MicroRNA expression profile in granulocytes from primary myelofibrosis patients

Experimental Hematology 35 (2007) 1708–1718

MicroRNA expression profile in granulocytes from primary myelofibrosis patients Paola Guglielmellia, Lorenzo Tozzia, Alessandro Pancrazzia, Costanza Bogania, Elisabetta Antoniolia, Vanessa Ponziania, Giada Polia, Roberta Zinib, Sergio Ferrarib, Rossella Manfredinib, Alberto Bosia, and Alessandro M. Vannucchia for the MPD Research Consortiumc a

Department of Hematology, University of Florence, Florence, Italy; bDepartment of Biomedical Sciences, Biological Chemistry Section, University of Modena and Reggio Emilia, Modena, Italy; c Myeloproliferative Disorders Research Consortium, Mount Sinai School of Medicine, New York, NY., USA (Received 30 April 2007; revised 30 July 2007; accepted 14 August 2007)

Objective. Expression profiling of microRNA (miRNA) was performed in granulocytes isolated from patients with primary myelofibrosis (PMF), with the aim of identifying abnormally expressed miRNAs in comparison with normal subjects or patients with polycythemia vera (PV) or essential thrombocythemia (ET). Patients and Methods. Using stem loop–primed reverse transcription and TaqMan quantitative real-time polymerase chain reaction, the expression of 156 mature miRNAs was evaluated using pooled granulocytes from PMF patients, either wild-type or JAK2617VOF mutant with O51% allele burden, and control subjects. Differentially expressed miRNAs were then validated on additional control and PMF samples, and also on PV or ET granulocytes. Results. There was a global downregulation of miRNA expression in PMF granulocytes; 60 miRNAs, of 128 called present, displayed differential expression compared to normal samples. Twelve miRNAs, which had been selected based on statistically different expression level, were finally validated. In PMF granulocytes, levels of miR-31, -150, and -95 were significantly lower, while those of miR-190 significantly greater, than control and PV or ET samples; on the other hand, miR-34a, -342, -326, -105, -149, and -147 were similarly reduced in patients with PMF, PV, or ET compared to controls. Increased expression of miR-182 and -183 correlated with JAK2617VOF allele burden. Three in silico–predicted putative target genes (DTR, HMGA2, and MYB), showed deregulated expression in PMF granulocytes that correlated with expression level of regulatory miRNA. Conclusions. A defined miRNA profile distinguishes PMF granulocytes from those of normal subjects and, partially, also from PV or ET patients. Ó 2007 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc.

Primary myelofibrosis (PMF) [1] is a clonal disorder of a hematopoietic multipotent stem cell included in the Philadelphia chromosome-negative chronic myeloproliferative disorders (MPD), together with polycythemia vera (PV) and essential thrombocythemia (ET) [2]. PMF is characterized by extensive proliferation of abnormal megakaryocytes, which accumulate in the bone marrow (BM);

Offprint requests to: Alessandro M. Vannucchi, M.D., Department of Hematology, University of Florence, Viale Morgagni 85, 50134 Florence, Italy; E-mail: [email protected]

development of BM fibrosis and eventually osteosclerosis; constitutive release of hematopoietic progenitor cells and their accumulation in peripheral blood with extramedullary hematopoiesis; and a leukoerythroblastosis picture [3]. The 617ValOPhe mutation in exon14 of JAK2 represented the first reliable molecular marker of PMF [4–6]; however, unlike in PV, where the percentage of JAK2-mutant patients is y95%, only 50% to 60% of PMF patients are mutated, although this figure goes up to almost 100% in post-polycythemic/post-thrombocythemic myelofibrosis. More recently, MPL mutations (515WOL and/or 515WOK) have been detected in 5% of patients with

0301-472X/07 $–see front matter. Copyright Ó 2007 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc. doi: 10.1016/j.exphem.2007.08.020

