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Hematopoietic stem cell (HSC) ontogeny is accompanied by dynamic changes in gene regulatory networks. We performed RNA-seq and histone mark ChIP-seq to define the transcriptomes and epigenomes of cells representing key developmental stages of HSC ontogeny in mice. The five populations analyzed were embryonic day 10.5 (E10.5) endothelium and hemogenic endothelium from the major arteries, an enriched population of prehematopoietic stem cells (pre-HSCs), fetal liver HSCs, and adult bone marrow HSCs. Using epigenetic signatures, we identified enhancers for each developmental stage. Only 12% of enhancers are primed, and 78% are active, suggesting the vast majority of enhancers are established de novo without prior priming in earlier stages. We constructed developmental stage-specific transcriptional regulatory networks by linking enhancers and predicted bound transcription factors to their target promoters using a novel computational algorithm, target inference via physical connection (TIPC). TIPC predicted known transcriptional regulators for the endothelial-to-hematopoietic transition, validating our overall approach, and identified putative novel transcription factors, including the broadly expressed transcription factors SP3 and MAZ. Finally, we validated a role for SP3 and MAZ in the formation of hemogenic endothelium. Our data and computational analyses provide a useful resource for uncovering regulators of HSC formation.
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CD123 (α-subunit of IL-3 receptor) expression on primitive and committed human hematopoietic cells was studied by multicolor sorting and single-cell culture. The sources of cells included fetal liver (FLV), fetal bone marrow, umbilical cord blood, adult bone marrow and mobilized peripheral blood. Three subsets of CD34+ cells were defined by the levels of surface CD123: CD123negative, CD123low, and CD123bright. Coexpression of lineage markers showed that a majority of CD34+CD123bright cells were myeloid and B-lymphoid progenitors, while erythroid progenitors were mainly in the CD34+CD123negative subset. The CD34+CD123low subset contained a heterogeneous distribution of early and committed progenitor cells. Single CD34+ cells from the CD123 subsets were cultured in a cytokine cocktail of stem cell factor, interleukin 3 (IL-3), IL-6, GM-CSF, erythropoietin, insulin-like growth factor-1, and basic fibroblast growth factor. After 14 days of incubation, a higher cloning efficiency (CE) was observed in the CD34+CD123negative and CD34+CD123low fractions (37 ± 23% and 44 ± 23%, respectively) than in the CD34+CD123bright fraction (15 ± 21%). Using previously published criteria that colonies containing dispersed, translucent cells (dispersed growth pattern, DGP) were derived from primitive cells and that colonies composed solely of clusters were from committed cells, early precursors were distributed evenly in the CD34+CD123negative and CD34+CD123low subsets. When CD38 and CD90 (Thy-1) were used for further characterization of CD34+ cells from FLV, CE increased from 37 ± 23% in CD123negative to 70 ± 19% in CD123negativeCD38− and from 44 ± 23% in CD123low to 66 ± 19% in CD123lowCD38−. No significant increase in CE or DGP progenitors was observed when CD34+ cells were sorted by CD90 and CD123. We concluded that: A) high levels of CD123 were expressed on B-lymphoid and myeloid progenitors; B) early erythroid progenitors had little or no surface CD123, and C) primitive hematopoietic cells are characterized by CD123negative/low expression.
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TGF-β and its role in hemopoietic regulation Get access Ke-Fu Wu, Ke-Fu Wu Institute of Hematology, Chinese Academy of Medical Sciences, Tianjin, People's Republic of China Search for other works by this author on: Oxford Academic Google Scholar Yong-Ming Zhu, Yong-Ming Zhu Institute of Hematology, Chinese Academy of Medical Sciences, Tianjin, People's Republic of China Search for other works by this author on: Oxford Academic Google Scholar Mu Li, Mu Li Institute of Hematology, Chinese Academy of Medical Sciences, Tianjin, People's Republic of China Search for other works by this author on: Oxford Academic Google Scholar Qing Rao Qing Rao Institute of Hematology, Chinese Academy of Medical Sciences, Tianjin, People's Republic of China Search for other works by this author on: Oxford Academic Google Scholar The International Journal Of Cell Cloning, Volume 9, Issue S1, 1991, Page 235, https://doi.org/10.1002/stem.5530090728 Published: 18 March 2009
