K36 methylation.Telomeres play important roles in safeguarding the genome. The specialized repressive chromatin that assembles at telomeres and subtelomeric domains is key to this protective role. However, in many organisms, the repetitive nature of telomeric and subtelomeric sequences has hindered research efforts. The fission yeast  Schizosaccharomyces pombe provides a highly useful genetically tractable system for exploring chromosome biology, and recently, the full sequence of subtelomeric domains has been defined. This review provides a non-exhaustive summary of the mechanisms underlying the assembly of distinct chromatin subtypes that assemble at subtelomeres in fission yeast, providing an overview of recent findings.Telomeres, located at the ends of chromosomes, play critical roles in ensuring the maintenance of genomic stability. Not only do these structures provide a proteinaceous cap that protects the ends of the chromosomes from recombination but they also play key roles in ensuring proper chromosome movement during mitosis and meiosis. The function of telomeres and repetitive subtelomeric sequences is highly conserved amongst eukaryotes, although the exact DNA sequences are not highly conserved . For manS. pombe cells have a haploid genome consisting of three chromosomes: I, II, and III  [S. pombe are large satellite-rich domains that span 35–110 kb. The telomeres comprise ~300 bp of a repeat motif [G2-6TTAC[A]) [972h- strain. Some strains possess partial subtelomeric sequences adjacent to the telomeres of Chr III [ctively) . Centrom Chr III ,9. SubteTelomeric DNA, which spans about 300 nucleotides, is composed of double-stranded DNA repeats with a single-stranded overhang at the very end of the chromosome. Internal to this, the subtelomeric region spans about 100 kilobases to separate the telomeres from the euchromatic arms of the chromosomes . TelomerS. pombe genome assembles constitutive heterochromatin at the pericentromeres, the telomeres, the mating-type locus, and rDNA loci that replace subtelomeric sequences on chromosome 3 [Heterochromatin and euchromatin are made up of the same basic unit, the nucleosome, which consists of a histone octamer wrapped by 147 bp of DNA . Histonemosome 3 . The chrmosome 3 ,16, whicS. pombe, all H3 K9 methylation is catalyzed by a single methyltransferase, Clr4, which is a homolog of Suv39 [S. pombe is bound by the chromodomain-containing proteins Swi6 and Chp2 (homologs of HP1), Chp1, and by Clr4 itself [In of Suv39 . Clr4 isof Suv39 . ClrC inof Suv39 ,20,21,224 itself ,23,24,254 itself . Whether4 itself ,26.ClrC complex recruitment to chromatin is central to heterochromatin assembly, and different mechanisms contribute to ClrC recruitment at distinct loci. For example, at centromeres, the RNAi pathway is central to heterochromatin maintenance, whereas at subtelomeres, RNAi contributes, but robust alternate mechanisms also recruit ClrC, so heterochromatin is unaffected by disruption of RNAi ,28,29.S. pombe, the RNAi pathway contributes to both post-transcriptional gene silencing (where siRNA contributes to turn over of RNAs) and transcriptional gene silencing, with RNAi-directed recruitment of ClrC to assemble heterochromatin. Two important complexes of the RNAi machinery are RITS: RNA-induced initiation of transcriptional gene silencing, and RDRC (RNA-directed RNA polymerase complex) [In complex) ,30,31. Rcomplex) . Within complex) ,30,33,34complex) , which ccomplex) . Brieflycomplex) ,38,39,40Histone deacetylases (HDACs) remove acetyl groups from the ε-amino group of lysines within histone tails, a mark deposited by histone acetyltransferases (HATs) . This isAt the subtelomeric repeats, Sir2 HDAC activity is required for silencing ,44,45 siClr3 is a member of the Snf2/HDAC-containing repressor complex, SHREC, which can be recruited to heterochromatin via the Chp2 chromodomain protein binding H3K9me2/3. Clr3 was first identified in a genetic screen for mutants with disrupted mating type locus silencing  but alsoAll components of SHREC are required for its role in heterochromatin maintenance at centromeres, mating type loci, and subtelomeres , and ClrS. pombe span ~100 kb (Subtelomeres in  ~100 kb . They caS. pombe genome sequence was published in 2002 [The SH domains of subtelomeres have high levels of sequence identity (>90% identity) between the ends of chromosomes 1 and 2. Even though the  in 2002 , sequenc in 2002 ,58, and  in 2002 . These e in 2002 . Subtelo in 2002 ,61. The  in 2002 .SH domains can be further subdivided into SH-D or distal regions, which lie distal to the telomere, and the much more highly variable SH-P or telomere-proximal domains- also known as TAS sequences that occupy 5–10 kb of sequence and directly abut the telomeric repeats.TAS sequences are highly complex mosaic structures consisting of elements shared between the four subtelomeres but in mixed order and exhibiting nucleotide changes within the motifs. These characteristics indicate that these domains are hot spots for recombination and rearrangement . TAS seqtlh genes, all of which are non-essential. In the original sequence of the S. pombe genome, two tlh genes were assigned, but with the completion of sequence of subtelomeric domains, we now know that there are four of these very similar genes, one in each SH-D region [tlh gene sequences are elements known as dg and dh that are shared between sites of constitutive heterochromatin at centromeres, the mating type locus, and at subtelomeres [SH-D domains show very high homology between the four ends of chromosomes 1 and 2, and remnants can be found between the telomeres and rDNA repeats on chromosome 3 in some strains. Due to the difficulty in sequence analysis and mapping, their exact sequence has remained ambiguous until very recently. Several genes lie within SH-D, including telomere-linked helicases, or D region . Within elomeres . These eelomeres . Howeverelomeres . In the elomeres .S. pombe, the assembly of a highly condensed chromatin “knob” is conserved on these domains [ST domains comprise unique sequences that span approximately 50 kb and separate the euchromatic chromosomal domain from repetitive subtelomeric SH domains. Although the sequences of ST domains are not conserved across the subtelomeres of  domains .S. pombe, redundant mechanisms are responsible for the formation and maintenance of heterochromatin. As mentioned above, repetitive elements similar to the centromeric dg and dh repeats within SH-D can recruit ClrC via RNAi to form subtelomeric heterochromatin [At subtelomeric regions of hromatin . There ahromatin ,66. The hromatin .S. pombe. Taz1 (the ortholog of TRF1 and 2) binds directly to the double-stranded portion of the telomeric repeats [Shelterin performs multiple important functions including recruitment and activation of telomerase and recruitment of ClrC complex to telomeres and, through linking the single-stranded and double-stranded regions of the telomere, allows the spreading of ClrC into subtelomeric domains. Shelterin comprises the conserved subunits Pot1, Tpz1, Poz1, Rap1, and Taz1 and also includes Ccq1 in  repeats ,67, and  repeats . Tpz1 bi repeats ,69,70,71 repeats ,73,74,75Telomeric repeats are sufficient to recruit shelterin and ClrC, and analysis of mutants that disrupt interactions between shelterin components reveals the importance of shelterin in not only recruiting ClrC but also allowing it to spread from telomeric repeats to subtelomeric TAS and SH-D sequences .ccq1 mutant background [set2Δ K36 methyltransferase mutants, leading to increased TERRA transcription. Deletion of the HAT complex Mst2-C also leads to upregulation of TERRA transcripts, but in contrast to sites of constitutive heterochromatin (SH-D domains- see below), the combination of set2Δ and mst2Δ does not suppress the silencing defect at TAS [In contrast to other heterochromatic loci, the assembly of heterochromatin on TAS domains occurs independent of RNAi. TAS domains were recently shown to have very low nucleosome occupancy, with only about one third of the occupancy seen in euchromatic regions . This atckground . Importat at TAS , suggestdg and dh sequences in SH-D, and the telomere associated shelterin proteins, which reside at TAS and telomere repeats [The chromatin that assembles on SH-D domains resembles constitutive heterochromatin, with high levels of H3K9me2/3. It is established by multiple factors, including the RNAi pathway, which acts through the  repeats ,27,72. H repeats . In stra repeats . At two  repeats , and of  repeats .set2Δ alone. These examples highlight the importance of proper balance between chromatin modifiers in control of subtelomeric heterochromatin. Recent work has shed further light on the mechanism of Mst2 function. Within euchromatin, Mst2 acetylates the Brl1 ubiquitin ligase at K242, which is required for the activation of its ubiquitin ligase activity on H2BK119. According to Flury et al. [set2Δ at heterochromatic sites, the combination of set2Δ with the non-acetylatable Brl1 K242R mutant does not compensate for set2Δ’s heterochromatin silencing defect, suggesting that the role of Mst2 in counteracting heterochromatic silencing is independent of its role in H2B ubiquitin regulation [Whilst heterochromatic loci are associated with low levels of activatory chromatin marks, intriguingly, the loss of the enzymes that generate the activatory marks H3K4me3 and H3K36me3 leads to derepression of particular sites of heterochromatin. The loss of Set2 (K36 methylation) leads to derepression of all sites of constitutive heterochromatin and the loss of Set1 (K4 methylation) to defective heterochromatin at subtelomeres and heterochromatic islands. Both enzymes associate with the C-terminal domain of RNA Pol II and mark sites of transcriptional activity in euchromatin. In both instances, loss of the methylated histone products leads to deregulation of either the localization or the activity of different HAT complexes, leading to aberrant transcription of normally heterochromatic sites. The loss of the components of Set1-C results in the loss of H3K4 methylation and a loss of antagonism with the H3K4 histone acetyltransferase Mst1. In cells lacking Set1-C, Mst1 can then acetylate and derepress subtelomeric heterochromatin . In conty et al. , euchromy et al. . Howevergulation .The ST regions adjacent to the subtelomeric SH silent chromatin are distinguished by their unique chromatin features. These regions are characterized by very low levels of histone variant H2A.Z and histone H3 lysine methylation at K4, K9, and K36 ,82. The S. pombe [set2 gene (set2∆) results in complete loss of K36 methylation and almost completely blocks knob formation [set2Δ [Histone H3 lysine 36 (H3-K36) plays an important regulatory role in the formation of knobs on the ST chromatin . H3K36 cS. pombe  and elicS. pombe  but can S. pombe ,87. Deleormation , suggestn [set2Δ  (note thn [set2Δ )). This n [set2Δ , suggestFurther evidence supporting the involvement of acetylation status of H3K36 in knob