Wrapping of DNA into nucleosomes restricts accessibility to the DNA and may affect the recognition of binding motifs by transcription factors. A certain class of transcription factors, the pioneer transcription factors, can specifically recognize their DNA binding sites on nucleosomes, may initiate local chromatin opening and facilitate the binding of co-factors in a cell-type-specific manner. For the majority of human pioneer transcription factors, the locations of their binding sites, mechanisms of binding and regulation remain unknown. We have developed a computational method to predict the cell-type-specific ability of transcription factors to bind nucleosomes by integrating ChIP-seq, MNase-seq and DNase-seq data with details of nucleosome structure. We have demonstrated the ability of our approach in discriminating pioneer from canonical transcription factors and predicted new potential pioneer transcription factors in H1, K562, HepG2 and HeLa cell lines. Lastly, we systemically analyzed the interaction modes between various pioneer transcription factors and detected several clusters of distinctive binding sites on nucleosomal DNA. Open chromatin is thought to comprise actively transcribed genes, while compact chromatin contains repressed genes. However, many recent observations point to a limited association between DNA accessibility, chromatin compaction and gene transcription at the global genomic scale. Indeed, rapid transcription activation may occur by relatively small changes of DNA solvent exposure, and localized nucleosomal array structures can be dynamically exposed without the large-scale chromatin rearrangements5. Nucleosomes represent the basic subunits of chromatin structure and function. They comprise a histone octamer of four types of core histones of two copies each, and ~147 base pairs of DNA wrapped around them6. Wrapping of DNA into nucleosomes inherently restricts DNA accessibility and the recognition of binding motifs by transcription factors. Intrinsically disordered histone tails flank histone core domains and may also modulate DNA accessibility by forming transient interactions with the nucleosomal and linker DNA7. The control of the DNA accessibility at nucleosomal and subnucleosomal scales is of major importance in understanding of how certain transcription factors can target compact chromatin to induce transcription activation or repression10.In eukaryotic cell, DNA is packaged in the form of chromatin yet, it should be dynamically accessed during transcription and replication processes at high spatiotemporal precision12. Although nucleosomes generally hinder the binding of transcription factors (TFs), a certain class, so-called pioneer transcription factors (PTF), can specifically recognize their binding sites on nucleosomal DNA, in some cases initiating local chromatin opening and facilitating subsequent binding of other co-factors in a cell-type specific manner12. Several studies have revealed the critical roles of pioneer transcription factors in mediating the cell-type specific gene expression and establishment of cell lineage reprogramming13.The differentiation of cells into different lineages occurs through chromatin reprogramming, involving the cooperative behavior of various transcription factors18. Using the NCAP–SELEX approach, a recent study has characterized the interaction modes between nucleosomes and 220 transcription factors14. Another high-throughput protein microarray study of 593 human TFs systematically identified the structural features of TFs binding with nucleosomes 19. It has been revealed that the vast majority of TFs preferably bind naked DNA instead of nucleosomal DNA at physiological concentrations, whereas certain transcription factors specifically target nucleosomes at different locations and orientations16. Moreover, several structures of pioneer transacription factors in complex with nucleosomes have recently been resolved23. Despite significant advances in recent experimental approaches, the interaction modes of most transcription factors with nucleosomes remain obscure and their cell-type-specific pioneer activities are largely unknown. The development of new computational approaches can improve our understanding of binding properties of various TFs with nucleosomes, thereby helping to identify novel pioneer transcription factors.Significant efforts have been made to characterize the interaction landscape between various TFs and nucleosomese.g. ChIP–seq, ATAC-seq, Dnase-seq and MNase-seq data24), which allows us to gain insights into the chromatin structure and details of epigenetic binding events. The rapid growth of such data sets has further stimulated the development of computational methods to characterize cell-type specific transcription factors 28. Several machine learning models have been proposed to predict transcription factor binding sites and identify sequence context features critical for TF binding29. In addition, gene regulatory network-based approaches have helped identify the key transcription factors in cell fate determination31. Despite the success of computational methods in genome-wide prediction of binding sites of canonical transcription factors, the locations of pioneer transcription factors’ binding sites, mechanisms of binding and regulation have not been systematically explored.Through the advances in high-throughput sequencing techniques, a large volume of data has been generated  with the data on DNA binding events, available through ChIP-seq and other methods, can reveal the interplay between transcription factor binding and nucleosome positioning, providing insights into the mechanisms of pioneer transcription factor interactions with nucleosomes and their ability to modulate chromatin accessibility36. Then, the downloaded fastq files were processed using the mnaseseq pipeline from nf-core (https://github.com/nf-core/mnaseseq)37, a recently developed bioinformatics pipeline for MNase seq data analysis. Adapter trimming of sequencing reads was performed with Trim Galore. Then, adapter-trimmed reads were mapped to the reference genome using Burrows-Wheeler Aligner (BWA)38. Human genome GRCh37 was used as a reference genome for reads mapping and the maximum number of mismatches in alignment was set to 4. The minimum and maximum insert sizes for filtering of mono-nucleosome paired-end reads were set to 120 and 180 base pairs. Duplicate reads were marked using Picard MarkDuplicates command (http://broadinstitute.github.io/picard/) and discarded from the analysis to avoid PCR duplication artifacts (less than 10% of reads were duplicated). Read libraries of replicates from the same experiment condition were merged into the analysis. Next, the BAM sequence alignment files were converted into BED format using bedtools v2.30.039. The aligned reads with fragment sizes from 146 to 148 base pairs were selected for mapping of the representative dyad positions .The overall workflow of our computational framework is shown in 40 and dyad positions were determined as midpoints of mapped MNase-seq reads41. Namely, we used a triweight kernel 40.To determine the representative dyad position of nucleosomes, we implemented and modified a previously developed nucleosome mapping protocolfunction  and the Here 42 and the minimum distance between the neighboring local maxima was set to 150 base pairs with “find local-extrema-maxima-min-sep=150”. Then, for every 60-bp window centered at each local maxima, one representative dyad location was determined as the dyad location with the highest number of dyad counts in this interval. Other dyad positions within the same 60-bp interval were discarded. If two or more dyad positions had the same dyad counts within the same interval, the dyad position located closest to the local maximum of the smoothed counts was selected as the representative dyad. Last, nucleosome regions (NRs) were determined as genomic regions centered at the representative dyad positions and flanked by 73 bp segments on each side.Next, we identified the genomic locations with the local maximum values of the smoothed dyad counts using bwtool44. The open chromatin regions were identified as genomic regions centered at narrow peaks and flanked by 1000 bp segments on each side. The active enhancer regions were defined as the open chromatin regions overlapped with both H3K27ac and H3K4me1 ChIP-seq narrow peaks. The nucleosome-depleted regions (NDRs) represent genomic regions free of nucleosomes and were identified as genomic regions not covered by any mono-nucleosome fragments (120 to 180 base pairs length) in MNase-seq data in all