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Article26 October 2020Open Access Transparent process Interactions between lineage-associated transcription factors govern haematopoietic progenitor states Iwo Kucinski orcid.org/0000-0002-9385-0359 Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Nicola K Wilson Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Rebecca Hannah Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Sarah J Kinston Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Pierre Cauchy Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany Search for more papers by this author Aurelie Lenaerts Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany International Max Planck Research School for Molecular and Cellular Biology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany Search for more papers by this author Rudolf Grosschedl orcid.org/0000-0002-0058-1250 Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany Search for more papers by this author Berthold Göttgens Corresponding Author [email protected] orcid.org/0000-0001-6302-5705 Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Iwo Kucinski orcid.org/0000-0002-9385-0359 Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Nicola K Wilson Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Rebecca Hannah Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Sarah J Kinston Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Pierre Cauchy Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany Search for more papers by this author Aurelie Lenaerts Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany International Max Planck Research School for Molecular and Cellular Biology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany Search for more papers by this author Rudolf Grosschedl orcid.org/0000-0002-0058-1250 Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany Search for more papers by this author Berthold Göttgens Corresponding Author [email protected] orcid.org/0000-0001-6302-5705 Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK Search for more papers by this author Author Information Iwo Kucinski1, Nicola K Wilson1, Rebecca Hannah1, Sarah J Kinston1, Pierre Cauchy2, Aurelie Lenaerts2,3, Rudolf Grosschedl2 and Berthold Göttgens *,1 1Wellcome–MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK 2Department of Cellular and Molecular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany 3International Max Planck Research School for Molecular and Cellular Biology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany *Corresponding author. Tel: +44 1223 336829; E-mail: [email protected] EMBO J (2020)39:e104983https://doi.org/10.15252/embj.2020104983 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Recent advances in molecular profiling provide descriptive datasets of complex differentiation landscapes including the haematopoietic system, but the molecular mechanisms defining progenitor states and lineage choice remain ill-defined. Here, we employed a cellular model of murine multipotent haematopoietic progenitors (Hoxb8-FL) to knock out 39 transcription factors (TFs) followed by RNA-Seq analysis, to functionally define a regulatory network of 16,992 regulator/target gene links. Focussed analysis of the subnetworks regulated by the B-lymphoid TF Ebf1 and T-lymphoid TF Gata3 revealed a surprising role in common activation of an early myeloid programme. Moreover, Gata3-mediated repression of Pax5 emerges as a mechanism to prevent precocious B-lymphoid differentiation, while Hox-mediated activation of Meis1 suppresses myeloid differentiation. To aid interpretation of large transcriptomics datasets, we also report a new method that visualises likely transitions that a progenitor will undergo following regulatory network perturbations. Taken together, this study reveals how molecular network wiring helps to establish a multipotent progenitor state, with experimental approaches and analysis tools applicable to dissecting a broad range of both normal and perturbed cellular differentiation landscapes. SYNOPSIS Principles of gene regulation underlying cellular differentiation remain poorly understood. Combining sequencing and computational methods, this resource defines the complex transcription factor (TF) network of multipotent blood progenitors, and reveals how coregulation by lineage-associated transcription factors maintains murine hematopoiesis. Individual depletion of 39 TFs identifies the gene networks depending on them in a defined lympho-myeloid progenitor cell line model. Some 17,000 TF-target interactions reveal co-regulation, TF