scholarly journals Establishment of Enhancer Elements during Erythro-Megakaryopoiesis

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 ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Histone H3 ◽  
Gene Set Enrichment Analysis ◽  
Lineage Commitment ◽  
Open Chromatin ◽  
Hematopoietic Stem ◽  
Gene Set ◽  
Primary Mouse ◽  
Accessible Chromatin

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.

scholarly journals Enhancer Accessibility during Erythropoiesis and Megakaryopoiesis Correlates with Lineage-Specific Gene Expression

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3576-3576
Author(s):  
Elisabeth F. Heuston ◽  
Cheryl A. Keller ◽  
Stacie M. Anderson ◽  
NIH Intramural Sequencing Center ◽  
Ross C. Hardison ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Histone H3 ◽  
Luciferase Assay ◽  
Lineage Commitment ◽  
Specific Gene ◽  
Open Chromatin ◽  
Hematopoietic Stem ◽  
Primary Mouse ◽  
Accessible Chromatin

Abstract Enhancers are epigenetic regulatory modules critical to lineage-specific transcript expression. Understanding the development and maintenance of enhancers will help clarify lineage commitment decisions. The ENCODE Project Consortium used a variety of cell lines to define enhancer regulatory maps. Our goal is to build ENCODE-like enhancer maps for primary mouse hematopoietic cells. To accomplish this we have performed genome-wide open chromatin surveys (Assay for Transposable-Accessible Chromatin [ATACSeq]) and transcriptome analysis (RNASeq) in enriched populations of hematopoietic stem and progenitor cells (LSK), common myeloid progenitors (CMP), megakaryocyte-erythroid progenitors (MEP), and erythroid and megakaryocytic progenitors (CFUE and CFUMeg, respectively). In addition to ATACSeq and RNASeq we performed ChIPSeq in erythroblasts (EB) and megakaryocytes (MEG). Together these data provide a high-quality map of essential enhancers and correlated transcription profiles at specific stages of hematopoiesis. Enhancers are associated with DNase I hypersensitivity and monomethylation at lysine 4 of histone H3 (H3K4me1), while active enhancer regions (AERs) are also acetylated at lysine 27 of histone H3 (H3K27ac). We identified several thousand candidate enhancer regions (cERs) in EB and MEG (Table). More than 70% of cERs also contained the H3K27ac mark and thus were candidate AERs (cAERs). In 90% of cases cAERs were closer to an active transcriptional start site (TSS) than to any other TSS. 23 of these erythroid cAERs were shown to be active in a luciferase assay. We next evaluated the major trends in acquisition and maintenance of cERs during hematopoietic differentiation and their correlation with altered gene expression. We focused on cAERs and examined their appearance and retention in progenitor cells (Table). Almost all cell-specific (97% of EB and 93% of MEG) cAERs are in accessible chromatin as monitored by ATACSeq (HC cAERs). This high degree of correlation allows us to use overlaps with progenitor ATACSeq data to estimate the presence of the cAERs identified in the mature cells. Our initial results show that the mature cell HC cAERs overlap with progenitor cell ATACSeq data (MEP and CMP) is greater for EB cAERs than MEG, suggesting that chromatin in these cells is more similar to that of EB. However, in LSK, 50% of both EB and MEG cAERs overlap with ATACSeq peaks, suggesting that half of cell-specific cAERs are present in LSK while the rest are established during differentiation. In contrast, 82% of shared EB and MEG cAERs overlapped with LSK ATACSeq peaks and were maintained throughout differentiation. Candidate super enhancers (cSEs) are the cAERs with the highest levels of H3K27ac as measured by ChIPSeq. Within the top 2% of AERs we identified 101 EB and 98 MEG HC cSEs (i.e., overlap ATACSeq peaks). There was no overlap between the EB and MEG cSEs, indicating that cSEs are more cell-specific than all cAERs (Χ2 ≤ 0.04). RNASeq data confirmed that 92% of cSEs were closest to an active TSS. These data indicate that cSEs are more cell-specific than cAERs and are associated with increased gene expression. Approximately 96% of EB-specific cSEs correlated with ATACSeq peaks in CFUE chromatin, 94% in MEP, 82% in CMP, and 65% in LSK. In contrast, 95% of MEG-specific cSEs correlated with ATACSeq peaks in CFUMeg chromatin, 81% in MEP and CMP, and 75% in LSKs. The higher percentage of MEG cSEs in LSK is significantly different from all MEG cAERs (Χ2 ≤ 0.003), indicating that, unlike cAERs, MEG-specific cSEs are established early and maintained throughout differentiation. We have shown that cAERs, especially cSEs, are highly cell-specific in primary murine erythroid and megakaryocytic cells and correlate with gene expression. Examining changes in cAERs, cSEs, and gene expression allows us to map the specific epigenetic changes in chromatin that define erythroid and megakaryocytic differentiation. These results will enable us to test hypotheses about the mechanism of erythroid and megakaryocytic lineage commitment. Table. EB-specific MEG-specific Common No. Percent No. Percent No. Percent cER 7153 n/a 4196 n/a 619 n/a cAER 6097 100% 3064 100% 468 100% HC cAER 5934 97% 2848 93% 434 93% HC cAER + CFUE ATACSeq 5818 98% n/a n/a n/a n/a HC cAER + CFUMeg ATACSeq n/a n/a 2606 92% n/a n/a HC cAER + MEP ATACSeq 5591 94% 1509 53% 394 91% HC cAER + CMP ATACSeq 4754 80% 1473 52% 385 89% HC cAER + LSK ATACSeq 3719 63% 1368 48% 351 81% Disclosures No relevant conflicts of interest to declare.