replicate experiments.To map the open chromatin and enhancer regions, we used DNase-seq and H3K27ac and H3K4me1 ChIP-seq data from the following five human cell lines  are defined as those that have less than 20% overlap between open regions in H1 embryonic cell line and open regions in one of differentiated cell lines so that these regions are closed in one cell line type and open in another45  and YY/RR  dinucleotides on NRs. Then, we aligned NRs by superimposing their dyad positions and computed the frequency of observed dinucleotides at each location of nucleosomal DNA (as a function of distance in base pairs from the nucleosome dyad). As a result, we observed pronounced dinucleotide patterns at specific nucleosomal DNA positions for all cell lines which are indicative of the high qulity of the data and nucleosome mapping procedure .43 with the MNase-seq data for the same human cell lines   . In case“motif profiles”. To do that, we first mapped ChIP-seq TF motifs for a given TF to the closest nucleosome dyad position and then aligned all dyad positions and corresponding ChIP-seq TF motifs. For each TF binding motif, we considered sequences in both strands of DNA. Then, we counted the number of base pairs from different TF binding motifs that mapped at each location of nucleosomal DNA and flanking DNA regions (+/− 1000 bp) around the dyad.Next, we calculated the transcription factor binding motif profiles or briefly Pioneer transcription factors (PTFs) can engage nucleosomal DNA, while truly canonical TFs cannot bind to nucleosomes. To predict PTFs, we calculated the binding motif enrichment of different TFs on NRs compared to NDRs. Namely, we counted a number of base pairs of ChIP-seq TF motifs overlapped with the NR and NDR regions and constructed the following contingency table . We thenhttps://egg2.wustl.edu/roadmap/data/byDataType/rna/expression/) and identified TFs which are highly expressed in each cell line .Here Receiver operating curves (ROC) and precision-recall (PR) curve analyses were further performed to evaluate the power of enrichment scores in discriminating pioneer transcription factors from other transcription factors. 32 known pioneer transcription factors from the literature (which were included in our list of 225 TFs) were used as positives  and otheTo identify the prevalent interaction modes of various TFs with nucleosomes, we have performed clustering of TF binding motif profiles. Prior to clustering, we filtered out potentially low-quality binding profiles using the following criteria. First, we removed under-represented TFs with the total genome-wide cumulative sum of ChiP-seq TF motifs on nucleosome regions of less than 500 base pairs. Second, due to the two-fold symmetry of DNA in nucleosome structures, ChiP-seq TF motifs on DNA complementary plus and minus strands should be structurally superimposed if the nucleosome structure is rotated by 180 degrees. If we are analyzing relative large number of binding motifs on both DNA strands, binding motif profiles should be symmetrical with respect to the nucleosome dyad. Therefore, we calculated the Pearson correlation coefficient of motif profiles between two symmetrical nucleosomal halves  for each TF and removed those with Pearson correlation coefficient values less than 0.4.48. Then, the projected data were subjected to k-medoids clustering using the pam function from the R package with the optimal number of clusters equal to six  and projected all profiles onto two dimensions using the Rtsne function from the R packagel to six . The silSince Micrococcal Nuclease has a sequence bias and cleaves DNA upstream of A or T more efficiently than of G or C nucleotides, a certain nucleotide preference exists around the ends of nucleosomal DNA reads produced by the MNase-seq experiments. It may potentially bias the TF binding profiles near the nucleosomal DNA ends. Therefore, in our analysis, we excluded regions near the nucleosomal DNA ends. To identify binding modes between TFs and nucleosomes, we clustered binding motif profiles of different TFs centered at the nucleosomal dyad locations (+/− 60 base pair from dyad) using k-medoids.49. Therefore, we hypothesized that DNA binding sites of pioneer transcription factors should not be depleted and, in some cases, should be enriched on nucleosome regions (NRs). At the same time, those canonical transcription factors that can preferentially bind to the naked DNA should exhibit the depletion of binding sites on nucleosome regions and enrichment in nucleosome-free regions. To quantitively assess these trends, we have performed binding motif enrichment analysis for each of the 225 transcription factors and calculated the motif enrichment scores  as positives . As can be seen in 53 were strongly depleted at nucleosomes  showed significantly lower enrichment scores compared to other TFs . Yamanakleosomes . It has entity56 .However, when we repeated our analysis by redefining differentially open regions as those closed in differentiated cell lines and open in H1 embryonic cell line, then ESSRB and Yamanaka pioneer transcription factor POU5F1 (OCT4) showed significantly higher enrichment scores . This codifferentially open and conserved open chromatin regions but the performance in classifications of PTF was not significantly affected by the threshold choice  and weak binders, which bind weakly to both naked DNA and nucleosomal DNA (cluster 2). Cluster 3 consists of strong binders which bind strongly to naked DNA but weakly to nucleosomal DNA. We found that TFs from their cluster 3 had the lowest enrichment scores although this trend was not statistically significant because of a lack of the data in this cluster. As to TFs in cluster 1 and 2, half of them had binding sites enriched on nucleosome regions  which correspond to strong nucleosome binders, and another half had binding sites enriched on nucleosome depleted regions  experiments on human TFs which systematically assessed transcription factor binding preferences to nucleosomal DNA versus naked DNA regions .57 and enrichment analysis on all open chromatin regions, we have identified 35 TFs in H1, K562, HepG2 and HeLa cell lines (no expression data was available for MCF-7) that ranked highly in the enrichment analysis and were also highly expressed in the corresponding cell lines  box protein that could bind to nucleosomes 60 and CUX1 could specifically interact with its recognition motif in a nucleosomal context 61.Next, using the enrichment score calculated based on NRs located in differentially open chromatin regions and NDRs located in conserved open chromatin regions, we identified 40 transcription factors that could act as PTFs with essential roles in cell differentiation in K562, HepG2 and HeLa cell lines. These identified TFs had their DNA binding sites significantly enriched on nucleosomal DNA of differentially open chromatin regions and were highly expressed in the corresponding cell lines . Among tThe enrichment analysis described above is the first step to estimate the propensity of transcription factor binding sites to be located on nucleosomal footprints. The enrichment analysis might have high specificity but a compromised sensititivity as it can misclassify those pioneer transcription factors that can bind to both naked DNA and nucleosomes at similar concentrations. Therefore, the next step would be to evaluate the actual locations of TF binding sites with respect to the nucleosomal dyad. To this end, we tested if there is a significant association between the binding motif profiles see  and nucl58. With the exception of a few cases, none of transcription factors was identified as being pioneer in all cell types because pioneer activity is often cell-type specific. For example, CTCF was identified as having a significant correlation  between binding motif profile and nucleosome occupancy for the human embryonic stem cell line , but not for other somatic cell lines  14, binding signals were compared by calculating the mutual information (MI) content between 3-mer distributions at two non-overlapping positions of the ligand, aimed at finding if SELEX ligand may contact these positions at the same time. Since nucleosomes can form on most DNA sequences, whereas TFs bind to only a few specific sequences, the NCAP-SELEX study calculated the enriched MI (E-MI) score to separate the TF signals from nucleosome signals by limiting the MI measure to the top ten most enriched 3-mer pairs. E-MI penetration score corresponds to E-MI drop by half compared to the E-MI maximum and larger values pointed to the favorable binding to the dyad regions14. As our