scholarly journals Single Cell RNASeq Demonstrates That Mouse Erythroid Cells Are the Last Lineage to Emerge

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1274-1274
Author(s):  
Elisabeth F Heuston ◽  
Bethan Psaila ◽  
Stacie M Anderson ◽  
NISC Comparative Sequencing Program ◽  
David M. Bodine

Abstract The hierarchical model of hematopoiesis posits that hematopoietic stem cells (HSC) give rise to myeloid progenitors (CMP), that can become further restricted to bipotential granulocyte/monocyte progenitors (GMP) or megakaryocyte/erythroid progenitors (MEP). We and others have shown that this model may not accurately depict hematopoiesis. Recent studies have shown that shown that populations of mouse hematopoietic stem and progenitor cells (LSK) have a strong megakaryocyte (Mk) transcriptional profile (Heuston, 2018, Epig. & Chrom.), and single cell studies have identified lineage committed cells in progenitor populations thought to be multipotent. For example, we recently reported that human MEP contain 3 populations: erythroid (Ery) primed, Mk primed, and bipotential (Psaila, 2016; Gen. Bio.). To determine when Mk and Ery cells emerge during mouse hematopoiesis, we performed single cell RNASeq on 10000 LSK, 12000 CMP, 6000 MEP and 8000 GMP cells. Clustering analysis (Satija, 2018, Nat. Biotech.) of all 4 populations identified 33 transcriptionally distinct clusters. In 30 of 33 clusters, 85% of cells were from a single defined population (e.g. MEP). LSK and CMP clusters grouped closely together. We used gene set profiling (Gene Set Enrichment Analysis, GO and KEGG) to correlate transcriptional profiles of clusters with specific hematopoietic lineages and cellular activities. In LSK, the most common transcriptional profiles correlated with active cell cycling. Mk-associated genes (Meis1, Myct1, and Fli1), were co-expressed with lymphoid genes in 56% of all LSK. Consistent with previous studies, we conclude that cells with Mk transcriptional profiles are abundant in LSK. No cells with an Ery RNA signature were observed in LSK. 23% of all CMP cells expressed Mk genes (e.g., Pf4, Itga2b, and Fli1) and were enriched for processes involved in platelet biology (p < 3E-18). 12% of CMP had an Ery RNA signature (low expression of Gata1, Klf1, and Nfe2) and decreased Mk gene expression (e.g., Gata2 and Gfi1b, [p < 3E-18]) compared to other CMP clusters. The high ratio of Gata2/Gata1 expression (1.90) suggests that this cluster contained immature Ery cells. More than 94% of all mouse MEP had Ery RNA signatures. Clusters could be distinguished by gene expression (e.g., Gata1, Klf1, Tfrc) and biological processes (ribosome synthesis and heme-biology processes [p < 4 E-10]). Based on the transcriptional profiles, we determined the most mature erythroid cells in MEP were late BFU-E. To compare the differentiation of Mk and Ery cells, we pooled our LSK, CMP, and MEP data for analysis using the Monocle software package. GMP contained only clusters expressing granulocytic or monocytic genes and were excluded from the analysis. Monocle arranges cells into trajectories based on their transcriptional profiles, with more differentiated cells positioned further from a common node (Xiaojie, 2017, bioRxiv). We found that LSK cells near the node had overlapping lymphoid and Mk transcriptional profiles. Closest to the node, we found 38% of CMP expressed a profile similar to LSK. An additional 45% of CMP formed one trajectory with lymphoid and granulocyte RNA signatures. Another 17% of CMP formed a second trajectory, with cells expressing an Mk signature closest to the node, cells with a mixed Ery/Mk signature further along the trajectory, and MEP cells with Ery-only signatures furthest from the node. To clarify the Mk/Ery divergence, we focused our analysis on the CMP populations expressing Mk RNAs (Figure1). We observed cells in G1/S phase with an immature Mk signature to the left of the node where the trajectories diverge. On the right, cells with immature Mk signatures were nearest the node and cells with a mixed Ery/Mk signature were at the end of the trajectory (upper right; Mk/Ery). Along the second trajectory, rapidly cycling G2/M Mk cells with an early endomitosis-associated RNA signature (e.g., Pf4, Gp1bb, Gp9, and Vwf) were located at the end of the trajectory (lower right; Mk early endomitosis). Our data are consistent with a model in which two waves of Mk differentiation begin in LSK and progresses to CMP. The Mk lineage is divided in CMP, producing cells that begin endomitosis and cells that have an Mk-repressing/Ery-activating cell program that gives rise to the Ery lineage. We conclude that the erythroid lineage is derived from an Mk-like precursor and is the last lineage to be specified in mouse hematopoiesis. Disclosures No relevant conflicts of interest to declare.