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
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Conflicts Of Interest ◽  
Gene Set Enrichment Analysis ◽  
Erythroid Cells ◽  
Erythroid Progenitors ◽  
Hematopoietic Stem ◽  
Transcriptional Profiles ◽  
Gene Set ◽  
Rna Signature

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.

scholarly journals Insufficiency of Non-Canonical PRC1 Complex Cooperates with an Activating JAK2 Mutation in the Pathogenesis of Myelofibrosis

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 100-100
Author(s):  
Daisuke Shinoda ◽  
Yaeko Nakajima-Takagi ◽  
Motohiko Oshima ◽  
Atsunori Saraya ◽  
Hironori Harada ◽  
...  
Keyword(s):  
Progenitor Cells ◽  
Target Genes ◽  
Erythroid Differentiation ◽  
Gene Set Enrichment Analysis ◽  
Severe Thrombocytopenia ◽  
Promoter Regions ◽  
Hematopoietic Stem ◽  
Gene Set ◽  
Stem And Progenitor Cells

Abstract Introduction: PcG proteins form two main multiprotein complexes, Polycomb repressive complex 1 (PRC1) and PRC2. They repress the transcription of target genes. Polycomb group ring finger protein1 (PCGF1) is a component of PRC1.1, a non-canonical PRC1.1 that monoubiquitylates H2A at lysine 119 in a manner independent of H3K27me3. Several groups including ours showed that the loss of Ezh2, a component of PRC2, promotes the development of JAK2 V617F-induced Myelofibrosis (MF) in mice. However, the role of PRC1.1 in hematologic malignancies is still not fully understood. We found that the deletion of PCGF1 in mice promotes myeloid commitment of hematopoietic stem and progenitor cells (HSPCs), and eventually induces a lethal myeloproliferative neoplasm (MPN)-like disease in mice (Nakajima-Takagi Y, unpublished data). Based on these findings, we investigated the role of PCGF1 in a mouse model of JAK2V617F-induced myelofibrosis. Methods: We transplanted BM cells from Cre-ERT2, PCGF1flox/flox;Cre-ERT2, JAK2V617F;Cre-ERT2, and JAK2V617F;PCGF1flox/flox;Cre-ERT2 mice into lethally irradiated recipient mice. We deleted PCGF1 by tamoxifen administration 4 weeks after transplantation. Results: JAK2/PCGF1 KO mice developed lethal MF significantly earlier than the other genotypes (p<0.01). JAK2/PCGF1 KO mice showed progressive anemia and severe thrombocytopenia. Bone marrow analysis of JAK2/PCGF1 KO mice revealed a significant reduction in HSPCs and an increase in the number of granulocyte-macrophage progenitors (GMPs). Erythropoiesis was severely impaired at the later stages of erythroid differentiation. To understand the molecular basis of MF-initiating cells in JAK2/PCGF1 KO mouse, we performed a gene expression analysis of LSKs/GMPs/MEPs isolated from the primary recipients 1 month after TAM injection. Gene set enrichment analysis of RNA-seq data clearly showed de-repression of PRC1 target