regulatory hierarchies, and target gene modules. Lymphoid TFs Gata3 and Ebf1 contribute to a CEBPA-dependent early myeloid program. Scoring inferred directions of cellular transition using single-cell RNA sequencing landscapes aids interpretation of the perturbation data. Introduction Mature blood cells are continuously replenished by a flow of differentiating cells originating from multipotent, self-renewing haematopoietic stem cells (HSCs), which give rise to multi, oligo and unipotent progenitors with decreasing self-renewal potentials. Potential structures for this differentiation hierarchy ("haematopoietic tree") have been proposed through decades of iterative sampling of cell subpopulations and functional testing using transplantation or colony assays (Eaves, 2015; Laurenti & Göttgens, 2018). More recently, single-cell functional assays, scRNA-Seq and barcoding approaches emphasise the landscape view of haematopoietic differentiation, proposing more gradual differentiation trajectories and a more probabilistic nature of lineage choices (Fig 1A; Nestorowa et al, 2016; Pei et al, 2017; Dahlin et al, 2018; Rodriguez-Fraticelli et al, 2018; Tusi et al, 2018; Watcham et al, 2019; Weinreb et al, 2020). Importantly, models based merely on the cataloguing of molecular data remain descriptive, with little insight into the mechanisms behind cellular decision making as cells traverse the differentiation landscape. The concept of differentiation landscapes was introduced by Waddington (Waddington, 1957), who proposed, right from the start, that beneath the landscape there had to be a complex molecular network which by, determining the shape of the landscape, controls cellular decision making. Figure 1. CRISPR/Cas9 screen with a transcriptomics readout Understanding the cell state landscapes of haematopoietic progenitors. (left) Annotated UMAP projection of a scRNA-Seq landscape—mouse LK + LSK populations (Dahlin et al, 2018) (middle) diagram representing haematopoietic hierarchy with gradual changes in cell fate potential (colour gradient) from HSCs to differentiated states (adapted from the Molecular Cell Biology, 9th edition under preparation). (right) Diagram of a molecular network. Differentiation capacity of Hoxb8-FL cells. Projection of Hoxb8-FL transcriptome onto the LK/LSK mouse landscape. The projection score (based on nearest neighbours) reflects relative transcriptional similarity to the Hoxb8-FL state for each LK/LSK cell. For the majority of cells, no neighbours are identified (grey), some cells exhibit low similarity (yellow), and a small set of cells exhibit high similarity (blue). Schematic of the screen layout, sgRNAs were cloned into the pBA439 backbone and introduced into Hoxb8-FL cells via lentiviral infection. Cells were cultured for either 2 or 4 days, followed by sorting for cells carrying sgRNA constructs (BFP+) into small pools and subsequent small-scale RNA-Seq analysis. Reproducibility of 2 independent experiments—correlation of observed changes in expression. Blue line indicates the linear fit with shaded areas as confidence intervals. Data information: Abbreviations: Meg—megakaryocytes, HSC—haematopoietic stem cells, Ery—erythrocytes, MC—mast cells, Bas—basophils, Mono—monocytes, DC—dendritic cells, Neu—Neutrophils, Ly—lymphoid, Myo—myeloid. Download figure Download PowerPoint Deciphering complex regulatory networks constitutes a formidable task due to the large number of components and an even larger number of possible interactions. For the past decade or so, much hope has been pinned on inference based on correlative evidence, e.g. trying to explain transcriptional regulation from variation in gene expression across many conditions or samples. Correlative inference approaches, however, commonly lack the means to identify causality or directionality. Regression-based methods, bayesian networks and differential equation models have all been proposed to overcome these shortcomings (Sanguinetti & Huynh-Thu, 2019), but unfortunately have had limited success so far (Marbach et al, 2012; Chen & Mar, 2018; Pratapa et al, 2020). Genome-wide binding profiles are often used for cross-validation. These, however, also face limitations, because singular TF binding events constitute a poor predictor of gene regulation (ENCODE Project Consortium, 2012; Calero-Nieto et al, 2014; Kellis et al, 2014; Vijayabaskar et al, 2019). Arguably, the main limitation is the lack of gold standards—sets of verified, functional connections which can be used to objectively evaluate and refine inference methods. There is a growing appreciation therefore