scholarly journals Drugs modulating stochastic gene expression affect the erythroid differentiation process

2018 ◽  
Author(s):  
Anissa Guillemin ◽  
Ronan Duchesne ◽  
Fabien Crauste ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Erythroid Differentiation ◽  
Causative Role ◽  
Stochastic Gene Expression ◽  
Differentiation Process ◽  
Modeling Tools ◽  
Erythroid Progenitor ◽  
Differentiated Cells

AbstractBackgroundTo understand how a metazoan cell makes the decision to differentiate, we assessed the role of stochastic gene expression (SGE) during the erythroid differentiation process. Our hypothesis is that stochastic gene expression has a role in single-cell decision-making. In agreement with this hypothesis, we and others recently showed that SGE significantly increased during differentiation. However, evidence for the causative role of SGE is still lacking. Such demonstration would require being able to experimentally manipulate SGE levels and analyze the resulting impact of these variations on cell differentiation.ResultWe identified three drugs that modulate SGE in primary erythroid progenitor cells. Artemisinin and Indomethacin simultaneously decreased SGE and reduced the amount of differentiated cells. Inversely, α-methylene-γ-butyrolactone-3 (MB-3) simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment.ConclusionUsing single-cell analysis and modeling tools, we provide experimental evidence that in a physiologically relevant cellular system, control of SGE can directly modify differentiation, supporting a causal link between the two.

scholarly journals Drugs modulating stochastic gene expression affect the erythroid differentiation process

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0225166 ◽  
Author(s):  
Anissa Guillemin ◽  
Ronan Duchesne ◽  
Fabien Crauste ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon
Keyword(s):  
Gene Expression ◽  
Erythroid Differentiation ◽  
Stochastic Gene Expression ◽  
Differentiation Process

scholarly journals The multifunctional role of EKLF/KLF1 during erythropoiesis

Blood ◽  
2011 ◽  
Vol 118 (8) ◽  
pp. 2044-2054 ◽  
Author(s):  
Miroslawa Siatecka ◽  
James J. Bieker
Keyword(s):  
Gene Expression ◽  
Erythroid Differentiation ◽  
Red Cell ◽  
Direct Role ◽  
Erythroid Progenitor ◽  
Cellular Events ◽  
Downstream Effects ◽  
Cell Phenotypes ◽  
Terminal Erythroid Differentiation

Abstract The cellular events that lead to terminal erythroid differentiation rely on the controlled interplay of extra- and intracellular regulatory factors. Their downstream effects are highly coordinated and result in the structural/morphologic and metabolic changes that uniquely characterize a maturing red blood cell. Erythroid Krüppel-like factor (EKLF/KLF1) is one of a very small number of intrinsic transcription factors that play a major role in regulating these events. This review covers 3 major aspects of erythropoiesis in which EKLF plays crucial functions: (1) at the megakaryocyte-erythroid progenitor stage, where it is involved in erythroid lineage commitment; (2) during the global expansion of erythroid gene expression in primitive and definitive lineages, where it plays a direct role in globin switching; and (3) during the terminal maturation of red cells, where it helps control exit from the cell cycle. We conclude by describing recent studies of mammalian EKLF/KLF1 mutations that lead to altered red cell phenotypes and disease.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals The regulatory role of interleukin 2-responsive T lymphocytes on early and mature erythroid progenitor proliferation

Blood ◽  
1986 ◽  
Vol 67 (6) ◽  
pp. 1607-1610
Author(s):  
Z Estrov ◽  
C Roifman ◽  
YP Wang ◽  
T Grunberger ◽  
EW Gelfand ◽  
...  
Keyword(s):  
T Lymphocytes ◽  
Colony Formation ◽  
Interleukin 2 ◽  
Erythroid Differentiation ◽  
Colony Growth ◽  
Dependent Manner ◽  
Erythroid Progenitor ◽  
Recombinant Interleukin 2

To analyze the role of T lymphocytes in human erythropoiesis, we evaluated the effect of recombinant interleukin 2 (IL 2) on marrow CFU- E and BFU-E colony formation in vitro. IL 2 resulted in an increase in CFU-E and BFU-E colony numbers in a dose-dependent manner. This increase could be prevented by anti-Tac, a monoclonal antibody to the IL 2 receptor. Moreover, anti-Tac on its own resulted in an overall decrease in colony numbers. Depletion of marrow adherent cells did not alter the effect of either IL 2 or anti-Tac on colony growth. Following the removal of marrow T lymphocytes, CFU-E and BFU-E colony formation proceeded normally; however, the effects of IL 2 and anti-Tac were markedly diminished. Readdition of T lymphocytes to the cultures restored the IL 2 effect. Although T lymphocytes were not themselves essential for in vitro erythropoiesis, our studies suggest that IL 2 and IL 2-responsive T cells can regulate both early and mature stages of erythroid differentiation.