formation came from study of a nearby residue in histone H3, H3-G34, which is mutated in pediatric high-grade glioma ,89,90,91gcn5∆ and mst2∆ reduces H3K36ac, represses transcription of ST domains, and concomitantly increases knob formation [set2∆ and mst2∆ results in proper silencing, transcripts within knob regions are upregulated in set2∆ but repressed in mst2∆, but the combined mutation of set2∆ and mst2∆ is not sufficient to restore silencing defect of a set2∆ strain.Yet further support for a regulatory role of histone acetylation in knob formation is that a double mutant of histone acetyl transferases ormation . Mst2 isormation . Brl1, iormation ,92,93. Tormation . IntriguAs mentioned earlier, Mst2 and Set2 are involved in a feedback loop to maintain euchromatin. Among these elements in the feedback loop, Set2 promotes while Mst2 and H2BK119ub inhibit knob formation. At a first glance, the opposing roles of Set2 and of Mst2 and H2BK119ub seem inconsistent. However, Mst2 acetylates H3K36 , and it sgo2∆ completely lose knobs [set2Δ mutants, the amount of Sgo2 localized at ST is decreased [mst2Δ gcn5Δ), there is increased Sgo2 localization at the subtelomeric regions, whereas in H3-G34V mutants, there is not [Shugoshin protein Sgo2 is localized at the centromere and contributes to precise chromosome segregation during mitosis ,97. Durise knobs , indicatecreased . Converse is not , consistS. pombe is related to condensed knob formation. RNAi mutants, ago1∆, dcr1∆, and rdp1∆, are defective in subtelomeric clustering in S. pombe [dcr1∆ cells, knob formation was only slightly reduced [dcr1∆ is sufficient for knob formation. We found that clustering of the subtelomeric regions requires Set2 (S. pombe. Furthermore, because gene expression levels in subtelomeric regions are similarly elevated in set2∆ and sgo2∆ [sgo2∆ indicates that clustering occurs independently of gene silencing at the subtelomeres.Subtelomeric regions are known to physically cluster in the nucleus in many eukaryotes ,99,100. S. pombe . In dcr1 reduced . This mares Set2 A, indicares Set2 B. Intereres Set2 C. Therefnd sgo2∆ ,79, the


Cellular heterogeneity in the human brain obscures the identification of robust cellular regulatory networks, which is necessary to understand the function of non-coding elements and the impact of non-coding genetic variation. Here we integrate genome-wide chromosome conformation data from purified neurons and glia with transcriptomic and enhancer profiles, to characterize the gene regulatory landscape of two major cell classes in the human brain. We then leverage cell-type-specific regulatory landscapes to gain insight into the cellular etiology of several brain disorders. We find that Alzheimer’s disease (AD)-associated epigenetic dysregulation is linked to neurons and oligodendrocytes, whereas genetic risk factors for AD highlighted microglia, suggesting that different cell types may contribute to disease risk, via different mechanisms. Moreover, integration of glutamatergic and GABAergic regulatory maps with genetic risk factors for schizophrenia (SCZ) and bipolar disorder (BD) identifies shared  and distinct cellular etiologies . Collectively, these findings shed new light on cell-type-specific gene regulatory networks in brain disorders. The cellular heterogeneity in brain obscures the identification of robust cellular regulatory networks. Here the authors integrate genome-wide chromosome conformation data from sorted neurons and glia, with transcriptomic and enhancer profiles, to characterize cell-type-specific gene regulatory landscapes in the human brain, and provide insights into cell-type-specific gene regulatory networks in brain disorders. Allelic variation in these elements is thought to have an influence on complex human traits via impacting gene regulation2, necessitating the understanding of gene regulatory architecture in the human brain. We and others have identified gene regulatory relationships in the developing and adult human brain by integrating multi-dimensional datasets that include transcriptomic, epigenomic, and higher-order chromatin interaction landscapes7. However, cellular heterogeneity poses significant challenges for unraveling the complexity in the gene regulatory architecture of the human brain, as it is comprised of heterogeneous cell populations, mostly different types of neurons and glia, each of which display distinct gene expression10 and chromatin accessibility profiles14. To address this issue, several groups have employed Hi–C and its derivatives (e.g. promoter-capture Hi–C) to build higher-order chromatin interaction maps in induced pluripotent stem cells (iPSC)-derived neurons and astrocytes15. However, these published studies rely on in vitro cultured cells that mark early brain development. Recently, promoter interaction profiles were inferred from three types of brain cells  obtained from the postnatal human cerebral cortex16.The majority of human genetic variants imparting risk for brain diseases are located within non-coding elements17 to sort neurons (NeuN+ cells) and glia (NeuN− cells), two major cell classes in the brain, and generated genome-wide chromosome conformation using Hi–C , which are local interaction hotspots that are enriched with active regulatory elements; and gene loops within NeuN+ and NeuN− cells which link promotors to presumed regulatory elements , schizophrenia (SCZ), and bipolar disorder (BD). Our results illustrate deciphering the epigenetic landscape in a cell-type-specific fashion offers substantial advantages for inferring the functional impact of genetic risk factors associated with brain disorders.Here, we analyze genome-wide chromosome conformation at cellular resolution to capture how chromatin structure affects cellular expression profiles. We use fluorescence-activated nuclear sorting 19. Notably, we found that samples were clustered by cell types, not individuals, and showed high correlation between the same cell types from different individuals  samples  switched from compartment A to B between NeuN− to NeuN+ cells, while 2098 regions switched from compartment B to A in NeuN+ to NeuN− cells. Importantly, genes located in compartments that switch from A to B between NeuN− and NeuN+ were highly expressed in oligodendrocytes and astrocytes, while those that switch from B to A in NeuN− to NeuN+ were highly expressed in neurons, suggesting that the difference in chromosome conformation between NeuN+ and NeuN− cells is associated with cell-type-specific gene regulation  variants and exhibit variability across individuals26. We therefore compared FIREs in NeuN+ and NeuN− cells to identify how local chromatin architecture differs among these major brain cell types. We detected 3966 and 3967 FIREs in NeuN+ and NeuN− cells, respectively , detecting 287 differential FIREs between NeuN+ 145) and NeuN− (142) cells  and found that they also displayed substantial cellular specificities. Only 9 super-FIREs were detected in both NeuN+ and NeuN− cells, leaving 253 and 157 cell-type-specific super-FIREs in NeuN+ and NeuN− cells, respectively . We were able to assign 10,167 and 11,242 cell-type-specific H3K27ac peaks to 7828 and 8851 genes via chromatin interactions in NeuN+ and NeuN− cells, respectively  neurons18 to deconvolute NeuN+ chromatin interactions into two major neuronal subtypes. We obtained 45,911 Glu-specific and 32,169 GABA-specific H3K27ac peaks and assigned them to 6234 and 4342 genes, respectively 41. Both modules were associated with synaptic transmission. This result demonstrates that cell-type-specific epigenetic dysregulation in AD is coupled with gene expression changes.We next used the cell-type-specific gene regulatory relationship to refine cell-type-specific aspects of disease vulnerability. Studies in AD have revealed changes in gene regulation manifested by differences in H3K27ac in bulk tissueaks Fig. . We deteaks Fig. . To deciaks Fig. . NeuN+ hlls Fig. . NeuN− hHD1 Fig. , a gene  41 Fig. . Among 142. We reasoned that cell-type-specific annotation of the regulatory impact of genetic risk factors would provide support or refine our knowledge of causal mechanisms underlying AD, which to date heavily implicate neural immune/microglial mechanisms44. Therefore, we first performed linkage disequilibrium score regression (LDSC)45 analysis to determine the enrichment of AD-associated common genetic variants in NeuN− and NeuN+ H3K27ac peaks. AD SNP-based heritability was highly enriched in NeuN− enhancers , suggesting that NeuN− gene regulatory relationships would be the most appropriate for refining biological insight from AD GWAS  based upon their potential regulation by associated non-coding variants  SNPs interact with the promoter of BIN1, whose transcript level is increased in AD brains48  pathways, lipoprotein assembly, and immune processes  confirmed that SCZ and BD displayed strong SNP-based heritability enrichment in NeuN+ cells  and divergence  among two highly genetically correlated disorders.In our previous study, we found that genes associated with psychiatric disorders display substantial molecular convergence within neurons, which was in stark contrast to neurodegenerative disorders like ADlls Fig. . We next BD Fig. . Since H BD Fig. . Glu-SCZ BD Fig. . We also BD Fig. . Glu-BD  BD Fig. . Neuronaore Fig. . Glu-SCZHere we provide a high-resolution map of chromosome conformation from two major brain cell classes, neurons (NeuN+ cells) and glia  or changes in regulatory landscape and cellular function  in AD, which requires future investigation.We first leveraged these networks to deconvolve epigenetic dysregulation in AD postmortem brain samples into the corresponding cell types. In their original work, Marzi et al.57. In linking AD genetic risk factors to their cognate genes, we also recapitulated a previously found association between rs6733839 and BIN1. Nott et al. found that this SNP is causally implicated in the regulation of an AD risk gene, BIN1, in a microglia-specific manner16 as predicted by our analysis.It is of note that while AD-associated hyperacetylation was linked to oligodendrocyte genes and astrocyte co-expression modules, genetic risk factors for AD were mapped onto a different glial cell type, microglia. These results are consistent with a newly emerging literature which indicates that different types of glia may contribute to the disease via different regulatory mechanisms61. However, given the strong genetic overlap between BD and SCZ62, we do not have much indication about specific biological pathways that are driving one disease versus the other. Here, we reasoned that neuronal subtype-specific gene regulatory networks would help refine the cellular etiology of disease and potentially identify discrete molecular neuropathology across psychiatric disorders. Indeed, when we applied Glu and GABA neuronal enhancer-gene networks to BD and SCZ, we were able to delineate discrete cellular substrates that may contribute to difference between BD (Ex1) and SCZ (Ex5), a distinction