that renewed emphasis needs to be given to direct experimental intervention as the way of identifying causal links. Targeted genetic/chemical perturbations have been used to reconstruct small networks (Jaeger, 2011; Briscoe & Small, 2015; Hill et al, 2016) with considerable success. However, scalability of conventional "functional" experiments has been limited. The CRISPR/Cas9 revolution has now firmly established the feasibility of large-scale gene perturbation screens. Moreover, the miniaturisation of next-generation sequencing protocols allows for significant cost savings thus enabling scalable genetic perturbations with simultaneous transcriptomic readout (Datlinger et al, 2017). In this study, we constructed an experimentally defined network connecting 39 TFs—chosen key regulators of haematopoietic differentiation—with their downstream targets. Due to extensive heterogeneity of primary cells and difficulties in maintaining their steady-state ex vivo, we utilised a multipotent cell line model—Hoxb8-FL (Redecke et al, 2013). By establishing a scalable screening pipeline to knock out single TFs and analyse the resulting transcriptomic changes by RNA-Seq, we identified 16,992 TF-target regulatory links across 7,388 target genes, revealing a range of target gene modules associated with specific functions including the maintenance of self-renewal and preventing dominance of specific lineage-specific programmes. To help attribute biological functions to analysed TFs, we also propose a new method—DoT score—which aids interpretation of transcriptomic changes using scRNA-Seq landscapes as a reference. Results A sensitive and scalable method to infer TF-target connections Hoxb8-FL cells represent a functional in vitro counterpart to lymphoid-primed multipotent progenitors (LMPP), which can be maintained as a self-renewing culture in the presence of Flt3 ligand and activation of a Hoxb8 oestrogen receptor fusion transgene, and can differentiate to myeloid and lymphoid cells both in vitro and in vivo (Redecke et al, 2013) (Fig 1B). To relate Hoxb8-FL cells to their likely counterparts in primary cell transcriptional landscapes, we identified the nearest neighbour cells connecting our previously published landscape of over 40,000 mouse HSPCs (Dahlin et al, 2018) with 82 single-cell transcriptomes from Hoxb8-FL cells cultured in self-renewal conditions (Basilico et al, 2020). As shown in Fig 1C, the primary HSPC cells that are most transcriptionally similar to Hoxb8-FL cells occupy a defined territory between myeloid and lymphoid progenitors, consistent with their LMPP-like properties. The value of Hoxb8-FL cells as a model for haematopoietic progenitors is enhanced further by previously generated genome-wide CRISPR/Cas9-dropout screen data (Basilico et al, 2020), which highlight genes critical for self-renewal of Hoxb8-FL cells. To establish functional links between TFs and their targets, we developed a CRISPR/Cas9-RNA-Seq screening approach (Fig 1D). Each TF was perturbed independently by three sgRNAs, introduced via lentiviral infection into Cas9-expressing Hoxb8-FL cells. These were subsequently analysed for transcriptomic changes after 2 or 4 days using an adapted Smart-Seq2 protocol (Picelli et al, 2014; Bagnoli et al, 2018) on 8 pools each of 375 cells. We avoided previously reported barcode recombination (Xie et al, 2018) by producing viral particles and infecting cells separately. As a negative control, we used two control constructs: sgRNA targeting GFP (sequence not present in the genome) and sgRNA targeting the Rosa26 locus. In parallel, we analysed pools of cells after switching off Hoxb8 ectopic expression for 18 h but maintaining Flt3L signalling (Hoxb8*), a condition ultimately leading to dendritic cell differentiation after 4–5 days. Gene knockout efficiency was confirmed by targeting the ubiquitously expressed CD45 locus, which was successfully inactivated in 48% of cells (Fig EV1A). Moreover, CRISPR/Cas9 perturbation also resulted in the loss of the corresponding TF protein as validated by the absence of Gata3 ChIP-Seq signal in single-cell clones derived from cells targeted with the Gata3 guide RNAs (Appendix Fig S6). Furthermore, high-throughput sequencing of loci targeted by 11 sgRNAs across 4 genes showed consistent frameshift in 30–50% DNA copies (Fig EV1B, Table EV1), indicating that targeted populations will contain some heterozygous and WT cells despite efficient editing. To ensure high-sensitivity in detecting expression changes, we therefore performed 8 replicate RNA-Seq experiments per condition (Fig EV1C). Differential