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 337-337
Author(s):  
Elisabeth F Heuston ◽  
Bethan Psaila ◽  
Cheryl A Keller ◽  
NISC Comparative SequencingProgram ◽  
Stacie M Anderson ◽  
...  

The hierarchical model of hematopoiesis posits that hematopoietic stem and progenitor cells produce common myeloid progenitors (CMP). CMP can become granulocyte/monocyte progenitors (GMP) or bipotential megakaryocyte/erythroid progenitors (MEP). MEP can produce megakaryocytic (Mk) or erythroid (Ery) cells. However, we and others have shown that early mouse and human progenitor populations express many Mk genes (Heuston, Epig. Chrom., 2016), while single cell studies have identified lineage-specific colony forming cells in progenitor populations thought to be multipotent (Psaila, Genome Biol., 2016). To identify the earliest mouse Ery and Mk cells, we performed single cell RNASeq on 10000 stem and progenitor cells (Lin-Sca1+Kit+), 12000 CMP (Lin-Sca1-Kit+CD16/32-CD34+), 6000 MEP (Lin-Sca1-Kit+CD16/32-CD34-) and 8000 GMP (Lin-Sca1-Kit+CD16/32+CD34+). TSNE analysis of expression in the 4 populations identified 33 clusters, which were correlated to biological functions using gene set enrichment analysis. In LSK, no cells with an Ery RNA profile were found, while 56% of cells co-expressed Mk-associated (e.g., Meis1, Fli1) and lymphoid genes. In CMP, 12% of the cells co-expressed Ery (e.g., Gata1, Fog1) and Mk (e.g., Pf4, Cd41) genes, while 23% had an Mk-specific profile (e.g., Fli1, Cd41) enriched for platelet biology processes (p&lt; 3E-18). Unlike traditional models, over 94% of MEP had Ery RNA profiles enriched for ribosome synthesis and heme-biology processes (p&lt; 4E-10). To establish developmental relationships, we performed pseudotime analysis using the Monocle and Scanpy software packages. These programs model differentiation by mapping similar transcriptomes together. Map nodes indicate lineage commitment points and cells further from a node are more differentiated. Combined analysis of LSK, CMP, and MEP generated a model with a single node and 2 trajectories. LSK with Mk and lymphoid RNA profiles diverged at the node, as did 14% of CMP. 31% of CMP with an Mk RNA profile were downstream of the node. Further downstream were cells with mixed Ery/Mk profiles, and furthest from the node were MEP with Ery profiles. A separate pseudotime analysis of CMP only 2 trajectories: one with decreasing Mk- and increasing Ery RNA profiles, and a second with an early Mk endomitotic RNA profile. Pseudotime analysis of MEP only identified a linear trajectory: cells at one end expressed early Ery RNA profiles, and cells at the other end had RNA profiles similar to those of burst-forming unit-erythroid (BFU-E). We generated a predictive set of RNAs for each TSNE cluster. We used index-sorting with 11 markers (Kit, Sca1, CD34, CD16/32, CD36, CD41, CD48, CD123, CD150, CD9, Flk2) to isolate single cells for custom high-throughput multiplex qPCR. This allowed confirmation of cell frequency within TSNE clusters while identifying surface markers for prospective isolation of cell subsets. We focused on 2 populations: CMP-E, which had an Ery RNA profile (10% of clustered CMP and 12% of CMP in the qPCR assay), and CMP-MkE, which had Mk and Ery RNA profiles (12% of clustered CMP and 13% of CMP in the qPCR assay). We prospectively isolated CMP-E and CMP-MkE to compare RNASeq profiles, ATACSeq profiles, and colony forming ability against those of bulk CMP, Ery, and Mk. In CMP-E, 54% of RNAs were expressed in both CMP and ERY, while 41% were expressed only in CMP (p &lt; 6E-72). In contrast, 41% of CMP-E ATACSeq peaks were present in CMP and ERY, while 57% of CMP-E peaks were present only in CMP (p &lt; 1E-3). We conclude that in CMP-E, the RNASeq profile is more erythroid than the ATACSeq profile. In CMP-MkE, 89% of RNAs were expressed in both CMP and Mk, while 7% were expressed only in CMP (p &lt; 8E-190). Likewise, 88% of CMP-MkE ATACSeq peaks were present in both CMP and Mk, while 3% were present only in CMP (p &lt; 1E-3). We conclude that in CMP-MkE, the RNASeq and ATACSeq profiles are equivalent. In soft agar assays, 21% of CMP-E and 3% of CMP-MkE colonies contained BFU-E, compared to 9% of control colonies. We conclude that the CMP-E and CMP-MkE populations are skewed towards the ERY and MK lineages, but are not erythro-megakaryocyte restricted. Our data support a model in which there are two megakaryocyte precursor populations and no erythroid populations in LSK. A third megakaryocyte population in CMP gives rise to erythroid cells. Finally, our data show that transcriptional changes precede chromatin accessibility changes in the earliest erythroid cells. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3669-3669
Author(s):  
Stephan Emmrich ◽  
Franziska Schmidt ◽  
Ramesh Chandra Pandey ◽  
Aliaksandra Maroz ◽  
Dirk Reinhardt ◽  
...  