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PMF, 1% with ET, and 10% with post-ET myelofibrosis [7,8]. Mice transplanted with hematopoietic cells bearing JAK2617VOF or MPL515WOL mutation develop a disease that has similarities with PMF and is preceded by a polycythemic phase in 617VOF mutants, supporting a pathogenetic role for both mutations in myelofibrosis [7,9,10]; however, since at least 40% of PMF patients still lack molecular abnormalities, other pathway(s) are presumptively involved. As an approach to identify genes/pathways possibly involved in disease pathogenesis, we have recently characterized the transcriptome from purified CD34þ cells of PMF patients [11]; a potential gene ‘‘molecular signature’’ of PMF CD34þ cells, which included CD9, CDH1, CXCR4, GAS2, NFE2, DLK1, HMGA2, and WT1, was identified. These differentially expressed genes were also validated in peripheral blood granulocytes, and allowed to reliably differentiate PMF from PV and ET granulocytes. Furthermore, WT1 quantification in granulocytes correlated with clinical characteristics and mirrored overall disease activity [11]. Mechanisms for deregulated gene expression in cancer cells are complex, and may be due to abnormal activation of genes downstream an involved pathway, to mutations in promoter region or in gene sequence involved in mRNA processing, and to epigenetic control of transcriptional activity. To add to this complexity, novel gene regulators have been identified in microRNA family (miRNA). miRNAs are short y22-nucleotide, phylogenetically conserved, non-protein–coding RNAs that are supposed to regulate gene expression through sequence-specific base pairing with target mRNAs [12,13]. miRNAs are transcribed as long RNA precursors (pri-miRNAs) that contain a stem-loop structure of about 80 bases, which is excised in the nucleus by the RNase III enzyme Drosha and DGCR8/ Pasha to form the pre-miRNA. Pre-miRNAs are then exported from the nucleus by Exportin-5 and processed to mature miRNA by the RNase III enzyme, Dicer; mature miRNA is finally incorporated into a RNA-induced silencing complex [12,14]. Most microRNAs in animals are thought to function by preventing effective mRNA translation of target genes through imperfect base-pairing with the 30 -untranslated region of target mRNAs [12,15]. miRNA targets are largely unknown, but estimates range from one to hundreds of target genes for a given miRNA, based on target prediction using a variety of bioinformatics approaches [16]. This apparently low level of specificity might help to explain why miRNAs have been implied in complex processes linked to cell development, differentiation, communication, and apoptosis [14], and, more recently, in oncogenesis [17]. With the aim to identify possible abnormalities in miRNA regulation in PMF, we have undertaken a comparative analysis of miRNA-expression profile in granulocytes from PMF patients with those obtained from healthy control subjects, and also from patients with PV or ET.

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Patients and methods Subjects Forty consecutive patients with a diagnosis of PMF were included in the study. Diagnosis of PMF was made according to the Italian Consensus Conference criteria (diffuse marrow fibrosis and absence of BCR-ABL rearrangement) and an algorithm based on variable combination of accessory criteria represented by splenomegaly, teardrop erythrocytes, circulating immature myeloid cells and erythroblasts, and clusters of abnormal megakaryocytes in the BM [18]. According to World Health Organization classification, all patients were in a typical fibrotic stage of disease, and all were primary forms of myelofibrosis [2]; they were studied either at diagnosis or during follow-up. Patients were included if they had not been previously treated with cytoreductive drugs or if treatment had been stopped for at least 3 months. Patients were assigned a prognostic score according to Dupriez et al. [19] and a ‘‘severity’’ score, as reported previously [20]. Twenty-five patients, each with PV or ET diagnosed according to WHO criteria [2], were also included for comparison; they were either at diagnosis or during follow-up, but all were chemotherapy-naı¨ve. Control granulocytes were obtained from peripheral blood (PB) of 25 healthy individuals. The study had received the approval from local ethics committees, and informed consent was obtained from subjects involved at the time of sample collection. Cell-sample preparation and RNA extraction Granulocytes were separated by differential centrifugation over a Ficoll-Paque gradient, starting from 20 mL PB; contaminating red cells were removed by hypotonic lysis, and cell pellets were resuspended in Trizol (Invitrogen Ltd, Paisley, UK, http://www.invitrogen.com). CD34þ cells were purified from 30 to 50 mL PB collected from PMF patients or from 5 mL BM aspirate obtained in preservative-free heparin of healthy donors. Density gradient– separated mononuclear cells were processed through two sequential steps of immunomagnetic CD34þ selection (Miltenyi Biotec, Bergisch Gladbach, Germany, http://miltenyibiotec.com); final purity was evaluated by flow cytometry after labeling with PEHPCA2 anti-CD34 monoclonal antibody (BD Biosciences, San Jose, CA, USA, http://www.bdbiosciences.com), and found to be O97% in all instances. Total RNA was extracted using Trizol. Disposable RNA chips (Agilent RNA 6000 Nano LabChip kit; Agilent Technologies, Waldbrunn, Germany, http://www.home. agilent.com) were used to determine concentration and purity/ integrity of RNA with Agilent 2100 Bioanalyzer. Analysis of JAK2617VOF and MPL515WOL/K mutations To quantify levels of JAK2617VOF RNA, an amplification refractory mutation system polymerase chain reaction (PCR) procedure on granulocyte cDNA was employed as originally developed in our laboratory [21]. Mutation-specific primers were labeled with 5-carboxyfluorescein (FAM) to permit resolution and quantification of amplicons in an ABI PRISM 3100 analyzer. Amplification was performed for 27 cycles at 62 C, with AmpliTaq Gold DNA polymerase (Applied Biosystems, Foster City, CA, USA, http:// www2.appliedbiosystems.com) starting from 50 ng cDNA. All patients were also genotyped for MPL 515WOL/K mutation by direct sequencing, as described previously [22].

