Hematopoiesis requires finely tuned regulation of gene expression at each stage of development. The regulation of gene transcription involves not only individual transcription factors (TFs) but also transcription complexes (TCs) composed of transcription factor(s) and multisubunit cofactors. In their normal compositions, TCs orchestrate lineage-specific patterns of gene expression and ensure the production of the correct proportions of individual cell lineages during hematopoiesis. The integration of posttranslational and conformational modifications in the chromatin landscape, nucleosomes, histones and interacting components via the cofactor–TF interplay is critical to optimal TF activity. Mutations or translocations of cofactor genes are expected to alter cofactor–TF interactions, which may be causative for the pathogenesis of various hematologic disorders. Blocking TF oncogenic activity in hematologic disorders through targeting cofactors in aberrant complexes has been an exciting therapeutic strategy. In this review, we summarize the current knowledge regarding the models and functions of cofactor–TF interplay in physiological hematopoiesis and highlight their implications in the etiology of hematological malignancies. This review presents a deep insight into the physiological and pathological implications of transcription machinery in the blood system. Lineage commitment to hematopoietic cells requires finely tuned regulation of lineage-specific gene expression patterns through the activation of lineage-specific transcriptional programs, and the concomitant suppression of programs related to the early multipotent state or alternative lineages. Hematopoietic transcription factors (TFs) bind directly to regulatory elements to elegantly regulate gene expression by forming transcription complexes (TCs) with cofactors (coactivators and corepressors) and basal transcriptional machinery in a context-dependent manner. TCs are involved in multiple steps of transcription, including preinitiation complex (PIC) formation, post-recruitment processes, DNA loop formation, transcriptional initiation, and elongation.Within TCs, cofactors play a pivotal role in modulating TF activity by “reading” the chromatin landscape or preparing the site for binding. Cofactors function in either an enzymatic or nonenzymatic manner. Generally, nonenzymatic cofactors include TBP-associated factors (TAFs) and Mediators (Meds). Enzymatic cofactors can be classified into two main mechanistically distinct groups: (1) histone-modifying cofactors, including histone deacetylase (HDAC), histone acetyltransferase (HAT), histone methyltransferase (HMT), and histone demethylase (HDT) and (2) ATP-dependent chromatin-remodeling cofactors, which are classified by their ATPase subunits into four major families, including the SWI/SNF, ISWI, Mi-2/NuRD, and INO80/SWR1 families.Normal cofactor–TF interactions or interplay are involved in multiple aspects of hematopoiesis, such as stemness maintenance and lineage commitment. Exome and whole-genome sequencing revealed some specific mutations or translocations that may produce gain/loss of function in cofactors. Deregulation of cofactors by abnormal expression or activity disrupts normal cofactor–TF interplay or facilitates formation of oncogenic TCs, which upsets gene regulatory networks. Therefore, cofactors are frequent targets of genomic alterations in cancer. In recent years, we have gained considerable knowledge about how TFs and chromatin landscapes control gene expression. Attention has now turned to understanding the dynamic and multifaceted interplay between these regulatory layers and how they cooperate to determine gene expression responses to cellular signals. Here, we provide unique insight into the interplay between TFs and cofactors and how dysregulation of their interplay results in aberrant transcriptional regulation programs. We also summarize the cofactors with a high frequency of association with genetic disorders and cofactor-targeting inhibitors in hematological malignancies.2 The hematopoietic TFs GATA1, EKLF, and NF-E2 have been shown to be associated with TFIID via interaction with TAFs. TAF/TF interactions are critical for dynamic changes in the occupancy of TAF-containing complexes. For example, the TAF10 and GATA1 interaction mediates the recruitment of two TAF10-containing complexes (TFIID and SAGA) to GATA1-responsive promoters and the GATA1 locus itself in mouse fetal erythroid cells. These complexes were significantly less enriched at the same locus in adult erythroid cells due to a reduction in their association. TAF10 ablation in fetal erythroid cells results in a block in erythropoiesis with downregulation of GATA1 and its downstream genes, suggesting that genetic inactivation of TAFs may phenocopy the effects of targeting their interacting TFs.3 In some cases, the TAF and TF interaction depends largely on the architecture of the gene promoter. For instance, during erythroid differentiation, TAF9 interacts with EKLF and functionally accentuates EKLF-mediated transcriptional activation of β-globin, in which TAF9 recruitment to the downstream promoter element (DPE) occurs in an EKLF-dependent manner. However, the AHSP gene, which does not contain a DPE, although dependent on EKLF for transcription, does not require TAF9.4 Specifically, ablation of the TAF9–β-globin interaction by β-thalassemia-causing mutations leads to a decreased level of promoter activity, suggesting that restoration of TAF complex occupancy at hematopoietic genes may be critical for normal hematopoiesis4  requires the general TF TFIID to assemble the Pol II PIC. TAFs, as components of TFIID, function in TATA-containing and TATA-less promoter recognition by engaging in direct and selective interactions with transactivators and/or core promoter sequences to facilitate PIC assembly.6 The TAF/TF interaction region or promoter recognition domains of TAFs may provide a therapeutic target to disrupt abnormal transcription programs. Indeed, NF-E2 lacking a TAFII130-interacting domain loses its ability to support enhancer-dependent transcription of globin genes.7 Targeting TAF1 bromodomains with the inhibitor BAY-299 produces antiproliferative effects via affecting GATA1 and MYC transcriptional programs in K562 cells.8 Furthermore, TAF12, in a heterodimer with TAF4, interacts with the transactivation domain of MYB, supporting transcriptional activation of MYB and protecting it from proteasome-mediated protein degradation. Perturbation of the TAF12/MYB interaction by peptides impairs MYB activity and leads to regression of AML in mice.9TAFs may guide the localization of oncogenic fusion TFs to promoter sites. In inv(16) acute myeloid leukemia (AML), the CBFβ–MYH11/RUNX1 complex interacts with TAFs and occupies genomic regions that have been implicated in hematopoietic stem cell self-renewal, such as TBP and RNAPII.β-globin gene expression but not in myeloid or lymphoid development, suggesting that Med1 has lineage-specific functions.10 In erythropoiesis, Med1 interacts with the N-terminal zinc finger of GATA1 and forms a GATA1–Med1–Med17–Pol II complex, which is critical for GATA-1-mediated transactivation.11 Through interaction with the nuclear receptors Vitamin D receptor (VDR) and retinoic acid receptor (RAR), Med1 is involved in the differentiation of HPCs toward monopoiesis and myelopoiesis, respectively.12 Furthermore, Med1 in stromal cells appears to play an important role in supporting hematopoietic stem and/or progenitor cell (HSPC) growth by upregulating VDR-mediated and Runx2-mediated transcription from the osteopontin promoter.13 During pre-B-cell differentiation, IKAROS, in the company of other B cell master regulators, such as Med1, PAX5, EBF1, and IRF4, defines a set of superenhancers (SEs) with a highly permissive chromatin environment that supports transcription of key pre-B-cell differentiation genes.14The Mediator is a large macromolecular complex that serves as a molecular bridge between enhancer-bound TFs and RNA Pol II, thus facilitating PIC assembly. The Mediator is composed of four separate modules, wherein the head, middle, and tail modules form a core that associates reversibly with a kinase module comprised of Med12, Med13, CDK8, and Cyclin C. The Med1/TRAP220 subunit, a component of the middle module, is involved in multiple hematopoietic lineages via interaction with GATA1, nuclear hormone receptors and IKAROS. Med1 knockdown mice show a specific block in erythroid development with a lack of 15 Med12 mutations are involved in abnormal hematopoiesis. For instance, a loss of function mutation of Med12 that leads to dissociation of Cyclin C-CDK8/19 from the core Mediator contributes to chronic lymphocytic leukemia (CLL) pathogenesis by activating NOTCH signaling.16 Interestingly, several cofactors interacting with Med12 in HSPCs are also frequently mutated in leukemia and lymphoma, including P300/CBP, KMT2D, WDR5, and KMD6A,19 suggesting that alterations in Med12-dependent enhancer regulation may be a potential pathogenic factor. In zebrafish, Keightley et al. identified a V1046D mutation in Med12 that can cause defects in myelopoiesis without affecting the erythroid lineage, which may be mediated by defective transcription of myc.20Med12 contributes greatly to the interaction of TFs with the Mediator complex. In HSCs, Med12 colocalizes with RUNX1, GATA2, and FL1, where it cooperates with additional cofactors, such as P300, LMO2, and TAL1, to maintain hematopoietic gene activity. Med12 deletion causes H3K27Ac depletion at enhancers and failure of hematopoietic-specific transcriptional programs.21 TCF/LEF/β-catenin,22 Smads,23 HIF1A,24 and NF-κB.25 CDK8/19-targeted substrate phosphorylation impacts TF activity and the transcription of lineage-controlling TFs. For example, CDK8 inhibitors enhance IL-10 production during innate immune activation in human and mouse primary macrophages and dendritic cells (DCs) via diminished phosphorylation of the c-Jun subunit of the AP-1 transcription complex.21 CDK8/19 can also inhibit gene expression of SE-associated lineage-controlling TFs identified in related CD14+ monocytes, including the tumor suppressors IRF1, IRF8, CEBPA, and ETV6, and the kinase activity of CDK8/19 can be pharmacologically targeted as a therapeutic approach to AML.26The Mediator kinase CDK8 and its paralog CDK19 act as major ingresses of developmental and oncogenic signaling through Mediator. CDK8/19 have been identified as cofactors of a number of TFs, such as c-Jun,27 In both T cells and MEFs, Med23 regulates the transcription of T cell transcription factor KLF2 by coactivating MEF2.27 Furthermore, individual subunits of Mediator interact specifically with different transcriptional modulators to selectively fine-tune the regulation of specific signaling pathways. For example, the ARC/Med6 complex links TGFβ/Activin/Nodal/Smad2/3 signaling to transcriptional activation by binding to the Smad2/3–Smad4 complex, which recruits the ARC/Med6 complex to Activin/Nodal-responsive promoters, in response to TGFβ.23 Furthermore, the transactivation domain of β-catenin interacts with the Med12 subunit in Mediator and activates transcription of Wnt-responsive genes via recruitment of the ARC/Med6 complex.28 CDK8 also stimulates Wnt/β-catenin signaling by phosphorylating/inhibiting E2F1, which is a negative regulator of β-catenin-mediated transcription.22 Bromodomain and extraterminal protein (BET) inhibitors are suggested to be applicable in targeting Med-mediated malignancies. In this respect, the BET protein BRD4 and the Mediator complex are linked coactivators that maintain MYB-specific transcriptional activation for AML maintenance. The BET inhibitor JQ1 exerts antileukemic effects by releasing the complex from the AML genome29  composed of 12–15 subunits and fall into two broad classes depending on whether they contain one of two ATPases, BRG1 or hBRM. SWI/SNF complexes are implicated in erythroid and myeloid lineage development via association with the hematopoietic TFs EKLF,39 Moreover, SWI/SNF complex components can be differentially recruited by TFs for activation or suppression at the same promoter. For instance, in primary T lymphocytes under basal conditions, hBRM is recruited by native STAT1 to the IFNγ-activated sequences (GAS) of the Hsp90α gene in a mSin3/HDAC corepressor complex. Upon IFNγ stimulation, hBRM is acetylated by p300 and dissociates from the corepressor complex, allowing STAT1 to become phosphorylated and recruit BRG1 to the GAS, leading to elevated gene transcription.40 Some SWI/SNF subunits perform specialized functions in hematopoiesis. For instance, BCL11A is indispensable for normal HSC function. BCL11A deficiency results in HSC defects with an aging-like phenotype and cell cycle delay caused by decreased Cdk6 expression.41 BCL11A is also required for normal lymphopoiesis. Bcl11a mutant embryos lack B cells and the expression of the downstream TFs Ebf1 and Pax5 and have alterations in several types of T cells.42 Bcl11b acts directly upstream of Gfi1 to maintain its expression in mature type 2 innate lymphoid cells.43 Moreover, BRG1 regulates T helper 2 (Th2) differentiation and Th2 cytokine transcription via its recruitment to the Th2 locus control region (LCR) by STAT6 and NFAT.44 During myeloid differentiation, RUNX1 interacts with subunits BRG1 and INI1, which supports binding of the SWI/SNF complex to RUNX1 target genes related to the myeloid lineage and generates active histone modifications.33 An interaction between C/EBPβ and hBRM is essential for activating the myeloid mim-1 gene in HD3 erythroblasts.35 During macrophage differentiation, ATF-3 associates with BRG1 and β-actin to transactivate the SLC11A1 promoter by initiating Z-DNA formation.34 BAF60B controls the differentiation of neutrophil granulocytes via recruitment of CEBPε to the promoters of neutrophilic granule genes. Adult mice with BAF60B-deficient hematopoietic cells develop myelodysplasia and excess blasts.45 Furthermore, inhibition of ACTL6A promotes granulocytic differentiation in NB4 and HL-60 cells via decreasing its interaction with the TF Sox2.46Although SWI/SNF complexes are suggested to antagonize chromatin-mediated repression, they can also associate with corepressors at a subset of genes, leading to transcriptional repression. For example, BRG1 acts as a component of the GATA-1–Scl/TAL1 complex, which inhibits protein 4.2 transcription in erythroid progenitors through recruitment of the corepressors mSin3A and HDAC2 and reduction of histone H3 and H4 acetylation at the promoter.BRG1/SMARCA4, SNF5/SMARCB1, BCL11B, and the BAF complex subunits SMARCA2 and ARID1A are prevalent in leukemia. The presence of BRG1 is critical for the oncogenic Myc expression of leukemia cells because it maintains TF occupancy at critical Myc enhancers and enables long-range chromatin looping interactions with the Myc promoter. Notably, these Myc enhancers coincide with a region that is focally amplified in ~3% of AML. BRD9 also supports AML cell proliferation and an undifferentiated cell state by sustaining enhancer-mediated Myc expression using its bromodomain, which mediates acetyl-lysine histone H3 recognition. Inhibition of the BRD9 bromodomain selectively suppresses the proliferation of human AML cell lines. These findings define a leukemia maintenance function of SWI/SNF that is linked to enhancer-mediated gene regulation49  is present in 20–25% of pediatric and up to 5% of adult T-ALL and defines a molecular subset of this malignancy. Fifty percent of T cell prolymphocytic leukemias display deletions at 22q11, the location of SNF5. Additionally, conditional deletion of SNF5 in mice leads to T cell lymphomas with short latency and 100% penetrance.50 Generally, specific mutations or translocations affecting SWI/SNF subunits may produce gain-of-function properties that result in an oncogenic SWI/SNF complex. In contrast, the inactivation or loss of some SWI/SNF subunits potentially affects TF activity,51 global histone modification52 or cooperating pathways,53 contributing to malignant hematopoiesis.Cancer genome sequencing has revealed that SWI/SNF subunits are the most frequently mutated (~20%) CRC in hematological malignancies. Inactivating mutations of 54 SNF2H also plays indispensable roles in the maturation of definitive HSCs and the completion of erythropoiesis. SNF2H is abundantly expressed in mammalian early HSCs and proliferating progenitors, whereas its levels are downregulated during erythroid terminal differentiation.55 Knockout of murine SNF2H abrogates definitive hematopoiesis within the fetal liver, causing anemia and lethality at E18.5. Inhibition of SNF2H levels significantly suppresses cytokine-induced erythropoiesis in human CD34+ progenitors and erythroid progenitor proliferation.56 Primary erythroid progenitor defects in SNF2H involved a slowed onset of definitive hematopoiesis and defective proerythroblast-to-basophilic erythroblast maturation, marked by reduced expression of GATA-1 and a defect in hemoglobin switching.57 Interestingly, SNF2H appears to interact with GATA-1 in mouse proerythroblast/basophilic erythroblastic MEL cells, suggesting that SNF2H and GATA1 may cooperate in erythroid differentiation.58 Furthermore, Dluhosova et al. found that in normal myeloid cells, the association of SNF2H with the cohesin complex facilitates CCCTC-binding factor (CTCF) binding to the imprinting control region (ICR) of the H19/Igf2 genes and an upstream regulatory element (URE) of the PU.1 gene, where SNF2H regulates the transcriptional outcome of CTCF as an enhancer-blocking cofactor.59The imitation switch (ISWI) is an evolutionarily conserved CRC that contains SNF2H/SMARCA5 or SNF2L/SMARCA1 as the ATPase subunit and a C-terminal SANT domain adjacent to a SLIDE domain (SANT-like ISWI), which together form a nucleosome recognition module. ISWI mediates DNA accessibility by catalyzing nucleosome spacing and sliding reactions without displacing the histone octamer from DNA. In mammals, seven different ISWI complexes have been described, including RSF, ACF/WCRF, CHRAC, WICH, NoRC, CERF, and NuRF. Among them, SNF2L is found in the NURF and CERF complexes, and SNF2H is found in the CHRAC, NoRC, ACF, RSF, and WICH complexes. To date, except for SNF2H, the identities of other components of this complex in hematopoiesis remain largely unknown. Zikmund et al. found that SNF2H regulates murine early T cell development by guiding the transcription of early differentiation programs at the DN3 stage, and SNF2H deficiency leads to thymocyte proliferation and survival defects via the activation of the DNA damage response.55 Moreover, the SNF2H/CTCF binding site at the PU.1 gene was methylated in AML, which prevented SNF2H/CTCF binding. Upon demethylation by 5-azacitidine (AZA) treatment, SNF2H/CTCF occupancy was partially restored, accompanied by decreased PU.1 expression and induced myeloid differentiation, suggesting that recruitment and occupancy of the SNF2H/CTCF complex is critical for normal myelopoiesis59  and GATA-1/FOG1-repressed genes  in erythroid cells and megakaryocytes (MKs), depending on the transcriptional and cellular context.61 The normal cooperation of the NuRD complex with other cofactor components is critical to the function of TCs. For example, the ability of FOG1 to augment GATA-1-induced transcription requires NuRD binding. Knock-in mice bearing three adjacent point mutations  within the NuRD-binding module of FOG-1 that abrogate NuRD binding displayed thrombocytopenia and anemia with splenomegaly and extramedullary hematopoiesis.61 Furthermore, mice harboring disrupted FOG1/NuRD interaction (Fogki/ki) produced Fogki/ki CMPs and Fogki/ki MEPs that gave rise to significantly fewer and more immature MKs and erythroid colonies while retaining multilineage capacity to differentiate into Mast cells (MCs) and other