Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Hydroxymethylation at gene regulatory regions directs stem cell commitment during erythropoiesis

ABSTRACT: CD34 positive hematopoietic stem cells were differentiated into erythroid lineage. Next generation sequencing (NGS) of 5hmC affinity pulldown and RNAseq were performed in four time point of different stages of erythroid differentiation. 4 RNA-Seq Samples (d0, d3, d7 and d10); 4 affinity-pulldown (d0, d3, d7 and d10), and 4 input samples (d0, d3, d7 and d10).

ORGANISM(S): Homo sapiens

SUBMITTER: Jozef Madzo

PROVIDER: E-GEOD-40243 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Hydroxymethylation at gene regulatory regions directs stem/early progenitor cell commitment during erythropoiesis.

Madzo Jozef J Liu Hui H Rodriguez Alexis A Vasanthakumar Aparna A Sundaravel Sriram S Caces Donne Bennett D DBD Looney Timothy J TJ Zhang Li L Lepore Janet B JB Macrae Trisha T Duszynski Robert R Shih Alan H AH Song Chun-Xiao CX Yu Miao M Yu Yiting Y Grossman Robert R Raumann Brigitte B Verma Amit A He Chuan C Levine Ross L RL Lavelle Don D Lahn Bruce T BT Wickrema Amittha A Godley Lucy A LA

Cell reports 20131227 1

Hematopoietic stem cell differentiation involves the silencing of self-renewal genes and induction of a specific transcriptional program. Identification of multiple covalent cytosine modifications raises the question of how these derivatized bases influence stem cell commitment. Using a replicative primary human hematopoietic stem/progenitor cell differentiation system, we demonstrate dynamic changes of 5-hydroxymethylcytosine (5-hmC) during stem cell commitment and differentiation to the erythr ...[more]

PMID: 24373966

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Project description:Global, genomic responses of erythrocytes to infectious agents have been difficult to measure, because these cells are e-nucleated. We have previously demonstrated that in vitro matured, nucleated erythroblast cells at the orthochromatic stage can be efficiently infected by the human malaria parasite Plasmodium falciparum. We now show that infection of orthochromatic cells induces change in 609 host genes. 592 of these transcripts are up-regulated and associated with metabolic and chaperone pathways unique to P. falciparum infection, as well as a wide range of signaling pathways that are also induced in related apicomplexan infections of mouse hepatocytes or human fibroblast cells. Our data additionally show that polychromatophilic cells, which precede the orthochromatic stage and are not infected when co-cultured with P. falciparum, up-regulate a small set of 35 genes, 9 of which are associated with pathways of hematopoiesis and/or erythroid cell development. These data unexpectedly predict that blood stage P. falciparum may induce host responses common to infections of other pathogens. Further P. falciparum may modulate gene expression in bystander erythroblasts and thus influence pathways of erythrocyte development. Human primary erythroid cells were differentiated from CD34+ hematopoietic stem cells isolated from growth factor-mobilized peripheral blood (ALL Cells, Inc.). Cells from five donors were cultured until polychromatophilic and orthochromatophilic stages of differentiation and served as uninfected control samples. Of the five donors, three were used to initiate Plasmodium falciparum (3D7) infection at a multiplicity of infection = 5. Infected cells were harvested 24 hours post-infection, and RNA was isolated with Trizol (Invitrogen) and purified with RNeasy columns (QIAGEN) according to manufacturer recommendations. Microarray labeling and hybridizations were done according to Affymetrix protocols using HG U133 plus 2.0 chips. For GenePattern analysis, all samples (5 control and 3 infected samples) were analyzed; for Dchip analysis, only three samples were analyzed (the same 3 donors served as control and infected samples).

Project description:Differentiation of hematopoietic stem cells to red cells requires coordinated expression of numerous erythroid genes. To understand the regulatory mechanisms governing lineage commitment we conducted a high resolution methylomic and transcriptomic analysis of six major stages of human erythroid differentiation. We observed widespread epigenetic differences between early and late stages of erythropoiesis with progressive loss of methylation being the dominant change during differentiation. Gene bodies, intergenic regions and CpG shores were preferentially demethylated during erythropoiesis. Epigenetic changes at transcription factor binding sites correlated significantly with changes in gene expression and were enriched for binding motifs for SCL, MYB, GATA and other factors not previously implicated in erythropoiesis. Demethylation at gene promoters was associated with increased expression of genes, while epigenetic changes at gene bodies correlated inversely with gene expression. Important gene networks encoding erythrocyte membrane proteins, surface receptors and heme synthesis proteins were found to be regulated by DNA methylation. Furthermore, integrative analysis enabled us to identify novel, potential regulatory areas of the genome that underwent differential methylation and correlated with corresponding changes in gene expression, as evident by epigenetic changes in a predicted PU.1 binding site in intron 1 of the GATA1 gene. This intronic site was found to be conserved across species and was validated to be a novel PU.1 binding site by qCHIP in erythroid cells. Altogether, our study provides a comprehensive analysis of methylomic and transcriptomic changes during erythroid differentiation and demonstrates that hypomethylation of the genome during erythroid differentiation has implications in modulating gene expression. We examined changes in methylation on days 0, 3, 7, 10, 13 and 16 of human erythroid culture. Two independent sets of experiments were performed.