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Quantification of miRNA expression level using QRT-PCR We designed a double-step analysis for identification and quantification of abnormally expressed miRNAs in PMF granulocytes. The first was a ‘‘panel’’ procedure that simultaneously evaluated expression level of 156 mature miRNAs by Taqman quantitative real-time PCR (QRT-PCR); the second was performed on individual miRNAs, which eventually resulted differentially expressed in panel experiments. For panel analysis, 1 mg each RNA from granulocytes of PMF patients (three JAK2617VOF mutant patients with O51% 617VOF allele and three wild-type patients) was pooled to obtain two pools each; as control, three RNA pools were prepared from three normal donors each. cDNA was synthesized from total RNA using microRNA-specific RT primers contained in the TaqMan MicroRNA Assays Human Panel Early Access Kit or the TaqMan microRNA Human Assays in case of individual miRNAs (Applied Biosystems). Briefly, single-stranded cDNA was synthesized from 10 ng total RNA in 15-mL reaction volume with the High-Capacity cDNA Archive Kit (Applied Biosystems) using 1 mM deoxyribonucleoside triphosphates, 50 U Multiscribe reverse transcriptase, 3.8 U RNase Inhibitor, and 50 nM of miR-specific RT primers. The reaction was incubated at 16 C for 30 minutes followed by 30 minutes at 42 C, and inactivation at 85 C for 5 minutes. Each generated cDNA was amplified by QRT-PCR with sequence-specific primers from the TaqMan microRNA Assays on an ABI Prism 7300 real-time PCR system (Applied Biosystems). PCR reactions included 10 mL 2 Universal PCR Master Mix (No AmpErase UNG), 2 mL each 10 TaqMan MicroRNA Assay Mix and 1.5 mL reverse-transcribed product; they were incubated in a 96-well plate at 95 C for 10 minutes, followed by 40 cycles of 95 C for 15 seconds and 60 C for 1 minute.

Normalization and data analysis Each pooled sample in the panel assay was analyzed in quadruplicate. Analysis of relative miRNA expression data was performed using DDCT method with hsa-mir16 as reference control [23]. Manufacturer’s data across several human tissues and cell lines indicate that hsa-miR-16 exhibits relatively even expression level and may be used as an endogenous miRNA control; in addition, we found it was equally expressed in PMF patients’ and controls’ granulocytes, displaying less than 1.3-fold variation among different samples when normalized to glyceraldehyde-3-phosphate dehydrogenase mRNA. To normalize data, DDCT for each sample was calculated using the mean of its DCT values subtracted from the mean DCT value measured in the entire population of healthy subjects, considered as a calibrator; relative quantification value was expressed as 2DDCT. Normalized DDCT values were uploaded onto GeneSpring software version 7.2 (Agilent Technologies), using the real-time data transformation. To prepare a ‘‘lowlevel filtered’’ gene list we excluded from analysis miRNAs, which presented a threshold cycle O35 in all pooled samples. The unsupervised analysis was performed on this gene list using the ‘‘condition tree’’ and ‘‘gene tree’’ options and applying the Pearson correlation equation. The low-level filtered gene list was further processed to select only those genes that showed a fold change O2 or !0.5 in comparison with normal samples; these were considered as ‘‘differentially expressed genes.’’ Furthermore, a Welch analysis of variance test (parametric test, with variances not assumed equal, p-value cutoff 5 0.05), using the Benjamini and Hochberg method to control the family-wise

error rate, was performed on the list of differentially expressed genes to finally generate a list of ‘‘statistically significant genes.’’ miRNA target prediction For miRNA target prediction, we used different algorithms: Miranda (http://www.microrna.org) [24], TargetScanS (http:// www.genes.mit.edu/targetscan) [25]; Pictar (hppt://pictar.bio.nyu. edu) [26]; and miRGen (http://microrna.sanger.ac.uk) [27]. Only miRNA-target genes identified by at least two of these algorithms were considered. QRT-PCR analysis of target gene expression For the analysis of expression level of DTR, HMGA2, and MYB in granulocytes, RNA was reverse-transcribed with random hexamers and Murine Leukemia Virus (MuLV) reverse transcriptase (Applied Biosystems). QRT-PCR was carried out with TaqMan Universal PCR master mix, using TaqMan gene expression assays (DTR/HBEGF:Hs00181813_m1; HMGA2: Hs00171569_m1; MYB: Hs00920563_g1), by means of StepOne real-time PCR system (Applied Biosystems). Assays were performed in quadruplicate. Gene expression profiling was achieved using the 2DDCT method as above, using glyceraldehyde-3-phosphate dehydrogenase as the housekeeping gene. Data are presented as percentchange using the mean value of control subjects as the reference value. Statistical analysis Comparison between groups was performed with Mann-Whitney U-test or Fisher’s exact test; associations between clinical characteristics and experimental data (logarithmically transformed) were assessed by Spearman’s or Wilcoxon–Mann-Whitney test, as appropriate, using SPSS software (StatSoft, Inc., Tulsa, OK, USA, http://www.statsoft.com), GraphPad InStat software (GraphPad Software, Inc., San Diego, CA, USA, http://www.graphpad.com), or ORIGIN software (V 7.5, OriginLab Northampton, MA, USA, http://www.originlab.com) for computation. Logistic regression analysis was performed according to SPSS software. The chosen level of significance from two-sided tests was p ! 0.05.