myeloid lineage cells in vitro. In particular, the NuRD/MeCP1 complex is required for GATA-2 and non-Meg/E gene silencing during early erythroid differentiation, suggesting that the FOG1/NuRD interaction is not only required for erythropoiesis and megakaryopoiesis but also associated with suppression of alternative lineage choices.62The NuRD/Mi-2 complex is a multifunctional epigenetic regulator that contains ATPase/helicase Mi-2, HDAC-1/2, Metastasis-associated Protein 1-3 (MTA1-3), methyl binding protein 3 (MBD3), and retinoblastoma-associated protein 46 and 48 (RbAp46/48). The NuRD/Mi-2 complex is itself part of a larger protein complex called the MeCP1 complex. The latter contains the additional components MBD2, P66, and P68.mb-1 gene via cooperative interactions between the major domains of Mi-2β with mb-1 promoter chromatin and the binding of methylated promoter CpGs by MBD2.63 Specifically, SWI/SNF and NuRD/Mi-2 complexes function in opposition to enable or limit activation of the mb-1 promoter by the TFs EBF and Pax5 during B cell development, suggesting that the NuRD/Mi-2 complex acts as a ‘gatekeeper’ by maintaining a high threshold for transcriptional activation.64 The MBD3/NuRD complex controls B versus T lineage fate and prevents the B cell commitment-associated transcriptional program by restricting Ebf1 transcriptional activity.65 Additionally, depletion of MTA3 in early thymic T cell progenitors leads to compensatory hyperproliferation of immature thymocytes and development of T cell lymphoma.65 MTA3 interacts with a transcriptional repression domain of BCL-6 that is required for BCL-6-dependent transcriptional repression. Their interaction is regulated by acetylation of BCL-6. Inhibition of NAD-dependent deacetylation by niacinamide, which results in the accumulation of the acetylated form of endogenous BCL-6, impairs its interaction with MTA3. Upregulation of BCL-6 and MTA3 in human plasma cell lines reprograms the cells to express markers of earlier stages of B lymphocytes,66 suggesting that the MTA3–BCL6 complex has a prominent role in B cell fate determination. MTA2 is critical for B cell maturation in the BM and spleen. Mechanistically, AIOLOS/IKAROS recruits the MTA2/NuRD complex to repress pre-BCR genes, such as Igll1 and VpreB1, in pre-B cells via regulation of H3K27 acetylation. MTA2 also cooperates with OCA-B to regulate the pre-B cell to immature B cell transition via repressing the Igll1 and VpreB1 genes.67The NuRD/Mi-2 complex has been implicated in regulating both B and T cell differentiation. Prior to B-lineage specification and commitment, the MBD2/NuRD complex inhibits transcription of the B cell-specific 68 In differentiating DP thymocytes, Mi-2β activates CD4 expression via recruitment of p300 and the E box-binding protein HEB to the CD4 enhancer, which causes histone H3 hyperacetylation of this regulatory region.68 NuRD/Mi-2β could determine the transcriptional activity of TFs in a stage-specific manner. Naito et al. found that in DN thymocytes, Ikaros binding to the CD4 silencer contributes to its repressive activity through interactions with HDACs and associated chromatin-remodeling complexes, such as the SWI–SNF complex. In DP thymocytes, concomitant binding of Mi-2β with Ikaros to the CD4 silencer antagonizes the repressive activity of Ikaros via the recruitment of HATs, i.e., MOZ and TAFII250, which allows for CD4 expression. This case suggests that concomitant interactions between functionally opposing chromatin-regulating machinery are an important mechanism of gene regulation during lineage determination.69 Interestingly, in T cells, a majority of Ikaros exists in the Mi-2/NuRD complex. A small fraction of Ikaros is found in association with SWI/SNF in a distinct complex. The locations of Ikaros/Mi-2/NuRD and Ikaros/SWI/SNF are differentially distributed during the cell cycle, implying that one TF could target distinct transcriptional machinery in a context-dependent manner.70 Furthermore, the modification status of TFs could affect TC activity via the recruitment of different cofactor components. For instance, during primary CD4+ T cell activation, PKC-mediated BCL11B phosphorylation switches BCL11B from a repressor to an activator of the IL-2 gene by disrupting interactions between BCL11B and the MTA/NuRD complex and recruiting p300.71The ATPase/helicase Mi-2β is essential for several steps in T cell development, including the double-negative (DN) to double-positive (DP) transition, the developmental expression of CD4 and the proliferation of mature T cells.RARβ2, which allows recruitment of Polycomb repressive complex 2 (PRC2) and DNMT3a, leading to promoter silencing. Knockdown of the NuRD complex not only prevents histone deacetylation and chromatin compaction but also impairs DNA and histone methylation, thus promoting cellular differentiation.73 In addition, the stem cell factor SALL4 contributes to AML leukemogenesis by recruiting the NuRD/HDAC complex to the PTEN and SALL1 promoters and suppressing their expression. Targeting AML can be achieved by blocking the interaction between SALL4 and the NuRD/HDAC complex. Thus, enzymatic components of the NuRD/Mi-2 complex may provide novel targets for pharmacological manipulation.74 Alterations in the composition of the NuRD complex also contribute to leukemogenesis. Biswas et al. found that MBD3 deficiency relieves HDAC1-associated transcriptional repression in a locus-specific manner, which in turn results in increased occupancy of KDM6A, H3K27Ac, and CBP, leading to DOCK5/8 expression and maintenance of the Rac GTPase program in AML cells.75The NuRD complex plays a key role in leukemogenesis Fig. . In APL,Histone-modifying cofactors are epigenetic enzymes that add or remove chemical residues from the histones they modify. Generally, HATs transfer acetyl groups from acetyl-CoA to lysine residues of histone proteins, which opens chromatin and allows gene transcription, whereas HDACs have a repressive effect on gene expression by deacetylating lysine residues on histone tails. Histone methylation is the methylation of lysine or arginine residues. Methyl marks are created by HMTs, so-called “writers,” and removed by histone demethylation enzymes, termed “erasers”.76 As an essential coactivator, CBP/p300 associates with large numbers of TFs in virtually all hematopoietic lineages. In stem/multipotent progenitor cells, CBP/p300-mediated acetylation of GATA-2 enhances its DNA binding and transactivation activities and suppresses GATA-2-mediated growth inhibition.77 Moreover, the GATA2–CBP complex induces GATA2 positive autoregulation.78 In committed progenitor/differentiated cells, GATA1-dependent displacement of the GATA2–CBP complex is involved in the GATA switch.78 Upon differentiation of erythroid cells, TFs, including EKLF,79 GATA1,80 NF-E2,81 and SCL/Tal1,82 associate with CBP/p300 or p300/CBP association factor (P/CAF) to promote normal erythropoiesis via modulating globin gene expression. Some TFs are themselves subject to acetylation by CBP/p300 or P/CAF, which increases TF transcriptional activity83 or DNA-binding activity84 and, in some cases, selectively stabilizes or destabilizes TF-associated CRCs.82 An important role of CBP/p300 in establishing lymphoid and myeloid differentiation is supported by their interactions with TFs, including RUNX1/AML1,85 PAX5,86 C/EBPβ,87 BCL11B,88 T-bet,89 c-Myb,90 Bcl-6,91 and the Ets family (Ets-1 and PU.1).93 These TFs are critical for myeloid-specific or lymphoid-specific gene transcription due to their regulation of the enzymatic activity of CBP/p300. For example, NF-E2, C/EBPβ, and PU.1 have been shown to inhibit93 or facilitate94 CBP/p300-mediated nucleosomal HAT activity.HATs typically exist in multiprotein complexes where they execute acetylation programs that acetylate not only histone tails but also nonhistone proteins, which modulates TC DNA binding or transcriptional activity. For example, in eukaryotic cells, CBP/p300 is a component of multiprotein complexes composed of the basal transcriptional apparatus , specific TFs, acetyltransferases  and other coactivator complexes .CBP and p300 genes have been identified in non-Hodgkin lymphoma and relapsed ALL patients.95 These mutations generally affect the HAT domain, reducing CBP acetyltransferase activity, which leads to impaired histone acetylation and diminished expression of target genes. In this case, reactivation of CBP/p300 has emerged as a potential therapeutic strategy.96 Some oncogenic TFs, such as C-myb, EVI1, E1A, and E2A, perform their transforming activity by relying on CBP or p300.99 HAT-containing fusion proteins  caused by chromosomal translocations also contribute to leukemogenesis. CBP fusions generate powerful chromatin-modifying activity, leading to transcriptional deregulation by promoting histone acetylation of genomic regions. MOZ fusions lead to repressed differentiation, hyperproliferation and self-renewal of myeloid progenitors through deregulation of MOZ-regulated target gene expression.100 Furthermore, a portion of oncofusion proteins perform their function via recruiting HATs  conventional DC differentiation via the reduction of the acetylation of both histone 3 and STAT6 and the subsequent transcriptional activation of Arg1.106 HDAC1 and HDAC7 are involved in terminal macrophage differentiation via their interaction with MEF2A/D heterodimers, which represses c-Jun.107 The reduced interaction of Runx1 with HDAC1/HDAC3 induced by Src kinase-mediated Runx1 phosphorylation is related to increased DNA affinity and the induction of granulopoiesis.108 Furthermore, Runx1 and PU.1 independently interact with the ETO2–SIN3A–HDAC2 corepressor complex and coactivate the expression of M-CSFR and GM-CSFR, which control the development and activation of granulocyte–monocyte cells.109 For the erythrocyte lineage, HDAC1/Sin3A can be recruited by EKLF to inhibit β-globin expression in undifferentiated EBHX11L cells, while