genes marked with H2AK119ub1 in hematopoietic stem and progenitor cells (HSPCs) from JAK2/PCGF1 KO mice. The gene set of megakaryocyte progenitors was also positively enriched in JAK2/PCGF1 KO HSPCs. ChIP sequencing of H2AK119Ub revealed that the levels of H2AK119Ub at promoter regions were mildly reduced in JAK2/PCGF1 KO LK cells compared with Pcgf1 KO LK cells. Among differentially expressed genes, we found that HoxA cluster genes were de-repressed in JAK2/PCGF1 KO progenitor cells including MEPs following significant reductions in H2AK119Ub levels at the promoter regions. Lin28b-Let-7-Hmga2 pathway genes that are activated in JAK2/Ezh2 KO progenitor cells were not altered in expression in JAK2/PCGF1 KO progenitor cells, suggesting different mechanisms operating in the pathogenesis of JAK2/Ezh2 KO and JAK2/PCGF1 KO MF. A selective AURKA inhibitor has been reported to promote differentiation of megakaryocytes with PMF-associated mutations and had potent antifibrotic and antitumor activity in vivo in mouse models of PMF (Wen et al., Nat Med 21:1473, 2015). Following this report, we treated JAK2/PCGF1 KO mice with JAK inhibitors and/or AURKA inhibitors. Both inhibitors improved MF-related phenotypes including impaired erythroid differentiation of JAK2/PCGF1 KO mice. Conclusions: Our findings suggest that dysregulated PRC1.1 function promotes JAK2V617F-induced MF with mechanisms distinct from MF associated with PRC2 dysfunction. Disclosures Harada: Celgene: Research Funding.

scholarly journals Intronic Architecture Links DNA Methylation to Gene Expression and Helps Drive Subtype-Specific Transcriptional Landscapes in DNMT3A- and IDH1/2-Mutant Acute Myeloid Leukemias (AML)

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3290-3290
Author(s):  
Aristeidis G. Telonis ◽  
Qin Yang ◽  
Hsuan-Ting Huang ◽  
Maria E. Figueroa
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Correlation Analysis ◽  
Gene Set Enrichment Analysis ◽  
Common Denominator ◽  
M Gene ◽  
Hematopoietic Stem ◽  
Alu Elements ◽  
Gene Set ◽  
Genomic Architecture

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.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

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 ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

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.

scholarly journals Hematopoietic Stem Cells Are Endowed with Erythroid Signature in Beta-Thalassemia

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 31-31
Author(s):  
Maria Rosa Lidonnici ◽  
Giulia Chianella ◽  
Francesca Tiboni ◽  
Matteo Barcella ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Hematopoietic Stem Cells ◽  
Gene Set Enrichment Analysis ◽  
Lineage Commitment ◽  
Erythroid Cell ◽  
Beta Thalassemia ◽  
Hematopoietic Stem ◽  
Multipotent Progenitors

