Identification of gene networks associated with erythroid differentiation

Blood Cells, Molecules, and Diseases 43 (2009) 74–80

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Blood Cells, Molecules, and Diseases j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y b c m d

Identification of gene networks associated with erythroid differentiation Shoshana Peller a,⁎, Yuval Tabach b, Miri Rotschild a, Osnat Garach-Joshua a, Yosef Cohen c, Naomi Goldfinger b, Varda Rotter b a b c

The Laboratory of Hematology, Assaf-Harofeh Medical Center, Zerifin, 70300, Israel Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel The Institute of Heamatology, Assaf-Harofeh, Medical Center, Zerifin, Israel

a r t i c l e

i n f o

Article history: Submitted 15 January 2009 Available online 28 March 2009 (Communicated by J.F. Hoffman, Ph.D., 4 January 2009) Keywords: Erythropoiesis JAK2 Expression microarrays Myeloproliferative disorders

a b s t r a c t Erythropoiesis is a multistep process involving a large number of genes, which balance between proliferation, differentiation and survival of the erythroid cells. To understand the molecular mechanisms of erythropoiesis and related pathological aberrations, we analyzed three stages of in vitro differentiating human erythroid cells by expression profiling. We identified distinct clusters of genes, each with a unique expression pattern during differentiation. As JAK2 was shown to play a central role in myeloproliferative disorders, we focused on one cluster which includes JAK2 and other genes with high correlation to JAK2 expression. These genes had a low expression at the early erythroblast which increased in the intermediate stage and further slightly increased in the last stage of differentiation. Our results indicate that gene networks may associate with JAK2 expression in erythroid differentiation. It is intriguing to determine whether the pathogenesis of polycythemia vera (PV), harboring a common or uncommon JAK2 mutation, involves alterations in independent gene pathways that underlie the normal erythropoietic process. © 2009 Elsevier Inc. All rights reserved.

Introduction Erythropoiesis is a lineage-specific proliferation and maturation of the hematopoietic pluripotent stem cell. The process is initiated by the binding of erythropoietin (Epo) to its receptor on the erythroid progenitor cells to activate down-stream signal transduction processes [1]. The varying morphology of the differentiating erythroid cells is a paramount event in erythropoiesis; nucleated cells reduce in size, their nucleus shrink and condense and eventually are enucleated in the mature hemoglobin-containing erythrocytes. Concomitant to these morphological changes, a vast number of genes are up or downregulated during erythropoiesis. Pope et al. [2] demonstrated by RTPCR of in vitro differentiating human erythroid cells, changes in expression of a few genes. They observed at the early stage of differentiation high expression of γ-globin, Sp1 and GATA-2 the latter remained highly expressed in the intermediate stage of differentiation and declined while other transcription factors such as GATA-1, EKLF, NF-E2 and EpoR reached their maximal expression. In the late stage of differentiation β-globin was found to be highly expressed. Using the same technique, Scicchitano et al. [3], also showed a varying profile of gene expression in three different stages erythropoietin-induced differentiation of human cord blood CD36+ erythroid progenitors.

⁎ Corresponding author. Fax: 97289778218. E-mail address: [email protected] (S. Peller). 1079-9796/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.bcmd.2009.01.020

Transcriptional regulation of erythropoiesis involves many genes that orchestrate and participate in the survival, proliferation and differentiation of the progenitor erythroid cells[4–7]. GATA-1 and FOG-1, for example, were found to play a central role in erythroid maturation and a balance between down-regulation of GATA-2 and up-regulation of GATA-1 was found to be essential to stop proliferation and enable erythroid differentiation. Interaction between EKLF and GATA-1 was reported to facilitate the switch from fetal to adult globin expression. C-Myb was also found to be down-regulated during erythropoiesis to promote differentiation [8]. It was also reported that in murine erythropoiesis, cell cycle control is essential for renewal and differentiation divisions of erythroid cells [9] and that the E2F transcription factors have a major role to promote cell cycle progression and cellular proliferation in fetal erythropoiesis [10]. The Rb (retinoblastoma) protein, a central regulator of the G1 to S-phase transition, was also found to play a role in the differentiation of early to late erythroblasts by coupling the process of mitochondria biogenesis with cell cycle exit [11]. Apoptotic mechanisms were also reported to control erythropoiesis as reviewed by Testa [12] and Opferman [13]. The activation of JAK2 by the binding of Epo to EpoR activates other EpoR-dependent pathways like Stat5. Activated Stat5 is translocated to the nucleus where it activates the expression of several antiapoptotic genes like Bcl-XL as well as genes involved in the control of cell proliferation and differentiation. Grebian et al. [14], nevertheless, demonstrated that Stat5 in mice allowed erythropoiesis in the absence of EpoR or