this complex can be converted to the EKLF-p300/CBP-SWI/SNF complex and promote β-globin expression during the differentiation of EBHX11L cells to a primitive erythroid phenotype.110 HDAC-containing TCs are also implicated in γ-globin to β-globin switching, highlighting that HDACs in association with globin gene switching may provide molecular targets for intervening in β-globin gene disorders.111 The function of class II HDACs, which shuttle TFs between the cytoplasm and nucleus, is critical for erythropoiesis. For instance, although HDAC5 does not have deacetylase activity, it could shuttle GATA1 and EKLF from the cytoplasm to the nucleus via the formation of an erythroid-specific HDAC complex.112 For the lymphocyte lineage, HDAC3 was identified as a component of the STAT5a–LSD1 complex in pro-B cells, where it plays dual roles in determining the activation or repression of STAT5a-targeting genes based on protein interactions, genomic-binding localization and binding affinities.113 In mature B cells, Bach2 recruits the HDAC3–NCoR1/NCoR2–Rif1 complex to repress Prdm1 transcription by deacetylating histone H3-K9, impeding the terminal differentiation of B cells into plasma cells.114 Moreover, HDAC7 specifically interacts with MEF2C in pro-B cells, and a HDAC7–MEF2C complex is involved in silencing lineage-inappropriate genes, ensuring correct B cell differentiation.116HDACs are widely involved in the development of various hematopoietic lineages, together with a variety of TFs. For example, in HSCs, HDAC3 cooperates with Ncor2 to repress the fos-vegfd cascade by modulating the acetylation level of the fos promoter, thereby stimulating HSC formation.118 Interactions with different HDACs confer different functions to TFs. For example, in B cell non-Hodgkin lymphoma (B-NHL), aberrant expression of the HDAC9–BCL6 complex contributes to lymphomagenesis.119 In follicular lymphoma (FL) and diffuse large B cell lymphoma (DLBCL), CBP mutations disable the acetylation of the HDAC9–BCL6 complex and lead to unopposed deacetylation by the BCL6–SMRT–HDAC3 complex at enhancers of B cell immune response genes, which promotes lymphomagenesis.120 In miR-155-induced pre-B cell leukemia/lymphoma, HDAC4 in complexes with BCL6 plays an important role in suppressing leukemogenesis.121HDACs are critical for the optimal oncogenic activity of leukemia fusion proteins Fig. . For exaHistone methylation signatures serve as an important regulatory mechanism in hematopoiesis. In HSCs, numerous genes involved in the differentiation of multiple lineages are marked by bivalent marks that display the repressive mark H3K27me3 and the active marks H3K4me2 and H3K4me3. Many lineage-specifying genes in these primitive cells possess a diverse range of histone modification patterns, suggesting that specific epigenetic combinations prepare these genes for selective expression or silencing during lineage commitment.122 The latter blocks myeloid differentiation by assembling a Methyl-RUNX1-dependent repressor complex that inhibits RUNX1 target genes.123 (2) Specific modification of histone marks via HMT-containing TCs. In megakaryocytic/erythroid progenitors, a RUNX1–PRMT6–Sin3A–HDAC complex has an impact on bivalent histone marking at megakaryocytic differentiation genes. This corepressor complex is exchanged with a RUNX1 coactivator complex following megakaryocytic differentiation.124 (3) Regulation of TF expression via proteasome-mediated degradation. For example, MLL stabilizes RUNX1/AML1 from ubiquitin–proteasome-mediated degradation by masking the RUNX1–proteasome interaction domain and possibly by methylating lysine residues. In MDS and AML, a subset of mutations at the N terminus of RUNX1 disrupt its interaction with MLL, leading to loss of H3K4me3 marks within PU.1 regulatory regions and decreased PU.1 expression.125 (4) Crosstalk between HMTs and other TCs. For example, PRMT4 has been found to engage in crosstalk with Mi2α/NuRD,126 SWI/SNF, and Mediator complexes to regulate hematopoietic signaling.127 Crosstalk between PRMT1 and the E3 ligase CNOT4 on RBM15 controls the RNA splicing of some hematopoietic TFs.128HMTs are implicated in hematopoiesis regulation via the following multilayer mechanisms. (1) Specific regulation of TF methylation based on the developmental stage of the cell. For instance, Runx1 is arginine-methylated by PRMT1 upon myeloid differentiation but by PRMT4 in HSPCs. The former prevents RUNX1 association with the SIN3A corepressor and functions as a coactivator for RUNX1-dependent transcription.Hox loci.129 Moreover, MLL fusion partners, such as AF4, AF9, AF10, and ENL, associate with DOT1L, which results in activation of H3K79me2, aberrant transcriptional elongation by recruitment of DOT1L to the Pol II elongation complex, and maintenance of an open chromatin state.130 A graded reduction of DOT1L recruitment to MLL-AF9 leads to differential loss of H3K79me2 and H3K79me3 at MLL-AF9 target genes.130 Interestingly, PRDM16 specifically suppresses MLL fusion protein-induced leukemogenesis via activation of Gfi1b, which in turn inhibits HOXA gene clusters131  selectively induces apoptosis in various hematological malignancies and promotes erythroid differentiation in K562 cells.133The aberrant recruitment of HMTs to key hematopoietic genes by oncofusion TFs is critical to transcription deregulation. The SET domain that is required for H3K4 methyltransferase activity is consistently lost in MLL fusions and is often compensated by interaction with alternative HAT/HMT enzymes through partner proteins. For example, the MLL–EEN fusion protein recruits CBP and PRMT1 to induce the transformation of primary myeloid progenitors via introduction of aberrant H4R3me2 at MLL target 131 Fig. . Thus, mHDTs are mainly categorized into two families: the LSD family (LSD1-2) and the JmjC family (KDM2-7). The LSD family contains flavin adenine dinucleotide-dependent monoamine oxidases, which erase methyl marks at the H3K4 and H3K9 residues. The JmjC family contains Fe(II)-dependent and α-ketoglutarate-dependent dioxygenases, which can be further divided into groups with H3K4-, H3K9-, H3K27-, or H3K36-demethylating activities based on the structure of the JmjC domain.etv2 gene in hemangioblasts is essential for the initiation of endothelial to hematopoietic transdifferentiation (EHT).134 In HSPCs, SALL4 dynamically controls the binding levels of LSD1 to hematopoietic regulatory genes, and LSD1 contributes to the repressive effects of SALL4 by affecting local chromatin structure.135 Specifically, lineage-restricted deployment of the LSD1/CoREST complex controls hematopoietic differentiation. For example, GFI1 recruits the LSD1/CoREST complex to epigenetically silence the endothelial program in the hemogenic endothelium and allow the generation of HSCs.136 In erythropoiesis, the LSD1/CoREST–BCL11A complex is required for full developmental silencing of mouse embryonic β-like globin genes and human γ-globin genes in adult erythroid cells.137HDTs are extensively involved in hematopoietic differentiation, ranging from embryonic to adult hematopoiesis, via HDT-TF interplay. For example, the LSD1-mediated downregulation of the 138 JMJD1C functions as a coactivator for the AML1-ETO-containing AETFC complex to drive leukemic programs by maintaining low H3K9me2 levels.139 JMJD1C is also an important mediator of MLL-AF9-driven and HOXA9-driven leukemia stem cell (LSC) function  Different genes regulated by the same TF may recruit different cofactors. (2) One cofactor could act as a coactivator or corepressor on different genes and utilize different domains to act on interacting TFs. (3) Cooperation of one cofactor with different TFs or TCs may produce distinct developmental or leukemic properties.151 The interplay between TFs and cofactors is multifaceted and involves direct and indirect regulatory mechanisms, which depend on the molecular properties of cofactors and can include the following: (1) Organization of TCs with scaffolding proteins. (2) Regulation of the transcriptional activity, DNA-binding capacity or protein modification of TFs. (3) Involvement in modifying the chromatin landscape. (4) Cooperation between cofactors within TCs, which is a prerequisite for TF transcription programs. TFs are important for guiding cofactors to particular genomic locations to modify the epigenetic environment. In some cases, TFs and cofactors could affect each other’s expression, activity, or binding states via signaling cascades, intermediate TF-mediated regulatory loops or crosstalk effects. For example, phosphorylation of RUNX1 by ERK enhances its transactivation activity by disrupting its interactions with mSin3A. TFs have also been found to mediate crosstalk between cofactors. For example, the recruitment of HDAC1 by Ikaros is critical for the repression of the demethylase KDM58.152Gene transcription determines cellular phenotype and function, and TFs play a key role in controlling gene expression profiles in response to cellular signals. However, the transcription process involves the interaction of TFs with not only the DNA sequence but also the cofactors within TCs. Cofactors modulate histone affinity for DNA and chromatin accessibility to their cognate-binding proteins by compaction of DNA/histone complexes. Their biochemical and molecular characteristics greatly impact the activity of TCs. Importantly, cofactors can have catalytic/noncatalytic153 (Tables 154 Drug resistance and limited therapeutic efficacy are key issues that hinder the clinical application of cofactor inhibitors. The ability to combine multiple chemotherapeutic or immunoregulatory agents is considered a major breakthrough in the treatment of hematological malignancies. For example, HDAC inhibitors have shown synergistic or additive effects with proteasome inhibitors, hormonal therapy, tyrosine kinase inhibitors, DNA-hypomethylating agents, and immune checkpoint inhibitors in preclinical and clinical settings.155One hallmark of hematological malignancies is somatic mutations and genetic rearrangements of cofactors that impact the activity and expression of cofactors or the composition of TCs that cause alterations in signal transduction and gene expression programs. In particular, the activity of oncogenic TFs and fusion proteins is reliant on their interactions with cofactors, which provides increased opportunities for intervention in the disease by targeting cofactors3 Tables –4. To da3 Tables . For exaThe interplay between cofactors and TFs globally affects the gene regulatory network. Future studies will be needed to identify more regulatory factors that dysregulate TC expression or function and determine how their dysregulation contributes to the pathogenesis of hematological malignancies. In particular, a functional switch in the cofactor–TF interplay is critical to malignant transformation. Hence, resolving specific cofactor components, the crystal structure of cofactors, identification of cofactors-related the epigenetic mechanisms, downstream genes, TF/cofactors or TF/DNA interactive interfaces may contribute to develop novel inhibitors in a given hematologic disorder.