that has not been previously recognized. It is also of note that common genetic risk for SCZ and BD also indicated shared pathways coalescing onto parvalbumin-positive interneurons, paralleling what has been among the most robust pathologic findings in SCZ and BD64. However, we note that these findings need to be further verified by neuronal subtype-specific Hi–C data (e.g. Glu and GABA Hi–C). Moreover, CRISPR/Cas9-mediated genome engineering can be used to target risk genes’ enhancers to further investigate their roles in a neuronal subtype-specific manner. Our study provides a foundation for such future study by characterizing cell-type-specific 3D chromatin structure in the adult human brain, which we show can be used to improve our understanding of gene regulatory landscape in brain disorders.In contrast to AD in which multiple glial cells are implicated, previous work has indicated neurons as the central cell-type harboring genetic risk for the majority of psychiatric disordersPrefrontal cortex with no history of neurological disease was procured from tissue collections at the Department of Neuroscience at Yale University School of Medicine. Additional specimens were procured from the Brain and Tissue Bank at the University of Maryland. Tissue was collected after obtaining next of kin consent and with approval by the institutional review boards at the Yale University School of Medicine, the National Institutes of Health, and at each institution from which tissue specimens were obtained. Tissue specimens were handled in accordance with ethical guidelines and regulations for the research use of human brain tissue set forth by the NIH and the WMA Declaration of Helsinki. All available non-identifying information was recorded for each specimen 2; 0.1 mM EDTA, pH8; 10 mM Tris-HCl, pH8; 1 mM DTT, 0.1% Triton X-100). Brain homogenate was fixed in 1% formaldehyde for 10 min prior to NeuN immunotagging66. Nuclei were isolated via ultracentrifugation in sucrose gradient. Isolated nuclei were subjected to NeuN immunotagging using 1:1000 anti-NeuN-488  and 1:1000 DAPI  diluted in DPBS with 0.2% BSA. For each Hi–C assay, 2–5 × 106 sorted NeuN+ or NeuN− nuclei were used. For each nuclear RNA-seq assay, ~5 × 104 sorted nuclei were used.Procedures in preparation for flow cytometry  for frozen never-fixed brain tissue specimens were previously described in detailSorted cells were fixed in 1% formaldehyde for 10 min. Cross-linked DNA was then digested by HindIII . Digested chromatin ends were filled, marked with biotin-14-dCTP , and ligated within the nucleus. DNA was sheared into 300–600-bp fragments , and biotin-tagged DNA was pulled down with streptavidin beads  and ligated with Illumina paired-end adapters. The resulting Hi–C library was amplified by PCR , and sequenced by Illumina 50 bp paired-end sequencing.67. Filtered reads were binned at 10 kb, 40 kb, and 100 kb resolution to build a genome-wide contact matrix at a given bin size, which was subsequently normalized using Iterative Correction and Eigenvector decomposition. We then used 100 kb resolution matrices for compartment analysis, 40 kb for TAD analysis, and 10 kb for loop detection. Heatmaps of Hi–C contact matrices at 10 kb resolution were plotted using R package pheatmap (v.1.0.12) with scale=“column”.The resulting Hi–C reads were mapped and filtered using hiclib (v.0.9)19 to measure reproducibility across biological replicates of Hi–C libraries using normalized Hi–C contact matrices at 40 kb resolution. HiCRep generates a smoothed contact matrix, and stratifies the matrix by the distance between the interacting regions of chromatin. From this stratified matrix, a SCC is defined, which provides a measure of similarity of genome-wide chromatin contacts across cell types.We used HiCRep (v.1.10.0)3, mature non-neuronal cells (NeuN−), mature neuronal cells (NeuN+), postmitotic neuron-enriched cortical plate from the fetal brain 4, progenitor-enriched ventricular zone from the fetal brain 4, iPSC-derived neurons (iPSC Neuron)15 and iPSC-derived astrocytes (iPSC Astrocytes)15.HiCRep was also used to compare similarities between brain-derived genome-wide normalized chromatin contacts at 40 kb resolution. We compared Hi–C datasets derived from homogenized adult brain tissue (Adult brain)68 was used to call compartments from normalized genome-wide chromatin contact matrices at 100 kb resolution68. Principal component analysis (PCA) was performed using 4 eigenvectors. PC values were selected for each chromosome and correlated with gene density to determine compartments. The correlations between PC values from each Hi–C data were then used to compare similarity in compartments across cell types.HiCExplorer (v.2.2.1.1)4. In brief, we quantified the directionality index by calculating the degree of upstream or downstream (2 Mb) interaction bias of a given bin (40 kb), which was processed by a hidden Markov model (HMM) to remove hidden directionality bias.We conducted TAD-level analysis as described previously24 to define FIREs and super-FIREs based on raw Hi–C contact matrices at 40 kb resolution. As FIREcaller defines FIREs based on a specific statistical threshold (p < 0.05 which corresponds to a FIRE score >1.56), NeuN+ or NeuN− specific FIREs defined by FIREcaller although displaying similar FIRE scores between two cell types, may be called significant only in one cell, as only one cell type exceeds the statistical cutoff (e.g. FIRE score = 1.6), while the other fails to reach the statistical cutoff (e.g. FIRE score = 1.5). Therefore, rather than using an all-or-none FIRE definition, we identified significantly differential FIREs based on the difference in FIRE scores between NeuN+ and NeuN−. NeuN+ differential FIREs were identified by obtaining the genomic regions that have NeuN+ FIRE scores greater than qnorm (0.975) and NeuN− FIRE scores lower than qnorm (0.9). NeuN− differential FIREs were defined in the opposite way; NeuN− FIRE scores<qnorm (0.9) & NeuN+ FIRE scores>qnorm (0.95). To link differential and super-FIREs to target genes, we intersected these differential FIREs and super-FIREs with gene promoters (defined as 2 kb upstream and 1kb downstream of TSS).We used FIREcaller (v.1.10)We also defined common FIREs, which are FIREs detected in both NeuN+ and NeuN− cells, and the difference of FIREs score between two cell types was less than 0.5. Common FIREs were then overlapped with promoters to identify common FIRE-associated genes. Since some of the common FIRE-associated genes also overlapped with NeuN+ and NeuN− FIREs, we removed those potential cell-type-specific genes to obtain common FIRE-associated genes.4. Briefly, we constructed background interaction profiles from randomly selected length- and GC content-matched regions to promoters. Using these background interaction profiles, we fit interaction frequencies into Weibull distribution at each distance for each chromosome using the fitdistrplus69 package in R. Significance of interaction from each promoter was calculated as the probability of observing higher interaction frequencies under the fitted Weibull distribution, and interactions with FDR < 0.01 were selected as significant promoter-based interactions. We overlapped promoter-based interactions with genomic coordinates of TADs, and found that the majority (~75%) of promoter-based interactions were located within the same TADs. We also applied Fit-Hi-C2 (v.2.0.7)70 with -U 2000000 -L 20000 -r 10000 -p 2 to call loops.Promoter-based interactions were identified as previously describedNuclei  were sorted into 300 µl of Trizol  such that the volume ratio of nuclei:Trizol was 1:3. Nuclei were lysed by pipetting 20 times in the Trizol solution on ice. RNA extraction was performed using the Zymo Directzol Microprep RNA Kit . RNA quality was evaluated using an Agilent Bioanalyzer 2100 with an RNA 6000 Pico Kit . The RNA was then converted into cDNA and prepared into RNA-seq libraries using the SmarterStranded kit . The libraries were size selected for an average fragment size of 300 bp using SPRI beads . Library quality was assessed with a Qubit and Agilent Bioanalyzer 2100 using the Agilent High Sensitivity DNA Kit (Cat#5067-4626).71 to check the quality of RNA-seq reads, and removed adapter with Cutadapt program (v.1.18)72. Next, clean reads were mapped to the human reference genome (Release 19 (GRCh37.p13)) from GENCODE database with HISAT2 (v.2.1.0)73 using default parameters. And, we assembled and quantified transcripts using StringTie (v.1.3.5)74. Differential analysis was done with DESeq2 (v.1.22.2)75. RNA-seq data from Glu and GABA neurons were obtained from a previously published study18.Once RNA-seq libraries were sequenced, we used FastQC (v.0.11.8)27. We re-analyzed previously published H3K27ac ChIP-seq data from Glu and GABA neurons18 to define differential peaks between Glu and GABA neurons. We first used FastQC (v.0.11.8)71 to check the quality of ChIP-seq reads18. Next, we used Bowtie2 (v.2.3.4.3)76 with—very-sensitive to align reads to human reference genome build (Release 19 (GRCh37.p13)) from the GENCODE database. We removed duplicate reads using Picard  MarkDuplicates function. We then called H3K27ac peaks using MACS2 (v.2.1.0.20150731)77 with—broad-cutoff 0.00001. Finally we used DiffBind (v.2.13.1)78 to analyze differentially binding regions between Glu and GABA ChIP-seq data.Differential H3K27ac peaks between NeuN+ and NeuN− cells were obtained from the previously published datasets27 and ATAC-seq peaks79 that are brought to the promoters via chromatin loops. We then performed differential motif analyses on these cell-type-specific distal regulatory peaks using GimmeMotifs (gimme maelstrom) (v0.14.1)30 with the default settings.To identify TFs that are involved in cell-type-specific distal regulation, we first extracted differentially accessible chromatin peaks by combining differential H3K27ac80 to identify GO terms that were overrepresented in particular gene sets such as genes assigned to common FIREs, differential FIREs, and super-FIREs. We set the arguments as following:We used gProfileR2 (v.0.1.9)gost)$resulthttp://revigo.irb.hr/)81 to remove redundant GO terms.Top GO terms for super-FIRE-associated genes were highly redundant, which hindered the identification of biological pathways implicated for super-FIREs. We therefore applied REVIGO with default arguments 80 was used to perform GO analyses for ranked gene lists from H-MAGMA (v.1.08)82 for AD, SCZ, and BD GWAS:To analyze functional enrichment of H-MAGMA genes, we used a previously reported rank-based approachp_value = 0.05, min_set_size = 15, max_set_size = 600, min_isect_size = 5, custom_bg = background gene set, correction_method = “fdr”, hier_filtering = “moderate”, include_graph = T, src_filter =“GO”)gprofiler;p value (Ranked gene list)input: background gene set was ordered based on We used gProfileR to perform GO analysis for NeuN+ hypoacetylated and NeuN− hyperacetylated genes:gprofiler.83. In addition, cellular expression profiles of