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JAK2. Epo deprivation, therefore, induces the activation of caspase-3, leading to apoptosis of erythroblasts. It was recently shown that the Notch pathway may also regulate apoptosis during erythroid differentiation in mice [15]. Previously we observed an overexpression of the p53 protein towards the final stage of erythroid differentiation and a decreased expression of caspase 3/7 in those cells, suggesting a mechanism for erythroid enucleation by nuclear apoptosis [16]. The application of expression microarrays provides a simultaneous panoramic view of genes interacting and cooperating in these complex mechanisms of erythropoiesis. A study on murine erythropoiesis [9] using expression microarrays, compared 588 genes' expression. The study that used primary as well as immortal p53-deficient murine erythroblasts, observed a cluster of 19 genes which were up-regulated and probably function in proliferation while, another cluster of 11 genes, significantly downregulated and contributes to the mature phenotype. A second study distinguished between 6 closely related progenitor populations originating from hematopoietic stem cells [17]. They demonstrated during differentiation, down-regulation of genes of hematopoietic stem cells and progressive up-regulation of a limited number of lineage-specific genes, like EpoR and GATA-1 for the erythroid cells. Kolbus et al. [18] studied the cooperative signaling between Epo, stem cell factor (SCF) and dexamethasone (Dex) to promote proliferation and to inhibit differentiation of murine erythroid cells. They demonstrated a different pattern of Epo/SCF and DEX-induced gene expression. Dex treatment of cells arrested differentiation without affecting the differentiation-induced proliferation arrest, while Epo/SCF enhances proliferation of erythroid progenitors without blocking differentiation. A study by Komor et al. [19] used human CD34+ cells in cultures with different growth factors to induce lineage-specific differentiation to erythropoietic, granulopoietic and megakaryopoietic cells. The expression microarrays of each lineage were studied. In erythropoiesis three stages of differentiation were observed and 21 genes were found to be continuously up-regulated like GTPase activator protein, a RASrelated protein and ARHGAP8, a GTPase involved in the regulation of actin cytoskeleton organization, membrane trafficking, gene expression and cell proliferation. A group of 58 genes were significantly down-regulated like genes associated with c-myc. A third group of approximately 40 genes were found to be differentially expressed in erythropoiesis. The intriguing process of erythropoiesis and the various diseases associated with impairment of this process like thalassemia [20] and myeloproliferative diseases [21], prompted us to study gene expression in differentiating normal erythroid cells by using expression microarrays. We observed 12 distinct clusters of genes with varying expression patterns in the 3 different stages of differentiation. In this study we focused on one cluster consisting of 34 genes, which includes the JAK2 gene and observed that its expression is rapidly up-regulated in the intermediate stage of differentiation. The expression of many genes in this cluster correlated to that of JAK2 which may suggest interactions between different gene networks. The implications of our results in patients with abrogated erythropoiesis like polycythemia vera may be paramount. The pathology of this disease may involve expression of a JAK2 mutated gene with alterations in the expression of other genes.

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The study was approved by the Helsinki committee of AssafHarofeh Medical Center, Israel. Cells and culture conditions Cells of buffy-coats were diluted in tubes with the same volume of phosphate-buffered-saline (PBS). The two phase liquid culture procedure described by Fibach et al. [22] was used. Briefly, samples were layered on Ficoll-paque (GE Healthcare, Sweden) and centrifuged for 20 min at 1400 rpm. The mononuclear layer was collected and washed twice with PBS. The cells were than layered again on Ficoll-paque to remove the rest of the neutrophils, washed three times and suspended in 50–70 ml hematopoietic cell karyotyping medium (Biological Industries, Beit Haemek, Israel) with 1.5 μg/ml cyclosporine A (Sandoz, Switzerland). The cells were cultured in phase I for 6 days at 37 °C with 5% CO2 and 90% humidity. Thereafter the non-adherent cells were collected and washed twice with MEMalpha medium (Biological Industries, Beit Haemek, Israel), suspended in the same medium and supplemented with 30% FBS (Biological Industries, Beit Haemek, Israel) 1% bovine serum albumin, 10− 6 M Dexamethasone, 10− 5 M mecaptoethanol (Sigma, USA), 1 U/ml erythropoietin (Roche, Germany) and 10 ng/ml recombinant human stem cell factor (Biological Industries, Beit Haemek, Israel). This phase II culture was incubated in the same conditions as phase I. Everyday from day 4 of the culture a sample of 200 μl was removed and cytospin slides were prepared and stained with May-GrunwaldGiemsa (MERCK, Germany). The slides were examined microscopically to determine the stage of differentiation and cell purity. Cells of phase II were collected on days 6–7, 9–10 and 12–13. The cells were washed twice with MEM-alpha medium, suspended in 1 ml Trireagent (MRC, Ohio, USA) and kept at − 80 °C till the extraction of total RNA. RNA isolation and cDNA synthesis Total RNA was isolated from samples of normal erythroid cells at 3 stages of differentiation, according to the instructions of the manufacturer of Tri-reagent. cDNA was synthesized using the kit SuperScript III reverse transcriptase (Invitrogen, USA). Briefly, 3 μg RNA were added to 13 μl DEPC-H2O and supplemented with 0.8 μl oligo(dT)20 0.5 μg/μl (Invitrogen, USA) and 1 μl of 10 mM dNTP mix (Roche, Germany). The tubes were incubated for 5 min at 65 °C and transferred to ice. The following were than added to each tube: 4 μl 5× first-strand buffer, 1 μl 0.1 M DTT 1 μl RnaseOUT and 1 μl of SuperScript RT. The tubes were incubated at 55 °C for 60 min and at 70 °C for 15 min, and were than kept at 4 °C. Preprocessing and filtering cDNA of samples of three stages of differentiation of normal erythroid cells were subjected to cDNA microarray in duplicates. For oligonucleotide microarray hybridization, RNA was labeled, fragmented and hybridized to an affymetrix HG-u133A oligonucleotide array. After the arrays were scanned, the expression value for each gene was calculated using affymetrix microarray software 5.0 (mas5). Probe sets that were absent in 5 or all samples according to affymetrix flags were removed (11380 probe sets remain). All values lower than 20 were adjusted to 20 to eliminate noise from the data.