expression (DE) statistic between matching perturbed and control samples was used to identify regulator–target relationships, with the observed log2(fold change) providing the weights for the resulting network edges. Two independent experiments targeting Gata3 show strong overlap and effect correlation across target genes (Fig 1E), and there is a strong correlation among the 3 sgRNAs targeting the same gene (Fig EV1D and F). Click here to expand this figure. Figure EV1. Parameters critical for the CRISPR/Cas9 screen Flow cytometry analysis of Hoxb8-FL cells transduced with sgRNA targeting CD45 (the Ptprc gene). Successfully transduced cells are BFP+, and mutant cells are BFP+, CD45−. Ptprc is successfully mutated in 48% of transduced cells, whereas almost all non-transduced cells remain CD45+. High-throughput sequencing analysis of genomic DNA reads with frameshift mutations at predicted cutting sites following treatment of Hoxb8-FL cells with 11 different sgRNAs. Experimental design applied to screening of 38 transcription factors, each gene was targeted with 3 sgRNAs in 8 replicates. Two sets of controls were used: sgRNA targeting the Rosa26 locus and sgRNA targeting a GFP sequence (absent in the genome). Hoxb8 ectopic expression was disabled by β-oestradiol withdrawal. R2 values for observed changes in expression for each pair of sgRNAs targeting the same gene (using genes differentially expressed in 2 out of 3 comparisons). A heatmap representing genes differentially expressed between the Gata3 sgRNA treated and control cells at all assayed time-points. The signature observed in the first three time-points disappears from 7 days onwards. Fraction of intronic reads is displayed above the heatmap. Barplot below shows the number of differentially expressed genes at each time-point. Related to (D), an example of correlation in gene expression changes across three sgRNAs targeting Gata3 sgRNA. Analysis performed using genes differentially expressed in at least 2 out of 3 comparisons. Blue line indicates the linear fit with shaded areas as confidence intervals. Relative survival analysis of cells transduced with sgRNAs against Cebpa, Gata3 and Myc. Control cells treated with sgRNAs targeting GFP or Rosa26 loci indicate background fluorescent population changes, with only a small loss of the positive population. The fraction of BFP+ has been normalised to a parallel control performed in Hoxb8 cells not expressing the Cas9 protein. Error bars—standard error of the mean. R26—4 replicates, GFP—2 replicates, Cebpa, Gata3, Myc—3 replicates per condition. Download figure Download PowerPoint Choice of time-point for the analysis is critical. There is a fine balance between the risk of analysing cells before the protein is sufficiently depleted if analysed too early and skewing data towards secondary (and higher order) effects at later time-points. Additionally, it takes approximately 1 day for the viral construct to integrate and transcribe/translate after the infection. When targeting the non-essential Gata3, we observed robust and reproducible signal between days 3 and 5 after perturbation (Fig EV1E); hence, we chose the 4 day time-point to provide sufficient time for gene knockout effects. For essential genes, we analysed cells mostly after 2 days to precede the drop in cell survival, as observed after removing Cebpa or Myc (Fig EV1G). A functional network of haematopoietic transcription factors We next applied the approach outlined above to identify the downstream targets of 38 TFs, chosen based on their haematopoietic function and expression in progenitor cells (Dataset EV1). We also assayed a cohesin complex component—Rad21, which plays an important role in haematopoiesis (Panigrahi & Pati, 2012) and regulates expression of pluripotency genes (Nitzsche et al, 2011). Twelve out of these 39 genes are essential for survival of Hoxb8-FL cells, i.e. their knockout leads to a competitive disadvantage when cultured with WT cells (Basilico et al, 2020) ("Dropout TFs"). Bioinformatic analysis of the more than 1,000 newly generated RNA-Seq datasets revealed a network of 39 TFs connected via 16,992 edges with 7,388 downstream target genes, i.e. differentially expressed following perturbation of one or more TFs (Dataset EV2). The number of differentially regulated genes included within the network is dependent on the chosen threshold, which balances sensitivity and specificity, and thus, some targets may have escaped our detection. Fig 2A and B provides specific numbers of target genes, and the network structure visualised as a force-directed layout, chosen subsets