Arabidopsis thaliana and Physcomitrella patens. The high G4 potential is balanced by the activity of G4-resolving enzymes. The ability of rDNA to undergo these “structural gymnastics” thus represents another layer of the rich repertoire of epigenetic regulations, which is pronounced in rDNA due to its highly repetitive character.Genes encoding 45S ribosomal RNA (rDNA) are known for their abundance within eukaryotic genomes and for their unstable copy numbers in response to changes in various genetic and epigenetic factors. Commonly, we understand as epigenetic factors (affecting gene expression without a change in DNA sequence), namely DNA methylation, histone posttranslational modifications, histone variants, RNA interference, nucleosome remodeling and assembly, and chromosome position effect. All these were actually shown to affect activity and stability of rDNA. Here, we focus on another phenomenon – the potential of DNA containing shortly spaced oligo-guanine tracts to form quadruplex structures (G4). Interestingly, sites with a high propensity to form G4 were described in yeast, animal, and plant rDNAs, in addition to G4 at telomeres, some gene promoters, and transposons, suggesting the evolutionary ancient origin of G4 as a regulatory module. Here, we present examples of rDNA promoter regions with extremely high potential to form G4 in two model plants,  Saccharomyces cerevisiae, Arabidopsis thaliana, Among many potential reasons to become interested in genes encoding ribosomal RNA (rRNA) is the possibility to study the wide range of regulatory mechanisms used to control their expression and genomic stability. When starting from the genomic level, genes for 45S rRNA (rDNA) usually form the most abundant gene family in most eukaryotes , replication and transcription polymerases can meet and occasionally collide, and DNA repair mechanisms must eventually solve problems arising from all this apparent turmoil.On the other hand, this mini-world has also developed numerous tools of precise regulation which began to be understood in molecular details recently. These include a phenomenon termed nucleolar dominance  structures (G4), which are based on guanine tetrads. This feature seems to be conserved throughout eukaryotes  and is tP. patens and A. thaliana.Here, we exemplify the role of G4 structures in rDNA of two model plants, Arabidopsis thaliana, it was found recently that RecQ-mediated genome instability protein 2 (RMI2) and Regulator of telomere elongation helicase 1 (RTEL1) contribute to the stability of the 45S rDNA copy number  functions as a Fe-S cluster helicase suppressing inappropriate recombination events by promoting disassembly of D-loop recombination. Furthermore, RTEL1 can dissolve quadruplex (G4) DNA structures that otherwise block the extension of telomeres by telomerase  but also in control of rDNA transcription.These results are consistent with the fact that tructure . The higtructure  coincidetructure . Presumathaliana , which mthaliana . These rP. patens rDNA is complicated by the linked arrangement between 18S-5.8S-25S units and 5S rRNA genes. This arrangement has been demonstrated recently , similar to atrtel1 mutants, also show a marked decrease of 18S rDNA copies (representing 45S rDNA), but, in addition, a comparable decrease of 5S rDNA is observed  be included among epigenetic mechanisms.In rDNA, epigenetic effects of G4 formation can be expected preferentially at active copies  or during replication when DNA strands are temporarily separated and noncanonical intrastrand structures can be formed. In addition to G4s formed by rDNA as discussed above, recent results suggest possible roles of G4s formed by rRNAs. Interestingly, these potential G4s are located on surfaces of both subunits of the human ribosome . When asAll datasets presented in this study are included in the article.KH performed annotation of rDNA region and prediction of G4 sites. JF wrote the manuscript. Both authors have read, edited, and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach that uses latent space models to better model accessible regions, termed ChromA. ChromA annotates chromatin landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. ChromA can analyze single cell ATAC-seq data, correcting many biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on multiple technologies and biological systems, including mouse and human immune cells, establishing ChromA as a top performing general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs. Most currently available statistical tools for the analysis of ATAC-seq data were repurposed from tools developed for other functional genomics data (e.g. ChIP-seq). Here, Gabitto et al develop ChromA, a Bayesian statistical approach for the analysis of both bulk and single-cell ATAC-seq data. Chromosomal accessibility plays a central role in several nuclear processes, including the regulation of gene expression and the structure and organization of the nucleus2. Chromatin remodelers modify chromatin state creating structural changes that affect gene expression4. Transcription factor proteins (TFs) are key transcriptional regulators and chromatin remodelers, binding to accessible DNA regions to control the expression of genes5 and to inaccessible chromatin, altering the accessibility of targeted regions6. Differential expression and regulation of TFs act as a combinatorial code that gives rise to the wide repertoire of cellular phenotypes observed in mammalian organisms8.The genome of eukaryotic cells is tightly packed into chromatin9. In part due to the small initial starting material required  and from a desire to query the chromatin structure of particular rare cellular types, ATAC-seq has become widely adopted. Recent advances have improved the technique and enabled the mapping of the accessible chromatin landscape of individual cells10. Following the same trend, low starting material techniques to probe the methylome landscape and different chromatin features have evolved from bulk assays to the single-cell domain13. These techniques raise the possibility of both describing the variability of chromatin accessibility, methylation states and chromatin fragments, and enable the study of epigenomic heterogeneity by classifying cellular types based on their chromatin structure15.The development of high-throughput chromatin accessibility assays  has enabled the analysis of chromatin accessible regions, the discovery of nucleosome positions and the characterization of transcription factor occupancy with almost single base-pair resolution16. ChromA is able to integrate information from different experiments, and draw statistical power to create consensus chromatin annotations. To validate our method, we use Th17 bulk18, A20 and GM12878 single-cell data sets (the Data availability section), identifying accessible chromatin and establishing ChromA as an effective platform for mapping the chromatin landscape in different cellular populations. We show that the method is readily adaptable to different experimental designs and technologies.Here, we present ChromA, a Bayesian statistical approach to characterize the chromatin landscape of aggregated and single cells and apply the method to multiple experimental technologies. In the case of ATAC-seq experiments, ChromA infers chromatin accessibility landscape and annotates accessible and inaccessible chromatin regions. ChromA harnesses recent developments in hidden semi-Markov models (HSMM) to create a scalable statistical inference method that can be applied to genome-wide experimentsChromA is a probabilistic graphical model developed to annotate chromatin regions as open (accessible) or closed (inaccessible) when experiments are performed on pooled (bulk), single cells, or a combination of both bulk samples and single cells. We first describe the results aimed at delineating accessible regions, and then illustrate extensions of the method to other tasks and technologies. Our algorithm takes as an input ATAC-seq-aligned sequencing reads (.bam files) or locations of Tn5-binding events (.tsv files) and produces chromatin accessibility annotations and quality control metrics for the data set Fig. .Fig. 1OvS) representing chromatin states (namely chromatin accessibility) at each base (b) and a set of observations (O) composed by the reads  and closed (Sb = 0). Bayesian inference creates posterior estimates of model’s parameters by combining our prior belief about parameter values with the likelihood of the observations being generated by the model. In our case, ChromA aims to estimate posterior chromatin state by combining our prior belief on the accessibility of each base with the likelihood of generating the observed reads.ChromA is based on a Bayesian statistical