Materials and methods

Gene expression profiling

Blood samples

We profiled the data as suggested by Netanaly [23]. The gene expression dataset samples are grouped by sample type and their expression values are averaged independently for each gene. The distribution of the averaged expression values is sliced to N bins (in our case 3 bins, representing low, medium and high expression), and

White blood cells (WBC) for research were obtained as buffy-coats from blood donations of healthy subjects, collected at the central blood bank in Israel.

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Fig. 1. Analysis of expression of microarrays. Results of expression microarrays of 3 stages of differentiating normal erythroid cells: S1, S2, and S3. The left side of the figure depicts the 3 levels of expression. The table in the middle of the figure summarizes the various clusters and their profiles.

each expression value is then mapped to one of the bins. The expression of every gene is then represented by a vector with an alphabet of size N (such as [1 3 1]). Using this simplified representation of the expression data, genes sharing the same profile are clustered together. The output of the profiling operation is a set of gene clusters, each one with a defined expression profile. Pearson's correlation analysis was performed between the genes in cluster 5 to that of JAK2 expression. Raw gene expression data is available at http://www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc=GSE12407. Table 1 Summary of 12 clusters observed by expression microarrays and GO data.

Gene-specific primers For real-time PCR the following pairs of primers were synthesized (Invitrogen, USA): DNAJB9, forward, 5′-GGTAAAGGACAAAGAGGTAGTGGAAGT-3′ reverse: 5′-CATCCTGGCGTGTCTGGAA-3′. ZNF354A, forward 5′-TGTGCTGTTTACCCGAGATGAG-3′, reverse: 5′CCAGTGAGACCAGGTTCCTATAGTTC-3′. ARG1, forward 5′-GAAGAAATCTACAAAACAGGGCTACTC-3′, reverse: 5′-GATTACCCTCCCGAGCAAGTC-3′.

S. Peller et al. / Blood Cells, Molecules, and Diseases 43 (2009) 74–80

ACVR2B, forward: 5′-CTCCCTCACGGATTACCTCAAG-3′, reverse: 5′TTTAAAGTCCCTGTGGGCAATAG-3′. JAK2, forward: 5′-ACACATCTCAGATATGCAAGGGTATG-3′, reverse: 5′-TCTTTGTCTTGTGGCAAGACTTTG-3′. GOLGA3, forward: 5′-GCTTCAAGAATCCAGAGGCTTTAG-3′, reverse: 5′-GCTGTTTCTAGGATGCTGTTGTGTT-3′. GAPDH, forward: 5′-TCGGAGTCAACGGATTT-3′, reverse: 5′-CCACGACGTACTCAGC-3′. Real-time PCR Real-time PCR was performed in capillaries using a LightCycler system (Roche, Germany). The reaction was run separately for each set of gene-specific primers in a total volume of 10 μl. The LightCycler faststart DNA master syber green I was used. 2.5 μl of a dilution of cDNA from each cell sample was added to each capillary. 0.6–0.8 μl of 25 mM MgCl2 was added (2.5 mM final concentration for the genes ACVR2B, DNAJB2, ZNF354A, ARG1, GOLGA3 and 2.9 mM for GAPDH and 3 mM for JAK2), 0.8 μl each of the relevant pair of primers (5 mM stock solution) and 1 μl of the master syber green in a total mixture of 10 μl. The program was: one cycle of 600 s at 95 °C and 40 cycles of 10 s at 95°, 10 s at 64° and 10 s at 72°. The annealing temperature was 64 °C for most genes except for ACVR2B — 62 °C and GAPDH — 60 °C. Standard curves were generated for each gene with 4 dilutions of the relevant cDNA to obtain a slope of −3.3 and maximum efficiency. To confirm amplification specificity, the PCR products from each reaction were subjected to melting curve analysis. We used the relative gene expression method [24] to quantitate the expression of each gene. Expression was normalized to GAPDH and results of normalized expression of the same target genes of mRNA from pooled normal

