Hypoxia is a common denominator in the pathophysiology of a variety of human disease states. Insight into how cells detect, and respond to low oxygen is crucial to understanding the role of hypoxia in disease. Central to the hypoxic response is rapid changes in the expression of genes essential to carry out a wide range of functions to adapt the cell/tissue to decreased oxygen availability. These changes in gene expression are co-ordinated by specialised transcription factors, changes to chromatin architecture and intricate balances between protein synthesis and destruction that together establish changes to the cellular proteome. In this article, we will discuss the advances of our understanding of the cellular oxygen sensing machinery achieved through the application of ‘omics-based experimental approaches. Molecular oxygen is best known for its connection to oxidative phosphorylation and ATP production. Changes to oxygen availability give rise to complex cellular and organismic responses in all multicellular organisms. Hypoxia is defined as a condition in which oxygen demand exceeds supply. Physiological responses to hypoxia in humans and other mammals have been long appreciated and known . Howeveregln1, egln2 and egln3). In well oxygenated cells and tissues prolyl hydroxylation of HIF-α subunits creates a high affinity binding site for the E3 ligase complex composed of Von Hippel–Lindau tumour suppressor (VHL), Elongin B/C and cullin 2 [Following the discovery of HIF, intensive research efforts identified the main components of the hypoxia signalling pathway in cells. HIFs are heterodimers containing an oxygen-sensitive α subunit, of which there are three homologues, HIF-1α, HIF-2α and HIF-3α, and a constitutively expressed β subunit  . The meccullin 2 ,9. The Vcullin 2 . This mocullin 2 . More recullin 2 ,12. Thescullin 2 . It thusSince the discovery of these important oxygen-sensitive transcription factors and enzymes, the use of ‘omic approaches such as transcriptomics and genomics has greatly propelled the field's knowledge, importance and reach into other disciplines. From the original physiology to molecular and cellular biology, medicine, veterinary sciences, chemistry and drug discovery, the impact of hypoxia research is now clear in all these areas. More recently, proteomics, epitranscriptomics and metabolomics have also started to be used in the field of hypoxia. This review will focus on how the use of omics and genome wide technologies where employed, has led to seminal discoveries in the research area of hypoxia .Fluctuations in oxygen availability/demand initiate co-ordinated responses, involving changes in the regulation of gene expression, protein stability and function, which impinge on many aspects of physiology and cell biology . CentralA limitation of microarrays and RNA sequencing is they only provide a measure of steady state RNA levels without any information on the relative contributions of RNA transcription and decay rates. Methods detecting nascent RNA, namely metabolic RNA pulse labelling sequencing, GRO-seq and precision run-on (PRO)-seq, have elucidated direct transcriptional changes in response to low oxygen ,38,42–45Most of our knowledge of transcriptome responses to varying oxygen levels comes from bulk cell analysis and despite the recent advances in single cell RNA sequencing, single cell resolution of transcriptome changes in response to oxygen fluctuations is lacking. The effects of chronic hypoxia on the transcriptome of single cells in the retina , and intGenomics tools have been fundamental to understanding gene transcription and chromatin regulation in hypoxia. This includes characterising HIF direct and indirect transcriptional control, identifying the chromatin environment as determining cell type specificity of hypoxia/HIF responses, defining the distinct binding profiles and transcriptional outputs of HIF-1α and HIF-2α, and the discovering new cellular oxygen sensors.Chromatin immunoprecipitation (ChIP)-chip microarrays  and ChIPGiven the drastic changes in gene expression in hypoxia, it is perhaps not surprising that the chromatin environment is also sensitive to oxygen. Post-translational modifications (PTMs) on histones and DNA, including histone methylation, histone acetylation and DNA methylation are associated with transcriptional regulatory mechanisms. ChIP-seq studies have shown hypoxia induces redistribution of the histone methylation landscape co-ordinating changes in hypoxia gene expression ,61–64. TAnalysis of the DNA methylome, using DNA immunoprecipitation sequencing (DIP)-seq, reveals DNA hypermethylation in hypoxia . MechaniGenomics approaches have also been used to investigate chromatin organisation and oxygen sensing. Most HREs overlap with DNase hypersensitivity sites which are present in normal oxygen tensions, indicating hypoxia is not required to open chromatin for HIF binding . Assay fTechnical limitations with ChIP-seq makes obtaining high quality genome wide occupancy data for transient and/or low abundance chromatin binding factors difficult and expensive. Newer genome occupancy sequencing technologies, namely cut and run, and cut and tag, are tackling these limitations. Use of these new technologies should facilitate genome wide mapping of chromatin/transcription regulators in response to oxygen fluctuations, such as chromatin remodellers, which is likely currently lacking due to prior technical limitations. Chromatin profiling approaches to study oxygen sensing have thus far been limited to bulk cells. However, with advances in single cell chromatin profiling approaches, researchers can now start to investigate chromatin and oxygen sensing at the single cell level. The challenge for future studies is to elucidate the complex cross-talk between the chromatin environment, gene transcription and oxygen signalling. Multiomics approaches will no doubt take centre stage in making progress in this area.The critical nature of the HIF-dependent changes in gene expression in response to hypoxia has led to a focus on identification of oxygen-dependent changes in transcription and chromatin architecture ,72. Howede novo protein synthesis, and these types of experiments will help to understand how acute hypoxia alters the proteome of hypoxic cells [The real power of proteomics experiments in the context of large groups of cells may be to understand the role of hypoxia in changing the total proteome through subtle changes to many genes through modulating global translation rates ,76. Indeic cells . CouplinIn addition to providing valuable insight into hypoxia-induced protein targets, unbiased MS experiments have been critical in identifying the key mediators of the hypoxic response by defining changes in protein–protein interactions and PTMs of HIF transcription factors ,83. The PTMs of HIF transcription factors can control protein stability, interaction with DNA or mediate protein–protein interactions with several co-activator and co-repressor proteins ,89. SomeLike most other signalling pathways, hypoxia is predicted to change the PTM landscape for the cell's proteome. Given, that hydroxylation and methylation levels are under direct control of dioxygenases, theses PTMs should be significantly altered in cells experiencing hypoxia. Unfortunately, studies investigating protein methylation have not been reported outside of histones ,91. SeveSumoylation is the covalent conjugation of the ubiquitin-like protein called sumo to another protein ,100. SumPhosphorylation is a crucial PTM regulating a many of the intra- and intercellular cell signalling pathways induced in normal and malignant hypoxic cells. As discussed, changes in phosphorylation states play key roles in regulating HIF signalling; however, large-scale changes in the phosphoproteome are observed in cells exposed to hypoxic stress ,108. A rOne major biological process altered by changes to oxygen availability is cellular metabolism . FurtherUnbiased metabolomics can be achieved via nuclear magnetic resonance (NMR) or using MS . AdvanceWork using metabolomics, mostly investigating diseased states, have helped to the identification of specific changes elicited by hypoxic stress . These iNANOG mRNA via ALKBH5 activates a breast cancer cell phenotype [RNA epigenetics (epitranscriptomics) is a rapidly growing and exciting field that represents an additional layer in the control of genetic information. RNAs carry a diverse range of chemical modifications; one of the most abundant in eukaryotes is N6 methyladenosine (m6A). Modulation of this highly dynamic and reversible modification on internal regions of mRNA and other types of nuclear RNA is orchestrated by m6A writers and erasers, with readers of the modification important in conferring its functional output . m6A imphenotype . m6A enrhenotype  and m6A henotype . To datehenotype . This sthenotype . ReprogrAlthough not necessarily an omics approach, genome wide screen using gene targeting approaches such as siRNAs and CRISPR have also contributed to our understanding of hypoxia. These approaches and their impact in the field have been nicely reviewed  and haveThe ability to conduct unbiased, omics analysis has greatly propelled the hypoxia research field forward. Perhaps, not surprisingly, sequencing related technologies have the front stage in this revolution. However, with technology development in the MS instrumentation, other approaches such as proteomics and metabolomics will become more accessible to the community and used more routinely in the future. However, bioinformatic expertise in these areas is still difficult and despite the advances in methods and instruments, data analysis, interpretation and integration remain a barrier for a research area dominated by physiologists, cell biologists and biochemists. As such, a team approach is much needed to bring bioinformatics knowhow to hypoxia research. Moving forward, systems and mathematical based approaches integrating omics datasets in a statistically rigorous fashion will allow us to comprehensively and accurately model the cellular response to hypoxia.