Abstract Long non-coding RNAs (lncRNAs) recently emerged as central regulators of chromatin and gene expression. We created a comprehensive lncRNA HemAtlas in human and murine blood cells. We sampled RNA from differentiated granulocytes, monocytes, erythroid precursors, in vitro maturated megakaryocytes, CD4-T and CD8-T cells, NK cells, B cells and stem cells (human CD34+ cord blood hematopoietic stem and progenitor cells [CB-HSPCs]) and subjected them to microarray analysis of mRNA and lncRNA expression. Moreover, the human LncRNA HemAtlas was complemented with human hematopoietic stem cells (HSCs; CD34+/CD38-), megakaryocytic/erythroid progenitors (MEPs; CD34+/CD38+/CD45RA-/CD123-), common myeloid progenitors (CMPs; CD34+/CD38+/CD45RA-/CD123+) and granulocytic/monocytic progenitors (GMPs; CD34+/CD38+/CD45RA+/CD123+) from fetal liver (FL), CB and peripheral blood (PB) HSPCs. The complete microarray profiling of the differentiated cells yielded a total of 1588 (on Arraystar® platform) and 1439 lncRNAs (on NCode® platform), which were more than 20-fold differentially expressed between the blood lineages. Thus, a core fraction of lncRNAs is modulated during differentiation. LncRNA subtype comparison for each lineage, schematics of mRNA:lncRNA lineage coexpression and genomic loci correlation revealed a complex genetic interplay regulating hematopoiesis. Integrated bioinformatic analyses determined the top 50 lineage-specific lncRNAs for each blood cell lineage in both species, while gene set enrichment analysis (GSEA) confirmed lineage identity. The megakaryocytic/erythroid expression program was already evident in MEPs, while monocytoc/granulocytic signatures were found in GMPs. Amongst all significantly associated genes, 46% were lncRNAs, while 5% belonged to the subgroup of long intervening non-coding RNAs (lincRNA). For human megakaryocytes, erythroid cells, monocytes, granulocytes and HSPCs we validated four lincRNA candidates, respectively, to be specifically expressed by qRT-PCR. RNAi knock-down studies using two shRNA constructs per candidate demonstrated an impact on proliferation, survival or lineage specification for at least one specific lincRNA per lineage. We detected a 3 to 4.5-fold increased colony-forming capacity upon knockdown of the HSPC-specific PTMAP6 lincRNA in methylcellulose colony-forming unit (CFU) assays. Inversely, knockdown of monocyte-specific DB519945 resulted in 3.5 to 5.5-fold reduction of the total number of CFUs. Likewise, the total CFU counts was 4.3-fold reduced upon knockdown of megakaryocyte-specific AK093872. Kockdown of the granulocyte-specific LINC00173 perturbed granulocytic in vitro differentiation as assessed by the percentage of CD66b+/CD13+ granulocytes (2-fold reduction) and nuclear lobulation (MGG-stained cytospins). The erythroid-specific transcript AY034471 showed 25 to 50% reduction in burst-forming units in collagen-based assays. Thus, our study provides a global human hematopoietic lncRNA expression resource and defines blood-lineage specific lncRNA marker and regulator genes. Disclosures: No relevant conflicts of interest to declare.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3328-3328
Author(s):  
Angela Mo ◽  
Linda Ya-Ting Chang ◽  
Gerben Duns ◽  
Xuan Wang ◽  
Gregg Morin ◽  
...  