Background Beta-thalassemia (Bthal) is a genetic disorder due to mutations in the ß-globin gene, leading to a reduced or absent production of HbA, which interferes with erythroid cell maturation and limits normal red cell production. Patients are affected by severe anemia, hepatosplenomegaly, and skeletal abnormalities due to rapid expansion of the erythroid compartment in bone marrow (BM) caused by ineffective erythropoiesis. In a classical view of hematopoiesis, the blood cell lineages arise via a hierarchical scheme starting with multipotent stem cells that become increasingly restricted in their differentiation potential through oligopotent and then unipotent progenitors. In human, novel purification strategies based on differential expression of CD49f and CD90 enrich for long-term (49f+) and short-term (49f−) repopulating hematopoietic stem cells (HSCs), with distinct cell cycle properties, but similar myeloid (My) and lymphoid (Ly) potential. In this view, it has been proposed that erythroid (Ery) and megakaryocytic (Mk) fates branch off directly from CD90-/49f− multipotent progenitors (MPPs). Recently, a new study suggested that separation between multipotent (Ery/My/Ly) long-term repopulating cells (Subset1, defined as CLEC9AhighCD34low) and cells with only My/Ly and no Ery potential (Subset2, defined as CLEC9AlowCD34high)occurs within the phenotypic HSC/MPP and CD49f+ HSCs compartment. Aims A general perturbed and stress condition is present in the thalassemic BM microenvironment. Since its impact on the hematopoietic cell subpopulations is mostly unknown, we will investigate which model of hematopoiesis/erythropoiesis occurs in Bthal. Moreover, since Beta-Thalassemia is an erythropoietic disorder, it could be considered as a disease model to study the 'erythroid branching' in the hematopoietic hierarchy. Methods We defined by immunophenotype and functional analysis the lineage commitment of most primitive HSC/MPP cells in patients affected by this pathology compared to healthy donors (HDs). Furthermore, in order to delineate the transcriptional networks governing hematopoiesis in Beta-thalassemia, RNAseq analysis was performed on sorted hematopoietic subpopulations from BM of Bthal patients and HDs. By droplet digital PCR on RNA purified from mesenchymal stromal cells of Bthal patients, we evaluated the expression levels of some niche factors involved in the regulation of hematopoiesis and erythropoiesis. Moreover, the protein levels in the BM plasma were analyzed by performing ELISA. Results Differences in the primitive compartment were observed with an increased proportion of multipotent progenitors in Bthal patients compared to HDs. The Subset1 compartment is actually endowed with an enhanced Ery potential. Focusing on progenitors (CD34+ CD38+) and using a new sorting scheme that efficiently resolved My, Ery, and Mk lineage fates, we quantified the new My (CD71-BAH1-/+) and Ery (CD71+ BAH1-/+) subsets and found a reduction of Ery subset in Bthal samples. We can hypothesize that the erythroid-enriched subsets are more prone to differentiate quickly due to the higher sensitivity to Epo stimuli or other bone marrow niche signals. Gene set enrichment analysis, perfomed on RNAseq data, showed that Bthal HSC/MPP presented negative enrichment of several pathways related to stemness and quiescence. Cellular processes involved in erythropoiesis were found altered in Bthal HSC. Moreover, some master erythroid transcription factors involved were overrepresented in Bthal across the hematopoietic cascade. We identified the niche factors which affect molecular pathways and the lineage commitment of Bthal HSCs. Summary/Conclusions Overall, these data indicate that Bthal HSCs are more cycling cells which egress from the quiescent state probably towards an erythroid differentiation, probably in response to a chronic BM stimulation. On the other hand,some evidences support our hypothesis of an 'erythroid branching' already present in the HSC pool, exacerbated by the pathophysiology of the disease. Disclosures No relevant conflicts of interest to declare.

scholarly journals Functional Investigation of the Argonaute Proteins in Human Hematopoietic Stem and Progenitor Cells

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-32
Author(s):  
Gordon G. L. Wong ◽  
Gabriela Krivdova ◽  
Olga I. Gan ◽  
Jessica L. McLeod ◽  
John E. Dick ◽  
...  
Keyword(s):  
Progenitor Cells ◽  
Erythroid Differentiation ◽  
Lineage Commitment ◽  
Myeloid Differentiation ◽  
Hematopoietic Stem ◽  
Erythroid Precursor ◽  
Lineage Differentiation ◽  
Erythroid Precursors