Recent efforts to measure epigenetic marks across a wide variety of different cell types and tissues provide insights into the cell type-specific regulatory landscape. We use these data to study whether there exists a correlate of epigenetic signals in the DNA sequence of enhancers and explore with computational methods to what degree such sequence patterns can be used to predict cell type-specific regulatory activity. By constructing classifiers that predict in which tissues enhancers are active, we are able to identify sequence features that might be recognized by the cell in order to regulate gene expression. While classification performances vary greatly between tissues, we show examples where our classifiers correctly predict tissue-specific regulation from sequence alone. We also show that many of the informative patterns indeed harbor transcription factor footprints. Complex multicellular organisms comprise a large number of different cell types, which all share the same genome. Nevertheless, cell morphology and function are determined by the combination of genes that are expressed ,2. To unHowever, this information alone can only be regarded as a first step toward understanding the regulatory program of a cell. Ultimately, we would like to understand why certain regions act as enhancer elements in particular cell types and how they drive gene expression. We believe that this information must be encoded in the genome in the form of binding sites for proteins such as transcription or pioneer factors. Hence, it should be possible to use the genomic DNA sequence not only for identifying enhancer elements but also for predicting the cell types in which the elements are active. Unfortunately, our knowledge of transcription factors, including their DNA binding preferences and interactions, remains incomplete. Instead, our goal is to quantify how much information about the cell type-specific activity of an enhancer element is actually encoded in the DNA sequence. For this, we may use enhancers identified from epigenetic marks and train classifiers that predict for each element the cell type in which it was found to be active. The classification performance then allows us to quantify to what extent there exists a correlate of cell type-specific epigenetic marks in the DNA sequence of enhancer elements.An increasing number of studies focus on the prediction of regulatory elements from DNA sequence. Especially, the DNA sequences of promoters have been studied in depth and several cell type-specific binding patterns have been identified . Since rk-mers that allows to efficiently compute sparse solutions on very high dimensional data. From the trained classifiers, we quantify how much cell type-specific regulatory information is actually contained in the DNA sequence of enhancers. Furthermore, the classifiers are easy to interpret and allow to identify DNA subsequences or code words that may drive cell type-specific gene expression. We use ATAC-seq data to validate our findings by showing that many of the informative patterns indeed harbor transcription factor footprints . The parameters 1-penalized log-likelihood functionn observations x = {x1, x2, …, xn} with labels yi ∈ {0, 1}. Each xi =  is a row vector of length m + 1, where m is the number of features. In the above formula, σ denotes the sigmoid function. λ. A common practice is to simply test a fixed set of values for λ. We avoid this by using leapfrog regularization . Reverse complements are considered equivalent; i.e. the vector xi has a single coordinate for both anntg and its reverse complement cannt, denoted by anntg|cannt. Hence, each xi is a vector of dimension 156 570 + 1 (m = 156 570). We consider either code word counts or occurrences (binarized counts). In the case of code word counts, we first normalize the data to unit variance. The data are not centered at zero to retain the sparse structure.For the KLR classifier, the set of features consists of all q. For the KLR classifier, we tested q = 10, 100, 200, …, 900, 1000, 2000, …, 6000.For the entire study, classifier performance is evaluated using 10-fold cross-validation. We do not know the optimal number of features (nonzero coefficients). Therefore, during each CV iteration, 10% of the training data are reserved as a validation set for selecting the best https://github.com/pbenner/kmerLr.Our software is available at k-mer frequencies as features. We mainly use it to benchmark the performance of the KLR classifier. However, the decisions that SVMs make are difficult to interpret and extracting important features from it is much more complicated than for the KLR classifier.LS-GKM  is a suphclust method in R). The clustered data collection is computed from this clustering in the following way. By removing an inner edge from the hierarchical clustering tree, a bipartition of tissues is generated. This bipartition corresponds to a single data set of the collection and is used for labeling the enhancers as positive or negative. Enhancers that appear in both the positive and negative sets are removed.The clustering of tissues is computed from ModHMM segmentations, where only the enhancer state is used. First, a similarity matrix between the tissues is computed by counting the number of overlapping enhancers between each pair of tissues. Afterward, this matrix is used to compute a hierarchical clustering  of embryonic mouse at day 15.5 from the ENCODE project . ModHMM As shown in the previous section, most enhancers are active in only a few tissues, which indicates that enhancers drive cell type-specific gene expression. A more detailed analysis on the location of the cell type-specific regulatory code can be found in leaves data collection. Furthermore, we constructed a second data collection where positive and negative sets consist of several tissues grouped together. The clustered data collection is defined in Table For testing different feature sets, we constructed an unbalanced data collection using a one-versus-rest strategy. It consists of eight data sets, each of which defines the enhancer elements of a single tissue as positive samples and the elements of all remaining tissues as negative. We refer to this collection as the k-mers as features, but found that k-mers generally yield better results (see k-mers (KLR), we consider all k-mers of length 4–8 with any number of gaps (denoted by n), which we also call gapped k-mers. A k-mer and its reverse complement are considered equivalent; i.e. the feature vectors have a single coordinate for both, say, anntg and its reverse complement cannt. We denote this pair as anntg|cannt and refer to it as code word. The KLR classifier uses mainly code word counts as features. However, for extracting most important code words from our classifiers, we only use code word occurrences as features, i.e. 1 if a code word appears in an enhancer sequence and 0 otherwise. This simplifies the interpretation of classifiers without much reducing their performance. To gauge the performance of the KLR classifier, we use an SVM with gapped k-mer string kernel . We tested motif scores and ults see . For logg kernel .We first tested the performance of the KLR and SVM classifiers on active enhancers from the clustered collection. We tested the classifiers on each data set of the collection using 10-fold cross-validation . Both clk-mers. The interpretation of SVMs, on the other hand, is much harder since there exists no natural way of extracting the importance of k-mers , then it suffices that classifiers learn patterns common to all enhancers or the genomic background, because it is highly unlikely that the negative set contains enhancers that are active in other tissues. Most related studies  use randk-mers, because it performs as well as SVMs and at the same time is easy to interpret. We consider two different scenarios: the positive set consists of either multiple tissues (clustered data collection) or only a single tissue (leaves data collection).The classification performances can be used as a proxy for the tissue-specific information contained in the DNA sequences of enhancers. This proxy provides only a lower bound on the tissue-specific information, as there might exist classifiers with a higher predictive accuracy. For quantifying this information, we use the KLR classifier, a simple logistic regression on The classification performance of the KLR classifier on the clustered data collection of active enhancers is shown in Figure Even more difficult is the discrimination of active enhancers from a single tissue versus all other tissues. Figure The KLR classifier can use as features either code word counts or occurrences. The former gives the number of times a code word is observed in the DNA sequence, while the latter is 1 if the code word is present and 0 if it is not. We wanted to understand whether there is additional information in the code word counts, i.e. whether transcription factors recognize single binding sites or whether the overall sequence affinity ,31 is ofTo identify code words that might be recognized by transcription factors to drive cell type-specific gene expression, we use the KLR classifier applied to the leaves data set. The coefficients of the logistic regression can be understood as the importance of the corresponding code words. However, even when data are standardized, the interpretation of coefficients is much simpler when only code word occurrences are used . Therefok-mers relevant to a single tissue, i.e. the positive set. The results must be interpreted with caution, because single code words do not determine cell type-specific activity. We only observe that weighted sets of code words are predictive of tissue-specific enhancer activity with varying levels of accuracy.Features are extracted from classifiers trained on the leaves data collection, because those contain information about single tissues. We generally use 10-fold cross-validation to evaluate predictive performances. Features are extracted by first merging the 10 classifiers estimated during cross-validation. For each feature, we take the coefficient with the minimum absolute value across all classifiers, which has the effect that only those features remain that have a nonzero coefficient across all training sets. Code words with high absolute coefficients are typically stable across all cross-validation iterations . We repotaatnaa|ttnatta or atnnatca|tgatnnat), which might reflect co-binding sites of transcription factors. A comprehensive list of code word matches with transcription factor motifs is given in Supplementary Table S26.Table As a further control, we looked at ATAC-seq footprints  around cSo far, we have extensively quantified the predictive accuracy of our classifiers. In a nutshell, genome-wide predictions of active enhancers are relatively easy (i.e. active enhancers versus random genomic regions). More difficult is the prediction of tissues in which an enhancer is active. Here, we demonstrate our findings on a single loci, where we compute sliding window predictions of active enhancers. More specifically, a sliding window of 2 kb is used to compute predictions of brain and liver enhancers along the genome. We first use a classifier that discriminates between enhancer elements and random genomic regions and combine this with a second classifier that separates brain from liver enhancers. Figure The sliding window prediction of brain enhancers shows a peak at only one of the active enhancers, but also very few false positives within this locus. This observation is in line with our previous results. We showed that random genomic regions can be easily distinguished from enhancers, but differentiating between active enhancers in particular cell types is hard. However, the sliding window predictions also detect the active promoter of Cdh10, possibly because we did not train the classifier to discriminate between promoters and enhancers. Nevertheless, the precision of predictions is very promising and rarely reported in the literature. By inverting the predictions of the second classifier (probability of complement), we obtain enhancer predictions for liver. The prediction of liver enhancers shows no peaks at the brain enhancers or the Cdh10 promoter. Throughout this locus, the probability of liver enhancers is very low.In this study, we constructed classifiers that predict tissue-specific enhancer activity from DNA sequence. This has been done by several other studies , which fThe classification performance of active enhancers strongly depends on the tissues we want to discriminate. Our classifier performs well when discriminating between highly dissimilar tissues, such as brain and non-brain tissues. However, especially when the positive class consists of enhancers from a single tissue, the performance drops in many cases. One possible reason is that the tissues we are dealing with contain too many different cell types or the data are simply too noisy. Another explanation is that we still have only a poor understanding of the cell type-specific regulatory code and the features required for predicting the activity of enhancer elements. It could be that not all the required information is contained in the DNA sequence of isolated enhancers and we are still missing a piece of the puzzle.There are many possible future research directions. For instance, single-cell ATAC-seq data could help to distinguish between cell types within a tissue and thereby help to train better classifiers. Furthermore, we know that transcription factors and other proteins bound to enhancers and promoters interact in order to initiate transcription. It is possible that the regulatory code is distributed among enhancers and promoters and that both must be considered jointly when training classifiers.lqab095_Supplemental_FileClick here for additional data file.