In NCAP-SELEX experimental analyses, to quantify the preference of PTF binding to nucleosomesnificant .65. We attempt to characterize the details of PTF binding motifs at near-single nucleotide resolution on nucleosomal DNA and performed K-medoids clustering of binding motif profiles for all 225 transcription factors  leads to the highest classification accuracy in discriminating pioneer from canonical transcription factors. This finding supports the view that TF binding to nucleosomes leads to DNA and chromatin opening and might correlate with the reprogramming potential19. Our study has also verified the known and predicted several dozens of new PTFs as nucleosome binders (The modulation of chromatin accessibility with high spatiotemporal precision is a subject of ongoing debate as it is a crucial factor in regulation of transcription, replication and DNA repair processes. The question of how chromatin accessibility is precisely modulated involves multiple aspects, including epigenetic modifications, PTF infiltration, ATP-dependent remodeling and spontaneous chromatin dynamics. There are about two thousand transcription factors in the human genome and a few hundred of them may have PTF properties. Yet, for the vast majority of human PTFs, the locations of their binding sites and mechanisms of binding and regulation remain unknown binders . These p73. Pioneer transcription factors can exploit DNA unwrapping and trap nucleosomes in a partially unwrapped states74. Transcription factor binding motifs close to the nucleosome entry-exit sites may have increased exposure but would be error-prone to capture using MNase-seq data as a nucleotide bias exists around the ends of nucleosomal DNA reads produced by the MNase-seq experiments. Moreover, as we showed recently, the degree of spontaneous DNA unwrapping from nucleosomes depends on the incorporation of histone variants, like H2A.Z75, which is, in turn, might be needed for pioneer transcription factor recruitment to promote embryonic stem cell differentiation76. The third group of limitations pertains to the nucleosome positioning in a genome, as binding dissociation constants for PTFs depend on where nucleosomes are located. Indeed, nucleosome positioning and stability is not uniform throughout the genome and depends on local nucleosomal DNA sequence, histone variant deposition and epigenetic chromatin modifications. Finally, pioneer transcription factors may exhibit multivalent binding recognizing not only DNA binding sites but also some parts of the histone core or histone tail regions. Deducing such dependencies from the experimental assays utilized in this study is very challenging, if not impossible.We should mention that our classification method and the data used in this study have certain limitations. First, interactions of PTFs with nucleosomes may depend on binding of other factors. Second, PTF binding relies on specific recognition of DNA binding sites and can be governed by nucleosome dynamics. Indeed, initial stages of binding can happen via the DNA site exposure through thermal fluctuations, leading to DNA unwrapping and the formation of DNA looping or twist defectsNucleosomes represent hub points in epigenetic signaling pathways and identifying complex epigenetic relationships at the level of single nucleosomes may yield functional insights into the mechanisms of binding and infiltration of pioneer transcription factors into chromatin during differentiation and reprogramming. Transcription factors regulate a large number of signaling pathways and their dysregulation contributes to a plethora of human diseases, including diabetes, cardiovascular diseases and many cancers. Conventional transcription factors have been used for a long time as biomarkers and drug targets, however, the targeted therapeutic potential of pioneer transcription factors is lagging. To fill this gap, integrative approaches using large-scale low- or medium-resolution data with precise molecular modeling or protein-protein docking may provide the required detailed characterization of many predicted PTFs in the future.Supplement 1


Transcription factors bind promoter or regulatory sequences of a gene to regulate its rate of transcription. However, they are also detected in anucleated platelets. The transcription factors RUNX1, GATA1, STAT3, NFκB, and PPAR have been widely reported to play key roles in the pathophysiology of platelet hyper-reactivity, thrombosis, and atherosclerosis. These non-transcriptional activities are independent of gene transcription or protein synthesis but their underlying mechanisms of action remain poorly defined. Genetic and acquired defects in these transcription factors are associated with the production of platelet microvesicles that are known to initiate and propagate coagulation and to promote thrombosis. In this review, we summarize recent developments in the study of transcription factors in platelet generation, reactivity, and production of microvesicles, with a focus on non-transcriptional activities of selected transcription factors. This trParadoxically, multiple transcription factors have been reported to express and be active in platelets . An obviPlatelets circulate along the vessel wall and act to stop bleeding at sites of vessel injury. This hemostatic process requires multiple ligand-receptor interactions to tether, activate, and aggregate platelets. The tightly controlled platelet activation and aggregation that occurs at the site of vascular injury during hemostasis can become dysregulated in pathological conditions, promoting thrombosis and inflammation. For example, platelets promote arterial thrombosis or thromboembolism when activated either on the surface of a ruptured atherosclerotic plaque or by pathological levels of high fluid shear stress in the area of arterial stenosis, leading to acute thrombotic events such as ischemic stroke and myocardial infarction . Emergin22.1In 1969, Weiss, et al. identified a family with an autosomal dominant inherited thrombocytopenia, caused primarily by decreased dense granule contents . A heterRUNX1 deficiency can result in pallidin dysregulation and deficient dense granules in platelets  and IIB (MYH10) genes, which encode non-muscle myosin II heavy chains; RUNX1 mutations are associated with dysregulated expression of MYH10 in platelets ; and the2.2GATA-binding protein 1 (GATA1) is a transcription factor that contains two zinc finger domains: a C-terminal zinc finger that binds the (T/A) GATA(A/G) motif of DNA and an N-terminal zinc finger that is required for stabilizing the C-terminal structure and also interacts with a nuclear co-factor protein called friend for GATA1 (FOG1), which stabilizes GATA1 binding , 37. GATEmbryonic stem cells from GATA1-deficient mice are smaller and show low expression of megakaryocytic markers, but have a high rate of proliferation . Complem-deficient mice  blocks collagen-induced platelet reactivity in a dose-dependent manner by targeting STAT3  and dimerized in platelets stimulated with thrombopoietin , 50, sugng STAT3 . Consistng STAT3 . These f3.2Nuclear factor kappa β (NFκB) is a well-defined redox-sensitive transcription factor that regulates the immune response and inflammation by controlling the expression of multiple genes activated by inflammatory mediators –57. Bloc3.3The peroxisome proliferator-activated receptors (PPARs) are ligand-activated receptors in the nuclear hormone receptor family. They contain three subtypes , which are essential in the regulation of cell differentiation, development, and metabolism –72. All While transcription factors are critically involved in megakaryocyte development and platelet production, they may also regulate platelet reactivity to conventional and specific platelet agonists (4Extracellular vesicles (EVs) are shed membrane fragments, intracellular organelles, and nuclear components from cells undergoing active microvesiculation –84 or ap5de novo transcription, but a number of transcription factors are found in platelets and may have non-transcriptional activities that regulate platelet function. Transferring transcription factors between platelets and target cells through platelet EVs could also be a novel regulatory mechanism of cell-cell communications and a potential therapeutic target for a variety of pathologies.Platelets lack a nucleus and HY and YL performed the literature search and compiled all the information from the researched articles and wrote the manuscript. ZZ, J-FD and JZ formulated, proposed, guided and wrote the manuscript. All authors contributed to the article and approved the submitted version.