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WBC were used to calculate the expression of the target genes with the equation: 2− ΔΔC. Results Establishment of the in vitro differentiation model A two phase in vitro culture of mononuclear cells was used. Cells collected at phase II of the culture, represented 3 stages of erythroid differentiation as described by Pope et al. [2], the early stage of proerythroblasts, the intermediate stage and the last stage of differentiation of Hb-containing orthochromatic normoblasts. It is of note that in our cultures we found by using cytospin slides a low contamination of lymphocytes in the cell fractions (6–8%) probably as a result of an increased concentration of cyclosporine A we added in phase I. For the microarray studies of the normal samples, we designated the three stages S1, S2 and S3 respectively, Microarrays analysis The samples obtained from the normal differentiating erythroid cells were subjected to expression profiling using expression microarrays (raw gene expression data is available at http://www.ncbi.nlm. nih.gov/geo/query/acc.cgi?acc=GSE12407). Analysis of the microarrays revealed 12 clusters with specific and unique expression pattern (Fig. 1 and Table 1). Fig. 1 depicts the expression of the 3 samples S1–S3 analyzed in duplicates. The middle of the figure summarizes the 12 different clusters, their number of genes and their profile. The three levels of expression were designated 1, 2 and 3 for low expression, medium expression and high expression, respectively. Table 1 shows that the

Table 2 The list of genes of cluster 5, their level of expression and correlation to the expression of JAK2. Gene symbol

Gene title

S1

S2

S3

Corr to JAK2

DNAJB9 KIAA1466 CDR2 ZNF354A GOLGA3 SAT LOC55831 SIGLEC6 ARG2 TM7SF2 SLC14A1 ARG1 MGC14376 RNGTT PLD1 ATF5 MAFF ABHD5 ENPP4 TRPS1 ACVR2B FUT2 CDC14A RPIP8 CTSF PDE4DIP PTPRH DLG5 CASP7 MMD PLCL2 SNX9 ID2 /// ID2B JAK2

DnaJ (Hsp40) homolog, subfamily B, member 9 KIAA1466 gene Cerebellar degeneration-related protein 2, 62 kDa Zinc finger protein 354A Golgi autoantigen, golgin subfamily a, 3 Spermidine/spermine N1-acetyltransferase 30 kDa protein Sialic acid binding Ig-like lectin 6 Arginase, type II Transmembrane 7 superfamily member 2 Solute carrier family 14 (urea transporter), member 1) Arginase, liver Hypothetical protein MGC14376 RNA guanylyltransferase and 5′-phosphatase Phospholipase D1, phophatidylcholine-specific Activating transcription factor 5 v-maf musculoaponeurotic fibrosarcoma oncogene homolog F (avian) Abhydrolase domain containing 5 Ectonucleotide pyrophosphatase/phosphodiesterase Trichorhinophalangeal syndrome I Activin A receptor, type IIB Fucosyltransferase 2 (secretor status included) CDC14 cell division cycle 14 homolog A (S. cerevisiae) RaP2 interacting protein 8 Cathepsin F Phosphodiesterase 4D interacting protein (myomegalin) Protein tyrosine phosphatase, receptor type, H Discs, large homolog 5 (Drosophila) Caspase 7, apoptosis-related cysteine peptidase Monocyte to macrophage differentiation-associated Phospholipase C-like 2 Sorting nexin 9 Inhibitor of DNA binding 2, dominant negative helix–loop–helix Janus kinase 2 (a protein tyrosine kinase

77 23 410 57 351 316 29 23 723 41 65 88 300 74 23 93 83 340 165 32 47 21 42 28 39 56 35 84 75 1120 260 23 393 304

157 114 907 247 537 945 50 50 2740 129 253 385 1211 161 47 211 365 750 280 80 95 76 86 120 89 103 94 196 123 2053 571 48 1094 528

149 186 869 176 520 985 48 49 6271 135 739 1672 3045 149 49 201 3750 860 298 83 86 52 91 378 82 113 131 232 110 2500 789 62 1210 489

0.99 0.78 0.98 0.94 0.99 0.92 0.98 0.97 0.64 0.86 0.56 0.50 0.63 0.96 0.93 0.90 0.41 0.90 0.95 0.87 0.96 0.90 0.91 0.47 0.90 0.91 0.83 0.82 0.95 0.88 0.84 0.79 0.75

Expression level of each differentiation stage is an average of duplicates.