Recently MEIS1 emerged as a major determinant of the MLL-r leukemic phenotype. The latest and most efficient drugs effectively decrease the levels of MEIS1 in cancer cells. Together with an overview of the latest drugs developed to target MEIS1 in MLL-r leukemia, we review, in detail, the role of MEIS1 in embryonic and adult hematopoiesis and suggest how a more profound knowledge of MEIS1 biochemistry can be used to design potent and effective drugs against MLL-r leukemia. In addition, we present data showing that the interaction between MEIS1 and PBX1 can be blocked efficiently and might represent a new avenue in anti-MLL-r and anti-leukemic therapy. Homeobox transcription factor MEIS1 belongs to the three-amino acids loop extension (TALE) sub-family of homeobox proteins. MEIS1 was discovered because a retroviral integration close to its regulatory region induced murine myeloid leukemia [PBX1 and HOXA9, MEIS1 is expressed in hematopoietic stem cells (HSCs) but downregulated during their differentiation [MEIS1 is highly expressed in the bone marrow, and its predominant and better-known role is in embryonic and adult hematopoiesis ,3,4,5. AMeis1 and Meis2 double knock-out) embryos are limbless, with undeveloped appendicular condensations.MEIS1 regulates growth and differentiation during vertebrate development  and is wGenome-wide association studies have linked polymorphism of MEIS1 to an increased risk of “Restless legs syndrome” ,12,13,14A direct connection between MEIS1 expression, leukemia, and cancer has been established from the start (see below) [One of the properties of TALE proteins is to interact with each other. Previous studies have defined that the primary binding partner of MEIS1 is PBX (another TALE protein), of which exist four different genes ,22. MEISPREP1 and MEIS1 share an overall similar organizational structure of the protein, in which the HR regions are almost identical. On the other hand, the homeodomain of MEIS1 and PREP1 is only partially conserved, which explains the slight distinction in DNA target specificity . ConversHOX cluster genes, playing crucial roles in development and hematopoiesis [MEIS1-PBX1 and PREP1-PBX1 dimers can bind DNA and form trimeric DNA-binding complexes with HOX proteins. During embryonic stages, trimerization of MEIS1/PREP1 with PBX1 and HOXB1 is necessary to express several opoiesis ,39,40,41opoiesis ,44,45,46opoiesis .hox genes regulation [The interaction of MEIS1 and PREP1 with PBX may lead to overlapping functions as they both share their interaction and bind to DNA target sequences that are very similar, although not identical . On the gulation ,48.−/−Meis1 and i/iPrep1 mice are similar [+/−Prep1 -+/−Meis1) embryos was unexpected [Likewise, the embryonic hematopoietic phenotypes of  similar , but theexpected . TherefoMeis1 acts as an oncogene only in the absence of Prep1 (or other tumor-suppressing genes), and the overexpression of Prep1 in Meis1-dependent tumor cells blocks their growth. In brief, Meis1 and Prep1 require Pbx in their respective tumorigenic or tumor suppressive functions and target the same molecular pathways, but in the opposite way [On the other hand, Meis1 and Prep1 compete in mouse tumorigenesis and leukemogenesis  because site way .MLL gene [The presence or absence of PREP1 has not yet been taken into account in the case of MLL-r, Mixed Lineage Leukemia, a leukemia due to translocation of several genes to the amino terminus of the MLL gene ,51,52, wMLL gene ,53, HOXAMLL gene .Meis1 is tightly controlled. Meis1 expression is stage-specific, with the highest levels observed in HSC and early progenitors; in later stages, Meis1 expression decreases [Meis1 is embryonic lethal: knock-out embryos die by E-day 12.5–14.5 because of hemorrhages caused by the absence of megakaryocytes, platelets [Meis1 is required during normal fetal murine hematopoiesis ,3. In moecreases . A homozlatelets  and sevePbx1 die at the same embryonic stage and die presenting severe hematopoietic defects [Prep1 deficiency appears to share a similar defect as Meis1 in the fetal HSC. Indeed, in Meis1 knock-out or Prep1 hypomorphic (i/iPrep1) mouse embryos, fetal HSCs are deficient in number and regenerating ability and display various definitive hematopoietic defects [In addition, there is a complete absence of functional HSC ,57. Addi defects . The deg defects ,60,61. I defects .Meis1 and Prep1 should show an embryonal blood defect. However, this does not happen, indicating that the two transcription factors do not exploit the same mechanisms; this represents a still unsolved part of the physiology of Prep1 and Meis1 [Regulation by Meis1 and Prep1 is, therefore, crucial to preserve the self-renewal of the stem cells pool and the homeostasis of the hematopoietic system. In addition, this primary function of Meis1 in maintaining the self-renewal of the stem cells pool might be relevant in its oncogenic role. On this basis, one would expect that mice heterozygous for nd Meis1  and requIn zebrafish, meis1 shows over 90% amino acid sequence-identity with vertebrate orthologues and plays an essential role in embryonic hematopoiesis ,64. AntiFurthermore, MO-meis1 display severe disruption of vessel lumen formation ,65: CvejCvejic and colleagues also tested a meis1 splice MO, designed to create aberrant splicing between exon 1 and 2. In spliced MO-injected embryos, exons 2 to 7 were removed, resulting in a meis1 truncated form, depleted of the pbx1-interaction motif and, therefore, incapable of binding pbx1. Similarly to the MO-meis1, meis1 splice MO-injected embryos displayed the same lack of red blood cells and reduced neutrophils and macrophages. Consistently, MO-pbx1 injected embryos showed a profound reduction in the number of erythrocytes and neutrophils and severe disruption of vascular patterning . TherefoMeis1 adult mouse hematopoietic system, as reviewed by Miller and colleagues [In a conditionally inactivated lleagues  and summlleagues . Ariki aMeis1 in all tissues of adult mice does not lead to hemorrhages, suggesting that the role of Meis1 is essential in embryonic development but might be dispensable in the adult, especially for proliferation and differentiation of committed hematopoietic progenitors [Deleting genitors .Prep1 leads to a major hematopoietic phenotype, similar to that of Meis1, adult conditional knock-out mice do not display a major hematopoietic defect. Moreover, the blood-regenerating activity of adult bone-marrow-derived i/iPrep1 HSC is as efficient as wild-type HSC  [Meis1 and Prep1 have different roles in the adult v. fetal hematopoiesis. However, the actual role of these two transcription factors in adult HSCs has not yet been carefully explored.While, in the embryo, the mutation in ed data) . TherefoThe effect of Meis1 knock-out is known to be partially mediated through reactive oxygen species (ROS). Indeed, loss of Meis1 leads to downregulation of hypoxia-inducible factors 1α and 2α (HIF-1α and Hif2a) and the consequent increase of ROS . MoreoveIn this regard, significantly higher ROS levels were found in Meis1-depleted HSCs than controls, while the phenotype was rescued by the presence of the ROS scavenger N-acetylcysteine . TherefoMeis1 should be well tolerated. Hence, MEIS1 might represent a valuable therapeutic target as one might hope to find few, if any, collateral effects in the hematopoietic system.In conclusion, as summarized in The many analogies of Meis1 and Prep1 roles in the hematopoietic system during the development and in the adult are remarkable. Therefore, given a possible therapeutic use of MEIS1 in human leukemias, it might be important to analyze better the common interactions with PBX.To our knowledge, no attempt has yet been made to target the MEIS1-PBX1 interaction surface, which would appear an obvious therapeutic target because of the requirement of PBX for MEIS1 oncogenic activity. Since we have recently described experiments that have shed further light on the PREP1-PBX1 interaction site , and, siThe large HOX/MEIS/PBX complex could offer several interfaces to be targeted to decrease their oncogenic function in leukemic patients. However, the development of small-molecule inhibitors of protein–protein interactions is challenging, especially for transcription factors, as surfaces are typically smooth and do not present the deep pockets present in enzyme active sites, making the development of small-molecule inhibitors more difficult ,73,74,75MEIS1 oncogenic function strictly requires heterodimerization with PBX ,47,76. IBiochemically, PREP1 competes with MEIS1 for PBX1 binding, which agrees with the lower MEIS1 binding affinity to PBX1  and pull-down assays, and impairs the nuclear localization of PREP1 without altering the overall folding of the protein .Like PREP1, MEIS1 requires dimerization with PBX to reach the nucleus. Therefore, we have cloned MEIS1-GFP in a retroviral vector and analyzed in mouse embryonic fibroblasts (MEFs) the ability of wild-type MEIS1 to reach the nucleus compared to quadruple mutants in the heptad repeats within HR1 or HR2 