Abstract Mutations in SKP1 and CUL1 (Zhang et. al. Oncol Lett 2018), which encode components of the SKP1-CUL1-F-BOX (SCF) ubiquitin E3-ligase complex, have previously been reported or characterized in AML. FBXO11, which encodes the substrate recognizing component, however, has not been studied in AML. We performed whole exome sequencing and RNA-seq on140 clinical AML samples and identified recurrent inactivating mutations in FBXO11. Of the components of the SCF FBXO11 complex, FBXO11 transcript expression is most significantly reduced in AML samples compared to normal. We show that loss of FBXO11 drives leukemogenesis through dysregulation of the novel target, LONP1, by reducing mitochondrial potential and promoting self-renewal. We found that UPS mutations co-occur with AML1-ETO (RUNX1-RUNX1T1) fusions and RAS mutations. Fbxo11 knockdown in mouse hematopoietic stem/progenitor cells (HSPC) cooperated with AML1-ETO to generate serially transplantable AML in mice. FBXO11 depletion in human cord-blood derived CD34+ cells (CD34+ CB), combined with AML1-ETO and a KRAS mutant, promoted stem cell maintenance and myeloid malignancy in a human xenotransplant model. Mass spectrometry analysis of FLAG-FBXO11 co-immunoprecipitating proteins in K562 cells identified mitochondrial protease, LONP1, as a top target. LONP1 protein expression did not vary with FBXO11 loss or overexpression, suggesting that LONP1 is not a degradation target of the SCF FBXO11complex. Knockdown of either FBXO11 or LONP1 resulted in myeloid bias in CD34+ CB in vitro, pointing to an activating role of FBXO11 on LONP1. Both FBXO11 and LONP1 depletion reduced mitochondrial membrane potential (MMP) in CD34+ CB and myeloid cell lines, aligning with the stemness phenotypes observed with FBXO11 depletion, as long-term hematopoietic stem cells (LT-HSCs) are characterized by low MMP (Mansell et. al. Cell Stem Cell 2021), and disruption of MMP promotes self-renewal in HSCs (Vannini et. al. Nat Commun 2016). As FBXO11 neddylates p53 to regulate transcription (Abida et. al. J. Biol. Chem 2007), we examined protein neddylation, and detected increased neddylation in immunoprecipitated LONP1 from FLAG-FBXO11-expressing K562 cells. As, neddylation regulates protein activation (Wu et. al. Nature 2005), our findings suggest that FBXO11 neddylation of LONP1 activates LONP1 to maintain mitochondrial function. Consequently, loss of FBXO11 function primes HSPC for self-renewal by reduction of MMP. To clarify the regulatory relationship between FBXO11 and LONP1, we performed RNA-seq on CD34+ CB cells expressing combinations of shRNAs targeting FBXO11 or LONP1, with overexpression of FLAG -FBXO11 or LONP1. Unsupervised clustering revealed that LONP1-overexpressing samples clustered with controls, suggesting that LONP1 requires modification by FBXO11 for functional effects. Using gene set enrichment analysis, we found that both FBXO11 and LONP1 depletion enriched for HSC and LSC (leukemic stem cell) gene sets. Knockdown of LONP1 reversed the effect of FLAG-FBXO11 overexpression, supporting a model of LONP1 being a downstream mediator of FBXO11 function. Both FBXO11 and LONP1 depletion enriched for a gene set composed of mitochondrial electron transport chain complex (ETC) genes, potentially reflecting a transcriptional response to loss of functional ETC activity, as suggested by accumulation of misfolded ETC proteins with knockdown of LONP1 (Ghosh et. al. Oncogene 2019). In this work, we demonstrate the leukemogenic effects of FBXO11 loss. We draw a novel connection between the UPS and the mitochondrial protease system with the identification of LONP1 as an FBXO11 target that regulates hematopoiesis. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1055-1055
Author(s):  
Wencai Ma ◽  
R. Eric Davis ◽  
Rodrigo Jacamo ◽  
Marina Konopleva ◽  
Ramiro Garzon ◽  
...  