Project description:MAPK cascade in solution (no scaffold) Description This model describes a basic 3- stage Mitogen Activated Protein Kinase (MAPK) cascade in solution. This cascade is typically expressed as RAF= =>MEK==>MAPK (alternative forms are K3==>K2==> K1 and KKK==>KK==>K) . The input signal is RAFK (RAF Kinase) and the output signal is MAPKpp ( doubly phosphorylated form of MAPK) . RAFK phosphorylates RAF once to RAFp. RAFp, the phosphorylated form of RAF induces two phoshporylations of MEK, to MEKp and MEKpp. MEKpp, the doubly phosphorylated form of MEK, induces two phosphorylations of MAPK to MAPKp and MAPKpp. Rate constant Reaction a10 = 5. MAPKPH + MAPKpp -> MAPKppMAPKPH a1 = 1. RAF + RAFK -> RAFRAFK a2 = 0.5 RAFp + RAFPH -> RAFpRAFPH a3 = 3.3 MEK + RAFp -> MEKRAFp a4 = 10. MEKp + MEKPH -> MEKpMEKPH a5 = 3.3 MEKp + RAFp -> MEKpRAFp a6 = 10. MEKPH + MEKpp -> MEKppMEKPH a7 = 20. MAPK + MEKpp -> MAPKMEKpp a8 = 5. MAPKp + MAPKPH -> MAPKpMAPKPH a9 = 20. MAPKp + MEKpp -> MAPKpMEKpp d10 = 0.4 MAPKppMAPKPH -> MAPKPH + MAPKpp d1 = 0.4 RAFRAFK -> RAF + RAFK d2 = 0.5 RAFpRAFPH -> RAFp + RAFPH d3 = 0.42 MEKRAFp -> MEK + RAFp d4 = 0.8 MEKpMEKPH -> MEKp + MEKPH d5 = 0.4 MEKpRAFp -> MEKp + RAFp d6 = 0.8 MEKppMEKPH -> MEKPH + MEKpp d7 = 0.6 MAPKMEKpp -> MAPK + MEKpp d8 = 0.4 MAPKpMAPKPH -> MAPKp + MAPKPH d9 = 0.6 MAPKpMEKpp -> MAPKp + MEKpp k10 = 0.1 MAPKppMAPKPH -> MAPKp + MAPKPH k1 = 0.1 RAFRAFK -> RAFK + RAFp k2 = 0.1 RAFpRAFPH -> RAF + RAFPH k3 = 0.1 MEKRAFp -> MEKp + RAFp k4 = 0.1 MEKpMEKPH -> MEK + MEKPH k5 = 0.1 MEKpRAFp -> MEKpp + RAFp k6 = 0.1 MEKppMEKPH -> MEKp + MEKPH k7 = 0.1 MAPKMEKpp -> MAPKp + MEKpp k8 = 0.1 MAPKpMAPKPH -> MAPK + MAPKPH k9 = 0.1 MAPKpMEKpp -> MAPKpp + MEKpp Variable IC ODE MAPK 0.3 MAPK'[t] == d7*MAPKMEKpp[t] + k8*MAPKpMAPKPH[t] - a7*MAPK[t]*MEKpp[t] MAPKMEKpp 0 MAPKMEKpp'[t] == -(d7*MAPKMEKpp[t]) - k7*MAPKMEKpp[t] + a7*MAPK[t]*MEKpp[t] MAPKp 0 MAPKp'[t] == k7*MAPKMEKpp[t] - a8*MAPKp[t]*MAPKPH[t] + d8*MAPKpMAPKPH[t] + d9*MAPKpMEKpp[t] + k10* MAPKppMAPKPH[t] - a9*MAPKp[t]*MEKpp[t] MAPKPH 0.3 MAPKPH'[t] == -(a8*MAPKp[t]*MAPKPH[t]) + d8*MAPKpMAPKPH[ t] + k8*MAPKpMAPKPH[t] - a10*MAPKPH[t]*MAPKpp[t] + d10*MAPKppMAPKPH[t] + k10*MAPKppMAPKPH[t] MAPKpMAPKPH 0 MAPKpMAPKPH'[t] == a8*MAPKp[t]*MAPKPH[t] - d8* MAPKpMAPKPH[t] - k8*MAPKpMAPKPH[t] MAPKpMEKpp 0 