domains. In MEIS1, we mutated into alanine the potential hydrophobic residues corresponding to those identified in PREP1. Hence, in the HR1 of MEIS1 we mutated L83/L86/L87/I90 and in the HR2 L149/I152/L156/L159 see .The subcellular localization of the transfected wild-type and mutants MEIS1-GFP was then determined by immunofluorescence A. Wild-tThe histogram of We previously reported that the inhibition of the PBX interaction can neutralize MEIS1 oncogenic properties ,47,79. IIn mouse models, the critical Meis1 interactor tested has been Pbx1. However, in human patients, it appears that the critical isoform is PBX3. This should pose no problems because of the sequence identity of the PBX orthologues in this region. Indeed, Garcia-Cuellar and colleagues identified in PBX3 the stretch 78-PALFSVLCEIKEKTGLSIRGAQE-100 E as fundConversely, in TR-FIA, PREP1 and MEIS1 retain similar binding behavior to PBX1 upon the deletion of four PBX1 stretches: the deletion of residues 43–55 retains 60% PREP1 and MEIS1 binding; the deletion of residues 77–84 retains 30% PREP1 binding and 20% MEIS1; deletion of the PBX1 stretch 105–113 has no significant influence on binding to PREP1 nor MEIS1; and the deletion of 197–204 retains 50% PREP1 and 35% MEIS1 . ConsistIn conclusion, we believe these data suggest a possible use of drugs preventing the MEIS1-PBX1 interaction in leukemias, particularly in MLL-r (see below). An even more accurate biochemical characterization of the MEIS1-PBX interface will further narrow the region to target and aid in developing small molecules effectively and specifically disrupting the MEIS1-PBX interaction.Many studies in the past have detailed the contribution of MEIS1 in cell transformation. Primary wild-type Mouse Embryonic Fibroblasts (MEFs) are never transformed by a single oncogene, like Ras. Neoplastic oncogene-induced transformation instead occurs when at least two oncogenes are used or when a tumor suppressor gene is also missing . Indeed,i/iPrep1 MEFs, a second oncogene  is, in fact, not required to induce transformation by MEIS1 overexpression.Furthermore, at the genetic level, the cooperation of Meis1 and Hoxa9 enables the transcription of Flt3, which is not efficiently activated by Hoxa9 alone . More rei/iPrep1 MEFs overexpressing Meis1, as well as soft agar colony formation, was drastically reduced when Prep1 was re-introduced. This result allowed the testing of Prep1 mutants that had lost part of their domains.The neoplastic transformation was demonstrated with all classical assays, including colony formation in soft agar and the injection of transformed cells in mice . Using tWhile the loss of the C-terminal fragment or the DNA-binding homeobox did still inhibit the growth of the Meis1 overexpressing cells in mice, the loss of the amino-terminal HR1 and HR2 abolished the ability of Prep1 to compete. As this domain is strongly and uniquely involved in the interaction with Pbx ,71,84, tRNA-seq data showed that the type of target pathways hit by both transcription factors were identical in the two cell types, except that Meis1 hits the promoting genes, while Prep1 hit the inhibiting ones . The abiThus, both Meis1 oncogenic and Prep1 tumor-suppressing activities require interaction with Pbx1. Indeed, the Pbx-interacting regions (HR1 and HR2 domains) ,34,71 arAcute leukemia arises when hematopoietic progenitor cells become locked into an immature state of perpetual proliferation. As HOXA9 and MEIS1 are involved in maintaining the HSC pool, aberrant overexpression of HOXA9 and MEIS1 can drive leukemogenesis ,85.By using quantitative real-time PCR to investigate gene expression in 100 children with acute leukemia and comparing them to those of healthy controls, Adamaki and co-workers  showed tThe overexpression of MEIS1 is implicated in childhood leukemia (AML and ALL), including B-cell ALL . Thus, hMLL-r is a particular type of leukemia. The chromosomal translocation of the mixed-lineage leukemia 1 gene  accounts for ~9% of adult, ~5% of children, and 80% of infants cases in acute lymphoblastic leukemia (ALL), together with over 10% of adult cases, ~15% of children, and above 70% of infant cases in acute myeloid leukemia (AML) ,89.The MLL1 fusion proteins  lack the SET domain necessary for catalyzing the mono- di and trimethylation of histone H3 lysine 4 (H3K4) since the methyltransferase activity of MLL1 is compromised. MEIS1 and HOXA9 have been identified as the most critical targets of MLL fusion proteins in MLL-r ,92,93,94Pbx genes, of which Pbx1 and Pbx3 express two different forms, short and long. In human MLL-r, the form of PBX that appears to be mostly involved is PBX3. However, all data available indicate that PBX1 and PBX3 are indistinguishable in terms of MEIS1 binding. From this point of view the dimer MEIS1-PBX3, and not just MEIS1, appears to be an interesting target for therapy.MLL-r leukemia has been extensively studied in the mouse by the laboratory of Michael Cleary, which has established the essential role of Meis1 in leukemia induction and maintenance. In any case, Meis1 does not act on its own but, as in its all-other known functions, as a dimer of one of the Pbx proteins. In mammals, there are four Liu and colleagues  identifiAML harboring NPM1 mutations represent one of the most common genetic lesions in adult AML. Moreover, it has a similar stem cell-associated gene expression program, which includes a well-characterized deregulated expression of the HOXA cluster genes and MEIS1 ,102,103.MEIS1 also plays a role in solid tumors; however, in this case the MEIS1 role is contradictory as it may behave as an oncogene or a tumor suppressor. The role of the whole MEIS protein family  in solid cancers has been recently reviewed by Girgin and colleagues . MEIS1 oFor example, Zargari and colleagues evaluated the expression pattern of MEIS1 in human esophageal squamous cell carcinoma, demonstrating that down-regulated expression of MEIS1 by shRNA decreased the mRNA expression of the most important stem cell markers , which can preserve self-renewal and proliferative potential via inhibiting differentiation signaling pathways during cancer initiation and development . HoweverUpregulation of MYC decreases MYC occupancy at the MEIS1 locus and leads to downregulation of MEIS1. On the other hand, knockdown of MYC activity leads to increased expression of MEIS1, following an increased occupancy of MYC at the MEIS1 locus . In addiIn addition, MEIS1 seems to function as a tumor suppressor in the progression of clear-cell renal cell carcinoma , and simAs described above, in mouse embryonic fibroblasts, Meis1 overexpression can directly transform in the absence of additional oncogenes, provided a tumor suppressor gene is absent . In thisAlthough this data has not been further explored, this spurious binding has a counterpart in the gene ontology of the genes bound. Indeed, overexpression targets both Prep1 and Meis1 towards the cell growth pathways, with a significant difference. Although the pathways are the same, Prep1 binds to genes inhibiting growth, whereas Meis1 binds to genes activating growth . It woulOverexpression of the HOX and MEIS1 genes triggers leukemogenesis and is associated with high-risk AML but up to now could not be directly targeted by drugs. Several epigenetic control strategies to decrease HOXA9 expression have been developed in the last decade. Currently, it is well recognized that HoxA9 expression alone is only weakly oncogenic in mouse leukemia models and usually requires a second “hit”, the overexpression of Meis1 ,119.As described above, recent results have highlighted a crucial role of MEIS1 in maintaining AML, while the role of HOXA9 is dispensable. Indeed, MEIS1 functions is essential during the progression of human AML and is an adverse prognostic factor independent of MLL1 abnormality . Thus, MSome epigenetic modulators, even of factors deregulated in leukemia, are used in clinics or have entered clinical trials. Lambert and colleagues  summarizDOT1L methyltransferase activity is critical to MLL-r leukemia  and is rDOT1L inhibitors, such as EPZ004777 and EPZ5676 , reduce HOXA9 and MEIS1 mRNA level expression and induce differentiation of AML cells . In precAnother selective inhibitor of DOT1L, SYC-522, an S-adenosyl-L-methionine (SAM) derivative, blocks the cell cycle at the G0/G1 phase and sensitizes MLL-r leukemia cells to chemotherapeutic agents , causing apoptosis. After 3–6 days of treatment with SYC-522, the expression levels of HOXA9 and MEIS1 also decreased in AML cells by 50% or more. Decreasing HOXA9 and MEIS1 gene expression, SYC-522 promoted cell differentiation . TherefoMethylation of the histone-H3 lysine-4 (H3K4) by the methyltransferase KMT2A (aka MLL1) is an epigenetic mark associated with actively transcribed genes and a master regulator for the transcription of genes important for embryonic development and hematopoiesis (including HOX and MEIS1 genes), but dispensable in the mature cell. MLL1 is frequently upregulated in cancers, resulting in increased expression levels of HOX and MEIS1 target