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Blood, 15 November 2004, Vol. 104, No. 10, pp. 3136-3147. Prepublished online as a Blood First Edition Paper on August 5, 2004; DOI 10.1182/blood-2004-04-1603.
HEMATOPOIESIS Global regulation of erythroid gene expression by transcription factor GATA-1From The Children's Hospital of Philadelphia, Division of Hematology, and the University of Pennsylvania, Philadelphia; the Cell and Molecular Biology and Combined Degree Graduate Programs, The University of Pennsylvania School of Medicine, Philadelphia; the Department of Biochemistry and Molecular Biology, Center for Comparative Genomics and Bioinformatics, Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA; and the Department of Cancer Biology, Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia.
Transcription factor GATA-1 is required for erythropoiesis, yet its full actions are unknown. We performed transcriptome analysis of G1E-ER4 cells, a GATA-1–null erythroblast line that undergoes synchronous erythroid maturation when GATA-1 activity is restored. We interrogated more than 9000 transcripts at 6 time points representing the transition from late burst forming unit–erythroid (BFU-E) to basophilic erythroblast stages. Our findings illuminate several new aspects of GATA-1 function. First, the large number of genes responding quickly to restoration of GATA-1 extends the repertoire of its potential targets. Second, many transcripts were rapidly down-regulated, highlighting the importance of GATA-1 in gene repression. Third, up-regulation of some known GATA-1 targets was delayed, suggesting that auxiliary factors are required. For example, induction of the direct GATA-1 target gene  major globin was late and, surprisingly, required new protein synthesis. In contrast, the gene encoding Fog1, which cooperates with GATA-1 in globin transcription, was rapidly induced independently of protein synthesis. Guided by bioinformatic analysis, we demonstrated that selected regions of the Fog1 gene exhibit enhancer activity and in vivo occupancy by GATA-1. These findings define a regulatory loop for globin expression and, more generally, demonstrate how transcriptome analysis can be used to generate testable hypotheses regarding transcriptional networks.
Studies of red blood cell development provide an important basis for understanding general mechanisms of gene regulation and tissue formation. Erythrocyte maturation is largely dedicated to producing hemoglobin and supporting its major function of oxygen delivery. Acquisition of this specialized phenotype is controlled by a concerted differentiation program that serves both generalized and cell type–specific requirements. Production of hemoglobin is coordinated to avoid the toxic accumulation of globins or heme synthetic intermediates. Special adaptations are required for iron import and defense against reactive oxygen species. The cell membrane must undergo remodeling to present developmentally appropriate cytokine receptors and ion transporters. In addition, erythropoiesis requires active mechanisms to prevent apoptosis and coordinate cell-cycle arrest with terminal maturation. Erythroid differentiation is highly regulated at the level of mRNA transcription, and factors controlling the expression of numerous erythroid genes, particularly globins, have been extensively characterized. Recent studies of primary hematopoietic precursors1-6 and immortalized cell lines7,8 are beginning to define global gene expression patterns during hematopoietic maturation, although a comprehensive view of erythropoiesis is lacking. This information is essential for new gene discovery and to better understand the regulatory networks that specify the formation and function of red blood cells. One general approach to understanding gene regulation during development is to identify lineage-determining transcription factors and then define the genes that they control. In this regard, studies of the nuclear protein GATA-1 have provided numerous insights into erythropoiesis.9-11 GATA-1 is a transcription factor essential for erythrocyte, megakaryocyte, mast cell, and eosinophil differentiation.12-17 Ablation of the GATA-1 gene in mice causes embryonic death from severe anemia due to maturation arrest and apoptosis of committed erythroid precursors.13,18,19 In humans, missense mutations in GATA-1 cause dyserythropoietic anemia and/or thalassemia.20,21 While virtually all erythroid genes examined are positively regulated directly by GATA-1, the full extent of its transcriptional actions is currently unknown. Defining the genes regulated by GATA-1 more comprehensively should better elucidate the molecular pathways that control erythropoiesis. To examine the role of GATA-1 in erythroid development, we created G1E cells (for GATA-1– erythroid), an immortalized GATA-1–null line derived from gene-targeted embryonic stem cells.22 G1E cells proliferate in culture as immature erythroblasts and undergo terminal erythroid maturation when GATA-1 function is restored. Thus, complementation of a defined loss-of-function mutation generates a synchronized cohort of differentiating erythroid precursors, an ideal system in which to delineate gene expression at specific stages of erythropoiesis and to investigate the transcriptional effects of GATA-1 in a physiologic context. Previously, we used subtractive hybridization to analyze transcripts up-regulated by GATA-1 in G1E cells.23,24 These studies identified known GATA-1 targets and also new genes important for erythroid maturation, but examined a limited number of transcripts at only one point in differentiation. Here we report transcriptome analysis using Affymetrix (Santa Clara, CA) oligonucleotide microarrays to simultaneously interrogate 12 451 murine transcripts corresponding to 9266 unique known genes and expressed sequence tages (ESTs). RNA from G1E cells was examined at baseline and at 5 time points after synchronous terminal maturation was induced by an estradiol-activated form of GATA-1. This model system allowed us to define both immediate and delayed actions of GATA-1 at specific developmental stages of erythropoiesis.