This study shows that signalling processes prime many enhancer elements at the chromatin level before the expression of their associated genes and that priming is essential for developmental stage-specific dynamic gene activation. Tissue-specific gene regulation during development involves the interplay between transcription factors and epigenetic regulators binding to enhancer and promoter elements. The pattern of active enhancers defines the cellular differentiation state. However, developmental gene activation involves a previous step called chromatin priming which is not fully understood. We recently developed a genome-wide functional assay that allowed us to functionally identify enhancer elements integrated in chromatin regulating five stages spanning the in vitro differentiation of embryonic stem cells to blood. We also measured global chromatin accessibility, histone modifications, and transcription factor binding. The integration of these data identified and characterised cis-regulatory elements which become activated before the onset of gene expression, some of which are primed in a signalling-dependent fashion. Deletion of such a priming element leads to a delay in the up-regulation of its associated gene in development. Our work uncovers the details of a complex network of regulatory interactions with the dynamics of early chromatin opening being at the heart of dynamic tissue-specific gene expression control. The development of multicellular organisms requires the activation of different gene batteries which specify the identity of each individual cell type. Such shifts in cellular identity are driven by shifts in the gene regulatory network (GRN) consisting of transcription factors (TFs) binding to the enhancers and promoters of their target genes resulting in the alteration of gene expression . Such geChromatin priming at specific cis-regulatory elements was first observed in genes specific for terminally differentiated cells which are not expressed in multipotent blood progenitors  to blood . In the Supplemental Data 1 and Supplemental Data 2). After filtering  ESCiltering , our metiltering   which genes were associated with primed enhancers, and (ii) how these genes were organised within a wider network of expressed TF genes. To this end, we identified genes inactive at one developmental stage that were associated with primed enhancers and linked them to expressed TFs via their binding motifs as described in RUNX1 locus (indicated by an arrow) was not expressed in ES cells, but was already primed by a cis-element containing a SMAD motif (note the arrow from SMAD to RUNX1). At the HB stage, Runx1 is barely expressed, but is now linked to genes activated by elements containing RUNX1 motifs. At the HB to HE and HE to HP transitions, Runx1 is linked to different primed genes which then become activated, some of them, such as Gfi1, by the direct action of RUNX1  motif use of primed elements changes during differentiation, as it does for active enhancers; (ii) that eRNA presence is a hallmark of enhancers associated with active genes; and (iii) that the polycomb-associated H3K27me3 mark is associated with the primed enhancer state only in ESCs but not in differentiating cells.Runx1. We also showed that VEGF needs to be withdrawn to enable the activation of hematopoietic enhancers, notably those of Runx1 which is essential for the expression of blood cell-specific genes such as Spi1 (PU.1)  performed with cells isolated from a serum-free ES cell differentiation system which allowed us to examine the effect of specific cytokines on enhancer activation . We foun1 (PU.1) . The presolation . We thersolation  to identsolation , as outlsolation . 34 geneis, PU.1 . This geis, PU.1 . Other gThese experiments demonstrate that extracellular signalling, in this case, VEGF signalling, is truly cell-fate instructive by impacting on primed enhancer elements via modulating the expression and activity of TF genes. Its presence or absence decides the fate of endothelial cells with its absence at the hemogenic endothelium stage being essential for the up-regulation of hematopoietic genes.Hand1 cardiovascular gene and the endothelial-specific gene Kdr encoding FLK1 (VEGF receptor) which we could link to their respective promoters using CHi-C data . Both genes were strongly up-regulated at the HB stage, but the stimulatory activity of their priming elements in isolation at this stage was barely measurable  of blast culture differentiation from FLK1+ HB cells . However, the actual developmental trajectory was not affected as at later stages of differentiation the proportion of cells committed to the hematopoietic fate (HP) within the population slowly catched up once other cis-elements became activated. Over time, a threshold of activation was crossed and the cells were able to transit into HP cells, most likely because of the accumulation of RUNX1 and other hematopoietic factors in the cell in the absence of cell division. In summary, our data demonstrate the importance of a priming element for correct cell stage-specific gene activation.To address this issue in more detail in the context of the entire HB cells  togetherHB cells . The devHB cells . In the  +340 kb . The 5′  +340 kb . Thereafed state . The remRUNX1 or Mecom) or a closed promoter (as in Dok2). When examined in isolation and during differentiation, priming elements do not always have stimulatory activity when assayed in isolation (as in KDR), and some are highly active and are then being repressed at a later stage (as in Mecom) and others acquiring strong enhancer activity at a late differentiation stage (such as Spi1). This complex interplay of different cis-regulatory elements is responsible for the timing of gene activation within a specific GRN.Our detailed analysis of the timing of gene activation at the level of chromatin highlights a number of important principles in how cis-regulatory elements activate developmentally controlled genes, summarised in The work described here also addressed the nature of the molecular difference between priming enhancers and enhancers associated with actively transcribed genes. Comparisons of multiple features of the two enhancer classes at different developmental stages revealed essentially no difference : All eleWe have also shown that in HP cells, 25% of our functionally identified enhancer elements are bound by RNA-polymerase II . EnhanceSpi1 (PU.1), Csf3r or BC11a at the HP stage in the absence of VEGF. An important VEGF-responsive gene is Mecom which is transcriptionally activated at the HE stage and is a major regulator for endothelial cell development . It has been shown that Mecom expression is crucial for the expression of VEGFR2 which is a major receptor for VEGF signaling (Spi1 (PU.1) −14 URE was mutated by eliminating