Results Patient characteristics For this study, we obtained RNA from purified granulocytes of 40 patients with PMF (12 samples were used in miRNA panel analysis, the remaining 28 in individual miRNA assays), and 25 each from patients with PV or ET (which were employed only in individual gene assays). Their main laboratory and clinical characteristics are reported in Supplemental Table 1. Median number of circulating CD34þ cells in PMF patients was 73.0  106/L, significantly higher than in PV or ET patients (p ! 0.002 for both). Three patients had an absolute CD34þ cell count higher than 300  106/L, a value that has been shown to harbor a greater risk of leukemic transformation [6]; however, they remained hematologically stable for at least 9 months after blood sampling for this study. The frequency of PMF, PV, and ET patients harboring JAK2V617OF mutation was

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65%, 100%, and 72%, respectively. All patients were negative for MPL515WOL/K mutation. Comparative analysis of miRNA expression profile in PMF and normal granulocytes In a first set of experiments, we analyzed miRNA differential expression in granulocytes from healthy subjects (three pools) or from PMF patients who were either wild-type or JAK2617VOF mutant with O51% 617VOF allele (two pools each). Stem-loop primers for 156 mature miRNAs were used for reverse transcription, followed by QRT-PCR. Twenty-eight miRNAs whose expression resulted undetermined in all cell populations were filtered out; the remaining 128 miRNAs generated a low-level filtered list. An unsupervised clustering analysis, performed using this low-level filtered list, paired the transcript profiles of granulocytes from PMF patients or normal subjects, respectively (Fig. 1). Using the filtering procedure described in the Patients and Methods section, we identified 60 miRNAs, which comprised a list of differentially expressed genes, showing a fold-change O2 or !0.5 between PMF and normal samples; of these, 23 were upregulated and 37 were downregulated. From this list, analysis of variance finally selected 12 miRNAs, which constituted a list of statistically significant genes (shown in bold in Table 1). Details on chromosomal localization of these 12 miRNAs are provided in Supplemental Table 2. Validation of the 12 statistically significant miRNAs in control and PMF granulocytes In order to validate the abnormal expression profile of miRNAs derived from panel analysis on pooled granulocyte samples, we focused on the 12 miRNAs belonging to the list of statistically significant genes selected by analysis of variance. An independent set of 28 additional patients with PMF and 16 healthy controls was analyzed by QRTPCR. As shown in Figures 2A, 2B, and 3, and detailed in Table 2, all 12 miRNAs were validated because their expression profile varied significantly between PMF and control granulocytes; in particular, miR-190, -182, and -183 were upregulated, while miR-31, -150, -95, -34a, -342, -326, -105, -149, and -147 were all downregulated in PMF granulocytes. Comparative analysis of the expression level of 12 statistically significant miRNAs in PMF, PV, and ET granulocytes We then compared PMF granulocytes with those obtained from patients with PV or ET. With a few exceptions (that are detailed in Table 2), these 12 miRNAs were abnormally expressed also in PV and ET granulocytes in respect to healthy subjects. However, when comparing the unique miRNA expression profile in the three MPDs, we found at least two different patterns of expression. In the case of miR-190, -150, -31, and -95 (Fig. 2A), the level measured

Figure 1. Unsupervised clustering analysis on ‘‘low-level filtered’’ gene list. Analysis was performed on four pools of granulocytes obtained from primary myelofibrosis (PMF) patients (two each from JAK2617VOF wild-type (WT) or mutant (Mu) patients) and three pools from normal controls; each pool was comprised of three subjects. All JAK2617VOF mutant patients had mutant allele burden in their granulocytes O51%. Clustering was performed using an unsupervised approach on low-level filtered gene list of 128 microRNA (miRNA) and applying several clustering algorithms provided by GeneSpring, as described in Patients and Methods section. A combination of two hierarchical clustering analyses (‘‘gene tree’’ and ‘‘condition tree’’) is presented; the gene tree is shown on the left, the condition tree on top. Gene coloring was based on normalized signals as shown at the bottom of figure.

in PMF granulocytes differed significantly also from that measured in PV or ET granulocytes (Fig. 2A and Table 2), to suggest that they were more typically associated with PMF. On the other hand, the expression level of the remaining eight miRNAs was substantially superimposable in the granulocytes of PMF, PV, and ET patients (Figs. 2B and 3, and Table 2).

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Table 1. List of differentially expressed microRNAs in primary myelofibrosis granulocytes Upregulated Gene name hsa-miR-183 hsa-miR-182 hsa-miR-144 hsa-miR-190 hsa-miR-368 hsa-miR-187 hsa-miR-135a hsa-miR-34a hsa-miR-9 hsa-miR-154 hsa-miR-126 hsa-miR-10a hsa-miR-9* hsa-miR-185 hsa-miR-154* hsa-miR-218 hsa-miR-130a hsa-miR-145 hsa-miR-106a hsa-miR-224 hsa-miR-142–3p hsa-miR-19a hsa-miR-17–5p