Abstract Abstract 1055 Cytogenetic and other evidence suggests that the mesenchymal stromal cell (MSC) is abnormal in bone marrow (BM) affected by acute myelogenous leukemia (AML). To gain further insight into molecular and physiologic abnormalities, we used Affymetrix HG-U133 Plus 2 microarrays to compare gene expression between BM-MSCs from 12 AML patients and BM-MSCs from 4 normal donors (ND). BM-MSCs were purified by in vitro culture as adherent cells with a purity of over 95%. Comparison at the single-gene level between AML and ND samples found only one differentially-expressed probe by t tests at a false-discovery rate (FDR) of 0.1. Comparison by the gene set enrichment analysis (GSEA) method of Subramanian et al., which is a more powerful way to find small differences that are significantly enriched within sets of biologically-related genes, first found that many enriched gene sets were predominantly the result of data from one AML sample. After excluding this sample, GSEA at an FDR of 0.25 found 115 downregulated gene sets for AML BM-MSCs from the Gene Ontology-based “C5” category of the mSigDB collection of gene sets. 19 of the 20 most significantly enriched downregulated gene sets were related to cell cycle progression, indicating that BM-MSCs are less proliferative in AML than in normal BM. An upregulated enriched gene set in AML BM-MSCs, from the “C2” category of curated gene sets, was composed of extracellular matrix genes for keratins, collagen, and laminin; while surprising, this is consistent with reports of BM-derived MSCs differentiating into epithelial cells after autografting, and suggest that BM-MSCs in AML may remodel the extracellular matrix. Overall, these results indicate that BM-MSCs in AML patients are substantially different from normal BM-MSCs. These and other differences could have substantial effects on the BM microenvironment and therapy response in AML, and should be studied further. Disclosures: No relevant conflicts of interest to declare.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1270-1270
Author(s):  
Mark van der Garde ◽  
Valgardur Sigurdsson ◽  
Visnja Radulovic ◽  
Svetlana Soboleva ◽  
Abdul Ghani Alattar ◽  
...  

Abstract The majority of adult hematopoietic stem cells (HSCs) are maintained in a dormant state under homeostatic conditions. In contrast, under stressed conditions such as myeloablation and infection, HSCs are known to proliferate and rapidly give rise to downstream progeny. However, it is unclear whether and how HSCs respond to severe anemic conditions. Here we report that HSCs rapidly expand with a biased differentiation towards erythroid cells upon the induction of acute anemia. Injection of 60 mg/kg of phenylhydrazine (PHZ) was used to induce hemolytic anemia, after which the peripheral blood (PB), bone marrow (BM) and spleen of the mice were analyzed for the blood profiles and stem/progenitor cell content. The red blood cell (RBC) count of the PHZ treated mice was at its lowest at day 6 post injection. BM analysis showed that the number of HSCs (CD150+CD34-c-kit+Sca-I+Lineage-) immediately started increasing, as well as megakaryocyte-erythroid progenitors (MEP, CD34-FcγIII/IIR-c-kit+Sca-I-Lineage-) with a peak at day 3-4 (3.0 and 3.4 fold increase, respectively). Interestingly, the number of common myeloid progenitors (CMP, CD34+FcγIII/IIR-c-kit+Sca-I-Lineage-) did not show a clear increase over time and the number of erythroid progenitors (Ter119+) started increasing at a later time point than the HSC/MEP expansion, suggesting that the expansion of primitive cells is a primary response to the anemic condition that possibly skips some of the regular stages that are observed in the normal differentiation towards erythrocytes. In contrast to the BM, in the spleen HSC expansion was modest while MEP and CMP were robustly expanded (5.7 and 6.6 fold increase, respectively). These findings indicate that the BM and spleen have distinct roles in the response to the anemic conditions. In order to accurately evaluate the lineage potential of HSCs in vitro, we developed a combined assay utilizing colony formation and flow cytometry analysis (CFU-FACS), with which all generated colonies were analyzed for the morphology and the frequency of each lineage. The result showed that HSCs isolated from control mice had a balanced differentiation towards megakaryocyte and erythroid cells with 20-25% of the colonies containing only granulocytes/macrophages and megakaryocytes, but not erythroid cells (GMMk colonies). In contrast, HSCs isolated from PHZ treated mice showed significantly increased the number of colonies containing a higher content of erythroid cells, whereas the ratio of GMMk colonies was decreased. Furthermore, 3-dimensional analysis of the three lineage potentials (myeloid, megakaryocyte and erythroid) in the colonies revealed an imbalanced lineage potential of HSCs from anemic mice, showing higher erythroid potential instead of the megakaryocyte potential. As an alternative method, phlebotomy was performed to induce acute anemia. Although phlebotomized mice did not display a clear expansion of the HSC population, CFU-FACS analysis showed an erythroid-biased lineage potential of the HSCs, indicating that the HSC expansion and the lineage bias may be caused by independent mechanisms. To demonstrate if the alterations in the HSCs affect the in vivo function of these cells, 50 HSCs isolated from control or PHZ injected Kusabira Orange (KuO) mice were transplanted into lethally irradiated mice. Two weeks after the transplantation, the ratio of KuO+ RBCs against KuO+ platelets was higher in the PHZ-HSC transplanted mice than control-HSC transplanted mice. This difference was not seen four weeks after transplantation and the long-term reconstitution (>12 weeks) levels did not differ between both groups, suggesting that the enhanced erythropoiesis is a transient event that does not reduce the stem cell capacity. In summary, we demonstrated that not only progenitor cells but also HSCs respond to severe anemic conditions and contribute to erythropoiesis through rapid expansion and a transient fate change, depicting a novel model of stress response. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3290-3290