a PU.1 autoregulatory binding site; up-regulation of gene expression was delayed until additional enhancers were activated  and replated onto gelatinised tissue culture plates at 1.2 × 104 per cm2.A HM-1 targeting ES cell line  was cultg for 5 min before being resuspended at 2.5 × 104/ml in I.V.D media  and then plated onto non-adherent dishes . The cells were then incubated at 37°C, 5% CO2 for 3 d to allow for the formation of floating embryoid bodies (EBs).Hematopoietic in vitro differentiation (I.V.D) using serum was performed as described in The floating EBs were then transferred to 50 ml centrifuge tubes and the EBs were allowed to settle by gravity over 5 min. The EBs were then washed with PBS before being allowed to settle again over 5 min. The EBs were then dissociated with TrypLE Express  with gentle pipetting.The dissociated cells were sorted for the FLK1 surface marker (forming the population referred to as the HB). The dissociated cells were incubated with a FLK1 biotin-coupled antibody (1:200) , and then with MACS anti-biotin beads . FLK1-expressing cells were sorted for by separation using a MACS LS column . FLK1-expressing HB cells were then taken for flow cytometry analysis and sequencing experiments and further differentiation in blast culture into HE1, HE2, and HP populations.4 cells per cm2 in Blast media  and were left to incubate for 2.5 d at 37°C, 5% CO2. HE1, HE2, and HP populations were FACS-sorted after incubation with KIT-APC (1:100) , TIE2-PE (1:200) , and CD41-PECY7 (1:100)  antibodies into HE1 , HE2 , and HP  populations.To achieve this FLK1+ HB cells were plated onto gelatine-coated tissue culture flasks at a concentration of 1.6 × 10We performed additional next-generation sequencing on library material previously used in The enhancer screen fragments were then further filtered, first against the negative control libraries (produced from WT cells with no enhancer reporter cassette integration) to remove PCR artefacts, then by the union of all plasmid library fragments to further remove PCR artefacts by ensuring the identical fragment was in the original plasmid libraries. Finally, the fragments were filtered for their presence in open chromatin by filtering against ATAC-Seq peaks. ATAC-Seq peaks were then assigned enhancer-positive status if they overlapped with at least 1 fragment from the enhancer screen-positive libraries. Enhancer activity levels were assigned by counting the number of high- and low-activity fragments (based on FACS sorting) at each enhancer-positive ATAC site, normalising against the total number of fragments and then assigning an activity state based on the ratio of high to low fragments at each site.Nuclear RNA-enriched RNA-seq libraries were produced using RNA extracted with the Cytoplasmic and Nuclear RNA Purification kit  based on the manufacturer’s instructions. RNA-Seq libraries were then prepared using the NEBNext Ultra II Directional RNA Library Prep Kit  total RNA with rRNA depletion protocol using 100 ng of input RNA. Libraries were sequenced using a NovaSeq 6000 SP 200 cycle flow cell.RNA-seq reads were first filtered using RiboDetector (v0.2.7) using optGSM2753058, runs: SRR5972842, SRR5972842, SRR5972842, SRR5972842, SRR5972842) and from HPC7 from GSM2702943 and GSM2702944, runs SRR5826938, SRR5826939) or where this was not possible, by closest gene, as previously published (P-adjusted < 0.05) twofold up-regulation across the stages with enhancer activity and were not expressed in the initial stage (CPM < 9). Gene ontology analysis of genes with primed enhancers was carried out using DAVID  testing positive for enhancer activity were linked to genes using published promoter capture HiC data from ES cells and HPC7 cell .Validation of the Mecom primed enhancer by removal of the enhancer region by CRISPR/Cas9. A guide sequence was designed to target each end of the enhancer region (CACCGGAACAGTAGCCTATCTGTCC and CACCGGGAAACCTACTGTCCCAGGA) (chr3:30353652-30354669). The guide sequences were purchased as DNA oligos and cloned into the PX458 Cs9 ad sgRNA expression vector by digestion with the BbsI restriction enzyme (NEB) and ligation using T4 Ligase (NEB). (pSpCas9(BB) − 2A-GFP (PX458) was a gift from Feng Zhang  with the P3 Primary Cell X kit . ES cell clones were picked and screened for successful homologous CRISPR by PCR using primers designed outside of the CRISPR guide region . The positive clone was then confirmed by Sanger sequencing showing removal of the intended enhancer region.GSE69101, GSE143460, GSE126496 and GSE79320). The percentage contribution of each feature to each enhancer type was then calculated and plotted.To study the features associated with enhancer elements, we first performed a TF binding motif searching using Homer annotatePeaks.pl  with thePreprint). In brief, networks of priming elements were made using the genomic coordinates of primed enhancers at each stage transition. The positions for each TF binding motif were retrieved from these peak sets using the annotatePeaks.pl function in Homer as described in the “Enhancer Feature Comparrsion” method above. We then added coordinates for the promoters of a wide range of transcription factors and gene expression values for each differentiation stage obtained from RNA-seq experiments. These data were then used to construct a network where transcription factor genes and their target genes are represented as nodes, with the presence of a TF-binding motif in a primed enhancer targeting a gene shown as an edge. The resulting network which shows which TFs have potential to regulate which primed enhancers (shown as their associated gene) was then plotted using Cytoscape (GRNs were constructed as previously described using custom Python scripts, available from ytoscape .GSE198775 and GSE244338. The genome-wide data generated in this study have also been provided as a UCSC Genome Browser Track-Hub and interactive versions of the networks are available from http://www.genomic-data.com/maytum2023.The Genome-wide data generated in this study have been deposited in the Gene Expression Omnibus database under accession codes