Identification of Alternative Splicing Changes in Erythroid Differentiation Using Human Exon Arrays.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1707-1707
Author(s):  
Miki L. Yamamoto ◽  
Jeong-Ah Kang ◽  
Josh A. Arribere ◽  
Amittha Wickrema ◽  
John G. Conboy
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Erythroid Differentiation ◽  
Transcriptional Level ◽  
Rt Pcr ◽  
Erythroid Progenitor ◽  
Array Data ◽  
Exon Arrays ◽  
Human Exon ◽  
A Genome

Abstract Terminal erythroid differentiation is accompanied by extensive structural remodeling as the cell enucleates and eventually assumes the biconcave disk morphology of the mature cell. Previous studies have documented many changes at the transcriptional level essential for erythroid differentiation. Changes in erythroid gene expression also occur at the level of pre-mRNA alternative splicing: the activation of 4.1R (EPB41) exon 16 splicing in late erythroblasts increases 4.1R affinity for spectrin-actin and mechanically strengthens the plasma membrane. We hypothesize that analogous changes in alternative splicing affect the structure and function of other erythroid proteins. To identify additional alternative splicing switches in erythroid genes, a genome-wide exon expression analysis was carried out using the new Affymetrix Human Exon 1.0 ST Array. Unlike traditional gene expression microarrays, this array has single exon resolution and can detect changes in expression due to alternative splicing. Samples for array analyses were prepared from RNA of human erythroid progenitor cells grown in culture for 7, 10, and 14 days, corresponding to basophilic, polychromatic, and orthochromatic stages. Analysis of this exon array data confirmed that 4.1R exon 16 splicing was activated in day 14 cells, and that a known inhibitor of exon 16 splicing, hnRNP A1, was down-regulated in coordination with the 4.1R splicing switch. As another positive control, we confirmed in array data the expression of a known erythroid-specific 3′ end in beta-spectrin mRNA in all three time points of erythroblasts, while array data from muscle tissue showed expression of only the non-erythroid 3′ end of beta-spectrin. Array data is now being analyzed to identify new cases of alternative splicing during erythropoiesis, and confirmation of several candidate splicing switches by RT-PCR and quantitative PCR is under way. A number of genes, including PIK3R1, SLC12A6, and TNPO2, show changes in alternative 5′ first exon usage during late erythropoiesis. A splicing change involving an internal cassette exon in MBNL2, which encodes a splicing regulator, was identified by array data and confirmed by RT-PCR. In addition, overall gene expression analyses confirm up-regulation of known genes expressed during erythroid differentiation, including Band 3, GLUT1, ALAS2, and BCL2L1. This preliminary analysis demonstrates the application of exon arrays toward the identification of splicing switches that occur during differentiation of human erythroblasts. Further validation of putative alternative splicing events is in progress, and investigation of the regulation of the validated events and the physiological implications of the predicted changes in the proteins will be pursued in the future.

scholarly journals TAF10 Interacts with GATA1 Transcription Factor and Controls Mouse Erythropoiesis

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2912-2912
Author(s):  
Petros Papadopoulos ◽  
Laura Gutierrez ◽  
Jeroen Demmers ◽  
Dimitris Papageorgiou ◽  
Elena Karkoulia ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Fetal Liver ◽  
Erythroid Differentiation ◽  
Erythroid Cells ◽  
Erythroid Progenitors ◽  
Erythroid Progenitor ◽  
Erythroid Progenitor Cells ◽  
Fetal Liver Cells ◽  
Gata1 Transcription Factor