Numerous erythroid markers and known GATA-1 target genes were induced, which, together with prior studies,22,24-29 validate the use of G1E cells as a model system for erythropoiesis. Our work illustrates several new aspects of GATA-1 function and erythroid biology. A surprising number of ESTs and transcripts encoding known proteins were altered at early time points, identifying genes likely to be regulated directly by GATA-1 and providing new insights into the control of erythropoiesis. Moreover, GATA-1 rapidly inhibited as many transcripts as it induced, highlighting an underappreciated role in gene repression. In addition, up-regulation of a subset of established GATA-1 targets was relatively delayed, suggesting that their activation requires induction of accessory factors. One important example was
Cell culture G1E-ER4 cells were cultured as described previously.22,26 Western blotting GATA-1 was detected in Western blots with antibody N-6 from Santa Cruz Biotechnology (Santa Cruz, CA) at 1:10 000 dilution. Microarray experiments
In 3 independent experiments, G1E-ER4 cells growing in log phase were induced for 0, 3, 7, 14, 21, or 30 hours with 10–7 M Analysis was performed with Affymetrix MAS 5.0 software using the statistical algorithm.31,32 Chip-wide signal was scaled to a value of 150 using all probe sets, without additional normalization, using default parameters. The signal for the 3 replicates of each probe set at each time point was averaged. Average signal log2 ratios (SLRs) were computed for comparative analyses by averaging the linearized SLR values for each probe set triplicate, followed by reconversion to a base-2 logarithm. Annotations were extracted from the Unigene and GeneOntology (GO)33 consortium databases using GenBank accession numbers assigned by Affymetrix. Results and annotations were merged into a Microsoft Access database (Microsoft, Seattle, WA). For comparative analyses between 2 time points, we adopted a stringent numeric filtering strategy based on the article by Cammenga et al4: all P values for each comparison in a triplicate had to satisfy the condition that P was less than .005 or greater than .995, and at least 1 of the 2 average signals being compared (baseline or experimental) had to be above a threshold value of 100, with an SLR more than 1.5. Hierarchic clustering of unfiltered data was performed using dCHIP.34 After normalization to the median intensity array, "PM only" model-based expression was calculated with default settings. Values obtained from triplicate samples were pooled for each time point, and genes were filtered using the following criteria: variation across samples after pooling: 0.6 less than standard deviation/mean less than 10; percent present calls in the arrays used 15% or more; replication variation such that 0 less than median less than 0.5 and signal level of 100 or more in 15% or more of samples. These criteria were selected to obtain a visual representation of about 100 genes that changed the most over the 30-hour time course. Gene expression analysis
Northern blotting was performed as previously described.23 To detect Predicting cis-regulatory modules in Fog1 by comparative genomics All bioinformatic predictions used whole-genome sequence alignments of mouse and human generated by blastZ36 or mouse, rat, and human generated by multiZ.37 For a measure of selection that indicates biologic function, we used L-scores, which give a log-likelihood estimate of the probability that an alignment would not be generated at the locally adjusted neutral rate.37 The conservation score data were plotted in the University of California Santa Cruz (UCSC) Browser display.39 The regulatory potential score (RP score) is a log-likelihood measure that estimates a probability that the function under selection is a cis-acting gene regulatory element.40 In this method, alignments are scored for how well they match patterns that distinguish alignments in regulatory regions from those in neutral DNA. The database of Genome Alignments and Annotations (GALA)41 was searched to find DNA intervals in the Fog1/Zfpm1 locus whose alignments had an RP score of at least 2.3, an empirically derived threshold based on identification of known regulatory elements in the Hbb locus.42 GALA was also queried for all mouse sequences in the Fog1 locus that match weight matrices for GATA-1 binding sites in TRANSFAC43 and for blocks in the mouse-rat-human 3-way alignments containing conserved matches to GATA-1 weight matrices (computed by the tffind comparative sequencing program).44 The DNA intervals exceeding the RP score threshold that also contain a conserved, predicted GATA-1 binding site were identified by intersection