Dear Editor,1 However, the relative contributions of the deposition and removal of H3K4 methylation to transcriptional control remain unclear. We found that rapid depletion of core COMPASS subunits led to a dynamic turnover of H3K4me2 and H3K4me3 that is mediated by KDM5 demethylases. Depleting H3K4me2 and H3K4me3 did not affect TFIID recruitment or initiating RNA polymerase II (Pol II) but instead reduced levels of paused Pol II, which could be attributed to increased enrichment of the Integrator-PP2A complex (INTAC).Modifications of histones are intricately linked with the regulation of gene expression, having demonstrated roles in various physiological processes and pathogenesis. Methylation of histone H3 lysine 4 (H3K4) implemented by the COMPASS family is enriched at promoters and associated cis-regulatory elements, with H3K4 trimethylation (H3K4me3) being a hallmark of active gene promoters.1 Among the four shared COMPASS subunits, RBBP5 is essential for complex assembly, nucleosome recognition, and catalytic activation, whereas DPY30 flexibly associates with, and stimulates, H3K4 methylation  into the N-terminus of the endogenous Rbbp5 and Dpy30 loci in mouse embryonic stem cells (mESCs) to allow rapid disruption of COMPASS  in dTAG-treated mRBBP5-dTAG mESCs at different time points. After 6 h of dTAG treatment, all significantly changed peaks of H3K4me3, and most significantly changed peaks of H3K4me2, had decreased occupancy  compared to DPY30 , in accordance with the notion of RBBP5 being more central for COMPASS functions   Fig. , indicat–4) Fig.  and Pol –4) Fig.  in mRBBP6 and TAF1, a scaffold subunit of TFIID,7 were selected for ChIP-Rx to quantify TFIID occupancy genome wide. BAP18, another reader protein of H3K4me3 was included as a control.8 Even after 24 h dTAG-induced RBBP5 degradation, the protein levels and chromatin occupancies of TAF3 or TAF1 exhibited no apparent alterations compared with DMSO-treated cells  assembly factor TFIID. Therefore, we sought to determine whether the reduced Pol II occupancy at promoters in COMPASS-disrupted cells can be ascribed to impaired recruitment of TFIID. TAF3, a subunit of TFIID, that directly interacts with H3K4me3,lls Fig. . Contraslls Fig. . We concPol II ChIP-seq signal reflects a combination of non-engaged polymerases within PIC and transcriptionally engaged but transiently paused Pol II, with the transition from PIC to pausing complex requiring the translocase activity of XPB/TFIIH Fig. . To deteTo further assess Pol II in the paused state, we conducted spike-in normalized precision run-on sequencing (PRO-seq) that specifically measures the engaged, paused polymerases but not the PIC form of Pol II. The level of PRO-seq signal decreased profoundly in RBBP5-depleted mESCs in both sense and antisense transcription Fig. . These c10 As measured by ChIP-Rx, SPT5 occupancy was diminished upon dTAG-induced RBBP5 degradation and reduction of H3K4me2/3 and Pol II at promoters (Fig. Paused but not initiating Pol II is tightly associated with SPT5, with SPT5 both stabilizing the paused form of Pol II and subsequently traveling with Pol II after being released into productive elongation.ers Fig. . As illuers Fig. . These rWe next sought to scrutinize the fate of paused Pol II loss upon RBBP5 and subsequent H3K4me2/3 depletion. Plotting Pol II occupancy from TSS to TES revealed that RBBP5 degradation led to decreased Pol II levels within gene bodies (Supplementary information, Fig. 11 As the dual-enzymatic INTAC complex is mainly responsible for premature termination at promoters, we measured its occupancy using ChIP-Rx of INTS5, a scaffold subunit of the “Shoulder” module that recruits the phosphatase module, and INTS11, the catalytic subunit of the RNA endonuclease module. In contrast to the reduced levels of paused Pol II and its associating factor SPT5, the binding of INTS5 and INTS11 showed a slight increase following RBBP5 depletion (Fig. 13 These data suggest that the observed decrease in paused Pol II upon H3K4 methylation loss is through premature termination by INTAC.The bulk levels of Pol II as assessed by western blotting remained unaltered following RBBP5 depletion, implying that Pol II loss from chromatin is unlikely a result of polymerase protein degradation (Supplementary information, Fig. ion Fig. . Using pion Fig.  or SPT5 ion Fig. , indicatWe next conducted spike-in normalized RNA-seq to examine impact of H3K4 methylation reduction on gene expression. Upon RBBP5 degradation, we observed that the number of significantly downregulated genes was four times as many as significantly upregulated genes Fig. . Moreove11 Most of the mechanistic studies of pausing have been focused on several Pol II-bound pausing regulatory factors, including but not limited to NELF, DSIF, PAF1C and INTAC.15 Our findings lead us to propose a causal role of H3K4me2/3 in retaining paused Pol II at promoters. Importantly, the loss of the transcriptionally pausing form of Pol II following reductions in H3K4me2/3 levels is accompanied by a further decline in elongating polymerases and the subsequent transcriptional downregulation, suggesting that Pol II in the transcriptionally paused state is terminated prematurely rather than being released into productive transcription elongation. Supporting this notion, the loss of H3K4me2/3 induces a relative enrichment of promoter-bound INTAC complex, which has recently been found to be a major regulator of premature termination in higher organisms.12 However, it is still unclear whether H3K4me2/3 regulate pausing directly through modulating chromatin structure or by recruiting effector proteins implicated in paused Pol II stability. In conclusion, the studies presented here identify, while raising new questions about, a previously unrecognized interplay between histone modifying enzymes and the regulation of transcription in metazoans.In summary, using rapid degradation systems, we found that dynamic deposition and removal of histone H3K4 methylation modulates the stability of RNA polymerase II pausing. Paused Pol II dwells at promoters for varying lengths of time, ranging from seconds to minutes, implying that the stability of pausing is subject to dynamic regulation.Supplementary information


Eukaryotic cells can rapidly adjust their transcriptional profile in response to molecular needs. Such dynamic regulation is, in part, achieved through epigenetic modifications and selective incorporation of histone variants into chromatin. H3.3 is the ancestral H3 variant with key roles in regulating chromatin states and transcription. Although H3.3 has been well studied in metazoans, information regarding the assembly of H3.3 onto chromatin and its possible role in transcription regulation remain poorly documented outside of Opisthokonts.Tetrahymena thermophila, to investigate the dynamics of H3 variant function in evolutionarily divergent eukaryotes. Functional proteomics and immunofluorescence analyses of H3.1 and H3.3 revealed a highly conserved role for Nrp1 and Asf1 histone chaperones in nuclear influx of histones. Cac2, a putative subunit of H3.1 deposition complex CAF1, is not required for growth, whereas the expression of the putative ortholog of the H3.3-specific chaperone Hir1 is essential in Tetrahymena. Our results indicate that Cac2 and Hir1 have distinct localization patterns during different stages of the Tetrahymena life cycle and suggest that Cac2 might be dispensable for chromatin assembly. ChIP-seq