Abstract The ordered assembly of a functional preinitiation complex (PIC), composed of general transcription factors (GTFs) is a prerequisite for the transcription of protein coding genes by RNA polymerase II. TFIID, comprised of the TATA binding protein (TBP) and 13 TBP-associated factors (TAFs), is the GTF that is thought to recognize the promoter sequences allowing site-specific PIC assembly. Transcriptional cofactors, such as SAGA (Spt-Ada-Gcn5-acetyltransferase), are also necessary to have tightly regulated transcription initiation. However, a new era on the role of the GTFs and specifically on the role of TFIID in tissue specific and promoter specific transcriptional regulation has emerged in the light of novel findings regarding the differentiation programs of different cell types1. TAF10 is a subunit of both the TFIID and the SAGA co-activator HAT complexes2. The role of TAF10 is indispensable for early embryonic transcription and mouse development as knockout (KO) embryos die early in gestation between E3.5 and E5.5, around the stage when the supply of maternal protein becomes insufficient3. However, when analyzing TFIID stability and transcription it was noted that not all cells and tissues were equally affected by the loss of TAF10. The contribution of the two TAF10-containing complexes (TFIID, SAGA) to erythropoiesis remains elusive. Ablation of TAF10 specifically in erythroid cells by crossing the TAF10-Lox with the EpoR-Cre mouse led to a differentiation block at around E13.5 with erythroid progenitor cells accumulating at a higher percentage (26% in the KO embryos vs 16% in the WTs at E12.5) at the double positive stage KIT+CD71+ and giving rise to fewer mature TER119+ cells in the fetal liver. At E13.5 embryos were dead with almost no erythroid cells in the fetal liver. Gene expression analysis of the fetal liver cells of the embryos revealed down-regulation of GATA1 expression and its target genes, bh1&bmaj/min globins and KLF1 transcription factor while expression of other genes known to have a role in mouse hematopoiesis remained unaffected (MYB, GATA2, PU.1). In order to get insight to the role of TAF10 during erythropoiesis we analyzed the composition of both TAF10-containing complexes (TFIID and SAGA) by mass spectrometry. We found that their stoichiometry changes slightly but not fundamentally during erythroid differentiation and development (human fetal liver erythroid progenitors, human blood erythroid progenitors and mouse erythroid progenitor cells) and no major rearrangements were generated in the composition of the TFIID as it was reported in other cell differentiation programs (e.g. skeletal differentiation, hepatogenesis). Additionally, we found GATA1 transcription factor only in the fetal liver and not in the adult erythroid cells in the mass spectrometry data of TAF10 immunoprecipitations (IPs), an interaction that we confirmed by reciprocal IP of TAF10 and GATA1 in MEL and mouse fetal liver cells. Most importantly, we checked whether TAF10 binding is enriched on the GATA1 locus in human erythroid cells during the fetal and the adult stage in erythroid proerythroblasts and we found that there is enriched binding of TAF10 in the palindromic GATA1 site in the fetal stage. Our results support a developmental role for TAF10 in GATA1 regulated genes, including GATA1 itself, during erythroid differentiation emphasizing the crosstalk between the transcriptional machinery and activators in erythropoiesis. References 1. Goodrich JA, Tjian R (2010) Unexpected roles for core promoter recognition factors in cell-type-specific transcription and gene regulation. Nature reviews Genetics 11: 549-558 2 .Timmers HT, Tora L (2005) SAGA unveiled. Trends Biochem Sci 30: 7-10 3. Mohan WS, Jr., Scheer E, Wendling O, Metzger D, Tora L (2003) TAF10 (TAF(II)30) is necessary for TFIID stability and early embryogenesis in mice. Mol Cell Biol 23: 4307-4318 Disclosures No relevant conflicts of interest to declare.

scholarly journals Role of Calcium Signaling Pathway-Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single-Cell Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-35
Author(s):  
Weiwei Lin ◽  
Yangxin Wang ◽  
Yisheng Chen ◽  
Qiangwei Wang ◽  
Zhaowen Gu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Calcium Signaling ◽  
Single Cell ◽  
Signaling Pathway ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Kegg Pathways ◽  
Calcium Signaling Pathway ◽  
Core Genes

Background. This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis. Methods. The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions. Results. The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex. Conclusions. Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.

scholarly journals Optimising expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Author(s):  
Anna S.E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