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two largest clusters; cluster 11 with 586 genes had a gradually downregulated profile, whereas cluster 2 with 494 genes showed a gradually up-regulated profile. Annotations were extracted from GeneOntology databases [25]. Cluster 11 included genes involved in DNA and RNA processing, and cluster 2 was compiled of many genes involved in arginine metabolism. Clusters 1 and 6 were comprised of many genes affecting the inflammatory response and various immunological functions. We could detect in the various clusters erythroid specific genes like; Hbβ, BTG1, FOXO3A, HbγA, glycophorin A, B, CAI, CAIII, CAIV, Heme binding protein, erythrocyte membrane protein, protoporphirin, Hb epsilon as well as many other genes mentioned in the introduction to be associated with various functions of the differentiating erythroid cells. (The complete list of genes in each cluster is enclosed in the supplement). We decided to focus on cluster 5 consisting of 34 genes which included the gene JAK2 that was shown to play a pivotal role in myeloproliferative disorders. Table 2, summarizes all the genes in this cluster and their level of expression (an average of the duplicates). Our analysis indicated that the profile of this cluster was found to be 1,3,3 indicating that the genes have a low expression in the very early erythroblast, they are rapidly up-regulated in the intermediate stage and further slightly increase towards the last stage of differentiation or remain at the same level of expression. The expression of a large number of genes in this cluster exhibited a high correlation to the expression of JAK2 whereas a low number of genes like arginase I exhibited a medium correlation only. The genes whose expression exhibited a medium correlation with JAK2 expression had also an additional up-regulation in the last stage of differentiation. The GO database showed that genes of this cluster are involved in phosphoric acid hydrolase activity and arginine and proline metabolism. Gene ontology also revealed that 22 genes of this cluster were involved in cellular metabolic processes. Indeed, JAK2, DNAJB9, ZNF354A and ACVR2B were found to be involved in regulation of biological processes. Genes of this cluster are known to take part in cell communication, localization, cell death, cell cycle and proliferation. Validation of gene expression patterns To validate the results obtained by expression microarrays of normal differentiating erythroid cells, we chose 6 genes from cluster 5

Fig. 2. Validation of expression of genes of cluster 5 by RT-PCR. RT-PCR of 5 genes of cluster 5 of 3 stages of erythroid differentiation (1,2,3). Genes of normal differentiation (A), RT-PCR of arginase of normal cells (B).

(Table 2) and performed quantitative RT-PCR. The genes were: DNAJB9, ZNF354A, GOLGA3, ACVR2B, JAK2 and ARG1. The expression of the first 4 genes exhibited a high correlation to the expression JAK2 whereas the expression of ARG1 had a medium correlation to JAK2 expression (Table 2). The results of the expression of these genes are depicted in Figs. 2A and B. The results show that all 6 genes had a low expression in the early erythroblasts and their expression rapidly increased in the intermediate stage (4.2 fold for JAK2, 8.9 for DNAJB9, 9.4 for ZNF354A, 3.8 for GOLGA3, 4.6 for ACVR2B and 8.1 for ARG1). In the last stage of differentiation ARG1 is the only gene that exhibits an up-regulation giving an over-all increase of 68 folds. The other 5 genes show an additional slight increase in the normoblasts (1–3 folds) or remained unchanged (ZNF354A). Altogether these results confirm data obtained by the expression microarrays profiling. Discussion To get a better insight into the understanding of the molecular mechanism that underlies erythroid differentiation we have adopted an expression profiling approach. Analysis of 3 defined stages of differentiating human erythroid cells identified 12 distinct clusters differing by their expression patterns. Unlike the study by Komor et al. [19] our cluster of genes showing a continuous up-regulation is larger and is comprised of 494 genes (Table 1, cluster 2). These genes were shown by the GO analysis to be involved in arginine metabolism and included also erythroid associated genes like Hbβ pseudogene, BTG1, FOXO3A, HbγA, glycophorin A, B, CAI and erythrocyte membrane protein, some of which were reported before in erythroid cells [26]. Further we also detected a large group of genes (cluster 11–586 genes) with a continuous down-regulation profile. These genes mainly involved various RNA and DNA processes and included also NPM, heme binding protein, integrin, Myb that was reported before to be down-regulated [8] and caspase 3 which we showed before to decrease during erythroid differentiation [16]. The other clusters summarized in Table 1, had various profiles of expression in the three stages of differentiation and included many erythroid specific and associated genes like caspases 6,7,8, Bcl2, aminolevolinate, HMOX1 (heme oxygenase), transcobalamine, protoporphorin, RhD, HIF3, CAIII, IV, ACTVA, and BMP2 [27]. In the present study we decided to further investigate cluster 5 comprising of 34 genes, mainly because one of its genes was JAK2. JAK2 is the first signal transducer following the binding of Epo to its receptor on the erythroid cells [1]. JAK2 plays a central role in the pathogenesis of myeloproliferative diseases especially since most patients with PV were found to harbor the same V617F activating mutation in this gene[28–31]. The genes in this cluster had a similar pattern of expression; they rapidly increased in the intermediate stage of differentiation, remained at the same level of expression or slightly increased in the last stage of the normoblasts. A few genes demonstrated a rapid increase also in the last stage of differentiation. Analysis of 6 genes representative of this cluster confirmed this pattern of expression. Interestingly, the expression of 18 genes of this cluster had a high correlation to the expression of JAK2 (R = 0.9–0.99), others had a medium correlation of expression to that of JAK2. One such gene is ARG1 which has a critical function in mammalian liver and serves as the final enzyme in the urea cycle responsible for the disposal of ammonia from protein catabolism. It was reported before that this enzyme is highly expressed in red blood cells in humans and had a profound effect on arginine levels [32]. This may also explain the increasing level of expression, detected by us, in a large number of genes of cluster 2 involved in arginine metabolism. Our results revealed an increased expression of ARG1during erythroid differentiation in normal cells. Recently, high levels of arginase activity were reported in patients with sickle cell disease, suggesting it plays a key role in the pathogenesis of the disease and that it may be a potential