Micro RNA (miRNA)-mediated gene silencing, largely mediated by the Argonaute (AGO) family proteins, is a post-transcriptional gene expression control mechanism that has been shown to regulate hematopoietic stem and progenitor cells (HSPCs) quiescence, self-renewal, proliferation, and differentiation. Interestingly, only the function of AGO2 in hematopoiesis has been investigated. O'Carroll et al. (2007) showed that AGO2 knockout in mice bone marrow cells interferes with B220low CD43- IgM-pre-B cells and peripheral B cell differentiation and impairs Ter119high, CD71high erythroid precursors maturation. However, the functional significance of other AGO proteins in the regulation of stemness and lineage commitment remains unclear. AGO submembers, AGO1-4 in humans, are traditionally believed to act redundantly in their function. However, our previous proteomic analysis from sorted populations of the human hematopoietic hierarchy shows each sub-member is differentially expressed during HSPCs development, suggesting each sub-member may have a specialized function in hematopoiesis. Here, we conducted CRISPR-Cas9 mediated knockout of AGO1-4 in human cord blood derived long-term (LT-) and short-term hematopoietic stem cells (ST-HSCs) and investigated the impact of the loss of function of individual AGOs in vitro and in vivo in xenograft assays. From the in vitro experiment, we cultured CRISPR-edited LT- or ST-HSCs in a single cell manner on 96-well plates pre-cultured with murine MS5 stroma cells in erythro-myeloid differentiation condition. The colony-forming capacity and lineage commitment of each individual HSC is assessed on day 17 of the culture. Initial data showed that AGO1, AGO2 and AGO3 knockout decreased the colony formation efficacy of both LT- and ST-HSCs, suggesting AGO1, AGO2 and AGO3 are involved in LT- and ST-HSCs proliferation or survival. As for lineage output, AGO1 knockout increases CD56+ natural killer cell commitment in LT-HSCs and erythroid differentiation in ST-HSCs; AGO2 knockout increases erythroid differentiation in both LT- and ST-HSCs and decreases myeloid differentiation in ST-HSCs; while AGO4 knockout seems to decrease erythroid output. For the in vivo experiment, we xenotransplanted AGO1 and AGO2 knockout LT-HSCs in irradiated immunodeficient NSG mice and assessed the change in LT-HSCs engraftment level and lineage differentiation profile at 12- and 24-week time points. We found that AGO2 knockout increased CD45+ engraftment at both 12- and 24-weeks. Aligning with our in vitro data, AGO2 knockout increases GlyA+ erythroid cells at 12- and 24-weeks. The increase in GlyA+ erythroid cells is a consequence of the 2-fold increase in GlyA+ CD71+ erythroid precursor cells, recapitulating previous findings that AGO2 knockout in mice impairs CD71high erythroid precursor maturation leading to the accumulation of undifferentiated CD71+ erythroid precursors (O'Carroll et al., 2007). Accumulation of early progenitors of the erythroid lineage, including the common myeloid progenitors (CMPs) and myelo-erythroid progenitor (MEPs) were observed, as well as their progeny including CD33+ myeloid and CD41+ megakaryocytes. For the myeloid lineage, AGO2 knockout shifts myeloid differentiation toward CD66b+ granulocytes from CD14+ monocytes. For lymphoid, AGO2 knockout decreases CD19+ CD10- CD20+ mature B-lymphoid cells, which again aligns with previous AGO2 knockout mice results. On the other hand, AGO1 knockout LT-HSCs share some similar phenotype with AGO2 knockout LT-HSCs, where AGO1 knockout increases CD71+ erythroid precursors. However, AGO1 knockout in LT-HSCs also results in unique phenotypes, with a decrease in neutrophil formation and an increase in CD4+ CD8+ T progenitor cells are observed. AGO3 and AGO4 knockout experiments are in progress. In summary, our AGO2 knockout experiments recapitulate the reported results from murine studies but also illustrate a more complete role of AGO2 in hematopoietic lineage differentiation. Moreover, AGO knockout experiments of individual submembers are revealing novel insights into their role in the regulation of stemness and lineage commitment of LT-HSCs and ST-HSCs. These data point to a unique role of different AGO isoforms in lineage commitment in human HSCs and argue against redundant functioning. Disclosures Dick: Bristol-Myers Squibb/Celgene: Research Funding.