MAPKpMEKpp'[t] == -(d9*MAPKpMEKpp[t]) - k9*MAPKpMEKpp[t] + a9*MAPKp[t]*MEKpp[t] MAPKpp 0 MAPKpp'[t] == k9*MAPKpMEKpp[t] - a10*MAPKPH[t]*MAPKpp[t] + d10*MAPKppMAPKPH[t] MAPKppMAPKPH 0 MAPKppMAPKPH'[t] == a10*MAPKPH[t]*MAPKpp[t] - d10* MAPKppMAPKPH[t] - k10*MAPKppMAPKPH[t] MEK 0.2 MEK'[t] == k4*MEKpMEKPH[t] + d3*MEKRAFp[t] - a3*MEK[t]*RAFp[t] MEKp 0 MEKp'[t] == -(a4*MEKp[t]*MEKPH[t]) + d4*MEKpMEKPH[t] + k6*MEKppMEKPH[t] + d5*MEKpRAFp[t] + k3*MEKRAFp[ t] - a5*MEKp[t]*RAFp[t] MEKPH 0.2 MEKPH'[t] == -(a4*MEKp[t]*MEKPH[t]) + d4*MEKpMEKPH[t] + k4*MEKpMEKPH[t] - a6*MEKPH[t]*MEKpp[t] + d6* MEKppMEKPH[t] + k6*MEKppMEKPH[t] MEKpMEKPH 0 MEKpMEKPH'[t] == a4*MEKp[t]*MEKPH[t] - d4*MEKpMEKPH[t] - k4*MEKpMEKPH[t] MEKpp 0 MEKpp'[t] == d7*MAPKMEKpp[t] + k7*MAPKMEKpp[t] + d9*MAPKpMEKpp[t] + k9*MAPKpMEKpp[t] - a7*MAPK[t]* MEKpp[t] - a9*MAPKp[t]*MEKpp[t] - a6*MEKPH[t]*MEKpp[t] + d6*MEKppMEKPH[t] + k5*MEKpRAFp[t] MEKppMEKPH 0 MEKppMEKPH'[t] == a6*MEKPH[t]*MEKpp[t] - d6*MEKppMEKPH[ t] - k6*MEKppMEKPH[t] MEKpRAFp 0 MEKpRAFp'[t] == -(d5*MEKpRAFp[t]) - k5*MEKpRAFp[t] + a5*MEKp[t]*RAFp[t] MEKRAFp 0 MEKRAFp'[t] == -(d3*MEKRAFp[t]) - k3*MEKRAFp[t] + a3*MEK[t]*RAFp[t] RAF 0.4 RAF'[t] == -(a1*RAF[t]*RAFK[t]) + k2*RAFpRAFPH[t] + d1*RAFRAFK[t] RAFK 0.1 RAFK'[t] == -(a1*RAF[t]*RAFK[t]) + d1*RAFRAFK[t] + k1*RAFRAFK[t] RAFp 0 RAFp'[t] == d5*MEKpRAFp[t] + k5*MEKpRAFp[t] + d3*MEKRAFp[t] + k3*MEKRAFp[t] - a3*MEK[t]*RAFp[t] - a5*MEKp[t]*RAFp[t] - a2*RAFp[t]*RAFPH[t] + d2* RAFpRAFPH[t] + k1*RAFRAFK[t] RAFPH 0.3 RAFPH'[t] == -(a2*RAFp[t]*RAFPH[t]) + d2*RAFpRAFPH[t] + k2*RAFpRAFPH[t] RAFpRAFPH 0 RAFpRAFPH'[t] == a2*RAFp[t]*RAFPH[t] - d2*RAFpRAFPH[t] - k2*RAFpRAFPH[t] RAFRAFK 0 RAFRAFK'[t] == a1*RAF[t]*RAFK[t] - d1*RAFRAFK[t] - k1*RAFRAFK[t] Generated by Cellerator Version 1.4.3 (6-March-2004) using Mathematica 5.0 for Mac OS X (November 19, 2003), March 6, 2004 12:18:07, using (PowerMac, PowerPC,Mac OS X,MacOSX,Darwin) author=B.E.Shapiro This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use . To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Dataset Information

Hydroxymethylation at gene regulatory regions directs stem cell commitment during erythropoiesis

Publications

Hydroxymethylation at gene regulatory regions directs stem/early progenitor cell commitment during erythropoiesis.

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