model that encompasses a set of latent variables  of each accessible region through an HSMM that exhibits a flexible negative binomial (NB) duration distribution20 as follows.To model the duration of accessible regions from ATAC-seq experiments, we reason that contextual information plays a key role in defining each base’s annotation. To improve upon the duration behavior of standard hidden Markov models (HMM)r > 0, and a probability parameter 0 < p < 1. We use this distribution to capture the notion that cis-regulatory transcriptional machineries, necessary for accessing DNA-binding domains, might occupy a certain characteristic length. This length is in turn reflected in the size of chromatin accessible regions. The maximum or mode of a NB distribution is given by its parameters  from a current state (i), Ai,j, and the probability of dwelling in the new state for a duration of d bases, P(τ = d|Sb = j) as follows.Recent developments in approximate posterior calculation provide efficient techniques for the estimation of HSMM parameters. These techniques are advantageous when the duration of HSMM states are distributed according to a NB distributionShiftedGeo(1 – p) is p(z|p) = pz(1 – p) with z an integer z ≥ 0. Equality 5 permits to write an HSMM’s transition matrix with NB distributed states, establishing a correspondence to a transition matrix, in which each state solely dependent on the previous one (HMM)  for each state.To facilitate inference, we begin by re-writing the NB distribution as a sum of shifted geometric distributionsMM) Fig. . The new9. After preferential binding to accessible DNA, Tn5 transposase tagments DNA, leaving behind a DNA adaptor. A correctly oriented second event can be used to sequence the intervening fragment and to identify tagmented locations21. However, we lack information about the total number of cells in the assay and the maximum number of binding events available to each cell on a base pair per chromosome level. Due to the sparse nature of each binding event  the presence of an accumulation of ChIP-seq-binding events . Instead, we reasoned that ChIP-seq locations can be used as a proxy to indicate chromatin accessible regions and therefore used the ChIP-seq data for validation experiments. Compared with other existing methods, ChromA’s predictions faithfully recover the greatest number of ChIP-seq calls while maintaining a comparable total number of peaks while controlling false discovery rate  represents the HSMM probability of transitioning from a state at base b − 1 into a state spanning bases b to b + d, given consensus variables at those bases. This probability factorizes into a HSMM transition term times a term linking each experiment to the consensus variables. We re-write the previous equality by using the HMM NB-embedding transition matrix, Ae, and a base-by-base consensus link transition matrix H.We augment individual experiment NB embedding to include a transition matrix, depending on the consensus representation. The link between each experiment and the consensus representation is possible because the HMM NB embedding, indicated with 27). In addition, to study the model’s robustness to outliers, we select a lower correlation data set . This correlation remains unaltered even, when the outlier is included into the analysis Focusing here on ATAC-seq experiments, ChromA exhibits several prominent features. First, by recovering wider accessible regions, ChromA captures valleys in read density associated with transcription factor footprinting. ChromA also exhibits higher sensitivity allowing for the recovery of less prominent peaks. As a result, single cells data sets, exhibiting an extended dynamic range compared with bulk measurements, can also be analyzed with our software. Finally, by integrating different experiments, ChromA is able to create a consensus annotation and thereby increase the signal-to-noise ratio (while still tolerating outlier regions or even mislabeled/outlier experiments/replicates). This analysis indicates that additional insights can be extracted by integrating different sources of information. In the future, we plan to extend ChromA to integrate different experimental procedures, extracting and combining information in a hierarchical fashion from a wide range of approaches.GSE113721. The following preprocessing pipeline was used to generate aligned reads. Adapters were trimmed using cutadapt. Reads were aligned using Bowtie2 to the murine mm10 reference genome and then filtered for mapping quality greater than Q30. Duplicates were removed using Picard (http://picard.sourceforge.net), and subsequently, mitochondrial, unmapped and chromosome Y reads were removed. For peak-calling, ChromA corrects the read start sites to represent the center of the tagmentation binding event, the +strand were offset b +4 bp, and all reads aligning to the—strand were offset −5 bp. In addition, ChromA filters peaks using a custom list that combines blacklisted genomic regions from the ENCODE project (http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/mm10-mouse/mm10.blacklist.bed.gz). This filtering step takes place when building the set of transposition events by removing all the events falling into the blacklisted regions.ATAC-seq libraries were downloaded from NCBI’s GEO Database under accession https://support.10xgenomics.com/single-cell-atac/datasets/1.0.0/atac_v1_hgmm_10k. Briefly, they consist of a mixture of fresh-frozen human (GM12878) and mouse (A20) cells collected with the Chromium Single Cell ATAC platform, and demultiplexed and pre-processed with the single-cell ATAC Cell Ranger platform. Cells were sequenced on Illumina NovaSeq with ~42 k read pairs per cell. Downsampled data sets are provided from the online website. TSV files are provided listing Tn5-binding events. ChromA incorporates the ability of importing TSV and Tabix files directly from Cell Ranger pipelines.Single-cell data sets were downloaded from 10× genomics ChromA reports different quality control metrics to assess data set quality. Given ChromA annotations, the fraction of reads in peaks (FRIP) is calculated as the number of reads laying within peaks versus the total number of reads in chromosome 1. This is calculated using properly paired and mated reads. SNR is calculated by defining promoter regions in the mouse or human genome as regions spanning 1 kb upstream, 3 kb downstream from gene start sites. Insert size distribution is reported as an additional file, and insert size metric is computed as the ratio between the number of reads with insert size between 190 and 210 bp to the number of reads with insert size between 60 and 80 bp for chromosome 1. Finally, we extrapolate the number of properly paired and mated reads by computing that number for chromosome 1 and multiplying by the total length of the genome and then dividing by the length of chromosome 1.To perform experiments to validate our algorithm, we ran ChromA in each sample individually using standard priors (described below). An example of running ChromA on a wild-type data set of Th17 cells, using the mouse genome with our bulk model is detailed next: ChromA -i “Th17_1_noMito.bam” -species mouse -sb th17_wt1.bed. We ran PeaKDEck . Peaks were identified using the MACS2 software. We run MACS2 using two sets of parameter, and always compare against the best-performing set .GSE40918), mapped to the murine genome (mm10) with Bowtie2 (2.2.3), filtered based on mapping score ), and duplicates removed (Picard). Peaks were identified using the MACS2 software (version 1.4.2) using the settings  and retained for raw p-value <10−10. All data sets were processed against an appropriate control. We retained summit locations to create a binding event localizing at a particular base pair.TF ChIP-seq and control sequencing data were downloaded from GEO (35 (http://pedagogix-tagc.univ-mrs.fr/remap/celltype.php?CT=gm12878) by filtering the database for the cell type GM12878. There are 131 TFs in this database that correspond to the particular cell line, among which we can find CTCF, Pou factors, and members of the Pax, Stat, and Etv families.Transcription factor-binding events for GM12878 were downloaded from ReMapTo compare ChromA against different algorithms, we used different metrics, the fraction of peaks covered, average peak coverage, and total coverage. We compute each metric from the intersection of bed files originating from the manually annotated regions versus algorithmically annotated regions. To compute the fraction of peaks covered (fpc), each manually annotated peak is intersected with the list of peaks algorithmically generated. If the intersection returns non-empty bases, the peak is considered intersected and recorded as such. The final metric value is computed by dividing the number of intersected peaks over the number of peaks Data sets for DNAse-seq experiments for cell lines GM12878 and K562 were downloaded from the Encode project (wgEncodeUwDnaseGm12878AlnRep1.bam/Rep2.bam and wgEncodeUwDnaseK562AlnRep1.bam/Rep2.bam). For each corresponding cell line, we download ChIP-Seqs experiments from the encode project and merge them. For each binding event, we keep the center base-pair location and consider that a peak captures the binding event if it superimposed with this base-pair location. ChromA annotated peaks with option “dnase”. For Macs2, we use the following command: macs2 callpeak -t $file -f BAM -g mm -n $name -p 1e-2 -nomodel -shift -75 -extsize 150 -keep-dup all. For hotspot2, we first generate a reference for the hg19 genome by running extractcentersize.sh and then we annotate each file by using: hotspot2.sh -c $fchrom -C $fCenter -P -f 0.01 $file $outdir.b at each base b is drawn independently through the process here described.Here, we present in more detail the entire ChromA’s generative process. The observed number of Tn5-binding events XSb denotes the chromatin state at base b, and it is distributed according to the transition matrix at the previous state. p is the probability of observing Xb number of binding events at base b given the current chromatin state. −4. a0 and b0 parametrize pseudocounts for the probability of observing a number of binding events in a particular base. We set these values to  for the state that represents closed chromatin and  for the state that represents open, however, the results are insensitive to these values. α0 denotes the prior pseudocounts for the initial state of the Markov process. Given our strategy that identifies batches surrounded by empty regions, we assume that the process starts in the closed state, α0 = . Again, the algorithm is insensitive to this value, as only the first few bases will be affected by it.To run ChromA on single-cell data sets, tsv files should be entered as input data, either as a raw file or as a tabix index file . ChromA automatically builds an observation vector recognizing the type of input data by pooling single-cell information.Further information on research design is available in the Supplementary InformationReporting Summary