Author(s):  
Aristeidis G. Telonis ◽  
Qin Yang ◽  
Hsuan-Ting Huang ◽  
Maria E. Figueroa

Abstract Mutations in DNMT3A and IDH1/2 are each found in ~20% of AML patients. 10-15% of AMLs carry mutations in both genes (herein, double mutants), resulting in a unique methylation landscape and upregulation of a signaling signature. In murine models, the presence of both mutations results in greater leukemogenic potential. However, the specific mechanism through which DNA methylation (DNAme) drives gene expression programs in double mutants remains unclear. We hypothesized that the link between DNAme and gene expression would be explained by more than simple proximity, and that the genomic architecture of the affected genes would play a key role. To test this, we first performed an unbiased correlation analysis of gene expression with DNAme at all CpG sites (mCs) located within the same topologically associated domain (TAD). We identified 406 genes with significant (FDR&gt; 5% and absolute rho &gt; 0.5) expression-methylation correlations with mCs proximal to the respective genes (herein the E-M gene set). In addition, another 2,088 genes (the L E-M set) were identified with long-range correlations (&gt;2Kb from the gene body) with mCs in the respective TAD (median distance = 451 Kb). As a set, the E-M genes significantly overlapped (P &lt; 10 -2) with genes identified as either differentially expressed (DE; n=890) or differentially methylated (DM; n= 4,006) between IDH1/2 and DNMT3A mutant AMLs. Notably, a simple overlap analysis of DE and DM genes showed no significant overlap between them, thus demonstrating that correlation analysis performed better in bridging the epigenome with the transcriptome. DAVID and Gene Set Enrichment Analysis on the genes ranked by correlation strength revealed that signaling, fructose and lipid metabolism pathways are enriched in the E-M gene set (FDR &lt; 5%) but not in the L E-M set. Analysis of transcription factor (TF) binding profiles did not reveal a common set of TF(s) binding to the mCs proximal to the genes of the identified pathways. Thus, we hypothesized that the E-M genes have other structural characteristics in common that drive regulation through DNAme, for which we focused on their genomic architecture. This analysis revealed that introns of genes in both the E-M and L E-M sets are significantly denser in Mammalian Interspersed Repeats (MIR) than expected by random chance (P &lt; 10 -2). Additionally, E-M genes were significantly sparser in endogenous retroviruses (ERVL) and primate-specific Alu elements. mCs with significant correlations were also enriched at MIR and depleted from Alu elements (P &lt; 10 -2), thus creating a regulatory network between mCs and genes with MIR sequences as the common denominator. Genome-wide, CpGs within retrotransposons that were differentially methylated among the three AML subtypes were enriched at enhancer regions or coding genes, particularly the E-M genes. Furthermore, the Dnmt3a knock-out (KO) or Idh2 R140Q knock-in mouse models display the same architectural biases at genes correlated with DNAme as the E-M genes identified in the human samples. Next, we sought to put our findings in the context of normal hematopoiesis and found that genes upregulated during normal hematopoietic differentiation are significantly denser in MIR elements and sparser of Alu elements than expected (P &lt; 10 -2). Alignment of the leukemic samples within normal differentiation trajectories revealed that double mutants resembled differentiated cell types more closely, while DNMT3A and IDH1/2 single mutants resembled hematopoietic stem cells. The E-M and L E-M sets significantly overlapped (P &lt; 10 -2) with those genes upregulated during myeloid but not erythroid or lymphoid differentiation, demonstrating that genes regulated by DNAme are at the core of the biology of these AMLs. In summary, our integrative work sheds light on a novel mechanism in which epigenetic modifications can regulate gene expression through MIR sequences within introns of hematopoietic-relevant genes and we posit that overlapping CpG dinucleotides may act as recruiters or substrates of DNMT3A and/or TET proteins. This mechanism seems to also be active in normal hematopoiesis and thus, is hijacked by leukemic cells. Therefore, our findings identify retrotransposons as a missing link in the understanding of epigenetic regulation of gene expression, reveal a previously uncharacterized role for these elements in leukemogenesis, and point to different cells of origin for each AML subtype. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3714-3714
Author(s):  
Melissa M Lee-Sundlov ◽  
Haley E Ramsey ◽  
Robert J Dickey ◽  
Martha Sola-Visner ◽  
Karin M Hoffmeister ◽  
...  