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target for therapy [33]. Interestingly, another gene in this cluster, SCL14A1 with a similar pattern of expression of that of ARG1, belongs to the Kidd blood group which encodes a urea transporter. It was demonstrated that the Kidd blood null phenotype is associated with the absence of the urea transporter in erythrocytes [34]. The genes displaying a high correlation of expression to that of JAK2 are of major interest. ACVR2B (activin A receptor type 2B) is a receptor for activin, a growth and differentiation factor which pertains to the TGF-β superfamily. Activin promotes the early stages of erythroid differentiation accompanied by hemoglobin synthesis. The receptor is a transmembrane protein and is considered to be a constitutively active kinase [27,35]. Other transmembrane proteins encoded by genes of this cluster are LOC55831 (TMEM111) and the PTPRH, a protein tyrosine phosphatase, receptor type H, a signaling molecule that regulates a variety of cellular processes including cell growth, differentiation, mitotic cycle and oncogenic transformation. The latter gene also known as SAP-1, was found to induce apoptotic cell death in cultured cells [36]. The expression of the gene DNAJB9 was found to have the highest correlation to the expression of JAK2, it pertains to a large group of heat shock proteins; Hsp40, that has been preserved throughout evolution and is important for protein translation, folding, unfolding, translocation and degradation, primarily by stimulating the ATPase activity of chaperone proteins [37]. Two genes of this cluster are associated with the Golgi apparatus. The gene PDE4DIP also known as myomegalin, binds actin and is involved in the actin cytoskeleton organization and biogenesis and was reported to localize components of the cAMP-dependent pathway to the Golgi/centrosomal region of the cell [38]. The second gene, GOLGA3, is a member of the golgin family of proteins which are localized to the Golgi and play a role in nuclear transport and Golgi apparatus localization. Mancini et al. [39] reported that the Golgi complex transduces pro-apoptotic signals through local caspases, like caspase 2 which is localized to the Golgi complex, and eventually regulate local effectors. It was reported that, in addition to the kinase activity of JAK2, its N-terminal domain is required for Golgi processing and expression of EpoR [40]. The promotion of EpoR by JAK2 was suggested to be carried out by enhancing EpoR folding that may explain the simultaneous high expression observed by us, of JAK2 and DNAJB9 that has a major role in folding of new proteins [37]. The ZNF354A gene, also known as Kid-1, is a transcription factor. It was found to have a binding motif present also in LC8 dynein light chain [41,42]. Dyneins are large enzyme complexes involved in intracellular transport and motility and the light chain of this complex (LC8) was reported also to bind to p53-binding protein 1 which assembles p53 to the dynein complex and translocate it from the cytoplasm to the nucleus in response to DNA damage [43]. The pro-apoptotic protein Bim of the Bcl-2 family also binds to LC8 of the dynein complex, apoptotic simuli disrupt this interaction and promote apoptosis [44]. The presence of the gene SAT1 (SSAT1) in this cluster was somewhat surprising since it is a rate-limiting enzyme in the catabolic pathway of polyamine metabolism and catalyzes the N(1)-acetylation of spermidine and spermine. It was, nevertheless, demonstrated by Baek et al. [45] in human cultured cells that SSAT1 binds to HIF-1α, a subunit of HIF-1, the regulator of oxygen homeostasis during erythropoiesis, and promotes its ubiquitination/degradation. Involvement of some gene networks in PV was already demonstrated. The study by Dai et al. [46] shows that increased erythropoiesis in patients with PV is associated with increased proliferation and increased phosphorylation of Akt/PKB and GSK3. Zeuner et al. [47] also demonstrated that JAK2 mutation of PV erythroblasts were associated with cytokine-independent activation of the JAK2 effectors Akt/PKB and ERK/MAP but also with deregulated expression of cFLIP a potent cellular inhibitor of death-induced apoptosis. They suggest that deregulation of erythropoiesis may be a result of inhibition of death receptor signaling.

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Taken together, a number of genes of our cluster 5 including that of JAK2 were reported to be pivotal for the initiation and progression of erythropoiesis that involves interaction and cooperation of many other cell process and cascades. Since patients with polycythemia vera harbor a JAK2 mutation it is possible that the process leading to the development of polycytemia vera requires alterations in the pattern of a few genes associated cluster 5. It is obvious that normal erythropoiesis and abrogation of this process are not a one gene's show. Analyzing gene expression of erythroid cells of patients with PV and investigating the plethora of genes participating in various stages of erythropoiesis will delineate the pathogenesis of PV and other diseases with impaired erythropoiesis and detect new targets for therapy. Conflict-of-interest disclosure The authors declare no competing financial interests.