Downregulated

Fold change

Gene name

Fold change

51.23 36.46 32.32 10.85 10.02 7.023 6.639 5.020 4.123 4.090 3.802 3.504 3.367 3.252 2.714 2.702 2.611 2.380 2.343 2.291 2.244 2.078 2.044

hsa-miR-29a hsa-miR-29b hsa-miR-27a hsa-miR-199-s hsa-miR-339 hsa-miR-139 hsa-miR-330 hsa-miR-222 hsa-miR-338 hsa-miR-200a hsa-miR-28 hsa-miR-134 hsa-miR-200b hsa-miR-155 hsa-miR-326 hsa-miR-182* hsa-miR-223 hsa-miR-133b hsa-miR-146 hsa-miR-342 hsa-miR-199a hsa-miR-150 hsa-miR-105 hsa-miR-100 hsa-miR-138 hsa-miR-34b hsa-miR-23b hsa-miR-370 hsa-miR-135b hsa-miR-214 hsa-miR-31 hsa-miR-99a hsa-miR-98 hsa-miR-149 hsa-miR-142–5p hsa-miR-147 hsa-miR-95

0.495 0.481 0.474 0.471 0.462 0.458 0.439 0.431 0.431 0.426 0.412 0.407 0.401 0.391 0.298 0.294 0.289 0.287 0.219 0.210 0.203 0.161 0.139 0.122 0.121 0.091 0.078 0.062 0.0545 0.0489 0.0431 0.0382 0.0353 0.0328 0.0316 0.0126 0.0102

The list includes 60 microRNAs (miRNA) that showed a fold change O2 or !0.5 in comparison with control granulocytes, following normalization and data analysis as described in the Materials and Methods section. The 12 miRNAs identified in bold passed analyses of variance, and represent the list of statistically significant genes. *Denotes the less predominant miRNA form.

Correlation of miRNA expression with JAK2617VOF mutational state To evaluate whether abnormal expression of miRNAs correlated with 617VOF allele burden, all MPD patients were grouped according to their mutational state (Fig. 3B). We found that miRNA-182 and -183, which were both upregulated compared to controls, presented significantly higher expression level in patients who had 617VOF allele burden O51% (p ! 0.001 vs both wildtype and patients with !50% mutant allele burden; Fig. 3B), while the difference between JAK2V617F wildtype patients and those with !50% mutant allele burden

did not reach the significance level. Using linear regression, the level of both miR-182 and -183 was significantly correlated to the burden of 617VOF allele (Fig. 4C). Finally, we found that expression level of miR-182 and -183 was significantly correlated with each other (r 5 0.711; p ! 0.001). miR-182 and miR-183 are abnormally regulated also in PMF CD34þ cells Relative expression level of miRNA may vary depending on cell differentiation. Therefore, we evaluated the expression level of miR-182 and miR-183 in CD34þ cells purified from the PB of PMF patients (n 5 15) and in CD34þ cells purified from the BM of healthy donors (n 5 7). As shown in Figure 4, both miRNAs were significantly overexpressed in PMF CD34þ cells, confirming data in the granulocytes (Fig. 3); however, while in granulocytes their relative changes were similar, in case of CD34þ cells, significantly higher levels of miR-182 than miR-183 were found (p 5 0.04). miRNA target prediction analysis Screening of potential targets by Miranda [24], TargetScanS [25], and Pictar [26] algorithms yielded from several dozen to hundreds of predicted genes for each miRNA (Supplementary Table 3). Because only a few predicted targets have been experimentally validated in vitro or in vivo [28], and in order to narrow the analysis to a manageable number of records, we choose to focus only on potential targets that had been eventually identified by at least two of the three inquired databases (see Patients and Methods section). A list of potential target genes for each of the 12 validated miRNAs is reported in Supplementary Table 2. Most predicted targets belong to transcription factor families (BACH2, FOXA1, FOP1, AKT3, RARB), homeobox genes (HOXA10, C4, C5, C8, B4), oncogenes (MYC, MYCN), cell-cycle regulators (BCL-2, NPM1, CCND2, CASP2), and signal transduction molecules (ARF4, PDGFRA, SOCS4, 5, 7). Among putative targets, we identified at least three genes, namely DTR, HMGA2, and MYB, which were originally comprised within a list of deregulated genes that have been identified in a global gene-expression analysis of PMF cells, previously reported by our group [11]. Thus, we tried to correlate the expression level of these coding genes with putative regulatory miRNAs by concurrently measuring their respective quantities in the same granulocyte preparation (Fig. 4). Although with some variability in the strength of regression, we found significant inverse correlation between the level of each miRNA and that of its putative target (Fig. 5), as expected for a repressive function of miRNA. In particular, we found that miR-182 and -183, which were both overexpressed, correlated with significantly reduced levels of mRNA for DTR, while putative targets for the downregulated miR-150, -149, and -105,

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Table 2. Statistical analysis of the differential expression of the 12 analysis of variance–selected statistically significant microRNAs in granulocytes from healthy controls and patients with PMF, PV, or ET

miR-190 miR-31 miR-150 miR-95 miR-34a miR-342 miR-326 miR-105 miR-149 miR-147 miR-182 miR-183