the disease risk genes were interrogated by plotting centered expression values for each cell type using the scRNA-seq data as described before46. Each neuronal subtype definition is provided in Supplementary Table To quantify the significance of cellular expression of the genes assigned to cell-type-specific chromatin architecture , we used EWCE (v.1.3.0)34 and Lips et al.35, respectively. We employed Fisher’s exact test to compare developmental and synaptic modules with genes engaged in NeuN+ and NeuN− enhancer–promoter interactions. AD-associated co-expression networks were obtained from Seyfried et al.41  genes that were linked to hyper and hypoacetylated genes in AD and (2) AD-associated genes identified by H-MAGMA38. Because these peaks were obtained from the brain homogenate that lacks cellular resolution, we overlapped them with NeuN+ and NeuN− differential peaks. Since we found that hyperacetylated peaks in AD significantly overlapped with NeuN− differential peaks, we used NeuN− loops to assign them to the target genes. On the other hand, hypoacetylated peaks in AD overlapped with NeuN+ differential peaks, so they were annotated to the target genes by NeuN+ loops.We downloaded AD-associated hyper- and hypoacetylated H3K27ac peaks from Marzi et al.n = 20,352 cases; 31,538 controls)84, SCZ 1, and AD 49.We downloaded the following GWAS summary datasets: BD (45 (v.1.0.0) to estimate the enrichment of SNP-based heritability for brain disorder GWAS in differential H3K27ac peaks between (1) NeuN+ and NeuN− cells and (2) Glu and GABA neurons. Genetic variants were annotated to differential H3K27ac peaks, and SNP-based heritability statistics were calculated using the GWAS summary statistics mentioned above. Enrichment statistics was calculated as the proportion of SNP-based heritability divided by the proportion of SNPs annotated to differential H3K27ac peaks.We implemented the LDSC software46. To provide SNP-gene relationships at a neuronal subtype resolution, we also generated Glu and GABA H-MAGMA input files. For example, we obtained SNPs that map onto Glu H3K27ac peaks, then SNPs active in Glu neurons were assigned to their genes by either genomic coordinates (when located in promoters and exons) or distal interactions (non-coding SNPs were mapped to their cognate genes via NeuN+ chromatin interactions). We used the same framework to obtain GABA SNP-gene relationships. Input files can be found in the github repository: https://github.com/thewonlab/NeuN.We generated H-MAGMA (v.1.08) input files that provide SNP-gene relationships based on chromatin interaction profiles from NeuN+ and NeuN− cells. Exonic and promoter SNPs were directly assigned to their target genes based on their genomic location. Intronic and intergenic SNPs were assigned to their cognate genes based on chromatin interactions with promoters and exons as previously describedBased on the SNP-based heritability enrichment result, we used NeuN− H-MAGMA to infer AD risk genes and Glu/GABA H-MAGMA to infer SCZ and BD risk genes.We set the arguments as following: magma_v1.08/magma—bfile g1000_eur –pval <GWAS summary statistics> use = rsid,p ncol = N—gene-annot <MAGMA input annotation file>—out <output file>.g1000_eur denotes the reference data file for European ancestry population.We used Python v.2.7 and R v.3.6.0.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting summary


Pervasive transcription is widespread in eukaryotes, generating large families of non-coding RNAs. Such pervasive transcription is a key player in the regulatory pathways controlling chromatin state and gene expression. Here, we describe long non-coding RNAs generated from the ribosomal RNA gene promoter called UPStream-initiating transcripts (UPS). In yeast, rDNA genes are organized in tandem repeats in at least two different chromatin states, either transcribed and largely depleted of nucleosomes (open) or assembled in regular arrays of nucleosomes (closed). The production of UPS transcripts by RNA Polymerase II from endogenous rDNA genes was initially documented in mutants defective for rRNA production by RNA polymerase I. We show here that UPS are produced in wild-type cells from closed rDNA genes but are hidden within the enormous production of rRNA. UPS levels are increased when rDNA chromatin states are modified at high temperatures or entering/leaving quiescence. We discuss their role in the regulation of rDNA chromatin states and rRNA production. Saccharomyces cerevisiae contains a single rDNA cluster organized as a linear array of 100–150 repeats on chromosome XII (Non-coding RNA (ncRNA) are key regulators in numerous chromatin-based processes. Both silent chromatin (heterochromatin) and active gene transcription can be regulated by specific ncRNAs . Extremesome XII A. Each rIn mammals, long non-coding transcripts (lncRNAs) have been proposed to regulate ribosomal RNA production and nucleolar integrity ,11,12,13In yeast, cryptic RNAPII promoters are present at the rDNA RNAPI-specific promoter  as demonIn this study, we characterized long non-coding RNAs generated from ribosomal the RNA gene promoter, called UPStream-initiating transcripts (UPS). We were able to show that they were produced in wild-type cells at very low levels in exponentially growing cells and were accumulated upon the inhibition of Rat1 exonuclease. They accumulated when rDNA genes were closing, suggesting a possible role in regulating rDNA chromatin structure.uaf30∆ strain contains a small fraction of pre-rRNAs synthesized from upstream initiation while the majority (90%) start at canonical RNAPI TSS [uaf30∆ strains, a large fraction of rRNA genes stays in the closed/inactive state, although in uaf30∆, a small fraction remains open and is heavily occupied by RNAPI [Transcription initiating immediately upstream from the RNAPI transcription start site TSS, see B was iniNAPI TSS . In bothby RNAPI ,27. Thusby RNAPI , while tby RNAPI . Howeveruaf30∆ and PSW strains were used as a positive control for the presence of the 5′-extended rRNAs . In order to visualize these transcripts, we used a primer extension method by the reverse transcription (RT) of RNAs with a specific radiolabeled primer B. Produced rRNAs . We coulot in WT ,24. RT oectively C. Intererase III . The detWe conclude that UPS are non-coding RNAs (ncRNA) of unknown function that are present in low amounts in the wild-type cells of budding yeast.Under the optimal growth conditions, the low level of UPS in wild-type cells, is a common feature of various classes of ncRNAs in yeast  . Most ofTRF4 and TRF5, which encode the subunits of the TRAMP complex, a co-factor of exosomes. The UPS level did not increase significantly in strains that had been mutated for the exosomes and TRAMP complex subunits nor in double mutants rrp6∆ trf4∆ . We als6∆ trf4∆ A. Furthe0∆ trf4∆ B althoug mutants .XRN1 deletion mutant, a major 5′-3′ exoribonuclease, which leads to the accumulation of XUTs [xrn1∆ strain . The UPS detected in the WT and uaf30∆ are both stable in the protection assay  or growth-limiting conditions  downregulate rDNA transcription in order to adapt cellular resources . During 600 = 2.5–3.0) and 11-day old cultures. The UPS were quantified using RT-qPCR. As shown in To address UPS transcriptional regulation, we quantified the UPS levels in various growth conditions with RT-qPCR. In agreement with the previous report from the Nomura lab, we observed a 2-fold accumulation of UPS after a shift to 37 °C  Figure A. In conFOB1 gene [To address the link between UPS transcription and the state of chromatin at the rDNA locus, we used strains with the copy number of rRNA genes stabilized by the deletion of the OB1 gene . The useOB1 gene ,42,43. IOB1 gene ,44. We mi.e., produced by RNAPI) progressively decreased during growth to the stationary phase and regularly increased after the return to exponential growth . rRNAs s, NUTs; .cis  or in trans . Althou mutants ,20, they mutants  nor anal mutants  C-pro—in mutants  and confrevisiae  and a fee [PHO84 ; GAL1-10am ncRNA ,57, or Tam ncRNA , could pxrn1∆, though degraded by Xrn1, suggests that these transcripts escape Rat1-dependent degradation after transcription termination. Subsequent degradation by Xrn1 could provide a double safety mechanism. Altogether, this suggests that the transcription event is important by itself, but is also suggests that UPS could act in trans.Using the mutants of RNA decay pathways, we have shown that exosomes and their co-factor TRAMP complex do not affect the accumulation of UPS. However, mutations in both Xrn1 and Rat1, 5′-3′ exoribonucleases have increased UPS levels when mutated or absent. This result has interesting implications; according to the torpedo/allosteric model, Rat1 could be associated with the elongation of RNAPII at the 35S gene and could promote UPS termination, as demonstrated for mRNAs . Its pre(1) UPS produced at a low level from closed rDNA copies by RNAPII-dependent transcription are important for the maintenance of the closed chromatin state. In response to environmental stimuli, the recruitment of the UAF complex represses the RNAPII that disengage from the rRNA promoter;(2) The RNAPII transcribing rRNA, revealed in wild-type cells by the presence of UPS, provides a platform for the elongation factors regulating rRNA production. Most of them are not essential for basal RNAPI activity but could be crucial for nucleosome eviction/assembly, chromatin remodeling during stress, and adaptation. Such recruitment could regulate the replication-independent closing of open rDNA genes ,62.Pervasive transcription as well as its output—the ncRNA, could become important in particular conditions. As mentioned, UPS accumulated in cells cultured at high temperatures, in post-diauxic shift, and in the stationary phase. In these cells, transcriptional programs change radically. Furthermore, in the stationary phase, the cell growth is arrested and rRNA synthesis decreases, shifting from a productive to non-productive rRNA processing pathway . The acci.e., ncRNA deletion or over-expression, because their genes mostly overlap with other protein-coding genes. In the case of the rRNA, the situation is even more complicated because of the coexistence of multiple copies in different chromatin states. We have shown here that in wild-type cells in permissive/optimal growth conditions, in addition to RNAPI, RNAPII transcribes the rDNA to produce lncRNAs—specially, UPS. The role of these transcripts as well as the fine characterization of their promoter awaits further investigation.To date, we are not aware of any easy methods to directly study the effects of ncRNA The strains used in this study are described in pMET3-RAT1 repression experiment, cells were pre-cultured ON in YPD at 30 °C. ON exponential cultures were used as the 0 time-point, and after careful centrifugation, cells were shifted to YPD medium containing 6 mM methionine. After 12 h of incubation, cells were centrifugated and collected for RNA analysis.Growth media and plates were prepared