Abstract Somatic mutations in the tyrosine kinase JAK2, the thrombopoietin (TPO) receptor Mpl and the chaperone calreticulin cause myelofibrosis due to constitutive TPO/Mpl signaling in abnormal hematopoietic stem cells (HSCs). Impaired Mpl-mediated endocytosis has been reported in myelofibrosis patients carrying the most frequent JAK2-V617F mutation. Mpl-mediated endocytosis is also impaired in Dnm2fl/fl Pf4-Cre (Dnm2Plt-/-) mice specifically lacking the highly conserved endocytic GTPase dynamin 2 (DNM2) in the megakaryocyte (MK) lineage. Consequently, Dnm2Plt-/-mice develop hallmarks of myelofibrosis such as elevated circulating TPO levels, constitutive JAK2 phosphorylation, marked expansion of HSCs, massive MK hyperplasia, bone marrow fibrosis, extramedullary hematopoiesis and splenomegaly. To determine whether the phenotype is due to unrestrained TPO/Mpl signaling in HSCs, Dnm2Plt-/- mice were crossed with Mpl-/-mice. Mpl-/- Dnm2Plt-/- mice were obtained with a normal Mendelian distribution at weaning, and bone marrow HSC and MK numbers were significantly reduced in Mpl-/- Dnm2Plt-/- mice, similar to those of Mpl-/- mice. Surprisingly, Mpl-/- Dnm2Plt-/- mice died at a median age of 26 days postnatal and presented a severe splenomegaly, similar to that of Dnm2Plt-/- mice. Complete blood counts were analyzed from birth to 3 weeks postnatal. Blood platelet counts increased over time in control mice, reaching maximal values at 3 weeks postnatal, and remained low in Mpl-/-, Dnm2Plt-/- and Mpl-/- Dnm2Plt-/- mice. Blood erythrocyte counts increased over time in control mice, were significantly slower in single Mpl-/- and Dnm2Plt-/- mice, and failed to increase in Mpl-/- Dnm2Plt-/-mice, resulting in severe anemia. Erythroid maturation in Mpl-/- Dnm2Plt-/- spleens was investigated by flow cytometry analysis using CD71 and Ter119 as erythroid markers, where immature erythroblasts are defined as CD71high/Ter119low and mature erythroblasts as CD71low/Ter119high. Approximately 75% of erythroid cells in control spleens were mature erythroblasts, and the distribution significantly decreased to 40% and 20% in single Mpl-/- and Dnm2Plt-/- spleens, respectively. In Mpl-/- Dnm2Plt-/- spleens, only 5% of erythroid cells were mature erythroblasts, consistent with severely impaired erythroid maturation. Flow cytometry phenotypic analysis of bone marrow MK and eryrthroid progenitors revealed a significant increase of the distribution of progenitors with both MK and erythroid potential (Pre-Meg-E) in Mpl-/- Dnm2Plt-/-mice. The data shows that TPO/Mpl signaling and endocytosis orchestrate erythropoiesis and thrombopoiesis to control the bone marrow environment. Mpl regulates both MK and erythroid progenitors, highlighting clinically relevant interactions between these two blood cell compartments in myelofibrosis development. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1486-1486
Author(s):  
Elisabeth F. Heuston ◽  
Cheryl A. Keller ◽  
Jens Lichtenberg ◽  
Stacie M. Anderson ◽  
NIH Intramural Sequencing Center ◽  
...  