PMF vs Ctr

PMF vs PV

PMF vs ET

PV vs Ctr

ET vs Ctr

PV vs ET

0.02 !0.000001 !0.00001 0.00007 0002 0.0002 0.001 0.0007 0.022 0.030 0.009 0.01

0.03 0.0003 0.0002 0.003 NS 0.005 NS NS NS NS NS NS

0.02 0.02 0.041 0.020 NS NS NS NS NS NS NS 0.01

NS 0.03 0.0006 0.043 0.001 NS 0.003 0.0006 0.0006 0.031 0.004 0.040

NS 0.0003 0.006 0.044 !0.000001 0.0005 0.002 0.00003 0.001 NS 0.002 0.044

NS NS NS NS NS 0.001 NS NS NS NS NS 0.008

This statistical analysis refers to the data presented in Figure 2 and 3. Ctr 5 healthy control subject; ET 5 essential thrombocythemia; NS 5 not significant; PMF 5 primary myelofibrosis; PV 5 polycythemia vera.

namely HMGA2 and/or MYB, resulted to be overexpressed in PMF granulocytes.

Discussion The importance of miRNAs in regulation of differentiation of hematopoietic stem cells toward different blood cell lineages is becoming increasingly apparent [28,29]. Among the best characterized, miR-181a, -146, and -223 are involved in murine B- and T-cell lymphopoiesis, respectively [30], while miR-150 is initially upregulated during developmental stages of B and T cells, and then repressed during differentiation of naı¨ve T cells into Th1 or Th2 effector cells [31]. Furthermore, overexpression of miR-150 prevented the formation of mature B cells in a murine transplant model [32]. Others have reported a critical regulatory role for miR-221 and -222 in human erythropoiesis, which was exerted through downmodulation of c-Kit receptor [33]; a miRNA signature associated with different erythroid maturation stages has been described in vitro cultured cord blood–derived CD34þ cells [34]. miR-223 is highly expressed in murine bone marrow [30] and in promyelocytic cell lines [35], and its expression is induced by granulocyte colony-stimulating factor or insulin-like growth factor-1 in differentiating 32D murine myeloid cells [36]; it is part of a minicircuity involving transcription factors NFI-A and C/EBPa in the control of myelopoiesis [35]. Specific downregulation of a defined set of eight miRNAs (miR-10a, -10b, -30c, -106, -126, -130a, -132, and -143) occurred during in vitro differentiation of human CD34þ cells toward the megakaryocytic lineage [37]. Finally, in a comparative study of BM and mobilized PB stem cell–derived normal human CD34þ cells, 33 miRNAs were identified as potential regulators of hematopoietic differentiation; some of them were also differentially expressed between BM and PB stem cell–derived CD34þ cells, and in partic-

ular, miR-95, -190, -182, and -183 were considered as BM-restricted [38]. Expanding knowledge of functional roles of miRNAs also points at their potential involvement in human cancer. Several miRNAs are deregulated in primary human tumors [39]; at least some of them are located at genomic regions linked to cancer [39–41], and may eventually act as regulators of proto-oncogenes, as is the case of let-7 family for RAS [42]. miRNA profile is characteristic of different cancer cells and reflects their developmental derivation and differentiation status, potentially allowing classification of poorly differentiated tumors better than messenger RNA profiling [43]. In some instances, altered miRNA expression profile in a variety of solid cancers has been associated with clinical outcomes and response to treatment [39]. Of particular interest is the mir-17 microRNA cluster, which is located in a region on human chromosome 13 that is frequently amplified in B-cell lymphomas [44]; overexpression of the mir-17 cluster cooperated with Myc to accelerate tumor development in a mouse B-cell lymphoma model, and mir-17 itself was induced by overexpression of Myc [45]. Downregulation of miR-15 and miR-16 has been demonstrated in the majority of patients with chronic lymphocytic leukemia [46], and a recent study described a unique miRNA signature in chronic lymphocytic leukemia associated with disease prognosis and progression [47]. In this study, we started to characterize miRNA expression in cells from PMF patients with the aim to identify a disease-associated expression profile, which might have diagnostic and, eventually, prognostic implication, and to gain insights into mechanisms involved in specific genes’ deregulation occurring in PMF cells. In our prior array mRNA expression study, we focused on CD34þ cells and identified a number of abnormally expressed genes; a subset of eight genes was further validated in granulocytes, and found to predict PMF cells with no misclassification error in the comparison with normal cells and an 81% correct

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Figure 2. Differential expression of selected microRNAs (miRNA) in primary myelofibrosis (PMF) compared to control, polycythemia vera (PV), or essential thrombocythemia (ET) granulocytes. The 12 miRNAs comprising the list of ‘‘statistically significant genes’’ presented in Table 1 were validated with quantitative real-time polymerase chain reaction in 28 additional PMF samples, 16 healthy subjects, and 25 patients each with PV or ET. In (A), the four miRNAs whose expression level differed significantly between PMF and either PV or ET granulocytes, in addition to controls, are presented. Data are shown as percent change using the mean value of control subjects as the reference value. In (B), percent change of six additional miRNAs, which were differentially expressed in PMF, PV, and ET granulocytes compared to normal cells, but did not differ among the individual myeloproliferative disorders, are shown. For details of statistical analysis, please refer to Table 2. Boxes represent the interquartile range that contains 50% of the subjects, the horizontal line in the box marks the median, the small square inside indicates mean value, and bars show the range of values.