operations in GALA. Data from GALA were displayed as custom tracks in the UCSC Genome Browser to provide a consolidated view. Generation of G1E-ER4 cells overexpressing bcl-xL The coding region of mouse bcl-xL was cloned into the retroviral vector pGD and G1E-ER4 cells were transduced as described.22 A polyclonal population of cells expressing ectopic bcl-xL was generated by culturing the transduced cells for 24 hours in the absence of erythropoietin and kit ligand. Under these conditions virtually 100% of G1E-ER4 cells transduced with vector alone died, while about 50% of the bcl-xL–transduced cells survived. After this selection step, all of the bcl-xL–transduced cells survived 24-hour cytokine deprivation and underwent erythroid differentiation in response to estradiol-induced activation of GATA-1. Chromatin immunoprecipitation
Chromatin immunoprecipitation (ChIP) assays were performed as described.45,46 PCR products were quantified using SYBR green dye on an ABI 7000 real-time PCR machine. PCR product signals were referenced to a dilution series of the relevant input. Primer pairs used for PCR were as follows: Enhancer assays by transient transfection The ability of predicted cis-regulatory module 1 (preCRM1 or R1) to enhance the level of expression from an erythroid promoter was tested by measuring its effect on expression of a gamma-globin-promoter-luciferase reporter gene plasmid, gammaLuc,47 after transient transfection of uninduced K562 cells. The DNA segment from mouse Chromosome 8, positions 121 975 271 to 121 975 514 (February 2003 assembly, mm338) contains preCRM1, located in the first intron of Fog1. It was added upstream of the promoter in the plasmid gammaLuc to make R1gammaLuc. The parental and test plasmids were transfected in triplicate into K562 cells using Tfx50 (Promega, Madison, WI), along with a control plasmid with a Renilla luciferase reporter gene. The firefly luciferase activity encoded by gammaLuc plasmids and the Renilla luciferase activity from the control were measured using the Promega Dual-Luciferase Reporter (DLR) Assay kit, and the ratio of these activities (FF/Ren) was calculated for each replicate. The experiment was performed twice. In order to present the results on a similar scale, the FF/Ren ratios were normalized by dividing by the mean FF/Ren value for the parental gammaLuc samples in each experiment. Microscopy The image in Figure 1A was obtained using an Axioskop 2 microscope equipped with an AxioCam camera and a 100 x/1.3 Plan-Neofluor oil immersion objective lens (Carl Zeiss, Munchen-Hallbergmoos, Germany). Images were processed with Axiovision 3.1 software (Carl Zeiss).
The complete microarray dataset was deposited at the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO; accession no. GSE628 [NCBI GEO] ). From this point forward, "supplemental data" refers to analysis, raw data, and the G1EDb database containing results and annotations found at http://stokes.chop.edu/web/weiss/G1Eindex.html and its subdirectories.49 We used G1E-ER4 cells, a G1E subclone that constitutively expresses a conditional, estradiol-activated form of GATA-1 (GATA-1–ER, GATA-1 fused to the estrogen receptor ligand binding domain).25,26,48 Upon addition of estradiol, G1E-ER4 cells undergo synchronous and homogeneous erythroid maturation (Figure 1A). G1E-ER4 cells express GATA-1–ER at a level similar to that of endogenous GATA-1 in murine erythroleukemia (MEL) cells (Figure 1B) and estradiol has no apparent effects on parental G1E cells that do not express GATA-1–ER (Rylski et al25; Gregory et al48; and not shown). These control experiments indicate that erythroid maturation of G1E-ER4 cells is caused by physiologic restoration of GATA-1 and is not due to its overexpression or irrelevant estradiol effects. We used Affymetrix GeneChips (MG-U74Av2) to profile mRNA expression in G1E-ER4 cells at 0, 3, 7, 14, 21, and 30 hours after activation of GATA-1. These time points were selected to examine both early and late effects of GATA-1 and to encompass several key aspects of erythroid maturation, including loss of proliferative capacity, extensive hemoglobin synthesis, and characteristic morphologic changes25,48 (Figure 1A). For each time point, 3 biologic repeats were performed to minimize random variations in gene expression. There was strong agreement in datasets from identical time points of separate experiments (supplemental data49). In filtering genes that changed versus time zero, we favored specificity over sensitivity, using stringent criteria requiring large fold induction and highly significant P values; for a full listing of genes considered to be induced/repressed at each time point, see supplemental data.49 