Acknowledgments This study was supported by Tel-Aviv University, Sackler School of Medicine (Meirbaum grant). Authorship Contribution: S Peller — designed research, collected data, analyzed and interpreted data, wrote the manuscript. Y Tabach — collected data, analyzed and interpreted data. M Rothchild, O Garach-Joshua and N Goldfinger — performed research and collected data. Y Cohen — clinical adviser. V Rotter — Designed research and wrote the manuscript. References [1] T.D. Richmond, M. Chohan, D.L. Barber, Turning cells red: signal transduction mediated by erythropoietin, TRENDS cell Biol. 15 (2005) 146–155. [2] S.H. Pope, E. Fibach, J. Sun, et al., Two-phase liquid culture system models normal human adult erythropoiesis at the molecular level, Eur. J. Haematol. 64 (2000) 292–303. [3] M.S. Scicchitano, D.C. McFarland, L.A. Tierney, et al., In vitro expansion of human cord blood CD36+ erythroid progenitors: temporal changes in gene and protein expression, Exp. Hematol. 31 (2003) 760–769. [4] C. Perry, H. Soreq, Transcriptional regulation of erythropoiesis fine tuning of combinatorial multi-domain elements, Eur. J. Biochem. 269 (2002) 3607–3618. [5] A.B. Cantor, S.H. Orkin, Transcriptional regulation of erythropoiesis: an affair involving multiple partners, Oncogene 21 (2002) 3368–3376. [6] J. Zheng, K. Kitajima, E. Sakai, et al., Differential effects of GATA-1 on proliferation and differentiation of erythroid lineage cells, Blood 107 (2006) 520–527. [7] S.L. Kim, H.E. Bresnick, Transcriptional control of erythropoiesis: emerging mechanisms and principles, Oncogene 26 (2007) 6777–6794. [8] A. Vegiopoulos, P. Garcia, N. Emambokus, J. Frampton, Coordination of erythropoiesis by the transcription factor c-Myb, Blood 107 (2006) 4703–4710. [9] H. Dolznig, F. Boulme, K. Stangl, et al., Establishment of normal, terminally differentiating mouse erythroid progenitors: molecular characterization by cDNA microarrays, FASEB J. 15 (2001) 1442–1444. [10] K.M. Kinross, A.J. Clark, R.M. Lazzolino, P.O. Humbert, E2f4 regulates fetal erythropoiesis through the promotion of cellular proliferation, Blood 108 (2006) 886–895. [11] V.G. Sankaran, S.H. Orkin, C.R. Walkley, Rb intrinsically promotes erythropoiesis by coupling cell cycle exit with mitochondrial biogenesis, Genes Dev. 22 (2008) 463–475. [12] U. Testa, Apoptotic mechanisms in the control of erythropoiesis, Leukemia 18 (2004) 1176–1199. [13] J.T. Opferman, Life and death during hematopoietic differentiation, Curr Opin Immunol. 19 (2007) 497–502. [14] F. Grebien, M.A. Kerenyi, B. Kovacic, et al., Stat5 activation enables erythropoiesis in the absence of EpoR and JAK2, Blood 111 (2008) 4511–4522. [15] A.R. Moreno, L. Espinosa, M.J. Sanchez, et al., The notch pathway positively regulates programmed cell death during erythroid differentiation, Leukemia 21 (2007) 1496–1503. [16] S. Peller, J. Frenkel, T. Lapidot, et al., The onset of p53-dependent apoptosis plays a role in terminal differentiation of human normoblasts, Oncogene 22 (2003) 4648–4655. [17] A.V. Terskikh, T. Miyamoto, C. Chang, et al., Gene expression analysis of purified hematopoietic stem cells and committed progenitors, Blood 102 (2003) 94–101. [18] A. Kolbus, M. Blazques-Domingo, S. Carotta, et al., Cooperative signaling between cytokine receptors and the glucocorticoid receptor in the expansion of erythroid

80

[19]

[20]

[21] [22]

[23]

[24] [25] [26]

[27]

[28] [29] [30] [31] [32] [33]