AbstractSingle-cell RNA-sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states, and promises to improve our understanding of genetic regulation across tissues in both health and disease. While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimise sc-eQTL mapping. Here, we evaluate the role of different normalisation and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches and provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

Regulation of Erythro-Megakaryocytic Lineage Divergence By the Transcriptional Repressor Gfi1b and Its Rgs Targets

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2385-2385
Author(s):  
Ananya Sengupta ◽  
Ghanshyam Upadhyay ◽  
Sayani Sen ◽  
Shireen Saleque
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Target Genes ◽  
Transcriptional Repressor ◽  
Erythroid Differentiation ◽  
Mapk Signaling ◽  
Erythroid Cells ◽  
Hematopoietic Progenitors ◽  
Lineage Divergence

Abstract Introduction: Appropriate diversification of hematopoietic lineages from multi-potent progenitors is essential for normal development and health. The molecular programs that govern the divergence of erythroid and megakaryocytic lineages remain incompletely defined. Gene targeting experiments have shown the transcriptional repressor Gfi1b (Growth factor independence 1b) to be essential for erythro-megakaryocyte lineage development. Transcriptional repression of Gfi1b target genes is mediated by the cofactors LSD (lysine demethylase) 1 and Rcor (CoREST) 1. To understand the mechanism of Gfi1b action, its target genes were identified by chromatin immunoprecipitation (ChIP on Chip) screens. Three members of the Rgs (Regulator of G protein signaling) family were prominently represented in this target gene pool. In this study we present the role of Rgs18, a GTPase activating protein (GAP), in modulating erythro-megakaryocytic lineage divergence in hematopoietic progenitors. The results presented below demonstrate Rgs18 as a key arbitrator of this process in murine and human contexts. Approach: Following identification of Rgs18 as a potential Gfi1b and LSD1 target, its regulation by these factors was ascertained in erythro-megakaryocytic cells. Subsequently, to interrogate the role of Rgs18 in erythro-megakaryocyte differentiation, cDNA and shRNA mediated manipulations were performed in primary hematopoietic progenitors and cell lines, and the resulting phenotypes were analyzed. Finally, to trace the underlying mechanistic alterations responsible for these phenotypes the status of two branches of the MAPK (mitogen activated protein kinase) pathway and gene expression patterns of the mutually antagonistic transcription factors Fli1 (Friend leukemia integration [site] 1/Klf1 (Krupple like factor 1) were determined in Rgs18 manipulated cells. Result: Rgs18 expression was found to be low in immature megakaryoblasts in keeping with strong Gfi1b and LSD1 expression, but was reciprocally upregulated in mature megakaryocytes following declining Gfi1b and LSD1 levels in cells and on the rgs18 promoter. In contrast, expression of Gfi1b was strong in immature erythroid cells and increased further in mature cells, while Rgs18 expression which was modest in immature erythroid cells exhibited a reciprocal decline during maturation. Manipulation of Rgs18 expression in murine hematopoietic progenitors and a bipotential human cell line produced divergent outcomes, with expression augmenting megakaryocytic, and potently suppressing erythroid differentiation and vice versa. These phenotypes resulted from differential impact of Rgs18 expression on the P38 and ERK branches of MAPK signaling in the erythroid and megakaryocytic lineages. Repercussions of these signaling changes impacted relative expression of the mutually antagonistic transcription factors Fli1 and Klf1 and were compensated by ectopic Fli1 expression demonstrating activity of this transcription factor downstream of Rgs18. Conclusion: These results identify Rgs18 as a critical downstream effector of Gfi1b and an upstream regulator of MAPK signaling and Klf1/Fli1 gene expression. Sustained Gfi1b expression during erythroid differentiation represses Rgs18 and limits megakaryocytic gene expression. However during progression of megakaryocytic differentiation, declining Gfi1b levels results in robust expression of Rgs18 and lineage progression. Overall, this study provides new perspectives on lineage determination by highlighting multi-tier interactions between transcriptional and signaling networks in orchestrating hematopoietic lineage divergence. These insights could exemplify generic mechanisms exhibited by this large family of signal modulators in mediating lineage diversification in various contexts. Disclosures No relevant conflicts of interest to declare.

Sign in / Sign up

Export Citation Format

Share Document