experiments in growing Tetrahymena show H3.3 enrichment over the promoters, gene bodies, and transcription termination sites of highly transcribed genes. H3.3 knockout followed by RNA-seq reveals large-scale transcriptional alterations in functionally important genes.We used the nuclear dimorphic ciliate protozoan, Our results provide an evolutionary perspective on H3.3’s conserved role in maintaining the transcriptional landscape of cells and on the emergence of specialized chromatin assembly pathways.The online version contains supplementary material available at 10.1186/s13072-023-00484-9. The fundamental repeating unit of chromatin is the nucleosome which is composed of two copies each of the four core histones H2A, H2B, H3, and H4 around which about 147 bp of DNA is wrapped . W. W48]. Wrahymena  and, conTetrahymena, where the interrupted TPR2 is flanked by TPR1 and TPR3/4 /H4 are transferred through several protein complexes in the cytoplasm before their entry into the nucleus , 14. AsfTetrahymena, we engineered knock-in cell lines with tagged NRP1-FZZ and NRP1-GFP at the endogenous MAC site, then performed AP-MS using Nrp1-FZZ as a bait  and Aip2, have been suggested to function in the H3/H4 transport pathway due to their interaction with Asf1Tt and Importinβ6  and B2086 [Mpr+/Mpr+ ] of inbreeding line B, as well as ∆HHT3 (Stock ID: SD01318) cells were obtained from the Tetrahymena Stock Center, Cornell University, Ithaca N.Y. (http://tetrahymena.vet.cornell.edu/). Cells cultured were maintained axenically at 30 °C in 1 × SPP media as previously described . After adding 500 units of Benzonase (Sigma E8263) extracts were rotated on a Nutator for 30 min at 4 °C. WCEs were clarified by centrifugation at 16,000 × g for 30 min. The resulting soluble material was incubated with 50μL of packed M2 agarose (Sigma) at 4 °C for at least 2 h. The M2 agarose was washed once with 10 mL IPP300 , twice with 5 mL of IP100 buffer , and twice with 5 mL of IP100 buffer without detergent . 500 μL of 0.5 M NH4OH was added to elute the proteins by rotating for 20 min at room temperature.Affinity purification was carried out essentially as described , 46. TetFor tandem affinity purification performed for all analyzed baits except for the H3-GFP and CSKB1-FZZ which were subjected to one-step affinity purification described above, clarified WCEs were incubated with 250 μl packed beads volume of IgG-Sepharose chromatography resin for the first step of affinity purification for 4 h at 4 °C. Beads were washed once with IPP300 and twice with 1 × Tev buffer  before being treated overnight with TEV protease. Next day, the supernatant was incubated with 50 μL of packed M2 agarose (Sigma) and the procedure was carried out as described above. Preparation of protein eluates for mass spectrometry acquisition is detailed in Additional file https://blast.ncbi.nlm.nih.gov/Blast.cgi), as well as by performing SMART domain analysis (http://smart.embl-heidelberg.de/). All MS files used in this study were deposited at MassIVE (http://massive.ucsd.edu) and assigned the identifier MSV000090060. Cytoscape  was useThe ChIP experiments were performed as described previously with modifications detailed in the Additional file http://tfgd.ihb.ac.cn/) [We used microarray data (accession number GSE11300) (.ac.cn/)  to examiTetrahymena during vegetative growth using the RNeasy extraction kit (Qiagen) following the manufacturer’s instructions. Two independent biological samples for each condition were generated. RNA was treated with DNase, and total RNA was quantified using Qubit RNA BR  fluorescent chemistry. 1000 ng per sample was processed using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina , including PolyA selection. 1 uL top stock of each purified final library was analyzed on an Agilent Bioanalyzer dsDNA High Sensitivity chip . The libraries were quantified using the Quant-iT dsDNA high sensitivity  and were pooled at equimolar ratios after size adjustment. The quantified pool was hybridized at a final concentration of 2.215 pM, and single-end reads were obtained on an Illumina NextSeq 500 platform using a full High-Output v2.5 flowcell at 75 bp read lengths.Total RNA was extracted from wildtype and H3.3 KO Tetrahymena cells using TRIzol (Life Technologies) according to the supplier's instructions. The isolated RNA was treated with DNase I . cDNA was prepared using iScript™ Reverse Transcription Supermix for RT-qPCR. qPCR was performed in technical triplicates from three individual KO cell lines. The data were normalized to the expression levels of beta Actin.For RT-qPCR analysis, total RNA was extracted from the H3.3 KO or WT Cells were fixed during vegetative growth, 24-h starvation, and conjugation  to perform indirect immunofluorescence as previously described . Cells wTetrahymena Asf1, Cac2, and Hir1 homologs, respectively.The complete open-source version of DeepMind’s AlphaFold V2.1.0 tool was used to fold and model each full-length protein , 86, andWhen predicting the co-structure of each protein pair, residues experimentally determined as important for interaction were not provided to AlphaFold, allowing for an unbiased prediction of interaction surfaces. AlphaFold model parameters under Creative Commons Attribution 4.0 license were used for modeling of both individual proteins and co-structures . In addiTetrahymena genome using STAR (ver 2.7.1) with default settings.ChIP-seq analysis was performed essentially as previously described . BrieflyAfter removal of duplicate reads, peaks were called jointly on immunoprecipitated and input samples with MACS2 (version 2.1.2) . The metTetrahymena genome annotation (T_Thermophila_MAC_2021-Updated Gene Names). We filtered out genes with less than 10 counts across the sum of all RNA-seq samples. To plot differentially expressed genes as volcano plots, R-package EnhancedVolcanoplot (https://github.com/kevinblighe/EnhancedVolcano) was used. GO/KEGG enrichment was performed using ShinyGO (v0.61), which utilizes a hypergeometric distribution followed by FDR correction, where the FDR cut-off was set to 0.05.To identify the differentially expressed genes from RNA-seq data, we used DESeq2 (ver 3.11)  on gene http://massive.ucsd.edu) and assigned the identifier MSV000090060. ChIP-seq and RNA-seq data generated can be found online at Gene Expression Omnibus  with unique identifier GSE210903. All MS files generated in this study were deposited at MassIVE  and micronuclear linker histone Mlh1-FZZ (right) in growing Tetrahymena. Note: DAPI was used to stain the nuclei and the positions of the MAC and MIC are indicated with arrows and arrowheads, respectively. Figure S3. H3 (H3.3)/H4 chaperones show similar expression profiles. Heatmap representation of microarray expression values for Asf1, Hir1, Cac2, and Nrp1. Z scores