with standard methods using YPD containing 2% glucose. Standard growth conditions were employed , and heat shock was performed at 37 °C for an indicated time. For galactose growth, YPGal medium  was used. The time course experiment was performed in YPD media at 30 °C. 500 mL culture was grown, and samples for RNA extractions were collected at indicated time-points. After 11 days of culturing, cells were considered as stationary. After centrifugation, all cells were resuspended in fresh YPD medium, and each 60 min, samples were collected for RNA analysis. For the 600 = 0.4–0.8, followed by DNA contaminant removal using RNase-free DNaseI. RNA concentration was measured on a nanodrop spectrophotometer. For the protection assay and pMET3-RAT1 experiment, RNA extractions were performed as previously described [Total RNA was extracted using the hot phenol extraction method from 10- to 50-mL of cultures at ODescribed .uaf30∆, and wild type. The total volume of the RT reaction was loaded on a denaturing 10% acrylamide gel. The gel was subsequently dried and exposed.The RT reaction was performed using the same method as the standard reverse transcription reaction but with radiolabeled RT primer A. 100 ngscR1 signals. Error bars correspond to standard deviation over at least two or more independent cultures.For the RT reaction, 1 μg of total RNA was subjected to reverse transcription  with a mix of locus-specific (target and control genes) or random primers for 60 min at 42 °C. cDNA were quantified with real-time qPCR using iTaq universal SYBR Green Supermix  and the ViiA7 AB Applied Biosystems . Primer pairs used for amplification are listed in


Transcriptionally active chromatin is marked by tri-methylation of histone H3 at lysine 4 (H3K4me3) located after first exons and around transcription start sites. This epigenetic mark is typically restricted to narrow regions at the 5`end of the gene body, though a small subset of genes have a broad H3K4me3 domain which extensively covers the coding region. Although most studies focus on the H3K4me3 mark, the broad H3K4me3 domain is associated with a plethora of histone modifications  and is therein termed broad epigenetic domain. Genes marked with the broad epigenetic domain are involved in cell identity and essential cell functions and have clinical potential as biomarkers for patient stratification. Reducing expression of genes with the broad epigenetic domain may increase the metastatic potential of cancer cells. Enhancers and super-enhancers interact with the broad epigenetic domain marked genes forming a hub of interactions involving nucleosome-depleted regions. Together, the regulatory elements coalesce with transcription factors, chromatin modifying/remodeling enzymes, coactivators, and the Mediator and/or Integrator complex into a transcription factory which may be analogous to a liquid–liquid phase-separated condensate. The broad epigenetic domain has a dynamic chromatin structure which supports frequent transcription bursts. In this review, we present the current knowledge of broad epigenetic domains. The broad H3K4me3 domain is absent or considerably shorter in MCF7 and in “normal” MCF10A1 cells. The PPP1R15A gene encodes protein phosphatase 1 regulatory subunit 15A . The PPP1R15A protein is a pleiotropic regulator that has key functions in multiple cellular processes including DNA damage pathways, endoplasmic stress responses, and chromatin remodeling. The latter is inferred from the observation that PPP1R15A binds to hSNF5/INI1, a component of the SWI/SNF chromatin remodeling complex [PPP1R15A gene constitutes one of the genes in the hypoxia-related prognostic signature for breast cancer [PPP1R15A gene also has a broad H3K4me3 domain signature in multiple cell types, including erythroleukemic K562, and lung epithelial A549 cells [SPRY4 is a tumor suppressor gene that inhibits the receptor-transduced mitogen-activated protein kinase signaling pathway and is a negative regulator of interferon signaling [SPRY4 expression in MDA-MB-231 cells increases cell proliferation, migration, and metastatic properties [In MDA-MB-231 breast cancer cells, which represent a more aggressive form of breast cancer, the  complex . Consistt cancer . The bio49 cells . The SPRignaling . Silencioperties , 83.FOXA1 gene has the broad epigenetic (H3K4me3) domain. Further, the GATA3 gene has a broad epigenetic (H3K4me3) domain in MCF7 cells  and in MCF7 cells (FOXA1 or GATA3) demonstrates the importance of these genes in maintaining the cellular properties of these two different types of breast cancer cells. Likewise, reducing expression of these genes may increase the metastatic potential of tumor cells and potentially lead to a poor prognosis. Reduction of SPRY4 expression, for example, increases cancer stem cell properties in MDA-MB-231 cells, while reduced expression of FOXA1 and GATA3 by inhibiting the mitogen-and stress-activated protein kinase (MSK1) enhances the metastatic progression of ER + breast cancer. Silencing these genes with the broad H3K4me3 domain results in a more aggressive form of cancer.In MCF7 cells, but not in MDA-MB-231 and MCF10A1 cells, the identity . Reducedsurvival . HighligThe H3K4me3 chromatin distribution of sorted neuronal and non-neuronal nuclei in human post-mortem, non-human primate, and mouse prefrontal cortex were analyzed through extensive bioinformatics approaches of epigenomic and transcriptomic data, in the context of cell-type-specific regulation . In mousCalm1, Fos, and Npas4). Because Fos and Npas4 are immediate early genes, it appears that genes marked by a broad epigenetic domain are poised for activation and become highly expressed following stimulus exposure [Behavioral studies in mice  established that broad epigenetic domains are specifically present in genes activated during memory formation in cornu ammonis (CA1) neurons of the hippocampus  domains were preferentially involved in 3D chromatin interactions that were required for transcription of the relevant genes , 86, 87 Super-enhancers have been identified by the presence of the Mediator complex (and/or Integrator complex), H3K27ac, and CBP/p300 –92. NoteThe interactions between the enhancers/super-enhancers and broad epigenetic domain marked genes are dynamic. Contributing to this dynamic interaction may be polyADP-ribosylation of the histones , 94. DurIt remains to be determined how broad epigenetic domains are established for specific genes. However, transcription factor circuits that include pioneer and cell type specific transcription factors binding to enhancers, super-enhancers and upstream promoter elements would likely establish the interactome between these regulatory elements  , 97, 98.PRMT1 binds to CBP/p300 as well as many other proteins. The PRMT1 product, H4R3me2a, stimulates that activity of CBP/p300 to acetylate H3K27. Further, CBP/p300 co-condenses with transcription factors, and the co-condensation modulates transcriptional bursting. Thus, PRMT1 may have a role in activation of enhancers via the enzyme’s recruitment of CBP/p300 and H3K27 acetylation and in interaction of the enhancer/super-enhancer/locus control region with the target promoter, a step that can take place before enhancer activation . RecentlThe recruitment of the general transcription machinery would lead to the first round of transcription, which is often needed to recruit chromatin modifiers (KATs) and chromatin remodelers , and wThe bioinformatic pipeline for peak detection from ChIP-seq data often includes a step where the issue of duplicate reads poses the challenge whether these reads should be filtered out or kept in. The duplicates are often artifacts from PCR bias but sometimes can be due to genomic regions that inherently get overamplified resulting in artifacts and noise . These rOf the various broad peak calling tools, MACS2 (broad setting) is the most used. The other tools include: Broadpeak, Zinba, RSeg, SICER, and histoneHMM , 119–123A note of caution is warranted regarding the interpretation of these data when it comes to enhancers and super-enhancers. In a previous study, H3K27ac was deemed the mark of choice to identify a large fraction of super-enhancers, while minimizing erroneous DNA regions. Thus, in many of the bioinformatic studies, super-enhancer prediction entailed stitching H3K27ac peaks together within 12.5 kb of one another in the linear genome . However6-adenine methyltransferase) is called Fiber-seq [We envisage that each gene allele with a broad epigenetic domain is in a state of flux with CHD1-mediated nucleosome dissolution and reformation along the gene body Fig. A. The nuiber-seq . For botSPRY4 in triple negative breast cancer. Mutations in the DNA sequence transcription factor binding sites in enhancers and super-enhancers, altered expression of transcription factors, and expression of oncohistones (replacement histones such as H3.3) will impact the formation and function of the broad epigenetic domain marked genes [Genes with the broad epigenetic signature have critical roles in cell identity and essential cell functions. Thus, identification of marked genes provides information about essential genes in normal and diseased states of cells as highlighted in our example of genes with the broad epigenetic domain in human breast cancer cells and in chicken polychromatic erythrocytes. Of clinical relevance, epigenetic therapy approaches , 133 thaed genes . The chred genes , 135. Ex