Among the 12 451 murine probe sets printed on the microarray, 3419 (27.4%) were designated present (in triplicate) in at least one time point. In general, activation of GATA-1 triggered numerous rapid changes in gene expression: at 3 hours, 1.3% of the probe sets represented on the microarray were increased and 0.8% were decreased versus time zero. By 30 hours, 5.6% were increased, and 10.5% were decreased, consistent with a more restricted pattern of gene expression during cellular maturation. Data were compiled into a custom database, G1EDb, available for public download (supplemental data49). Expression data and corresponding annotations can be retrieved gene by gene in a visually intuitive manner and filtered according to present/absent calls, numeric criteria, and functional categories. The G1EDb also includes hypertext links to public and Affymetrix databases. Our microarray data have also been deposited at the NCBI Gene Expression Omnibus (GEO)49 (www.ncbi.nlm.nih.gov/geol) (accession no. GSE628 [NCBI GEO] ). Validating the experimental system
We addressed 2 important validation concerns: does the experimental system recapitulate GATA-1–dependent erythropoiesis and does the microarray accurately reflect transcript levels? Regarding the first issue, numerous erythroid-expressed genes, many of which are known GATA-1 targets, were up-regulated after GATA-1 activation (Figure 2). In addition, our dataset is in agreement with an earlier study that examined expression of 600 genes during erythroid maturation of primary fetal liver cells3 (Figure 3). Although erythroid and megakaryocytic lineages arise from a common precursor and are both GATA-1 dependent, platelet-expressed transcripts such as platelet factor 4, glycoprotein 1b, coagulation factors 8 and 10, and integrin
Developmental stages of G1E cell maturation defined by gene profiling Erythroid precursors develop through distinct stages that are defined by proliferative capacity, cytokine requirements, cell-surface markers, morphology, and patterns of gene expression.50 G1E cells without GATA-1 most closely resemble the late burst forming unit–erythroid (BFU-E) stage.5,27,48 They exhibit an immature morphology, are negative for the proerythroblast marker Ter 119,51 and require kit ligand (KL, stem cell factor) for proliferation and survival. By 24 hours after GATA-1 activation, G1E cells become division arrested, down-regulate c-Kit, the receptor for KL, and require erythropoietin to prevent apoptosis. By 30 hours, most of the cells stain for hemoglobin and resemble basophilic erythroblasts. The maturation of G1E cells in relation to designated developmental stages of erythropoiesis is further defined by the current gene expression studies (Figure 5). For example, expression kinetics of erythrocyte membrane protein Rh50 (Rhag), glycophorin A (Gypa), and band III (Slc4a1) mRNAs during G1E cell maturation resembled those observed in primary erythroid cultures, with Rh50 appearing first and band III appearing last on basophilic erythroblasts.52 Several developmental switches in protein isoforms that occur during normal erythroid maturation, including the shifts from carbonic anhydrase 1 (Car1) to carbonic anhydrase 2 (Car2)53 and transferrin receptor 2 (Trfr2) to transferrin receptor 1 (Trfr1),54 were recapitulated during GATA-1–induced maturation of G1E cells (Figure 5).
Global GATA-1–induced changes in gene expression Analysis of transcripts according to their expression kinetics provides a basis to define the regulatory hierarchy through which GATA-1 controls erythroid development. A broad overview of transcripts that change during GATA-1–induced maturation of G1E cells is illustrated by hierarchic clustering (Figure 6). This method selects the genes that change the most over the entire 30-hour time course and graphically sorts them according to the similarity of their transcriptional profiles. Since each transcript is scaled relative to its own median value for the experiment, all transcripts can be viewed at once, despite a range of absolute signal intensities. As a complementary approach, we identified the most significant changes in gene expression at each time point relative to the baseline level at time zero. This pairwise comparison was particularly useful to identify rapidly modulated genes (Tables 1 and 2; supplemental data49). Together, these studies define groups of genes that are regulated by GATA-1 at early and late time points. These genes represent various functional classes including transcription factors, signaling molecules, enzymes, and structural proteins. Insights into GATA-1 actions and erythroid development deriving from these analyses are discussed below.