were calculated across the rows for each gene to examine its differential expression across growth, starvation, and developmental stages. L1–LH: Logarithmic growth phase, S0–24: Starvation for 24 h, C: Conjugation where 0–18 are hours post mixing the different mating types. Hierarchical clustering was used to examine the expression profiles. Figure S4. Asf1Tt structure is conserved in Tetrahymena. A) AlphaFold-predicted structure of TTHERM_00442300 (Asf1Tt) B) Model alignment comparison of Asf1Tt from this study with the Human ASF1A homolog model generated by the AlphaFold Deepmind consortium (AlphaFold Database ID: Q9Y294). Asf1Tt is colored in green; Human ASF1A is colored in magenta. C) Predicted structure of Asf1Tt colored by pLDDT per residue confidence score ranging from orange (very low: pLDDT<50) to dark blue (very high: pLDDT>90). D) Model alignment comparison of Asf1Tt from this study with Human ASF1A colored by pLDDT score. Figure S5. Cac2Tt forms β-propeller-like structure. AlphaFold-predicted structure of TTHERM_00442300 (Cac2Tt) protein depicting side (A) and top (B) views respective to the βpropeller motif. The B-domain of Cac2Tt is colored in red. Figure S6. Hir1Tt structure prediction. AlphaFold-predicted structure of TTHERM_00046490 (Hir1Tt) protein depicting side (A) and top (B) views respective to the β-Propeller motif. The B-domain of Hir1Tt is colored in red. Figure S7. Protein complex prediction of Cac2Tt and Asf1Tt. Overall AlphaFold-predicted structure of TTHERM_00219420 (Cac2Tt) bound with TTHERM_00442300 (Asf1Tt). Cac2Tt is colored in cyan and Asf1Tt is colored green. The B-domain of Cac2Tt is colored in red. Figure S8. Visualization of the predicted binding interface between Cac2Tt and Asf1Tt. Cac2Tt is colored in cyan, whereas Asf1Tt is colored green and the B-domain of Cac2Tt is highlighted in red. Labeled residues  are predicted to form polar intermolecular contacts between Asf1Tt and Cac2Tt within 3Å, and an intramolecular π interaction (F393-K535) involving a lysine residue within the B-domain of Cac2Tt (T527-Y545). All interactions are shown as dashed yellow lines and arrows. Figure S9. Visualization of the predicted binding interface between Hir1Tt and Asf1Tt. A: AlphaFold-predicted co-structure of TTHERM_00046490 (Hir1Tt) with Asf1Tt. Hir1Tt is colored in gray, Asf1Tt is colored green. The B-domain of Hir1Tt is colored in red. No significant intermolecular interactions were detected in our predictions. B: Left, AlphaFold-predicted co-structure. Hir1Tt is colored by pLDDT per residue confidence scores ranging from orange (very low: pLDDT<50) to dark blue (very high: pLDDT>90). Asf1Tt is colored in green. Right: AlphaFold-predicted by-residue pLDDT confidence score plot for the 5 highest-confidence Hir1Tt models. All models display low predictive confidence for residues of the Hir1Tt B-Domain (res. 453:476) and directly up and downstream of the B-domain. Figure S10. Endogenous tagging of Tetrahymena Hat1. A: Western blotting analysis using whole cell lysates prepared from vegetative Tetrahymena cells expressing Hat1-FZZ. The blots were probed with the indicated antibodies. B: Indirect immunofluorescence analysis of Hat1-FZZ in growing Tetrahymena. DAPI was used to stain the nuclei, and the positions of the MAC and MIC are indicated with arrows and arrowheads, respectively. Untagged wildtype Tetrahymena were used as a control. Figure S11. Strategy to confirm the correct integration of NEO cassette. Top, Schematic representation of the confirmation of the accurate integration of the NEO cassette at the locus of interest. Positions of PCR primers are indicated. The reverse primer is designed complementary to sequence within the NEO cassette, whereas the forward primer is specific to sequence upstream of the gene of interest. A PCR product will be observed only if the NEO cassette is integrated into the desired locus. WT cells will not show PCR products. Primers designed to amplify DNA from the promoter regions of each target gene were used as loading controls. Bottom, Agarose gel electrophoresis using genomic DNA extracted either from KO or WT Tetrahymena cells. Figure S12. KO analysis of CAC2Tt and HIR1Tt in Tetrahymena. A: Left, Fluorescence (DAPI) of vegetative and starved ∆CAC2 and wildtype Tetrahymena cells. Right, Bar plots showing the quantification of mean diameter of MACs in ∆CAC2Tt compared to wildtype Tetrahymena. Diameters were measured in micrometers for 40 individual Tetrahymena cells. Images used were taken at 40X magnification in a 1360x1024 frame. Field of view at 40X was 360 micrometers. B: Fluorescence (DAPI) analysis of conjugating wildtype, ΔHIR1Tt, and ΔCac2TtTetrahymena cells. Hours post mixing the Tetrahymena cells of different mating types are indicated on the left. Note: DAPI was used to stain the nuclei. Figure S13. Indirect immunofluorescence analysis in starved Tetrahymena. Indirect immunofluorescence analysis in starved Tetrahymena. A: Expression profile of Hir1 during growth and starvation in Tetrahymena. For growing cells, L-l corresponds to ~1X105 cells/mL. For starvation, ~2X105 cells/mL were collected at 0, 3, 6, 9, 12, 15, and 24 hours referred to as S-0, S-3, S-6, S-9, S-12, S-15, and S-24. Microarray data was acquired from http://tfgd.ihb.ac.cn/search/detail/gene/TTHERM_00046490  B: Indirect immunofluorescence analysis of H3.3-FZZ in starved Tetrahymena. H3.3 is found in the MAC only. C: Indirect immunofluorescence analysis of RebL1-FZZ in starved Tetrahymena. RebL1 is found in the MAC only. DAPI was used to stain the nuclei. The positions of the MAC and MIC are indicated with arrows and arrowheads, respectively. Figure S14. H3.3 ChIP-seq replicates correlate with each other. A: Principal component analysis (PCA) of two H3.3 ChIP-seq replicates and their corresponding inputs. B: Fingerprint plot to examine the quality of H3.3 ChIP signal in comparison with inputs. ChIP-seq is enriched as more reads are found in smaller number of bins for ChIPs compared to the input. Figure S15. GO enrichment analysis of H3.3 ChIP-seq targets. A: Bar plot depicts the % overlap of upregulated and downregulated genes in H3.3 KO cells with those genes classified as highly expressed during Tetrahymena vegetative growth. B: KEGG pathway enrichment analysis of H3.3 bound genes. Number of genes for each term is indicated beside each bar. C: Dot plot representation of pfam domain enrichment analysis in H3.3-target genes (Q< 0.05). Figure legend is provided. Figure S16. Predicted CKII sites on Cac2Tt and Cac1Tt proteins. The red box shows the conserved sequence, whereas star indicates the serine residue predicted to be phosphorylated by CKII. The prediction was performed using Netphos web server https://services.healthtech.dtu.dk/services/NetPhos-3.1/.Additional file 3: Methods S1.