genes, which link MLL1 with its tumorigenic properties ,94,130.In MLL-r leukemia, chromosome translocation produces an oncogenic fusion protein consisting of the N-terminal DNA-interacting domains of MLL1 fused with one among over 60 fusion partners ,131. TheInhibition of the MLL-menin interaction in MLL-r cells leads to downregulation of MEIS1 and HOXA9 and other MLL-fusion target genes. Most importantly, the disruption of the MLL-menin complex abrogates the oncogenic properties of MLL fusion proteins .MI-1, MI-2, MI-3, and MI-NC ,133 reprFurthermore, MI-2 was shown to inhibit the oncogenic proliferation and to induce apoptosis of four MLL translocation-harboring human leukemia cell lines (by >95% in 12 d at 6 µM), but not of non-MLL leukemia lines Kasumi-1 and HAL-01 (≤16% inhibition at 12 µM) . At the Other reversible and irreversible menin inhibitors have been developed in the last years, all suppressing MEIS1 and HOXA9 expression without affecting adult hematopoiesis: MI-463 and MI-503 , MI-3454Krivtsov and colleagues  recentlyLoss of MLL binding led to changes in gene expression, differentiation, and induced apoptosis. Interestingly, VTP50469 antiproliferative activity in MLL-r cell lines induced the repression of MEIS1 but left the HOX-gene transcription intact and resulted in low toxicity. Inhibitor VTP50469 demonstrated for the first time that effective targeting of only MEIS1 is sufficient to abrogate the oncogenic properties of MLL-r cells. Mice treated with VTP50469 had substantial reductions of cancerous tissue, and many animals remained disease-free several weeks after treatment. Further research involving patient-derived xenograft models of NPM1-mutated AML also demonstrated responses to the menin inhibitor VTP50469 , again tTo date, two menin-MLL1 inhibitors  have demonstrated preclinical activity in HOXA/MEIS1 genes-driven leukemias, including MLL-r and NPM1-mutant AML, and have entered clinical testing with a focus on patients with MLL-rearrangements or NPM1 mutations.The catalytic activity of MLL1 is regulated by the formation of a multi-subunit complex consisting of MLL1, WD repeat domain 5 (WDR5), retinoblastoma binding protein 5 (RbBP5), and the Absent Small Homeotic-2-like protein (Ash2L) . In partStarting from the 12-mer MLL1 WIN peptide and through systematic analysis, Karatas and colleagues  determinHoxa9/Meis1-driven tumor in a mouse model.Spleen tyrosine kinase (SYK) signaling is another proposed target in AML. SYK promotes cellular differentiation and survival, and is expressed broadly in most hematopoietic cells. SYK expression is modulated by HOXA9 and MEIS1 . More spHox9/Meis1 regulatory loop and prolongs the survival of mice. Sensitivity to Syk inhibition has been linked to HoxA9 and Meis1 overexpression in preclinical studies [Inhibition of Syk disrupts the  studies . EntosplEntospletinib has been shown to produce complete remission rates of 83–90% in a phase Ib/II trial (NCT02343939) when administered in combination with chemotherapy to patients with AML bearing a HOXA9/MEIS1 signature  ,144, sugDirect MEIS1 downregulation in the treatment of AML represents an attractive target . HoweverBased on the high-resolution structure of the MEIS1 homeodomain, Turan and colleagues  recentlyThe two inhibitors identified using high-throughput in silico screening (MEISi-1 and MEISi-2) effectively reduced MEIS-Luc reporter activity in a dose-dependent manner. Furthermore, MEISi-1 and MEISi-2 were also effective in downregulating HIF-1a and Hif-2a in hematopoietic cells and several HSC quiescence modulators. At the same time, the expression profile of other TALE proteins, such as PBX, HOXA9, TGIF, and PREP1, were unaltered in vivo.Turan and colleagues demonstrated that both MEISi-1 and MEISi-2 injected intraperitoneally into wild-type mice were able to downregulate MEIS expression profile, induce HSCs expansion in the bone marrow , and downregulate MEIS target genes. The study of Turan and colleagues highlights the possibility to use MEIS1 DNA binding inhibitors as therapeutics for directly modulating the activity and the proliferation of HSCs.i/iPrep1, overexpressing Meis1), Meis1 acts as an oncogene and Prep1 antagonizes Meis1: when Prep1 is present, the oncogenic phenotype is suppressed/inhibited [One of the distinctive properties of the Meis1 and Prep1 dimerization with Pbx is often their increased stability in vivo ,42,47. Wnhibited , and is nhibited , it can In addition, Garcia-Cuellar and colleagues demonstrated that a 5-alanine mutation in the HR2 (L152A/F154A/L156A/L157A/L159A) partially stabilized Meis1 by prolonging the protein half-life and blocking proteasomal degradation. Indeed, in a cellular system expressing the Meis1-Pbx3 complex, Meis1 wild-type, or Meis1 mutant in the presence of cycloheximide decreased, after 2 h of treatment, the amount of Meis1-Pbx3 complex to 60%. At the same time, the amount of mutant Meis1 decreased to 20% and that of wild-type Meis1 to almost zero.The instability of Meis1 is due to its proteasomal degradation that leads to a short half-life when not in complex with Pbx3; complex formation with Pbx3 completely prevented the generation of ubiquitin-modified Meis1. Possibly, Pbx3 competes for the binding of a ubiquitin ligase; additionally, in immunoprecipitation studies, it was demonstrated by the same authors that in vitro Pbx3 increases Meis1 affinity for Hoxa9 in the presence of DNA . As mentAs most potential Meis1 inhibitors thus far developed are intended to act in the nucleus, they have to overcome the nuclear membrane barrier. It might be, therefore, more convenient to target MEIS1 before it reaches the nucleus, i.e., while or immediately after it has been synthesized. Newly synthesized MEIS1 or PREP1 immediately dimerizes with PBX, forming a stable complex that is then able to enter the nucleus and, in addition, acquire the ability to bind DNA.Therefore, preventing the formation of a MEIS1-PBX dimer would have a dual effect: not only the prevention of DNA binding before but also not allowing the localization of MEIS1-PBX to the nucleus. The MEIS1-PBX interaction domain was identified a long time ago, but only recently, due to its homology to the PREP1-PBX1 interaction domain, were the essential residues identified in detail . In addiTranscription factor MEIS1 drives myeloid leukemogenesis in the context of HOX gene overexpression; however, MEIS1 is still considered a challenging transcription factor to target, for the absence of structural details, besides the homeodomain . Neverth


Pelteobagrus fulvidraco, explored the mRNA tissue expression and deciphered their promoter regions. Yellow catfish TSPO and SMAD4 shared the similar domains to the corresponding genes from other vertebrates. The TSPO and SMAD4 mRNAs were widely expressed in the detected tissues, but at different levels. Several transcription factors were predicted, such as Sp, GATA, AP1, SOX1, SRY, STAT, HNF4α, PPARγ, Pu.1 and FOXL2. PPARγ overexpression increased but STAT3 overexpression reduced TSPO promoter activity, and FOXL2 overexpression inhibited the promoter activity of TSPO and SMAD4. The site mutation and EMSA analysis indicated that TSPO promoter possessed STAT3 and FOXL2 sites. Overall, our provided the novel understanding into the transcriptionally regulatory mechanisms of TSPO and SMAD4 in fish.The steroid hormones are required for gonadal development in fish. The present study was undertaken to characterize the cDNA and promoter sequences of TSPO and SMAD4 genes in yellow catfish  Mothethe fish . Similarthe fish . HoweverIn eukaryotic organisms, promoter regions have many cis-acting elements and regulate the expression of genes at the transcriptional level by binding with transcriptional factors . TherefoPelteobagrus fulvidraco, widely distributed freshwater fish, is an important economic fish in some Asian countries due to the high economic value and delicious meat quality. At present, studies were scarce on the regulation of steroidogenesis for the fish species [Yellow catfish  species . To thisP. fulvidraco were 952 bp and 2221 bp in length, respectively (The full-length cDNAs of TSPO and SMAD4 genes from ectively . Their pectively .P. fulvidraco had the five α-helix transmembrane structure (TM1–5), the C-terminal cholesterol-recognition amino acid consensus domain (CRAC) , the DNA binding motif, the nuclear localization and export signals (NLS and NES) (The TSPO protein sequence from n (CRAC) . The P. and NES) .P. fulvidraco, but their mRNA levels were variable among the tissues  did not compete with the PPARγ binding sequence as the probe, indicating that PPARγ could not bind with this region A. HoweveFor SMAD4 promoter, when the FOXL2 binding sequence was used as the probe, the 300-fold unlabeled FOXL2 binding site (−777 bp/−789 bp) did not compete for the binding, demonstrating that FOXL2 could not bind to this region D.TSPO and SMAD4 play an important role in steroid synthesis ,3, but tP. fulvidraco TSPO had similar domains with mammals, such as five α-helix transmembrane structure (TM1–5), the conserved