Nedd4-2/NEDD4L, an E3-ubiquitin ligase involved in salt-sensitive hypertension, drawing from detailed genetic dissection analysis and the development of genetically engineered mice model. Based on our analyses, targeting therapeutic regulations of ubiquitination in the fields of cardio-vascular medicine might be a promising strategy in future. Although the clinical applications of this strategy are limited, compared to those of kinase systems, many compounds with a high pharmacological activity were identified at the basic research level. Therefore, future development could be expected.Ubiquitination is a representative, reversible biological process of the post-translational modification of various proteins with multiple catalytic reaction sequences, including ubiquitin itself, in addition to E1 ubiquitin activating enzymes, E2 ubiquitin conjugating enzymes, E3 ubiquitin ligase, deubiquitinating enzymes, and proteasome degradation. The ubiquitin–proteasome system is known to play a pivotal role in various molecular life phenomena, including the cell cycle, protein quality, and cell surface expressions of ion-transporters. As such, the failure of this system can lead to cancer, neurodegenerative diseases, cardiovascular diseases, and hypertension. This review article discusses  Hypertension is a multifactorial disease determined by both genetic and environmental factors, including dietary habit, and is a representative public health issue due to its high prevalence and risk for cardiovascular diseases. The relationship between oral salt intake and elevation of blood pressure were widely observed in clinical trials such as the INTERSALT Study , PURE StLifton et al. determined that the genes involved in the development of hereditary human hypertension are restricted to the sodium transporter and its associated proteins in the renal tubule . The mosThe function of the kidney is to produce urine by ultrafiltration, during which the primitive urine passes through the renal tubules, and the quality and quantity of the body fluids and electrolytes are maintained through concentration by water re-absorption, appropriate reabsorption of electrolytes, and appropriate secretion of the bicarbonate ions ,25. MostDuring the first part of the process, >90% of the ultrafiltered sodium is reabsorbed. A physiological regulatory mechanism of the renal microcirculation  between the macula densa and the afferent arterioles, detects the chloride ions in the renal tubular fluid flowing into the distal nephron and imports the anions, thus adjusting the diameter of the afferent arterioles. By adjusting the diameter of the afferent arterioles according to the difference between the amount of filtered and reabsorbed sodium chloride (NaCl), the glomerular filtration pressure and amount were adjusted, and the glomerular filtration rate was maintained according to the oral salt intake. A myogenic reaction specific to the afferent arterioles occurs, in which the smooth muscle around the arterioles reflexively contracts due to the blood flow brought to the afferent arterioles, which regulates the diameter of the afferent arterioles. Similar to TGF, this mechanism adjusts the diameter of the afferent arterioles and maintains a constant glomerular filtration pressure and volume. Moreover, the renal tubules distal to the macula densa are collectively called aldosterone-sensitive-distal nephrons (ASDN), and sodium reabsorption is fine-tuned by the regulation of the expression of sodium ion transporters on the apical side of renal tubular cells, mainly through the action of aldosterone, an endocrine factor. Sodium reabsorption at this site comprises <5% of the total reabsorption, but because of an absent or insufficient feedback/compensation mechanism as in TGF, abnormalities about sodium reabsorption at this site is thought to cause blood pressure abnormalities such as the Liddle syndrome or type I pseudo-hypoaldosteronism (type I PHA). Conversely, abnormalities in sodium reabsorption due to irregularities in the ion transporter from the proximal tubule to the macula densa can be canceled and corrected by powerful TGF and myogenic reactions. Even if such irregularities exist in nature, they might not be recognized due to these physiological compensations.The onset of salt-sensitive hypertension caused by an enhanced renal tubular renin-angiotensin system (RAS) might be caused by abnormalities in the sodium reabsorption mechanism in the terminal nephron, i.e., the ASDN. Our findings in the C57Bl6/J mice showed that angiotensinogen secretion in the proximal tubular cells was enhanced as a result of the enhanced filtered sodium ion ,11,12, dThe function of renal tubules, that comprise a highly differentiated and complex regulatory system, is difficult to describe in a few words. However, their function can be summarized to facilitate understanding, as follows ,30,31,32The outline of sodium transport in the ASDN renal tubular epithelial cells is as shown  Figure . Na-K-ATα, β, and γ subunits, at a ratio of 1α:1β:1γ. The expression of its function requires all three subunits [As ENaC is inhibited by the diuretic amiloride, it is referred to as an “amiloride-sensitive” epithelial sodium channel and is localized in epithelial cells with polarity. That is, it is expressed only in the apical side of the distal tubule, the alveolar epithelium, and colonic mucosa. It comprises subunits ,35. HoweThe endocrine factors principally regulate intracellular ENaC expression. In addition to aldosterone action through binding to MR, the action of vasopressin (ADH) secreted from the posterior pituitary gland appears to regulate the expression of ENaC via V2 receptor-adenylyl cyclase-cAMP, as well as insulin via the insulin receptor. As it acts on renal tubule cells through the blood stream, ADH acts via a receptor present on the basolateral side ,27,28. ALiddle syndrome is a type of hereditary hypertension described by Liddle et al., in 1963, that has an autosomal-dominant inheritance pattern similar to that of primary aldosteronism, without elevated renin and aldosterone. Patients with Liddle syndrome exhibit hypokalemic alkalosis. The mechanism of this disease was discovered by Lifton et al. in 1995. ,39 They Sodium reabsorption mediated by ENaC in ASDN does not possess sufficient physiological compensatory/feedback mechanisms such as TGF in the upstream nephron and the myogenic reaction in afferent arterioles. Therefore, the sodium reabsorption mechanism fails due to a dysfunctional ENaC-Nedd4 system, whether it is a gain or loss of function, and can eventually lead to abnormal blood pressure, without effective intra-renal correction.NEDD4L and salt-sensitive hypertension was confirmed, based on the following—the discovery and naming of NEDD4L, ENaC as a binding factor with respect to Liddle mutation, the discovery of KIAA0439 and NEDD4L (Nedd4-2), and our discovery of the human NEDD4L C2 domain and the V13 G/A mutation [The current paradigm of mutation   and the fourth among the molecules numbered 1 to 10, in order of identification, was NEDD4, which Staub et al. (1996) found to be associated with hypertension [NEDD4 was highlighted, but its significance shifted. With the discovery of the mechanism of ENaC degradation and the failure of retrieval from the cell membrane during the onset of Liddle syndrome, the gene encoding a protein related to this system is notable as a promising candidate associated with salt-sensitive hypertension. However, NEDD4 was not a potential candidate gene because, new findings came to light, which led to other insights into NEDD4/NEDD4L.Kumar et al. (1992) studied changes in gene expression during the development of neural precursor cells, using subtraction cloning . A grouprtension . Using tNEDD4L gene on human chromosome 18 with a 67% homology to human NEDD4 [Nedd4-2, which showed a high homology with Nedd4 (Nedd4-1) in mouse cultured cells [Nedd4-2 [Ishikawa et al. (1997) sequenced and published 78 unknown cDNA sequences from a human brain cDNA library . Among tan NEDD4 . Around ed cells ,50. A co[Nedd4-2 ,52. ThusNEDD4/Nedd4 with NEDD4L/Nedd4-2 and found that NEDD4 had a C2 domain unlike NEDD4L/Nedd4-2, which was a critical difference between the two molecules. However, phylogenetic analyses of gene homology showed that human/mouse NEDD4L/Nedd4-2 (KIAA0439) has a high homology with NEDD4 in Xenopus, which has only one NEDD4, and the NEDD4L gene was thought to have appeared early during the evolutionary process. Based on this finding, the apparent difference between the two genes could not be explained.While the model of the ENaC-NEDD4 system continued to shift towards NEDD4L/NEDD4-2, we clarified significant differences that led to further findings. We and other authors, compared human/mouse NEDD4L) associated with salt-sensitive hypertension. We focused