GATA-1 as a repressor of transcription. One of the most striking findings is that numerous transcripts were rapidly down-regulated, highlighting a potential role for GATA-1 in gene repression. Traditionally, GATA-1 is viewed as an activator of erythroid-specific transcripts; potential roles in gene repression have been implicated in prior studies, but are poorly understood.25,55-58 The current findings indicate that gene repression by GATA-1 is more prevalent than previously recognized and likely to play a critical role in hematopoiesis. We recently demonstrated that repression of Gata259 and Myc25 in erythroid cells is associated with GATA-1 binding to the respective genes, indicating potentially direct mechanisms for transcriptional inhibition. GATA-1 may repress gene expression through its interaction partner Fog-1 (Zfpm1, "Friend of GATA-1"), which binds corepressors and inhibits GATA-1–mediated transcription in specific promoter and cellular contexts.46,58,60 Transcription factors were prominent among the most rapidly repressed transcripts, for example: Eto-2 (Cbfa2t3h), Gata2, Lyl1, Nab2, and Tieg (Table 1). It is likely that many pleiotropic effects of GATA-1 are transmitted through modulation of some or all of these nuclear proteins, although their specific roles in erythropoiesis require further study. GATA-2 is of particular interest; this GATA-1 relative is expressed in hematopoietic stem cells and/or early progenitors61 and is derepressed in GATA-1– erythroblasts, which may account for their baseline transcription of GATA-dependent target genes.13,62 These findings suggest functional overlap between GATA-1 and GATA-2 in vivo. However, distinct roles for these proteins are illustrated by the ability of GATA-1, but not GATA-2, to repress transcription through a defined cis-element in the GATA-2 gene.46,59,60 Hence, it is possible that competition between GATA-1 and GATA-2 regulates the transcriptional output of some GATA-regulated genes and reflects a requirement for GATA-2 repression during normal erythropoiesis. Also of note, several genes associated with oncogenic transformation were down-regulated after GATA-1 activation. These include Myc, Myb, Kit, and Nab2. GATA-1 binding to the Myc gene is associated with its transcriptional repression during erythroid maturation.25 GATA-1 may also repress Myb through cognate elements in the proximal promoter.63 Hence, GATA-1 may promote hematopoietic maturation by repressing mitogenic genes. This hypothesis is consistent with recent findings that link GATA-1 mutations to a subset of megakaryocytic leukemias.64
Genes that are rapidly induced by GATA-1. Rapidly induced genes are shown in Tables 1 and 2. Some of the earliest up-regulated genes might encode proteins that participate in the initial cascade of GATA-1–induced maturation. For example, transcripts encoding EKLF (Klf1) and Fog-1 (Zfpm1), nuclear proteins that augment GATA-1 activities, were induced at early time points. Another rapidly up-regulated gene was Bach1, which encodes a CNC-type transcription factor that interacts with control elements in the Facilitating new insights into erythroid function and physiology. Many of the genes up-regulated by GATA-1 at early and late time points participate in specialized aspects of terminal erythropoiesis (Figure 2). Examination of additional induced and repressed genes not previously implicated in erythroid development or function should provide further insight into these processes (Tables 1 and 2; Figure 6; and supplemental data49). To facilitate the discovery of genes that participate in erythrocyte biology, we annotated transcripts in the G1EDb according to functional classes as predicted by prior studies or the presence of signature protein motifs. These include transcription factors, signaling molecules, antioxidants, and others. Data retrieval according to gene function and expression patterns provides a broad overview of specific aspects of erythropoiesis and illustrates potential new pathways that can be tested experimentally in future studies. Analysis of GATA-1–regulated gene expression according to functional categories is discussed in supplemental data.49 In addition, several poorly characterized ESTs were up-regulated by GATA-1 in G1E-ER4 cells. As indicated by our prior studies,23,24 characterization of these ESTs should identify novel proteins that participate in erythroid development or function. Regulatory hierarchies defined by the kinetics of GATA-1–regulated gene expression
Numerous established erythroid GATA target genes including those encoding alpha globin (Hba-a1), band 3 (Slc4a1), and
Delayed kinetics of To test whether Fog1 is a direct GATA-1 target gene, we first applied bioinformatic predictions to find candidate GATA-1 binding sites in Fog1 that are likely to be involved in transcriptional regulation (Figure 8A). Whole-genome sequence alignments between mouse and human were analyzed to find sequences likely to be functional (conservation score) and sequences more likely to be involved in regulation of gene expression (regulatory potential40). Also, matches to weight matrices for GATA-1 binding sites that are conserved in human, mouse, and rat were identified. As shown in Figure 8A, 17 intronic segments have prominent peaks for both regulatory potential and conservation and all of these contain one or more conserved GATA-binding sites. The presence of potential GATA sites in regulatory regions of erythroid-expressed genes is not unexpected. However, the Fog1 locus is remarkable in the large number of predicted regulatory regions that all contain conserved GATA motifs. This suggests that GATA-1 may be a critical protein in regulating Fog1 and that the multiple binding sites could mediate concentration-dependent effects.