S. Peller et al. / Blood Cells, Molecules, and Diseases 43 (2009) 74–80 progenitors: molecular analysis by expression profiling, Blood 102 (2003) 3136–3146. M. Komor, S. Guller, C.D. Baldus, et al., Transcriptional profiling of human hematopoiesis during in vitro lineage-specific differentiation, Stem Cells 23 (2005) 1154–1169. S. Gardenghi, M.F. Marongiu, P. Ramos, et al., Ineffective erythropoiesis in βthalassemia is characterized by increased iron absorption mediated by downregulation of hepcidin and up-regulation of ferroportin, Blood 109 (2007) 5027–5035. P.J. Campbell, A.R. Green, The myeloproliferative disorders, N. Engl. J. Med. 355 (2006) 2452–2466. E. Fibach, D. Manor, A. Oppenheim, E.A. Rachmilewitz, Proliferation and maturation of human erythroid progenitors in liquid culture, Blood 73 (1989) 100–103. D. Netanely, Gene expression analysis of mesenchymal stem cell differentiation and leukemic over expression of tissue specific genes. (M.Sc thesis,) 2006. www. weizmann.ac.il/physics/complex/compphys/group_papers.htm#dvir n. K.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using realtime quantitative PCR and the 2− DDT method, Methods 25 (2001) 402–408. M. Ashbumer, C.A. Ball, J.A. Blake, et al., Gene ontology: tool for the unification of biology; the gene ontology consortium, Nat. Genet. 25 (2000) 25–29. W.J. Bakker, M. Blazquez-Domingo, A. Kolbus, et al., FoxO3a regulates erythroid differentiation and induces BTG1, an activator of protein arginine methyl transferase1, J. Cell Biol. 164 (2004) 175–184. V. Maguer-Satta, L. Bartholin, S. Jeanpierre, et al., Regulation of human erythropoiesis by activin A, BMP2, and BMP4, members of the TGFβ family, Exp. Cell Res. 282 (2003) 110–120. C. James, V. Ugo, J.P. Le Couedic, et al., A unique clonal JAK2 mutation leading to constitutive signaling causes polycythaemia vera, Nature 434 (2005) 1144–1148. A.V. Jones, S. Krell, K. Zoi, et al., Widespread occurrence of the JAK2 V617F mutation in chronic myeloproliferative disorders, Blood 106 (2005) 2162–2168. R. Kralovics, F. Passamonti, A.S. Buser, et al., A gain-of-function mutation of JAK2 in myeloporolifarative disorders, N. Engl. J. Med. 352 (2005) 1779–1790. E.J. Baxter, L.M. Scott, P.J. Campbell, et al., Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders, Lancet 365 (2005) 1054–1061. P.S. Kim, R.K. Iyer, K.V. Lu, et al., Expression of the liver form of arginase in erythrocytes, Mol. Genet. Metab. 76 (2002) 100–110. E.W. Iyamu, C. Ekekezie, G.M. Woods, In vitro evidence of the inhibitory capacity of chloroquine on arginase activity in sickle erythrocytes, Brit. J. Haematol. 139 (2007) 337–343.

[34] N.M. Irshaid, N.I. Eicher, H. Hustinx, et al., Novel alleles at the JK blood group locus explain the absence of the erythrocyte urea transporter in European families, Br. J. Haematol. 116 (2002) 445–453. [35] B.K. Hilden, T. Tuuri, M. Eramaa, O. Ritvos, Expression of type II activin receptor genes during differentiation of human K562 cells and cDNA cloning of the human type IIB activin receptor, Blood 83 (1994) 2163–2170. [36] T. Takada, T. Noguchi, K. Inagaki, et al., Induction of apoptosis by stomach cancer-associated protein-tyrosine phosphatase-1, J. Biol. Chem. 277 (2002) 34359–34366. [37] X.B. Qiu, Y.M. Shao, S. Miao, L. Wang, The diversity of the DnaJ/Hsp40 family, the crucial partners for Hsp70 chaperones, Cell. Mol. Life Sci. 63 (2006) 2560–2570. [38] I. Verde, G. Pahlke, M. Salanova, et al., Myomegalin is a novel protein of the Golgi/ centrosome that interacts with a cyclic nucleotide phosphodiesterase, J. Biol. Chem. 276 (2001) 11189–11198. [39] M. Mancini, C.E. Machamer, S. Roy, et al., Caspase-2 is localized at the Golgi complex and cleaves golgin-160 during apoptosis, J. Cell Biol. 149 (2000) 603–612. [40] L.J.H. Huang, S.N. Constantinescu, H.F. Lodish, The N-Terminal domain of Janus kinase 2 is required for Golgi processing and cell surface expression of erythropoietin receptor, Mol. Cell. 8 (2001) 1327–1338. [41] R. Witzgall, E. O'Leary, R. Gessner, et al., Kid-1 a putative renal transcription factor: regulation during ontogeny and in response to ischemia and toxic injury, Mol. Cell Biol. 13 (1993) 1933–1942. [42] I. Rodriguez-Crespo, B. Yelamos, F. Roncal, et al., Identification of novel cellular proteins that bind to the LC8 dynein light chain using a pepscan technique, FEBS Lett. 503 (2001) 135–141. [43] K.W. Lo, H.M. Kan, L.N. Chan, et al., The 8-kDa dynein light chain binds to p53 binding protein 1 and mediates DNA damage-induced p53 nuclear accumulation, J. Biol. Chem. 280 (2005) 8172–8179. [44] H. Puthalakath, D.C. Huang, L.A. O'Reilly, et al., The proapoptotic activity of the Bcl-2 family member Bim is regulated by interaction with the dynein motor complex, Mol. Cell. 3 (1999) 287–296. [45] J.H. Baek, Y.V. Liu, K.R. McDonald, et al., Spermidine/Spermine N1-acetyltransferase-1 binds to hypoxia-inducible factor-1 α (HIF-1α) and RACK1 and promotes ubiquitination and degradation of HIF-1α, J. Boil. Chem. 282 (2007) 33358–33366. [46] C. Dai, I.J. Chung, S.B. Krantz, Increased erythropoiesis in polycythemia vera is associated with increased erythroid progenitor proliferation and increased phosphorylation of AKt/PKB, Exp. Hematol. 33 (2005) 152–158. [47] A. Zeuner, F. Pedini, M. Signore, et al., Increased death receptor resistance and FLIP short expression in polycythemia vera erythroid precursor cells, Blood 107 (2006) 3495–3502.