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Figure 3. Correlation between abnormal expression of miR-182 and -183 and JAK2617VOF mutational state. In (A), expression level of miR-182 and -183 in primary myelofibrosis (PMF), polycythemia vera (PV), or essential thrombocythemia (ET) granulocytes is presented as percent change using the mean value of control subjects as the reference value. For details of statistical analysis, please refer to Table 2. The correlation between microRNA expression level and JAK2617VOF mutational load is shown in (B). The level measured in patients with O51% mutant allele burden (all myeloproliferative disorder patients were grouped together for convenience) was significantly higher than that measured in wild-type patients or in those with !50% mutant allele burden (p ! 0.001 for both). Data are presented as relative quantity (RQ) compared to control subjects (Ctr); see Patients and Methods section for details. Boxes represent the interquartile range that contains 50% of the subjects, the horizontal line in the box marks the median, the small square inside indicates mean value, and bars show the range of values. In (C), the correlation between miR-182 and -183 RQ and 617VOF allele burden is presented; linear regression parameters are reported inside each plot.

classification in case of PV or ET [11]. This study also supported the usefulness and appropriateness of granulocytes as source of RNA for expression analysis of disease-associated genes. Indeed, granulocytes have been successfully employed in similar expression studies in PV [48,49]. We have employed a quantitative real-time PCR approach for quantification of well-characterized 156 human miRNAs; while QRT-PCR has the advantage of being more sensitive and reproducible than other methods, such as Northern blotting and arrays, the number of genes we have analyzed is definitely smaller than the number of miRNAs identified to date, that is O450 (July 2006 release of miRBase) with O1000 in silico predicted miRNAs [50]. Thus, we acknowledge that our analysis cannot be considered exhaustive, and that the number of miRNAs potentially of interest for PMF might increase in the near future, depending on availability of more detailed genomic information on novel miRNAs as well as of sensitive and high-throughput analytical methods. Not with standing this potential limitation, data from experiments presented herein have started to provide interesting information about abnormalities of miRNA profile in PMF granulocytes. We observed a general downregulation of miRNAs, which is in line with observation in other tumors, and which can supposedly be ascribed to an abnormal state of cellular differentiation; indeed, induction of many miRNAs coincident with terminal myeloid differentiation has been described in all-trans retinoic acid–treated HL-

60 cells [43]. Twelve statistically significant deregulated miRNAs were identified and validated in PMF granulocytes; among these, miR-95, -182, and -183 were upregulated while the others (miR-190, -31, -150, -34a, -342, -326, -105, -149, -147) displayed reduced levels compared to control granulocytes. Interestingly, the abnormally

Figure 4. Abnormal expression of miR-182 and miR-183 in the CD34þ cells from primary myelofibrosis (PMF) patients. Expression level of miR-182 and -183 was measured in CD34þ cells purified from the peripheral blood of PMF patients or the bone marrow of control subjects. Data are expressed as percent change using the mean value of control subjects as the reference value.

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Figure 5. Correlation between the level of mRNA for DTR, HMGA2, and MYB and the level of putative regulatory microRNAs (miRNAs). Quantification of expression level of putative target RNAs and of regulatory miRNAs was performed by quantitative real-time polymerase chain reaction in the same granulocyte cell preparation; levels are presented as relative quantity (RQ) compared to control subjects (Ctr); see Patients and Methods section for details. In (A), RQ of mRNA level for DTR, HMGA2, and MYB, in primary myelofibrosis (PMF) and control granulocytes is shown; all were significantly different at p ! 0.001. In (B), concurrently measured expression level of potentially regulatory miRNAs are displayed. Linear regression parameters are presented inside each plot.

increased level of two of three overexpressed miRNAs (miR182- and -183) was found to linearly correlate with allelic burden of JAK2617VOF mutation. Because expression of miRNAs may fluctuate according to cell differentiation [31], we also measured the expression level of miR-182 and miR-183 in PMF CD34þ cells, and we have been able to confirm their abnormal expression in comparison to control CD34þ cells; however, the fact that their relative expression profile was heterogeneous unlike in granulocytes would suggest differentiation-specific modification. Thus, it is likely that assessment of global miRNA profile in purified PMF CD34þ cells will reveal, at least partially, a different profile than the one we characterized in granulocytes.