Abstract Enhancers are cis acting regulatory modules associated with lineage-specific gene expression. The Encyclopedia of DNA Elements project (ENCODE) showed that enhancers are in open chromatin regions identified by the Assay for Transposable-Accessible Chromatin (ATAC) and bound with histone H3 is mono-methylated at lysine 4 (H3K4me1). Chromatin regions marked by H3K4me1 alone identifies "poised" enhancers (not active), while the additional presence and histone H3 acetylated at lysine 27 (H3K27ac) identifies "active" enhancers. To establish a genome-wide enhancer map in the erythro-megakaryocytic lineage, we performed ChIPSeq of H3K4me1 and H3K27ac in primary erythroblasts (EB) and megakaryocytes (MEG) isolated from mouse bone marrow. We also assayed primary mouse EB, MEG, hematopoietic stem and progenitor cells (LSK), and common myeloid progenitor cells (CMP) for open chromatin regions with ATAC and transcriptome profiling by RNASeq. Finally, we compared histone-defined enhancers in mature cells with the corresponding ATAC regions in progenitor cells to identify the preservation of poised and active enhancers through hematopoiesis. We identified 6565 and 3543 active enhancers in EB and MEG respectively; approximately 10% (434) were shared. We further refined our enhancer set to the ~90% of EB and MEG active enhancers that overlap with ATAC regions (AER, histone-marked active enhancer within an ATAC region). To identify enhancers in the open chromatin of progenitor cells, we overlaid EB and MEG AER with CMP ATAC sites. This revealed that 82% (5226/6399) of EB AER and 87% (1437/3302) of MEG AER were present in CMP. Overlaying the EB and MEG AER onto LSK ATAC showed that 67% (4278/6399) of EB-specific AER and 79% (2594/3302) of MEG-specific AER overlapped with LSK ATAC sites. To identify the EB and MEG AER in LSK-accessible chromatin that are active (not poised), we compared our LSK enhancer set with the indexing-first ChIP (iChIP) histone marks identified by Lara-Astiaso et al., (Science, 2014). 1840 of the 4278 (43%) LSK-accessible EB AER overlapped with LSK iChIP H3K4me1 marks; 632 of these (15% overall) also had the active H3K27ac mark. 1083 of the 2594 (42%) MEG AER that were present in LSK overlapped with LSK iChIP H3K4me1 marks; 241 of these (9% overall) had the H3K27ac mark. For both EB and MEG, AER not marked by iChIP K4me1 were within gene bodies. To further characterize enhancer roles in lineage commitment, we profiled super enhancers (SE), which have highly lineage-specific activity. We defined SE as the 2% of AER with the highest H3K27ac levels (Hnisz et al., Cell, 2013) and identified 101 EB and 98 MEG SE; all of these were cell-specific. We found that 65% (66/101) of EB SE and 87% (85/98) of MEG SE overlapped with LSK ATAC sites. 30 of the 66 (45%) LSK-accessible EB SE overlapped with LSK iChIP H3K4me1 marks; 9 of these (14% overall) also had the active H3K27ac mark. In comparison, 15 of the 85 (18%) LSK-accessible MEG SE overlapped with LSK iChIP H3K4me1 marks; 4 of these (5% overall) also had the active H3K27ac mark. We correlated our LSK-accessible, iChIP-marked active AER with gene expression by assigning each AER to the nearest gene. We then used RNASeq data to perform gene set enrichment analysis via Ingenuity Pathway Analysis. We found that the LSK-accessible EB-specific AER gene set included erythropoietin-regulated genes (p £ 9x10-5) and genes associated with Fanconi anemia (6x10-4). Conversely, LSK-accessible and iChIP-active MEG AER were associated an increase of progenitor cell populations and proliferation activities for several hematopoietic lineages (p £ 2x10-5). However, the genes in the non-megakaryocyte pathways were significantly down-regulated as LSK committed to the megakaryocyte lineage. In summary, our results demonstrate the establishment of poised and active enhancers in hematopoietic progenitors and their preservation through erythro-megakaryopoiesis. We show that >40% of EB and MEG enhancers were also enhancers in LSK and CMP; the EB and MEG enhancers that were not LSK enhancers were primarily within gene bodies. We also found that MEG, but not EB, super enhancers were less likely than conventional enhancers to be established in LSK. Finally, our data show that, while LSK-established EB enhancers target EB-specific functions, LSK-established MEG enhancers have more universal hematopoietic functions that are down-regulated during megakaryocytic lineage commitment. Disclosures No relevant conflicts of interest to declare.


2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lars Velten ◽  
Benjamin A. Story ◽  
Pablo Hernández-Malmierca ◽  
Simon Raffel ◽  
Daniel R. Leonce ◽  
...  

AbstractCancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells.


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