CRAC domain in the C-terminus, in agreement with other studies [Our study found that the protein sequences of  studies ,19. Thes studies . Our stu studies ,22. Stud studies ,24. P. fulvidraco, indicating that steroid synthetase plays a wide role in these tissues. Similarly, studies suggested the major sites of steroidogenesis included several gonadal and non-gonadal tissues, such as head kidney, intestine, liver and adipose tissue [P. fulvidraco revealed the functional differentiation of these proteins and reflected the tissue-specific metabolic regulation. In this study, we demonstrated the tissue distribution of these genes, which provided the basis for elucidating their functions. Our results indicated that their mRNAs were existent in ten tissues in the e tissue ,26. Our e tissue  reportede tissue . The hige tissue . Their dIn eukaryotes, the identification of the core regions of promoters is crucial for exploring the mechanism of transcriptional initiation . In the The identification of transcription factor binding sites (TFBS) is significant to reveal the regulatory mechanism of genes . Our stuPPARγ, STAT3 and FOXL2 regulate the transcription of key enzymes in steroidogenesis ,38. The In summary, we cloned the cDNA sequences of TSPO and SMAD4 genes from yellow catfish, explored their mRNA tissue distribution, and characterized the TSPO and SMAD4 promoter regions. The promoters of TSPO and SMAD4 genes presented different structures on their core regions. The present study demonstrated that TSPO was the direct target gene of STAT3 and FOXL2. These studies are of great significance to demonstrate transcriptional regulation of key genes of steroid synthase in vertebrates.All animal experiments followed the Institutional Ethical Guidelines of Huazhong Agricultural University (HZAU) for using the vertebrates and were approved by the Experimentation Ethics Committee of our university  .The present study included two experiments. We cloned TSPO and SMAD4 genes, and explored their mRNA tissue distribution in Expt. 1, characterized the upstream 5′ flanking regions of the TSPO and SMAD4 genes and explored their promoter activity by the deletion and mutation analysis in Expt. 2. Juvenile yellow catfish  for cDNA and promoter cloning were purchased from a commercial farm . They were maintained in indoor cylindrical fiberglass tanks (300-L water volume) at ambient temperature for 2-week acclimation. All fish were fed a commercial pellet diet twice a day and provided with continuous aeration to maintain the dissolved oxygen level near saturation. At the end of 2-week acclimation, fish were fasted for 24 h and then euthanized with MS-222 (100 mg/L). The heart, brain, liver, kidney, muscle, spleen, fat, intestine, testis and ovary were sampled and stored at −80 °C for the subsequent analysis. The HEK293T cell line was from Cell Resource Center in Fishery College, Huazhong Agricultural University. Dulbecco’s Modified Eagle’s medium (DMEM), fetal bovine serum (FBS) and 0.25% trypsin-EDTA were from Gibco . Other reagents were from Sigma-Aldrich . Using ClonExpress II One Step Cloning Kit , the overexpression plasmid was labeled with pcDNA3.1 (+) vector.http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi (accessed on 20 April 2021)) was used to analyze the domains. RNA isolation and cDNA cloning of TSPO and SMAD4 followed the protocol described in our previous study . The 3′-−ΔΔCt method after normalizing to the geometric mean after the best combination of two genes, based on geNorm analysis. Prior to the analysis, we performed experiments to check the stability of housekeeping genes, and β-actin and ubce (M = 1.2669) presented the most stable expression level for analyzing the tissue distribution.qPCR method  was usedVia RNA ligase-mediated rapid amplification of 5′ cDNA ends (RLM-5′ RACE) method, we identified the 5′ cDNA sequences and the transcription start sites (TSS) of TSPO and SMAD4. We cloned the promoter sequence, based on the published draft genome of yellow catfish  and our http://www.genomatix.de/ and http://jaspar.genereg.net/ (accessed on 20 April 2021)). We used the Clustal-W multiple alignment to evaluate sequence alignments.For sequences analysis of the TSPO and SMAD4 promoters in yellow catfish, their TFBSs were predicted by the online tools on August 9, 2020 , based on the manufacturer’s instruction. The reporter plasmids were used in equimolar amounts in Opti-MEM . They were co-transfected with 20 ng pRL-TK as a control. After 4-h transfection, the medium was replaced by DMEM plus 10% FBS. Then, after 24-h incubation, we harvested cells to determine the luciferase activity by Dual-Luciferase Reporter Assay System  after calculating the ratio of firefly luciferase to Renilla luciferase. These experiments were performed in triplicates.To explore the overexpression-induced changes of promoter activities, we co-transfected the overexpression plasmid or the same amount of pcDNA3.1 (+) plasmid (control) with the TSPO and SMAD4 luciferase reporter plasmids into HEK293T cells using Lipofectamine 2000 . The remaining steps are the same as above.To identify the corresponding PPARγ, STAT3 and FOXL2 binding sites on the yellow catfish TSPO and SMAD4 promoters, we performed the site-directed mutagenesis, based on the instruction of QuickChange II Site-Directed Mutagenesis Kit . The pGl3-TSPO-2015 and pGl3-SMAD4-1558 were used as the template, respectively. The mutagenesis primers were presented in EMSA was performed to analyze the PPARγ, STAT3 and FOXL2 functional binding sites on the regions of TSPO and SMAD4 promoters after Xu et al. . Cytoplat test, we determined the differences between two groups. Difference was thought to be statistically significant at p < 0.05. The SPSS 19.0 for Windows  was used for the statistical analyses.The data were showed as means ± standard error of mean (SEM). Before the statistical analysis, all data were tested for distribution normality using the Kolmogornov-Smirnov test. Using the unpaired two-tailed Student’s


Transcription factors (TFs) control gene expression by direct binding to regulatory regions of target genes but also by impacting chromatin landscapes and modulating DNA accessibility for other TFs. In recent years several TFs have been defined that control cell fate decisions and effector functions in the immune system. Plasmacytoid dendritic cells (pDCs) are an immune cell type with the unique capacity to produce high amounts of type I interferons quickly in response to contact with viral components. Hereby, this cell type is involved in anti-infectious immune responses but also in the development of inflammatory and autoimmune diseases. To date, the global TF reservoir in pDCs early after activation remains to be fully characterized.To fill this gap, we have performed a comprehensive analysis in naïve versus TLR9-activated murine pDCs in a time course study covering early timepoints after stimulation  integrating gene expression (RNA-Seq) and chromatin landscape (ATAC-Seq) studies. To unravel the biological processes underlying the changes in TF expression on a global scale gene ontology (GO) analyses were performed. We found that 70% of all genes annotated as TFs in the mouse genome (1014 out of 1636) are expressed in pDCs for at least one stimulation time point and are covering a wide range of TF classes defined by their specific DNA binding mechanisms. GO analysis revealed involvement of TLR9-induced TFs in epigenetic modulation, NFκB and JAK-STAT signaling, and protein production in the endoplasmic reticulum. pDC activation predominantly “turned on” the chromatin regions associated with TF genes. Our in silico analyses pointed at the AP-1 family of TFs as less noticed but possibly important players in these cells after activation. AP-1 family members exhibit (1) increased gene expression, (2) enhanced chromatin accessibility in their promoter region, and (3) a TF DNA binding motif that is globally enriched in genomic regions that were found more accessible in pDCs after TLR9 activation.In this study we define the complete set of TLR9-regulated TFs in pDCs. Further, this study identifies the AP-1 family of TFs as potentially important but so far less well characterized regulators of pDC function.The online version contains supplementary material available at 10.1186/s12863-021-00991-2. TFs al states . These ll states . Chromatl states , 7, whicl states  and TFCal states , amongstl states , 11. ThiPlasmacytoid dendritic cells (pDCs) comprise a rare population of 0.2 to 0.8% of peripheral blood mononuclear cells . They weSpi-B-deficient mice, Runx2-deficient animals exhibited normal pDC development in the BM but reduced pDC numbers in the periphery due to a reduced egress of mature pDCs from the BM into the circulation [Tcf4-encoded TF E2–2 is essentially required for pDC development as either its constitutive or inducible deletion in mice blocked pDC differentiation [Over the last years, different TFs have been determined as cell fate-instructive TFs in DCs. In particular, absence of the interferon regulatory