Blood malignancies often arise from undifferentiated hematopoietic stem cells or partially differentiated stem-like cells. A tight balance of multipotency and differentiation, cell division, and quiescence underlying normal hematopoiesis requires a special program governed by the transcriptional machinery. Acquisition of drug resistance by tumor cells also involves reprogramming of their transcriptional landscape. Limiting tumor cell plasticity by disabling reprogramming of the gene transcription is a promising strategy for improvement of treatment outcomes. Herein, we review the molecular mechanisms of action of transcription-targeted drugs in hematological malignancies (largely in leukemia) with particular respect to the results of clinical trials. For a long time, gene transcription has been considered non-druggable because of inevitable general toxicity. However, gene transcription in tumor cells has unique patterns (reviewed in ). MalignAberrant activation of signaling pathways is common in tumors. Moreover, leukemias are often caused by formation of new chimeric transcription factors (TFs) as a result of chromosome rearrangements . SpecifiSuper-enhancers (SE) are the cis-acting elements on DNA recognized by different components of the transcriptional machinery. Unlike conventional enhancers, these potent regulatory elements are capable of switching gene transcription in an ‘all-or-nothing’ manner in response to relatively small changes in chromatin modification, TF concentration, etc. Commonly, SEs control highly but not ubiquitously expressed genes, for example, the genes involved in the maintenance of the pluripotent stage and lineage specificity . These tTwo aspects make ‘transcriptional’ drugs suitable for combinations with other chemotherapeutics. First, the transcriptional modulators can synergize with conventional cytotoxic drugs in achieving antitumor efficacy with better tolerance, which is extremely important in children and elderly patients ,12. SecoMLL rearrangements (MLL-r) in acute myelogenous leukemia (AML) or BCR–ABL1 in chronic myelogenous leukemia (CML)) and mutations (point mutations in EZH2 in acute lymphocytic leukemia (ALL)) can affect chromatin state .,83.82,83EED, EZH2, and SUZ12 are found in 42% of early T-cell precursor (ETP) ALL cases, as well as in 12% of T-cell ALL cases in non-ETP malignancies [Enhancer of zeste homologs 1 and 2 (EZH1 and EZH2) are catalytic subunits of the Polycomb repressive complex 2 (PRC2) responsible for transcriptional repression via trimethylation of H3K27  23,65,8,865,87. gnancies . EZH2 pognancies , whereasgnancies ,91. All S-adenosyl homocysteine hydrolase which disrupts methionine metabolism leading to inhibition of methyltransferases. Suppression of EZH2 by DZNep has been shown to inhibit the formation of leukemia colonies and reduce H3K27me3 [S-adenosylmethionine (SAM), a substrate for normal and mutant EZH2, and inhibit the enzyme > 50 times more selectively than EZH1. All three inhibitors showed activity against lymphoma cells with EZH2 mutation [3-Deazaneplanocin A (DZNep) inhibits H3K27me3 . HoweverH3K27me3 . Inhibitmutation ,93,94.Tazemetostat (EPZ-6438) is more potent than EPZ005687 and has better pharmacokinetic properties, including oral bioavailability . TazemetDouble EZH1/2 inhibition increased the efficacy against MLL leukemia compared to single-isoform inhibition. For example, UNC1999  and SAH-MLL-r leukemia [HOXA9 and MEIS1 genes associated with MLL translocation and leukemogenesis, leading to decreased proliferation, increased differentiation, and apoptosis of MLL–AF9 cells, indicating its potential for AML therapy [HOXA genes, which leads to methylation of H3K79 and constitutive activation of these genes [DOT1L is a methyltransferase that catalyzes mono-, di-, and trimethylation of H3K79  65,97,9,99, whic therapy ,65,97,99se genes ,101,102.HOXA and MEIS1 cluster [HOXA and MEIS1 gene regions [HOXA9 and MEIS1 [KDM1A (LSD1 protein-coding gene) which may indicate a relationship between HOXA9 and LSD1 [EPZ4777 is a selective DOT1L inhibitor . The hig cluster . EPZ4777 regions . Pinomet regions ,99,104 and MEIS1 . Pinometand LSD1 . ResistaArginine methylation is catalyzed by arginine methyltransferases (PRMTs) classified into types I and II. All PRMTs catalyze the formation of a monomethylated intermediate, and type I PRMTs  additionally catalyze the production of asymmetrically dimethylated H4 while the type II PRMTs (PRMT5 and 9) generate dimethylated H4 . PRMT1 aKAT2A expression has been found in children with AML [KAT2A is a histone acetyltransferase whose inhibition stimulates LSCs to differentiate via stabilization of gene expression programs . An incrwith AML . KAT2A ewith AML . Both cowith AML . KAT2A iwith AML , therebyMYC [The lysine acetyltransferase paralogs CREBBP  and EP300 (KAT3B) are transcriptional coactivators that regulate many cellular processes . SEs areMYC  and induMYC . GATA1/MMYC .Drosophila melanogaster) is critical for the regulation of gene expression and differentiation [RUNX1-regulated promoters . These interactions are associated with chromatin remodeling during myeloid differentiation [The ATP-dependent BAF chromatin-remodeling complex  represent a family of 20 serine/threonine kinases that control critical cellular processes. CDKs are divided into two groups: cell-cycle regulators  and transcription regulators . The first group controls the transition between cell-cycle phases, while the second group regulates gene transcription. Transcription-associated CDKs are recruited to chromatin as parts of much larger complexes. Upon binding to their cyclin partners, CDKs phosphorylate serine 2, 5, and 7 in the CTD of RNA Pol II, leading to transcription initiation and elongation ,123. SomCDK7 and CDK9 are considered key regulators of the transcriptional machinery. In a simplified model, CDK7, a component of the TFIIH (transcription factor IIH) complex, phosphorylates Ser5 and Ser7 at CTD of RNA Pol II, leading to initiation of transcription. CDK9 is the catalytic subunit of the P-TEFb complex, and CDK9 phosphorylates Ser2 at CTD of RNA Pol II, enabling transcriptional elongation. However, functions of both CDKs are context-specific, and these kinases are, to some extent, complementary and interchangeable . AML oftR)-roscovitine) is active against CDK1, 2, 5, 7, and 9. Primary effects of alvocidib and seliciclib include CDK9 inhibition, thereby suppressing SE transcriptional targets and activating apoptosis due to the loss of myeloid cell leukemia-1 (Mcl-1) protein. Mcl-1 is a member of the Bcl-2 family of proteins involved in the progression and survival of AML cells as well as chemotherapeutic drug resistance. A high MCL1 expression in tumor cells is mediated by SEs [Pan-CDK inhibitors alvocidib and seliciclib can be used to combat AML. Alvocidib (formerly flavopiridol) is a CDK1, 2, 4, 6, 7, and 9 inhibitor with a pronounced potency against CDK9. Seliciclib . However, while these inhibitors have demonstrated a robust response in AML trials, their clinical utility is limited due to a low selectivity for CDK9 and other CDKs . This leL models .MCL1 and BCL2 genes. Inhibition of CDK2 sensitizes AML cells to apoptosis [Fadraciclib (CYC065) is a more selective and potent derivative of seliciclib directed against CDK9 and CDK2. Inhibition of CDK9 by fadraciclib attenuates transcription of the poptosis . FadraciMYC and MCL1, in LDC067 treated cellsis thought to be the cause [LDC067 is another highly selective CDK9 inhibitor that abolishes phosphorylation at Ser2 of RNA Pol II and selectively inhibits transcription. Treatment with LDC067 led to apoptosis in several cancer cell lines and AML blasts. The loss of short-lived mRNAs, including he cause . Accordihe cause .THZ1, a covalent CDK7 inhibitor  exhibiteCT7001 is a novel CDK7 inhibitor. In preclinical models, CT7001 effectively impeded growth of many AML cell lines and led to tumor regression in MV-4-11 xenograft mice ,139. CurCDK8 is a part of the kinase module that associates with the Mediator complex. CDK8 kinase module consists of four subunits: CDK8, MED12, MED13, and cyclin C. CDK8 or its paralog CDK19 regulates transcription by phosphorylating RNA Pol II and TFs  is extremely potent and relatively selective CDK8/19 inhibitor. Dual CDK8/19 inhibition by CA showed antiproliferative activity in MV-4-11 and SET-2 AML mice xenograft models . In addiSenexin A and Senexin B have been discovered in a high-throughput screening for low-molecular-weight inhibitors of p21-induced transcription . SenexinMCL1 transcriptional silencing [SEL120-34A is a substituted tricyclic benzimidazole that represents a novel, robust, and selective CDK8 inhibitor . SEL120-ilencing . SEL120-ilencing .Beyond its kinase activity, CDK8 has been identified as a key mediator of BCR–ABL1-driven leukemia. CDK8 was found to be involved in the PI3K/mTOR signaling pathway, and its loss increased sensitivity to mTOR inhibitors. As a result, combined inhibition of CDK8 and mTOR in AML and ALL was proposed. YKL-06-101 is a dual CDK8 degrader and mTOR inhibitor that was generated by combining THZ4-55, an integrated mTOR/CDK8 inhibitor, with thalidomide, which acts as a degrader. This degradation of CDK8 is crucial for its action in order to eliminate kinase-independent activity of CDK8. In both BCR–ABL1-positive and BCR–ABL1-negative CML and ALL, treatment with YKL-06-10 caused growth arrest and cell death, while other members of the Mediator kinase module remained unchanged .CDK12 and CDK13 are closely related CDKs with approximately identical kinase domains but different N- and C-terminal domains. CDK12 and CDK13 exert their kinase activity by binding to cyclin K. CDK12 regulates gene transcription, genomic stability, and DNA damage response, and it is crucially important for embryonic development. CDK13 controls gene transcription as well. There is considerable evidence (obtained using genetic methods) for the roles of CDK12/13 in development of breast, ovarian, colorectal, and pancreatic cancer . HoweverNot only transcriptional CDKs can be regarded as therapeutic targets. Although chemical inhibitors have not yet been developed, there are genetic indications that other members of coactivator complexes can be successfully targeted to treat AML. For example, Xu and colleagues demonstrated that TAF12, a member of the TFIID complex, which also binds to and stabilizes the proto-oncogenic TF MYB, is critical for AML suppression .Transcriptional addiction is a hallmark of AML that could be exploited therapeutically, making transcriptional CDKs plausible targets. Despite the notion that targeting transcription is considered somewhat a risky approach due to non-selectivity toward tumor cells, these highly proliferative cells are more vulnerable to transcriptional inhibitors. Several small-molecule inhibitors of CDKs have already reached clinical studies for blood malignancies, while others are still in preclinical stages . CurrentPML–RARA, t RUNX1–RUNX1T1, t MLL-r, and inv(16)(p13q22) core-binding factor β (CBFβ)–smooth muscle myosin heavy chain  (CBFB–MYH11). The oncogenic fusion receptor tyrosine kinase BCR–ABL1 generated upon t deserves to be mentioned among other fusion proteins because it upregulates several important signaling pathways. Inactivation of the enzymatic activity in the BCR–ABL1 fusion protein with the small molecule imatinib (Gleevec®) cures or leads to remission in BCR–ABL1-positive malignancies [Chromosomal rearrangements, mainly translocations, and the corresponding gene fusions may lead to tumor initiation or progression. Fusion proteins were the first TFs identified as cancer drivers. Fusion oncogenes are crucial in the diagnosis and treatment of various subtypes of leukemia . Since tgnancies . HoweverPML–RARα is a transcription factor which underlies the pathogenesis of acute promyelocytic leukemia (APL). With the discovery of ATRA that, in pharmacological doses, selectively binds to the mutant TF to induce leukemia cell differentiation , APL hasPML gene on chromosome 15 to the retinoic acid receptor alpha RARA gene on chromosome 17, and the resulting PML–RARα fusion protein is the master driver of APL [A balanced translocation t fuses the promyelocytic leukemia r of APL . PML hasr of APL ,160.PML–RARα is a diagnostic hallmark of APL, the unique subtype of leukemias, which accounts for 10–15% of AML. PML–RARα behaves as an altered TF repressing RARα targets and antagonizes the formation and function of PML NBs . The chiAnother potent anti-APL agent suitable both for relapsed patients, as well as for primary APL therapy, is