of the data are shown in Fig EV2. The periphery of the network is occupied by genes regulated by single TFs, whereas the centre contains coregulated genes (i.e. genes which are downstream of > 1 TF). Large groups of double-regulated targets can be distinguished in between the two zones. We observed large transcriptomic changes for 10 TFs, previously not identified as essential in Hoxb8-FL cells (Basilico et al, 2020) (> 200 target genes). Reassuringly, we detected strong effects for several essential TFs, proving that analysis at an appropriate time-point permits the capture of transient cell stages. For more detailed downstream analysis, we focused on the 19 TFs with > 200 targets (essential + non-essential) and considered three aspects of the network: how TFs coregulate their targets, how TFs regulate each other's expression and which target genes form functional modules with common regulatory mechanisms. Figure 2. A functional network connecting 39 transcription factors with their targets Number of target genes identified using differential expression for each assayed TF. *genes identified as essential for self-renewal of Hoxb8-FL cells in (Basilico et al, 2020). A force-directed graph displaying perturbed transcription factors (orange dots) and target genes (grey dots). Edges indicate if the target gene is differentially expressed, blue for genes downregulated and red for genes upregulated. Size of the nodes is proportional to their degrees. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Key TF subnetworks controlling the progenitor state A–D. Subgraphs isolated from Fig 2B. In each panel, the indicated TFs were isolated together with their downstream targets. Download figure Download PowerPoint TF coregulation, regulatory modules and common regulatory mechanisms Focussing on the 19 TFs with > 200 targets, we next calculated gene overlaps and correlations in expression changes for all pairs of TFs to highlight potential functional relationships between them (Fig 3A and B, Appendix Fig S3A and B). As expected, Myc and Max, known to operate in the same complex, share a large fraction of target genes with very high correlation. Of note, not all TFs exhibit strong target overlap as Fos shares only a small fraction of its 228 targets with other factors. Importantly, defining target genes by gene expression changes means that the network model will contain primary and secondary (or higher order) targets. Thus, a target gene with two upstream regulators (TF1, TF2) may receive both inputs in parallel or sequentially. Consequently, if TF1 activates TF2, their shared targets would be expected to change in the same direction. Our network shows examples of such behaviour (see below), but it may not be a universal trait due to other regulatory factors (e.g. a feed forward loop dampening the response) or the time required to manifest secondary and tertiary effects. While resolution of primary and secondary targets is difficult without dynamic data, our network captures some hierarchical regulation, as it contains information on cross-regulation of the 19 TFs (Fig 3C). A case in point is Cebpa, a key myeloid regulator and essential for Hoxb8-FL cell survival (Avellino & Delwel, 2017; Basilico et al, 2020). We detect 748 genes downstream of Cebpa, including a large number of myeloid factors such as Irf8, Trem3, Prtn3, Hp and Anxa3 (Appendix Fig S2A). A wide range of TFs bind the Cebpa locus (Cooper et al, 2015; Avellino et al, 2016) but their relevance was unclear (Avellino & Delwel, 2017). Our network pinpoints the Cebpa regulators Erg, Lmo2 and an unexpected input from Gata3. An example of cross-regulation of TFs through core circuits is illustrated by the observation that Cebpa, Gata3 and Lmo2 coregulate 37 genes, including activation of myeloid genes like Prtn3, Mmp8, Ctsg, Anxa3, Nrg2 and suppression of B-cell genes Cd79a, Mzb1, Myl4 or megakaryocytic gene Cd9 (Fig EV2A, Appendix Fig S2C). Figure 3. Network analysis provides insight into hierarchy and relations among TFs and their downstream transcriptional programmes A representation of the pairwise degree of overlap in targets (size of the circle) and correlation in gene expression changes (colour) among overlapping targets for indicated TF perturbations. Red indicates positive correlations and blue negative correlations. Network view of (A) showing relations among TFs based on their target correlation/anti-correlation. Edge width is proportional to the absolute value of the correlation. To increase readability, connections with |correlation| < 0.4 are not plotted; all correlation values are