We examined 2 regions in the Fog1 locus, designated preCRM1 and preCRM4 (for predicted cis-regulatory module), for in vivo GATA-1 occupancy by chromatin immunoprecipitation (ChIP) (Figure 8B). An upstream region of the Fog1 promoter without any conserved GATA sites was selected as a negative control (labeled as "U" in Figure 8A and as "Upstream" in Figure 8B). ChIP was performed in G1E-ER4 cells before and 24 hours after estradiol treatment. Occupancy of the GATA-1–ER fusion protein was measured using 2 different antibodies, 1 directed against GATA-1 and 1 against the ER moiety. Both CRM1 and CRM4 regions exhibited significant GATA-1 occupancy after estradiol treatment, as detected with both antibodies, roughly 50- to 100-fold greater than isotype control or irrelevant antibodies and comparable with GATA-1 occupancy at hypersensitive site 3 (HS3) of the
Next, we tested preCRM1 for its ability to augment gene expression in erythroid cells. We inserted this region upstream of a minimal
Considering that Fog-1 interacts with GATA-1 both functionally and physically and that both nuclear proteins are required for optimal Defining erythroid development through GATA-1 gene complementation Use of microarrays for mRNA profiling is a powerful tool for examining gene expression during hematopoiesis.4,5,85-87 The current study validates the utility of G1E cells as a biologically relevant model system and provides a comprehensive transcriptome analysis of the transition through several well-defined stages of erythropoiesis, illustrating new aspects of its metabolic and developmental control. In addition, our experimental system provides a unique view into erythroid maturation from the perspective of GATA-1, further defining its regulatory hierarchy and mechanisms of action. In this regard, the kinetics of gene expression in G1E cells, which are arrested by loss of GATA-1, may differ somewhat from that in maturing primary erythroid cells where GATA-1 is induced in a more gradual and physiologic manner. Therefore, it will be interesting and important to contrast the current findings to comprehensive transcriptome studies performed using other model systems in which the GATA-1 gene is intact and under endogenous control.1-3 Such comparisons should better define the developmental block conferred by loss of GATA-1. Ultimately, it is likely that these complementary experimental approaches will intersect to provide a greater understanding of the transcriptional program in erythropoiesis. In the future, it will also be important to analyze G1E-ER4 cells and other model systems using proteomic approaches, as many aspects of erythroid gene expression are regulated at the level of protein translation, modification, or degradation.88-92
We thank Ernst Müller and Jeffrey Miller for sharing their cDNA and subtraction hybridization data. We are also grateful to Mortimer Poncz for reviewing this manuscript.
Submitted April 30, 2004; accepted June 22, 2004.
Prepublished online as Blood First Edition Paper, August 5, 2004; DOI 10.1182/blood-2004-04-1603.
Supported by National Institutes of Health (NIH) grant R33 CA94393 (L.A.C.), NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grant R01 DK065806 (R.C.H. and M.J.W.), Abramson Cancer Center of the University of Pennsylvania Pilot Project Grant, and Johnson and Johnson Focused Giving Award (M.J.W.).
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: Mitchell J. Weiss, The Children's Hospital of Philadelphia, Division of Hematology, 34th & Civic Center Blvd, Abramson Research Center, Philadelphia, PA 19104; e-mail: weissmi{at}email.chop.edu.
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Abstract
Introduction
and 
globin gene promoter, and preCRM1 (R1) was added upstream of the promoter to make the plasmid R1 gammaLuc. The normalized levels of expression are plotted for 2 experiments, with each sample tested in triplicate in both; the error bars indicate standard deviations. The activity from R1 gammaLuc is significantly higher than that from gammaLuc, with a P value no more than .005 for either by Student t test. (D) GATA1 and Fog1 act in a feed-forward loop to activate the