arsenic trioxide (ATO) . ATO is RUNX1–RUNX1T1  is one of the most common chromosomal alterations found in AML. Perturbation of RUNX1–RUNX1T1 levels and its DNA binding affects chromatin accessibility and TF occupation at multiple gene loci associated with changes in gene expression levels [RUNX1–RUNX1T1-mediated transcription program is a complex regulatory network promoting leukemic self-renewal and propagation. The RUNX1–RUNX1T1 fusion has been detected in up to 5% of AML cases [n levels . RUNX1–RML cases ,169. AMLML cases .RUNX1  gene on chromosome 21 and the RUNX1T1 (ETO or MTG8) gene on chromosome 8 join together in the course of t translocation. The resultant RUNX1–RUNX1T1 fusion protein is a leukemia-initiating TF that interferes with the hematopoietic master regulator RUNX1 function. RUNX1 is a key TF that, together with its heterodimerization partner, the core-binding factor beta (CBFβ), interacts with other TFs and transcriptional coregulators  to regulate genes involved in hematopoiesis [N-Cor/SMRT, mSin3A, and HDAC that inhibit transcription of genes involved in hematopoiesis [The opoiesis . These iopoiesis . Essentiopoiesis . The resopoiesis .Although TFs are difficult to target, several promising attempts of RUNX–RUNX1T1 inhibition have been made. Current approaches to directly target RUNX1–RUNX1T1 in vitro include the use of small interfering (si) RNAs targeting the fusion site of chimeric mRNA or suppression of oligomerization by polypeptides or low-molecular-weight compounds ,176,177.RUNX1–RUNX1T1 gene after short hairpin (sh) RNA coding vector transduction dramatically reduces the growth rate and leads to proapoptotic signaling in AML Kasumi-1 cells [RUNX1–RUNX1T1 oncogene expression in t AML by microRNA let-7b targeting 3′-untranslated regions (UTRs) of fusion transcripts [RUNX1–RUNX1T1 uses 3′UTRs of the RUNX1T1 gene, which is not normally expressed in hematopoietic cells. The authors concluded that the mechanisms regulating RUNX1–RUNX1T1 expression via 3′UTRs are therapeutically relevant.Spirin and coworkers demonstrated the silencing of the -1 cells . Furthernscripts . RemarkaApproaches to suppress RUNX1–RUNX1T1 include inhibition of interaction partners of the fusion protein. These interactions are critical for its subcellular localization and function. The development of inhibitors that prevent the formation of the RUNX1/CBFβ complex  inspireMLL (MLL1 renamed as lysine-specific methyltransferase 2A or KMT2A) gene on chromosome 11q23 is frequently disrupted by chromosomal rearrangements that occur in the unique group of acute leukemias. MLL-r including the translocations involving 11q23 with >30 sites resulting in MLL fusion genes have been described in ALL and in 5–10% of AML [MLL gene is affected by chromosomal translocations that fuse it in-frame to one of over 70 gene partners. The fused MLL acts as a TF and aberrantly regulates gene transcription while retaining H3K79 methyltransferase activity [HOX clusters [N-terminal motif that binds with the transcription coactivators Menin [MLL-r forms the chimeric fusion oncoprotein (MLL and the partner) that regulates the expression of a set of critically important genes. Therefore, disrupting protein–protein interactions sounds intriguing for treatment of MLL-positive leukemias.The % of AML . The MLLactivity ,187,188.clusters , whose dclusters ,191. Twoclusters . One is rs Menin ,193 and rs Menin . These irs Menin . The mosrs Menin . Thus, MMEN1 gene) is an essential oncogenic cofactor for MLL oncoproteins in leukemogenesis. Association of Menin with MLL upregulates HOXA9 or MEIS1 that are critical for enhanced self-renewal in AML [NPM1) gene is important for gene expression in AML [Menin  [The group of Grembecka and Cierpicki have developed and optimized a novel class of inhibitors of Menin–MLL fusion protein interaction ,201,202.4065399) . Moreove4065399) . This fiCBFB and smooth muscle myosin heavy chain MYH11, resulting in the formation of CBFB–MYH11 chimera [In 1993, Liu and colleagues identified that the recurrent balanced chromosomal rearrangement inv(16)(p13q22) and its variant t in AML lead to fusion of genes coding for the transcriptional coactivator core-binding factor β  chimera . The fus chimera ,206. Thu chimera .−/−runx1 or cbfb−/− [KIT, FLT3, NRAS, and KRAS genes) is required for acute leukemia development [Castailla and colleagues have demonstrated that, in a knock-in mouse model, the CBFβ–MYH11 fusion protein blocks a definitive hematopoiesis repressing the RUNX1/CBFβ function. A similar phenotype is observed in mice with the complete knockout of  cbfb−/− ,208,209.elopment ,212,213.MYC expression by replacing the BAF complex component BRG1 with the Polycomb repressive complex component RING1B, thereby leading to apoptosis [The first anti-CBFβ–MYH11 inhibitor, AI-10-49, was reported in 2015 . Its mecpoptosis . Combinapoptosis .Richter and colleagues demonstrated that HDAC1 is a cofactor of CBFβ–MYH11 that regulates its activity. The HDAC1 inhibitor entinostat blocks the growth of CBFβ–MYH11-positive leukemia cells and promotes their differentiation, indicating that HDAC inhibitors may be useful for treatment of inv(16) AML .The formation of CBFβ–MYH11 fusion occurs persistently in AML and is a highly specific hallmark of the disease. Recent studies indicate that the CBFβ–MYH11 fusion neoantigen is present on AML blasts, thereby enabling T-cell recognition and killing of tumor cells .BCR and ABL1 genes; the 5′ part of the BCR gene at 22q11 and the 3′ part of the ABL1 tyrosine kinase-encoding gene at 9q34 are joined together, leading to the creation of a hybrid BCR–ABL1 oncoprotein with increased tyrosine kinase activity . The formation of the BCR–ABL1 chimera is a critical event in CML pathogenesis. The rearrangement t results in the formation of an aberrant Philadelphia (Ph) chromosome. Molecular characterization revealed the occurrence of a new chimeric oncogene that consists of fused iewed in ). This iiewed in . Howeverhibitors . Anotherhibitors . Asciminhibitors ,223,224.Skp2 E3 ubiquitin ligase [BCR–ABL1 activates a variety of pathways such as MAPK, JAK/STAT5, and PI3K/AKT. Autophosphorylation of BCR–ABL1 at Tyr177 promotes the formation of a GRB2 (growth factor receptor-bound 2) complex with GAB2 (GRB2-associated binder) and son-of-sevenless (SOS), triggering activation of RAS GTPase and recruitment of PI3K and the SH2-containing protein tyrosine phosphatase SHP2 ,226,227.n ligase , and actn ligase . The sign ligase ,232. STAn ligase . AltogetSTAT5 has a pivotal role in the resistance of leukemic cells to treatment with tyrosine kinase inhibitors and promotes survival of LSCs. Thus, blocking STAT5-mediated transcriptional activity is an important potentially druggable target . In addiUnfortunately, fused TFs in AML remain a difficult target for development of drug candidates. The abovementioned approaches including inhibition of protein–protein interactions are partially successful preclinically but less often clinically . One of As a result of the successful development of different cytotoxic and target drugs, the mean 5year survival in leukemia surpassed 80% in recent decades . NeverthTwo main scenarios of nongenetic resistance emergence have been proposed . The firPVT1 enhancers in AML resistance to BRD4 inhibitors. PVT1 is a noncoding RNA locus that normally acts as a tumor-suppressive chromatin boundary element, but it contains intergenic enhancers capable of driving MYC expression. PVT1 enhancer-driven MYC expression was shown in murine and human cells resistant to BRD4 inactivation. Administration of the CDK7 inhibitor in combination with the BRD4 inhibitor interrupted RNA Pol II loading at the PVT1–MYC transcription complex, suppressing the growth of resistant cells [MYC and BCL2 enhancers of resistant cells; BRD4 inhibitors successfully alleviated this resistant phenotype [Guo and colleagues analyzed the role of the nt cells . Knoechehenotype .MYC are largely dependent on SE genes that are prone to an epigenetic switch because of their ‘all-or-nothing’ expression profile. Activation of lineage-specific genes can also help to overcome the resistance in the stem-like cells.Another possible scenario is attributed to a small population of stem-like tumor cells with distinct transcriptional patterns, which are present at the diagnosis and are not eradicated by conventional therapy ,245,246.−/CD11b1−phenotype. In the absence of the promoter-bound BRD4 , the transcription of key genes was governed by β-catenin. Genetic inactivation and pharmacologic inhibition of Wnt/β-catenin promoted a Gr11+/CD11b1+differentiated phenotype and restored sensitivity to I-BET [Shlush and colleagues proposed two similar non-genetic situations: a relapse from very few cells with hematopoietic stem phenotype and a relapse from a larger number of committed cells but with strong stem-like transcription patterns . Resistato I-BET . Bell anto I-BET . These eAlong with the disclosure of specific mechanisms of gene regulation in tumor cells, as well as with the development of chemical and genetic tools to target these mechanisms, gene transcription became an important area of experimental and clinical investigations. In this respect, hematological malignancies represent a leading disorder where targeting transcription, as a monotherapy and/or in combinations with conventional chemotherapeutic and targeted drugs, emerges as a novel promising approach.Of special interest are the attempts to combat blood cell plasticity  via interference with transcriptional reprogramming. This unique mechanism, being commonly nonfunctional in the adult organism, becomes critical when cell survival or a switch in function requires rapid adaptation of the transcriptional machinery. Importantly, modern inhibitors of transcriptional reprogramming are virtually nontoxic over the course of a prolonged oral administration. These agents are attractive mainly for therapeutic combinations although, in particular situations , inhibition of transcriptional reprogramming kinases becomes vital. Furthermore, experimental evidence is growing in support of the role of this mechanism in de novo acquisition of antileukemia drug resistance. These observations set the stage for combination strategies aimed at improving patient outcome. It remains to be elucidated whether the effects of patented pharmacological agents  and investigational PROTAC degraders are similar, given that transcriptional kinases have both enzymatic enzyme-independent functions.These considerations are likely to be attributable to other classes of transcription-regulating agents analyzed in the present review. Definitely, the relevance of individual transcriptional mechanisms to the biology of various blood tumors differs; therefore, general interventions could be of limited success. The field of transcriptional drugs is in its infancy. Nevertheless, the fundamental importance of this regulation and the perspective of initial studies provide a strong hope for patients and clinicians.