shown in A. Network view of TF-TF cross-regulation. Directed edges indicate how transcription factors regulate each other's expression. Edge width is proportional to the magnitude of gene expression change (for clarity capped at a value corresponding to absolute log2(fold change) of 0.8). Identification of target gene modules—groups of genes with common regulatory patterns by TFs. Colour indicates the fold change (adjusted for significance) of gene expression following each TF perturbation. Rows (perturbed TFs) and columns (target genes) are hierarchically clustered. Forty-four modules for target genes are shown. Modules: 1, 2 and 3 are omitted for clarity, all modules are listed in Dataset EV3. Selected modules with highlighted overall regulation pattern and example genes listed below. Gene enrichment analysis for indicated modules is provided in Dataset EV5. Data information: For clarity, only TFs with > 200 target genes detected are shown in all panels. Data for all TFs are available in Appendix Fig S3A and B. Hoxb8*—gene ectopic expression is disabled by β-oestradiol withdrawal. Download figure Download PowerPoint Hoxb8-FL cells rely on Hoxb8 activation to suppress myeloid differentiation (Redecke et al, 2013). Interrogation of our network model reveals that Hoxb8 opposes Cebpa as well as other myeloid factors such as Spi1 and Myb (Fig 3A and B). This function of Hoxb8 appears to be executed at least in part by activating Hoxa9 and Meis1, previously reported anti-myeloid factors (Zeisig et al, 2004). We observe strong correlation in target gene expression between Hoxb8 and Meis1 and to a lesser extent between Hoxb8 and Hoxa9 (Fig 3A). This involves repression of numerous myeloid factors: Mpo, Prtn3 (regulated by all three factors), Il6ra, Irf8 (Meis1 and Hoxb8), Elane and Hp (Hoxa9 and Hoxb8) (Appendix Fig S2D). Of note, only a limited number of targets were shared between Meis1 and Hoxa9 suggesting that they may play complementary roles in suppressing myeloid differentiation. Additionally, the network model highlights a negative correlation between Tcf3/E2A and Cebpa and to some extent Gfi1. Tcf3 classically plays a pro-lymphoid role (Boller & Grosschedl, 2014), consistent with Tcf3 activating lymphoid factors Gata3 and Ebf1 in Hoxb8-FL cells. Moreover, Ebf1 and Tcf3 coregulated B-cell lineage factors such as Mzb1 and Igll1 (Appendix Fig S2F). Cbfb, Runx1 and Runx2 are all essential for Hoxb8-FL cell growth, and their targets exhibit high correlation (Appendix Fig S3B), consistent with the known dimerisation of Runx and Cbfb proteins (Warren et al, 2000; Yan et al, 2004). Of note, Runx/Cbfb targets appear to be involved in promoting myeloid gene expression and antagonise the Hoxb8 programme. For instance, myeloid lineage genes Mpeg1, Afap1, Nrp1 and Dtx4 are activated by Cbfb but repressed by Hoxb8 (Appendix Fig S2B). Interestingly, Runx1/Runx2 and Cbfb show different regulatory patterns with several other factors (Gfi1, Mitf, Rad21; Appendix Fig S3B), suggesting that Runx1/Runx2 and Cbfb may play roles outside of their common protein complex. In addition to the TF-TF regulation, we identified 47 target gene modules (Dataset EV3). These represent groups of genes with common patterns of regulation by the assayed TFs, for instance modules 6 and 10 are enriched for genes co-activated by Myc, Max and Ebf1, while genes in module 12 are mostly co-activated by Myc/Max/Gfi1 but repressed by Ebf1 (Fig 3D, Appendix Fig S3C–G). For instance, modules 6 and 10 contain genes involved in replication, biosynthesis and mitochondrial biogenesis (enrichment analysis is provided in Dataset EV5) which are co-activated by Myc, Max and Ebf1, highlighting a novel function of Ebf1. On the other hand, module 19, with genes involved in replication and translation (Dataset EV5), is similarly activated by Myc and Max but instead of Ebf1 receive inputs from Cebpa. Importantly, correlation of Myc/Max and Ebf1 targets is not universal and depends on the gene module. Module 12 contains multiple cell cycle genes (Ccne1, Ccnb1, Cenpt, Dataset EV5) activated by Myc and Max but suppressed by Ebf1, suggesting that Ebf1 may play a balancing role between cell growth and proliferation. The module analysis explains to a large degree observed coregulation between Myc/Max and other lineage-specific factors like Cebpa, Ebf1 and Gfi1 presented in Fig 3A and B. Altogether, our network reveals a wealth of relations among transcription factors at a single gene resolution, specific hierarchical TF regulation with novel roles in regulat
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