As expected from previous studies of mRNA profiling in MPD [11,51], there was large overlap also in miRNA differential expression among patients with the different clinical MPD entities, including PV or ET. Notwithstanding, we were able to identify four miRNAs, which presented statistically significant differences in their expression levels between PMF and PV or ET patients, although this number was too small to employ mathematical models of prediction. We believe this observation deserves further investigation. One current limitation of studies on miRNA profiling is the paucity of information concerning their target genes, because for only a few miRNAs, a direct proof of target gene regulation has been obtained through knockout or overexpression experiments in a defined cellular context;

P. Guglielmelli et al./ Experimental Hematology 35 (2007) 1708–1718

a search of the database providing computational prediction of potential gene targets is the most widely employed approach to identify putatively regulated genes. Taking these caveats in mind, we have attempted to correlate miRNA profile in PMF granulocytes and in silico–predicted target genes by taking advantage of our own database of gene-expression profile in PMF cells ([11], and personal unpublished data). We focused on three possible gene targets, DTR, HMGA2, and MYB, and provided evidence for a correlation between their expression level and that of putative regulatory miRNA. In particular, HMGA2, an architectural transcription factor belonging to the high-mobility group proteins and a putative target for miR-150 and -149, was initially found overexpressed in two patients with a t(4;12) and t(5;12) rearrangement [52], and later in most PMF patients, even in the absence of chromosome 12 abnormalities [53]. HMGA2 turned out to be one of the most differentially expressed genes between PMF and PV or ET patients in our global gene analysis approach, and constituted one of the genes comprising a set of eight discriminatory genes in granulocytes [11]. DTR, or heparin-binding epidermal growth factor-like factor gene, is abnormally downregulated in CD34þ PMF cells, and our novel data indicate that also in granulocytes, its expression level is significantly reduced over controls. It was of interest that both miR-182 and -183, potentially regulators of DTR, are part of a miRNA cluster that includes miR-96 and are located at 7q32.2, a site possibly involved in chromosomal deletions in PMF (http://cgap.nci.nih.gov/Chromosomes/CytList). Heparin-binding epidermal growth factor-like factor stimulates the growth of a variety of cells in autocrine or paracrine manner, facilitates cell migration, and is a potent inducer of tumor growth and of angiogenesis [54]. Finally, MYB is involved in hematopoietic progenitor cell differentiation and proliferation [55], in particular in the megakaryocytic lineage [56,57]; mutations of Myb have not been found in PMF patients [58]. Myb was found to be upregulated in PMF CD34þ cells, and we have now validated its overexpression also in granulocytes, and correlated with concurrently reduced expression levels of miR105. However, before one can reliably conclude that these in silico–predicted interaction do play a role in PMF cell abnormalities and disease pathogenesis, functional studies of miRNA modulation will be required. Overall, our data support the existence of defined abnormalities in miRNA profile of PMF granulocytes and, at least for miR-182 and miR-183, also in CD34þ cells, and provide a starting tool for future studies of miRNA regulation in different hematopoietic cell subsets in patients with PMF and other chronic myeloproliferative disorders.

Acknowledgments This study was supported by Associazione Italiana per la Ricerca sul Cancro, Milano; Ente Cassa di Risparmio di Firenze; Ministero

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della Salute, Ricerca Finalizzata 2005; COFIN 2006067001_003. A.P. was the recipient of a fellowship from Associazione Italiana per le Leucemie, Instituto Tumor: Toscano, Firenze. Conflict of interest statement: there is no conflict to be declared.

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Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.exphem. 2007.08.020.

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Supplemental Table 1. Clinical characteristics, at the time of sampling of patients with PMF, PV or ET included in the study Characteristics

PMF (n = 40)

Age, year* Hemoglobin, g/dL* Leukocytes (x109/L)* Platelets (x109/L)* CD34+ (x106/L)* Drupiez score 0 1 2 JAK2 V617F Wild-type Mutant 617V > F allele burden (%) < 50% ≥ 51% Severity Score 0–2 3–6

PV (n = 25)

ET (n = 25)

67.0 (49–98) 12.1 (7.6–14.9) 13.0 (3.0–56.8) 265 (38–1,851) 73.0 (3–400)

58.0 17.2 11.0 438 8.1

(32–76) (15.2–19.3) (5.2–36.0) (104–1,052) (0–31)

56.6 14.4 8.5 698 3.3

27 10 3

n.a.

n.a.

14 26

0 25

7 18

10 16

13 12

17 1

14 26

n.a.

n.a.

(20–89) (11–17.6) (2.8–13.1) (460–1,702) (0–6)

*, values were expressed as median (range) values. n.a., not applicable.

Supplemental Table 2. Chromosome localization and possible cluster distribution of the 12 abnormally regulated and validated miRNAs. miRNA

CHROMOSOME LOCALIZATION

miR-190 miR-31 miR-150 miR-95 miR-34a miR-342 miR-326 miR-105 miR-149 miR-147 miR-182 miR-183

15q22.2 9p21.3 19q13.33 4p16.1 1p36.23 14q32.2 11q13.4 Xq28 2q37.3 9q33.2 7q32.2 7q32.2

CLUSTERING WITH

hsa-mir-105-1; hsa-mir-767; hsa-mir-105-2

hsa-mir-96; hsa-mir-183 hsa-mir-96 hsa-mir-182

Cluster assignation was derived from data in miRNA Cluster data-base at www.diana.pcbi.upenn.edu/cgi-bin/mirgen/v3/cluster.cgi.