The SAGA complex is an evolutionarily conserved transcriptional coactivator that regulates gene expression through its histone acetyltransferase and deubiquitylase activities, recognition of specific histone modifications, and interactions with transcription factors. Multiple lines of evidence indicate the existence of distinct variants of SAGA among organisms as well as within a species, permitting diverse functions to dynamically regulate cellular pathways. Our co-expression analysis of genes encoding human SAGA components showed enrichment in reproductive organs, brain tissues and the skeletal muscle, which corresponds to their established roles in developmental programs, emerging roles in neurodegenerative diseases, and understudied functions in specific cell types. SAGA subunits modulate growth, development and response to various stresses from yeast to plants and metazoans. In metazoans, SAGA further participates in the regulation of differentiation and maturation of both innate and adaptive immune cells, and is associated with initiation and progression of diseases including a broad range of cancers. The evolutionary conservation of SAGA highlights its indispensable role in eukaryotic life, thus deciphering the mechanisms of action of SAGA is key to understanding fundamental biological processes throughout evolution. To illuminate the diversity and conservation of this essential complex, here we discuss variations in composition, essentiality and co-expression of component genes, and its prominent functions across Fungi, Plantae and Animalia kingdoms.The online version contains supplementary material available at 10.1186/s13072-021-00402-x. The groundbreaking characterization of general control nonrepressed protein 5 (Gcn5) as the first histone acetyltransferase (HAT) linked to transcription from studies in Tetrahymena and yeast led to the discovery of the Spt-Ada-Gcn5 acetyltransferase (SAGA) complex –3. SAGA Yeast Gcn5 and its interacting partner, transcriptional adaptor 2 (Ada2), were found to be part of two complexes designated ADA and SAGA, with respective molecular masses of 0.8 and 1.8 megadalton . AffinitSAGA consists of multiple modules: HAT (renamed KAT to reflect its activity towards lysines in non-histone substrates) , DUB, TFADA2A and ADA2B genes are encoded solely in higher eukaryotic genomes, and whether the GCN5-containing KAT module is incorporated into ATAC or SAGA is determined by its assembly with an either ADA2A or ADA2B protein [The KAT modules of ATAC and SAGA differ in that they contain ADA2A and ADA2B, respectively . Paralog protein . In Dros protein , 29. Res protein –32. ThesBiochemical and genetic experiments have demonstrated that components of the core structural module, but not of KAT nor DUB modules, are required for the overall structural integrity of SAGA , 4. DeleSAGA composition has remained largely conserved throughout eukaryotic evolution, however duplicated homologues that increase structural and functional complexity have been found in higher eukaryotes. Arabidopsis has more duplicates of SAGA components compared to yeast and metazoans, namely TRA1A/B, histone acetyltransferase of the TAFII250 family 1/2 (HAF1/2), TAF6/TAF6B, TAF12/TAF12B, ADA1A/B and SGF29A/B  robustly co-purified with canonical SAGA components including ADA2B, SPT3 and TAF10 [Orthologues of yeast Spt8 have not been identified in higher eukaryotic SAGA complexes . Interestingly, a pattern of enhanced expression in brain tissues such as the cerebral cortex and cerebellum, the skeletal muscle and reproductive organs is observed for many SAGA component genes. The high expression of these genes in the cerebellum corresponds to the association of SAGA with a neurodegenerative disease, spinocerebellar ataxia type 7 . Catalytic subunit genes GCN5, PCAF and USP22 particularly show increased expression in brain tissues. In fact, the top five tissues for GCN5 expression are all those of the encephalon. Thalamus and amygdala are among the top five tissues for expression of both GCN5 and PCAF. Correspondingly, GCN5, PCAF, as well as other SAGA members have critical developmental functions in the central nervous system (further discussed in the following section) [Comprehensive expression studies of genes encoding SAGA components (hereafter referred to as SAGA component genes) in human tissues have not been reported, thus we generated a co-expression heatmap using the consensus dataset from the Human Protein Atlas (HPA), which combines HPA, GTEx and FANTOM5 datasets and includes RNA-sequencing data from 62 tissues and cell types  , 45.PCAF, ADA3, SGF29, ADA2B, ENY2, USP22, TAF10 and SF3B5 are all highly expressed in the skeletal muscle  expression in regulatory T cells  [lls Fig. , which msection) , 51.taf13 mutant displays embryo arrest at the 8–16 cell stage and over-proliferation in the endosperm, resembling phenotypes of plants with PRC2 mutations. In Drosophila, expression of nearly all SAGA component genes, including Gcn5, Usp22/Nonstop, Ada1, Ada2b, Ada3, Sgf11, Sgf29, Spt3, Spt7, Spt20, Taf5/Wda, Taf6/Saf6 and Taf9/E(y)1, is enriched in the ovary and during the earliest stages of embryogenesis [Gcn5 show aberrant chromatin dynamics and increased nucleosome retention in the sperm, ultimately leading to male fertility defects [Enrichment of SAGA component transcripts in reproductive tissues is conserved among higher eukaryotes. Additionally, numerous component genes are abundantly expressed in Arabidopsis and Drosophila embryos, but expression data in mammalian embryos are lacking –54. In Aogenesis , 54. In ogenesis , 57. MicGCN5 causes pleiotropic phenotypes including defective cell cycle progression and shortened replicative lifespan [GCN5 also display pleiotropic defects such as dwarfism and aberrant root, leaf and floral development, but a null mutant has yet to be reported [Gcn5 hypomorphic alleles cause abnormal appendages and absence of abdominal cuticle deposition, while null alleles hamper onset of oogenesis and metamorphosis [Gcn5 null mice die soon after gastrulation, reaching approximately one-third of embryo development [Gcn5 and Pcaf die earlier than those null for Gcn5, indicating partial redundancy between these KATs during early embryogenesis.SAGA KAT and DUB module components that are essential for growth and development in metazoans, of which mutations cause lethality, are contrarily often not essential in yeast and plants  but not in fission yeast (Schizosaccharomyces pombe), while deletions of the genes encoding the Taf proteins, which also function in the TFIID complex, cause lethality in both yeast species [UBP8 has a positive effect for extending lifespan of yeast cells [Trrap or Ada3 null mutations in mice is linked to cell cycle progression defects [Usp22 mutants is due at least in part to failure in placenta formation, which is related to inhibition of transforming growth factor beta (TGF-β) and receptor tyrosine kinase (RTK) signaling pathways that control vascularization [Tra1 is essential in budding yeast  domain lies within the C-terminus of Ada2a but appeared absent in Drosophila Ada2b. The SWIRM domain is annotated in both of the human paralogues, therefore it remains to be ascertained whether the C-terminal region consistently determines complex-specific incorporation in humans.Both SAGA-specific Ada2b and ATAC-specific Ada2a are essential in Drosophila, but at different developmental phases; of Ada2b . Arabidoenotypes . Of notece versa . Rescue gcn5 or ada1 deletion strains [GCN5, ADA2, ADA3, or SPT20 results in partially or completely defective unfolded protein response following ER stress due to failure of transcriptional induction of response programs [Yeast SAGA members contribute to activation of various sets of stress-regulated genes under conditions such as glucose, osmotic and ER stresses –78. For programs . Concordprograms .Populus trichocarpa [In Arabidopsis, GCN5 and ADA2B have been widely studied in abiotic stress response . Under hchocarpa . GCN5 anSAGA functions in stress response have been less characterized in metazoans, however they are repeatedly linked to disease states (discussed below). Commonly in mammals, physiological stress and disorders are closely associated. One example is the acetylation and inactivation of peroxisome proliferators gamma coactivator 1 (PGC-1) family of transcriptional coactivators including PGC-1α and PGC-1β, which are conferred by GCN5 and mediate glucose metabolism , 85. Dysinterleukin-2 (IL-2) promoter and to activate IL-2 transcription upon antigen stimulation [Gcn5 and Pcaf in mice Tregs decreases Treg stability in peripheral lymphoid tissues and subsequently causes lethal autoimmunity. Recently, CRISPR screens performed by two groups simultaneously identified SAGA components, including USP22, ATXN7L3, ADA1, ADA2B, ADA3, SGF29, SPT20, TAF5L and TAF6L, as novel regulators promoting expression of FOXP3, a master TF governing the stability and suppressor activity of Tregs [FOXP3 locus and concurrently mediates deubiquitination of FOXP3 protein, thereby sustaining transcription and preventing protein degradation. Finally, in addition to direct regulation of immune cells, GCN5 and PCAF suppress TANK-binding kinase 1 (TBK1) activity in the cytoplasm by blocking phosphorylation events on TBK1, ultimately inhibiting innate immune signaling and interferon production upon viral infection [SAGA components regulate the development of specific populations of immune cells. During granulocyte differentiation, GCN5 acts as a negative regulator through catalyzing acetylation of a TF, CCAAT enhancer binding protein alpha (C/EBPα), thereby inhibiting its DNA-binding ability . On the mulation . Acetylamulation . PCAF plmulation . Double of Tregs , 51. USPnfection . AltogetGcn5-deficient neural stem and precursor cells have a microcephaly phenotype caused by reduction of brain mass, whereas mice homozygous for a catalytic-dead allele of Gcn5 are defective in cranial neural tube closure, resulting in exencephaly [Pcaf null mice do not display developmental defects, they exhibit impaired memory and learning ability, as well as an overreactive emotional response to acute stress [Usp22 facilitates neural differentiation in the developing mouse brain [Gcn5 and Ada2b display similar defects in axon targeting as those observed in Nonstop and Sgf11 mutants, but are coupled with a clear reduction of global H3K9Ac levels. The flies bearing mutations interfering with DUB or KAT activities show overlapping and distinct transcriptional profiles, indicating the two enzymatic modules have both common and discrete targets.SAGA members are involved in neural development of the central and peripheral nervous systems, and aberrations in a single component could lead to detrimental consequences. Mice with ncephaly , 91. Alte stress . Knockdose brain . USP22 ise brain . KAT modrhodopsin (Rho) and G protein subunit alpha transducin 1 (Gnat1), leading to progressive loss of photoreceptor function in the SCA7 mouse model [Gcn5 in SCA7 mice accelerates retinal degeneration and onset of ataxia, further suggesting a crucial role for SAGA in SCA7 pathogenesis [Expansion of a polyglutamine tract in ATXN7 (PolyQ-ATXN7) results in SCA7, a disease that is characterized by cerebellar and retinal degeneration. PolyQ-ATXN7 can incorporate into the DUB module of SAGA, not altering DUB activity but forming a dominant-negative version of SAGA with inhibited coactivator activity , 94. Polse model . Deletioogenesis .MYCN-amplified neuroblastoma cell lines [USP22 as part of the 11-gene “death from cancer” signature driven by B cell-specific Moloney murine leukemia virus integration site 1 (BMI-1), a PRC1 member, in metastatic prostate tumors [Genome-wide CRISPR-based screens have identified human SAGA component genes as dependencies for viability of different cancer cell types. GCN5 was isolated to be essential for acute myeloid leukemia (AML) cell proliferation; ADA2B and several other SAGA components were identified as selective dependencies essential for the growth and survival of ll lines , 98. Thell lines , 100 revll lines , 102. Moll lines . GCN5 exll lines , 104–109ll lines . A microe tumors . This BMIn addition to MYC, SAGA components also regulate cancer development through post-translational modifications of other non-histone substrates such as chromatin modifiers. PCAF acetylates enhancer of zeste homologue 2 (EZH2), a histone methyltransferase and PRC2 subunit, augmenting silencing of tumor suppressor genes and resulting in progression of lung adenocarcinoma . AcetylaUnlike other GCN5-containing complexes such as the metazoan-specific ATAC complex, SAGA is uniquely conserved in all eukaryotes. The recently elucidated high-resolution structures of SAGA from two yeast genera and from humans enlightened our understanding of its dynamic conformation in unicellular and higher eukaryotes , 23, 33.Our comparison of the compositions, co-expression patterns, and defined functions for SAGA suggests the existence of alternative versions of the complex across kingdoms as well as within a species Figs. , 2. SomeThe discovery of Gcn5 as a gene-activating KAT more than two decades ago revolutionized our understanding of the connection between histone modification and transcriptional regulation . HoweverAdditional file 1: Table S1. SAGA composition and essentiality of each component in yeast, Arabidopsis, Drosophila and mice. *Deletion of TRA1 is lethal in S. cerevisiae but viable in S. pombe. **Homozygotes die at 7 to 8 weeks. ND, not determined.