factor 8 (IRF8) resulted in pDC-deficient mice , 35. Borculation , 39. Finntiation . Using antiation . Even thntiation . As an aIn the present study, we performed a detailed analysis on the changes in expression and chromatin accessibility for the complete set of all known TFs in pDCs in an early time course after activation. To this purpose, we used the AnimalTFDB data base and combined RNA-Seq, ATAC-Seq, and Gene Ontology analyses to define global TF gene expression, chromatin landscapes, and biological pathways in pDCs following activation. We defined epigenetic and transcriptional states using purified murine BM-derived Flt3-L cultured pDCs 2 h, 6 h, and 12 h after TLR9 activation as compared to steady state. Based on our findings, we suggest a novel set of CpG-dependent TFs associated with pDC activation. We further identify the AP-1 family of TFs, which are so far less well characterized in pDC biology, as novel and possibly important players in these cells after activation., which takes into consideration the library length of the RNA-Seq run and the gene length to determine whether the gene is expressed or not [Drosophila melanogaster, among the unclassified TFs. Other TF families showed expression of all family members in at least one condition , such as the Transforming growth factor-β stimulated clone-22 (TSC22) family in the Basic Domain group, Runt and Signal Transducers and Activators of Transcription (STAT) factors from the β-scaffold classification, and E2F and Serum response factor (SRF) factors in the Helix-turn-helix group. In summary, 70% of all genes annotated as TFs in the mouse genome (1014 out of 1636) were expressed either in naïve or activated pDCs , covering a wide range of TF classes based on different DNA binding mechanisms.To assess the impact of pDC activation on global TF expression in these cells, we simulated early events after virus infection in a time course study. To this end, we performed RNA-Seq of sorted BM-derived Flt3-L pDCs from C57BL/6N mice that were either left untreated or stimulated with CpG for 2 h, 6 h, or 12 h. This synthetic double-stranded DNA specifically activates endosomal TLR9 and is known to induce a robust type I IFN production . As the d or not . We found or not   was higher than the absolute number of TFs showing expression changes between the CpG conditions . This reflects the results from the Pearson correlation analysis , and late-responding up and down-regulated TFs . In a first analysis, motifs of immediate TLR9 signaling dependent TFs  were screened for occurrence among the promoter regions (− 1000 of TSS) of early-responding TF genes. We found 308 and 360 significant motif hits (p < 0.0001) in the promoter sequences of early-responding TF genes that were up or down-regulated, respectively. The significant motif hits occurred with a similar frequency for IRF3, IRF7, NFkB1 and NFkB2 ranging from 69 to 87 and 69–114 for up and down-regulated TF genes, respectively  for up and down-regulated late-responding TF genes, respectively. Interestingly, two TF motifs dominated among late-responding TF genes, namely KLF6 and STAT1 that appear 768 and 245 times for up-regulated genes and 649 and 341 times for down-regulated late-responding TF genes, respectively  for the biological replicates used in the respective conditions of the RNA-Seq data set. Notably, the differences in the Pearson correlation coefficient between the naïve and first stimulation time point CpG 2 h) were higher than the differences observed between the later CpG stimulation time points 6 h, 12 h)  between stimulated pDCs vs naïve pDCs . To evaluate patterns of expression changes for all 661 differentially expressed TFs, we next carried out hierarchical clustering of all TF genes based on the normalized expression in naïve and stimulated pDCs  or classical DC (cDC)  development [p < 0.05, pDC at steady state, or after CpG activation at 2 h, 6 h, 12 h). Further, pDC activation showed time dependent activating as well as inhibiting effects on the expression of TFs.We next evaluated the overlap of up or down-regulated TF genes . We further found GO terms enriched for the IκB/NFκB complex, NIK/NFκB signaling, and IκB kinase/NFκB signaling , which showed the highest fold enrichment (up to 25 fold) among all GO terms and clusters. In line with this, it is well known that CpG activates the canonical TLR9-Myd88-NFκB/IRF7 signaling pathway in pDCs [Smad1, Smad2, Smad3), signal transducers for TGFβ receptors, involved in receptor binding, signal transduction, and protein complex assembly. Of note, it is known that pDCs exposed to TGFβ lose their ability to produce type I IFN after TLR9 stimulation [Cebpb, Ddit3), an important site of intracellular protein and lipid assembly. GO terms containing TFs that regulate sumoylation , posttranslational modifications that e.g. coordinate the repression of inflammatory gene expression during innate sensing [Stat1, Stat2, Stat3) activated by binding of type I IFN to the type I IFN receptor. TFs affecting mRNA binding processes  which are required for synthesizing proteins at the ribosomes, were also affected. The fact that epigenetic modulators  were enriched highlights the importance of gene expression regulation of TFs in pDCs by modifications that alter the physical structure of the DNA after CpG stimulation. In summary, we found that CpG-dependent TFs are involved in a wide variety of biological processes, such as circadian regulation, mRNA binding, and signaling pathways such as the NFκB and JAK-STAT pathways. The analyses revealed the importance of these biological processes being affected by pDC activation in a hierarchical manner according to their attributed relevance. This opens up the opportunity to investigate specific TFs involved in processes that have not been fully elucidated for pDC biology.Next, downstream gene ontology (GO) analyses of RNA-Seq data were performed to unravel the biological processes in which CpG-dependent TFs are involved. For this purpose, functional annotation clustering with the 661 TF encoding genes defined as CpG-dependent  was performed on DAVID including terms for biological processes (BP), molecular functions (MF), and cellular components (CC). The analysis produced 16 clusters, out of which the 9 non-redundant and most relevant in the context of innate immunity are depicted in Fig.  in pDCs . Anothermulation , which mmulation , 57. Ano sensing , were alp < 0.05, Fig. Another hallmark of cell activation is the modification of the chromatin landscape. To better understand how the chromatin accessibility of different TF families is altered in pDCs in the course of activation, we performed ATAC-Seq in naïve and 2 h CpG activated pDCs. Pearson correlation analysis for the ATAC-Seq data reveals > 95% similarity for all biological replicates Fig. . A quantTo unravel the biological significance of the activation-dependent chromatin states for the more accessible . Using the MEME-FIMO search tool, we found an AP-1 motif in the proximal promoter site of the Tcf4 gene which encodes the E2–2 protein  to 1 kb downstream of the poly adenylation site. pDC activation altered the chromatin landscape of ~ 750 accessible regions associated with TF genes  and Zbtb18 . Our ATAC-Seq data show 11 regions with significantly more accessible chromatin within the Tcf4 gene after 2 h CpG stimulation of pDCs, which may well explain the increased expression of the gene after TLR9 activation. Zbtb18, on the other hand, showed only one region downstream of the gene  that is characterized by increased chromatin accessibility, but no significant chromatin changes in the promoter region or within the gene. The detected region downstream of Zbtb18 may represent an enhancer region that regulates its gene expression but requires further verification. Leylek et al. reported ZBTB18 as a zinc finger TF to be specifically expressed and active in human pDCs [Using CpG as an optimal TLR9 agonist and focusing on early events after virus infection, we found that after pDC activation more of the pDC chromatin landscape is "turned on" rather than "turned off“, both globally in the genome and also among the regions associated with TF genes themselves. Specifically, about 80% of all regions that show significant chromatin changes exhibited increased accessibility for TFs. However, with regard to gene expression, 2 h after pDC activation more genes were down-regulated than up-regulated as compared to the naïve state. One explanation could be that while DNA is more accessible, the TFs that possibly bind to these DNA stretches may inhibit rather than activate gene expression. Leylek et al. recently performed a global analysis of chromatin modulations in primary human DC populations including pDCs stimulated for 48 h by the TLR7 ligand imiquimod or CD40L . In contis paper . We, howman pDCs . Thus, tIn contrast, Ets family members belonging to the Helix-turn-helix family of TFs and Zinc-coordinating zf-C2H2 TFs were both found to have less access to DNA after pDC activation. Ets family members include SPI1, also