on the importance of the ENaC-NEDD4L system and selected NEDD4L as a target in a genetic analysis of salt-sensitive hypertension.The ENaC-NEDD4L system plays an important role in post-translational modification through ubiquitination, which regulates the ENaC expression on the cell membrane of the terminal nephron. Genetic analyses of the Liddle syndrome and hereditary hypertension found that ENaC, which is restricted to the terminal nephron and regulates sodium reabsorption, and its regulatory protein, NEDD4L, are promising candidate genes  for which the splice site on the genomic DNA sequence exon-end was noted and an exon (exon 2) common to the transcription products from both, were discovered in chromosome 18. Exon 2 had a high homology with the C2 domain of the ancestral Nedd4, such as that in Xenopus and fugu and was thought to be a shared C2 domain that was conserved during molecular evolution [Based on a draft sequence of human chromosome 18 determined using genomic cDNA expression sequence tag (EST) data such as KIAA0439, Xenopus Nedd4 cDNA extracted from GenBank, Human and Mouse EST, and other databases, we performed Basic Local Alignment Search Tool (BLAST) and cross-match analyses. We searched for SNP by analyzing common polymorphisms determined by re-sequencing the genomic DNA of 48 normotensive persons whose DNA samples were registered in the HyperGEN network. We also analyzed human volution   was found just before the exon 1 splice site. We spliced cDNA synthesized from human RNA through RT-PCR, immediately after the G base in those with a G allele, and the full-length C2 domain after exon 2 was encoded. In contrast, a frame shift occurred in those possessing an A allele because the splice site moved downstream by 10 bases, ending at the stop codon at exon 2. As a result, the gene product of exon 1 was knocked out . The resNEDD4L gene expresses two isoforms each with a C2 domain and another without a C2 domain. Among these, isoform I is a gene product resulting from a G/A mutation, a common variant, and isoform I was hypothesized to be associated with salt-sensitive hypertension. Therefore, we studied variant 13 (G/A) in 367 Japanese persons. An analysis based on genotype, alleles, and three inheritance patterns revealed that the GG and G alleles significantly correlated with essential hypertension. We then assessed the significance of the two types of C2 domains in the human Nedd4L gene and six types of transcriptional diversity. The results of a related study of Japanese patients revealed that a variant 13 (G/A) mutation correlated with essential hypertension [These results showed that the human rtension ,54,55. Irtension ,57,58.Nedd4L gene function in salt-sensitive hypertensive Dahl rats [Nedd4L gene in rats has a C2 domain. The results revealed an isoform with a C2 domain in rats and a novel exon on rat genomic DNA. The C2 domain of rat Nedd4L was 100% identical to the human C2 domain. Northern blotting using a novel “isoform A” possessing a C2 domain and a probe of the WW domain common to other isoforms, revealed the tissue-specific expression of Nedd4L gene transcription products, especially of isoform A, the expression of which was localized in the kidneys, lung, brain, and heart [We investigated the ahl rats . We initnd heart .Nedd4L expression in normal DR rats, but did not alter Nedd4L expression in salt-sensitive DS rats. The trend of Nedd4L expression determined by in situ hybridization was similar in kidney tissues [NEDD4L expression was restricted to late DCT and lower CNT, CCD, and collecting renal tubules, as in rats, and this distribution was consistent with that of ENaC expression, suggesting that NEDD4L is involved in the regulation of ENaC expression [Changes in salt loading were examined by quantitative PCR and in situ hybridization. The results showed that salt intake increased  tissues . A studypression .NEDD4L both with and without the C2 domain. A significant reduction in the amiloride-sensitive ENaC current by isoform II and III with ENaC cRNA injected was observed, when either isoform II or III cRNAs were injected into the Xenopus oocyte. The current was significantly restored when isoform I cRNAs were coinjected with other isoforms, indicating the dominant negative effects of the isoform I product against the downregulation of cell surface ENaC by isoforms II and III. Such interactions might abnormally increase sodium reabsorption in ASDN, suggesting that the human NEDD4L gene, especially the evolutionarily new isoform I, is a candidate gene for salt-sensitive hypertension in human [Subsequently, we performed in vitro functional experiments using heterologous gene expression systems using Xenopus oocytes. Initially, we successfully cloned three isoforms of human in human ,54,60,61Nedd4-2 (NEDD4L in human) using knockout mice and examined the detailed phenotypic manifestations to determine the critical roles of Nedd4-2 in salt-sensitive hypertension. First, we determined genetic variations of mice Nedd4-2 using in silico exploration of the transcriptional start site of the gene. As reported previously, both human and rodent NEDD4L/Nedd4-2 showed molecular diversity, with and without a C2 domain in their N-terminal. The NEDD4L/Nedd4-2 isoforms with a C2 domain were hypothesized to be related closely to ubiquitination of ENaCs. In silico gene identification analysis of mice Nedd4-2 C2 domain coding exon was performed using the BLAST and cross-match program. Then, the sequences of EST and cDNA in the GenBank database (nr) were aligned and compared with the sequence of mice Nedd4-2 exon 4, which was already known as the transcription start site of mice Nedd4-2 without a C2 domain. Cross-match analyses were performed repeatedly between the cDNA/EST sequences and genomic sequences, and the results were parsed with chromosome 18 of C57Bl6/J, to form a consistent assembly of EST, cDNA, and a genomic sequence. Thus, the expressed sequence tag and cDNA alignment and the results of the in silico bioinformatic database analysis of Nedd4-2 on mouse chromosome 18q showed a newly identified exon2 coding the C2 domain of mice Nedd4-2 [Lastly, we decided to develop genetic engineered model of salt-sensitive hypertension such as  Nedd4-2 .Nedd4-2 C2 KO mice did not show any phenotypic differences, according to blood pressure, urinary sodium excretion, urinary osmotic pressure, urine volume, and water intake. However, under high oral salt intake, Nedd4-2 C2 KO mice showed blood pressure elevation with reduced urinary sodium excretion [Nedd4-2 gene. These results suggested that ENaC itself act as a “sensor” of intra-tubular salt with intra-tubular epithelial “excite-transcription coupling”, which might regulate ENaC gene expressions paradoxically without the NEDD4-2 C2 domain [Subsequently, we started to create a targeting vector for the newly discovered “exon 2”, which codes the C2 domain of NEDD4-2 and generated genetically engineered mice without a C2 domain of NEDD4-2 . Mice wixcretion . Detailexcretion . ENaC mR2 domain . This coNedd4-2 C2 KO mice. Finally, us and other authors [Nedd4-2 C2 KO mice showed eplerenone-resistant salt-sensitive hypertension [Nedd4-2 isoform with C2 domain for cardio-renal association, with regards to target-organ damages of the subjects with salt-sensitive hypertension.As NEDD4-2 with C2 domain is expressed not only in renal tubular cells but also in cardiomyocytes working as ubiquitinating enzyme for voltage-gated sodium-channel, SCN5a, we performed subsequent experiments examining the electrophysiological change of the heart after myocardial infarction, in mice. Additionally, to determine detailed characteristics of salt-sensitive hypertension of the mice, we tried a mineral corticoid receptor antagonist treatment for  authors ,64, havertension  and enhartension , suggestNEDD4L gene [NEDD4L gene C2 domain expressed along urinary tubules [in vivo, resulting in a higher oral salt intake suppressing sodium excretion in urine and elevated blood pressures, the pathophysiology of which is called “salt-sensitive” [In summary, from earlier detailed re-sequencing experiments for the human D4L gene , we perfD4L gene . Other e tubules , heterol tubules , the dis tubules , and a g tubules ,54,55. Unsitive” . In acconsitive” ,66.NEDD4L were derived from a collaboration between 2000 and 2003 with Professor Robert Weiss at the Eccles Institute of Human Genetics at the University of Utah. Professor Robert Weiss is a bioinformatics expert who also participated in the Human Genome project, and some of the results described herein were derived from in silico analyses. This is an example of the ability to discover valuable information from vast databases using a desktop computer.The author’s studies of NEDD4L gene produces multiple transcripts. Thus, many genes might generate and maintain biological diversity via transcriptional diversity. The NEDD4L gene is also notable as an example of transcriptome analysis after genomic and proteomic analyses.The Human Genome project was completed in 2003 which provided many insights, such as the fact that only 30,000 to 40,000 genes are needed to create diverse humans, instead of the original prediction of 100,000 to 120,000 genes. We found that one NEDD4L might be the next candidate gene for salt-sensitive hypertension, following angiotensinogen. Although there is currently insufficient evidence to fully support this hypothesis, when the amount of information available about the NEDD4L gene becomes similar to that of the angiotensinogen gene, the significance of the NEDD4L gene in the onset and progression of salt-sensitive hypertension will surely be further clarified by investigating its functions in vitro, in vivo, and clinically. Furthermore, therapeutic and diagnostic applications targeting the NEDD4L gene should become available in the foreseeable future, including de-ubiquitination enzyme activating agents for antagonizing human NEDD4L.Human