Immunoglobulin gene expression in umbilical cord blood-derived CD34+ hematopoietic stem/progenitor cells

Gene ◽  
2016 ◽  
Vol 575 (1) ◽  
pp. 108-117 ◽  
Author(s):  
Jingfang Liu ◽  
Miaoran Xia ◽  
Pingzhang Wang ◽  
Chong Wang ◽  
Zihan Geng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cord Blood ◽  
Progenitor Cells ◽  
Umbilical Cord ◽  
Umbilical Cord Blood ◽  
Immunoglobulin Gene ◽  
Hematopoietic Stem

scholarly journals Sequential generations of hematopoietic colonies derived from single nonlineage-committed CD34+CD38- progenitor cells

Blood ◽  
1991 ◽  
Vol 77 (6) ◽  
pp. 1218-1227 ◽  
Author(s):  
LW Terstappen ◽  
S Huang ◽  
M Safford ◽  
PM Lansdorp ◽  
MR Loken
Keyword(s):  
Progenitor Cells ◽  
Lineage Commitment ◽  
Multiparameter Flow Cytometry ◽  
Differentiation Markers ◽  
Single Class ◽  
Hematopoietic Stem ◽  
Cd34 Antigen ◽  
Cd34 Cells ◽  
Cell Fractions ◽  
Cd38 Antigen

Abstract Multiparameter flow cytometry was applied on normal human bone marrow (BM) cells to study the lineage commitment of progenitor cells ie, CD34+ cells. Lineage commitment of the CD34+ cells into the erythroid lineage was assessed by the coexpression of high levels of the CD71 antigen, the myeloid lineage by coexpression of the CD33 antigen and the B-lymphoid lineage by the CD10 antigen. Three color immunofluorescence experiments showed that all CD34+ BM cells that expressed the CD71, CD33, and CD10 antigens, concurrently stained brightly with anti-CD38 monoclonal antibodies (MoAbs). In addition, the CD38 antigen was brightly expressed on early T lymphocytes in human thymus, characterized by CD34, CD5, and CD7 expression. Only 1% of the CD34+ cells, 0.01% of nucleated cells in normal BM, did not express the CD38 antigen. The CD34+, CD38- cell population lacked differentiation markers and were homogeneous primitive blast cells by morphology. In contrast the CD34+, CD38 bright cell populations were heterogeneous in morphology and contained myeloblasts and erythroblasts, as well as lymphoblasts. These features are in agreement with properties expected from putative pluripotent hematopoietic stem cells; indeed, the CD34 antigen density decreased concurrently with increasing CD38 antigen density suggesting an upregulation of the CD38 antigen on differentiation of the CD34+ cells. Further evidence for a strong enrichment of early hematopoietic precursors in the CD34+, CD38- cell fraction was obtained from culture experiments in which CD34+ cell fractions with increasing density of the CD38 antigen were sorted singularly and assayed for blast colony formation. On day 14 of incubation, interleukin-3 (IL-3), IL-6, and GM-CSF, G-CSF, and erythropoietin (Epo) were added in each well. Twenty-five percent of the single sorted cells that expressed CD34 but lacked CD38 antigen gave rise to primitive colonies 28 to 34 days after cell sorting. The ability to form primitive colonies decreased rapidly with increasing density of the CD38 antigen. During 120 days of culture, up to five sequential generations of colonies were obtained after replating of the first-generation primitive colonies. This study provides direct evidence for the existence of a single class of progenitors with extensive proliferative capacity in human BM and provides an experimental approach for their purification, manipulation, and further characterization.

Sign in / Sign up

Export Citation Format

Share Document