known as PU.1, which has been shown to drive the development of precursor cells toward cDC rather than pDC development . RegardiGene expression of the key pDC cell fate TFs IRF8, E2–2, and RUNX2 has been shown to steadily increase in expression during pDC precursor development into fully differentiated pDCs –38. Howe. A connection has been established between NFκB and AP-1 activity, which may be regulated by NFκB [Tcf4 gene, a prominent pDC cell fate TF. Grajkowska et al. showed that there are two Tcf4 isoforms, the expression of which is controlled during pDC differentiation by two respective promoters as well as distal enhancer regions within 600–900 kb 5′ and ~ 150 kb 3′ of the Tcf4 gene [Jun expression and chromatin structure combined with an increased global DNA binding accessibility in pDCs after activation. Further studies are required to assess the role of Jun regulation in pDCs upon a microbial stimulus or in a chronically activated state that might unravel unknown functions of this TF in immunity. While targeting TFs for therapeutic purpose has been proven difficult so far, recent advances have been made through novel chemistries and the use of staples peptides to disrupt protein-protein interactions [Our results therefore warrant further investigations of pDC cell fate TFs to explore the biological relevance of distinct expression patterns as well as the simultaneous gain and loss of accessibility to DNA by modulation of chromatin after pDC activation. We found that IRF7, NFκB1, and RELA as well as ATF3 and JUN, two AP-1 family members, fulfil three criteria relevant in this context: They exhibit (i) increased gene expression, (ii) enhanced chromatin accessibility for their gene regions, and (iii) enriched TF DNA binding motifs in the accessible genomic regions after pDC stimulation. We used this integrative omics approach to identify potential novel players important in pDC biology after cell activation. Increased gene expression (criterium i) of NFκB and IRF genes in TLR9 activated pDCs has been described already. It has been shown that pDCs sense MCMV via TLR9 mediated pathways  and that by NFκB  suggesti by NFκB . They sp by NFκB . In contcf4 gene . Howevercf4 gene . The halcf4 gene  and has cf4 gene . Our anaractions , 90. ThuIn the present study, we performed a detailed analysis on the changes in expression and chromatin accessibility for the complete set of all known TFs in pDCs for early time points after activation. To this purpose, we used the AnimalTFDB data base and combined RNA-Seq, ATAC-Seq, and Gene Ontology analyses to define global TF gene expression, chromatin landscapes, and biological pathways in these cells. Based on our findings, we suggest a newly defined set of TLR9-dependent TFs associated with pDC activation. We further identify the AP-1 family of TFs, which are so far less well characterized in pDC biology, as possibly important players in these cells after activation.C57BL/6N mice were originally obtained from Charles River Laboratories and housed under specific pathogen-free conditions in the animal research facility of the University of Düsseldorf strictly according to the German Animal Welfare Act. The mice were euthanized by cervical dislocation before bone marrow was harvested. All experiments were performed using bone marrow from sex and age matched littermates between 7 to 14 weeks of age.−CD19−CD11c+CD11blowB220+SiglecH+ CD317+) were FACS purified using FACS Aria III (BD). The pDCs were left untreated or stimulated with 1 μM CpG 2216  complexed to transfection reagent DOTAP (Roche) for 2 h, 6 h or 12 h. RNA was isolated by using the NucleoSpin II RNA mini kit (Macherey-Nagel) and subjected to RNA-Seq. For ATAC-Seq BM-derived pDCs (CD3−CD19−CD11c+CD11blowB220+SiglecH+CD317+) were FACS purified using FACS Aria III (BD). The pDCs were left untreated or stimulated with 1 μM CpG 2216 complexed to transfection reagent DOTAP (Roche) for 2 h. At the end of stimulation time, cells were kept on ice and stained for 7AAD (BD). Live cells (7AAD−) were further purified by FACS and kept frozen in complete RPMI medium containing 5% DMSO. The frozen cells were transported on dry ice to Active Motif (Belgium) for ATAC-Seq.BM-derived Flt3-L cultured pDCs were generated as previously described . For RNAThe following antibodies have been used: CD3-PerCP , CD19-PerCP-Cy5.5 , CD11c-PE-Cy7 , CD11b-APC-Cy7 , B220-FITC , SiglecH-APC , CD317-PE .DNase digested total RNA samples used for transcriptome analyses were quantified  and quality measured by capillary electrophoresis using the Fragment Analyzer and the ‘Total RNA Standard Sensitivity Assay’ . All samples in this study showed high RNA Quality Numbers . The library preparation was performed according to the manufacturer’s protocol using the Illumina® ‘TruSeq Stranded mRNA Library Prep Kit’. Briefly, 200 ng total RNA were used for mRNA capturing, fragmentation, the synthesis of cDNA, adapter ligation and library amplification. Bead purified libraries were normalized and sequenced on the HiSeq 3000/4000 system  with a read setup of SR 1 × 150 bp. The bcl2fastq tool was used to convert the bcl files to fastq files as well for adapter trimming and demultiplexing.Mus musculus   genome sequence. Samples  were grouped according to their respective experimental condition. Raw counts were next re-uploaded to the Galaxy web platform. The public server at usegalaxy.org was used to perform multi-group comparisons [p values were corrected for multiple testing by the false discovery rate (FDR) [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE170750 ).Data analyses on fastq files were conducted with CLC Genomics Workbench . The reads of all probes were adapter trimmed (Illumina TruSeq) and quality trimmed . Mapping was done against the parisons . Differeparisons , 94. Lowparisons . The reste (FDR) . A p valCells were harvested and frozen in culture media containing FBS and 5% DMSO. Cryopreserved cells were sent to Active Motif to perform the ATAC-Seq assay. The cells were then thawed in a 37 °C water bath, pelleted, washed with cold PBS, and tagmented as previously described , with sop-value 1e-7, without control file, and with the –nomodel option. Peaks that were on the ENCODE blacklist of known false ATAC-Seq peaks were removed. Signal maps and peak locations were used as input data to Active Motifs proprietary analysis program, which creates Excel tables containing detailed information on sample comparison, peak metrics, peak locations, and gene annotations. For differential analysis, reads were counted in all merged peak regions (using Subread), and the replicates for each condition were compared using DESeq2. ATAC-Seq data are deposited with NCBI’s GEO and are accessible through GEO Series accession number GSE171075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171075).Reads were aligned using the BWA algorithm . Duplicate reads were removed, only reads mapping as matched pairs and only uniquely mapped reads  were used for further analysis. Alignments were extended in silico at their 3′-ends to a length of 200 bp and assigned to 32-nt bins along the genome. The resulting histograms  were stored in bigWig files. Peaks were identified using the MACS 2.1.0 algorithm at a cut off of https://software.broadinstitute.org/morpheus). Pearson correlation matrices were calculated in R and plotted as heatmaps using gplots [www.graphpad.com). ATAC-Seq peaks were visualized using IGV [Volcano plots were created using ggplot2  and ggreg gplots . Pathwayg gplots . Venn dig gplots . Evolutig gplots . Bar grasing IGV , 105.p < 0.05) were used for motif analysis. The regions were adjusted to the same size (500 bp). The MEME-Centrimo differential motif analysis pipeline [ATAC-Seq regions that indicated differentially accessible chromatin regions between naive and 2 h CpG stimulated samples (DESeq2, |FC| > 2, pipeline  was run Additional file 1: Supplemental Table S1. FIMO analysis of immediate signaling-dependent TF motifs among promoters of early-responding TFs that are more expressed at 2 h vs steady state.Additional file 2: Supplemental Table S2. FIMO analysis of immediate signaling-dependent TF motifs among promoters of early-responding TFs that are less expressed at 2 h vs steady state.Additional file 3: Supplemental Table S3. FIMO analysis of early-responding TF motif presence in promoters of up-regulated late-responding TF genes.Additional file 4: Supplemental Table S4. FIMO analysis of early-responding TF motif presence in promoters of down-regulated late-responding TF genes.Additional file 5: Supplemental Table S5. Functional cluster analysis with 661 CpG-dependent TF genes.Additional file 6: Supplemental Table S6. Centrimo motif enrichment analysis using the HOCOMOCO database and 2174 gene promoter associated regions with increased chromatin accessibility after pDC activation.Additional file 7: Supplemental Table S7. FIMO analysis of immediate signaling-dependent TF motifs among promoters of early-responding TFs that are more expressed and have more accessible chromatin at 2 h vs steady state.