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Stem Cells 2005;23:631-637 www.StemCells.com
© 2005 AlphaMed Press

Detection of Aberrant Gene Expression in CD34+ Hematopoietic Stem Cells from Patients with Agnogenic Myeloid Metaplasia Using Oligonucleotide Microarrays

Letetia C. Jonesa, Ayalew Tefferib, Peter T. Vuonga, Julian C. Desmondc, Wolf-K. Hofmannd, H. Phillip Koefflerc

a Division of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA;
b Division of Hematology and Internal Medicine, Mayo Clinic and Mayo Foundation, Rochester, Minnesota, USA;
c Division of Hematology/Oncology, Department of Medicine, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California, USA;
d Department of Hematology, University Hospital, Frankfurt, Germany

Key Words. Hematopoietic stem cells • Agnogenic myeloid metaplasia • Aberrant gene expression

Correspondence: Letetia C. Jones, Ph.D., Division of Laboratory Medicine, University of California, San Francisco, 513 Parnassus Ave. S864, San Francisco, California 94143, USA. Telephone: 415-514-0815; Fax: 415-514-0815; e-mail: letetia{at}itsa.ucsf.edu


   ABSTRACT
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Agnogenic myeloid metaplasia (AMM) is a clonal stem cell disorder that leads to ineffective hematopoiesis, bone marrow fibrosis, and extramedullary hematopoiesis. The molecular mechanisms underlying the development of this syndrome are currently unknown. Therefore, the aim of this study was to characterize aberrant gene expression in CD34+ hematopoietic stem cells from patients with AMM. We used oligonucleotide microarrays to analyze gene expression profiles in CD34+ hematopoietic stem cells from patients with AMM compared with expression in CD34+ cells from healthy individuals. We identified 95 highly differentially expressed genes (48 upregulated and 47 down-regulated) that are potentially involved in regulating abnormal hematopoietic proliferation and differentiation and confirmed many of them by quantitative polymerase chain reaction. Using class membership prediction analysis, we identified 75 genes whose expression profiles can accurately differentiate AMM samples from the controls. Using these 75 genes, we were able to discriminate patients with AMM from the controls by hierarchical clustering (Spearman’s confidence correlation). The predictive power of these genes was verified by applying the algorithm to an unknown test set containing expression data from eight additional CD34+ samples (four AMM, four control). Our results indicate that a subset of genes may be used to differentiate patients with AMM from healthy individuals. Furthermore, we identify 95 genes whose aberrant expression may be involved in AMM.


   INTRODUCTION
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Agnogenic myeloid metaplasia (AMM) (also referred to as myelofibrosis with myeloid metaplasia) is classified as a chronic myeloproliferative disorder along with polycythemia vera, essential thrombocythemia, and chronic myeloid leukemia. The primary pathogenetic mechanism of AMM is a clonal stem cell disorder that leads to ineffective erythropoiesis, dysplastic megakaryocytic hyperplasia, and an increase in the ratio of immature myeloid cells to total granulocytes. This clonal myeloproliferation is characteristically accompanied by bone marrow fibrosis and extramedullary hematopoiesis in the spleen and other organs [1]. Although aberrant expression of fibrogenic and angiogenic cytokines has been demonstrated in patients with AMM [2, 3], the molecular events that lead to the disease process have not been characterized.

Like other cancers, malignant transformation of hematopoietic cells in myeloproliferative diseases results from a series of genetic changes. After an initial insult to the stem cell, additional genetic alterations occur in this cell that give it a growth advantage over other cells. Such alterations may influence the expression of cell cycle–related genes, those encoding transcription factors, or tumor suppressor genes. Because expression of CD34 is a marker for hematopoietic stem cells and the initial cascade of events leading to AMM occurs in this stem cell, we have focused this study on gene expression patterns in CD34+ cells from individuals with this disease.

To expand our understanding of the genetic events in hematopoietic stem cells that lead to AMM, we performed oligonucleotide microarray analysis on purified CD34+ hematopoietic stem cells isolated from patients with AMM. As a control, we compared expression to that observed in CD34+ cells from healthy individuals. Microarray technology provides a powerful tool for monitoring the expression of thousands of genes in a single experiment. Recent studies have demonstrated that multiple tumor types can be distinguished on the basis of their gene expression patterns. Furthermore, the gene expression arrays are capable of predicting the survival of patients in several types of cancer [4–6]. In this study, we have identified genes that can distinguish patients with AMM from healthy individuals. Second, we have identified numerous genes whose expression is aberrantly regulated in patients with AMM and that may contribute to the disease process. This study represents the first of its kind, providing a glimpse at gene expression profiles in hematopoietic stem cells from patients with AMM.


   MATERIALS AND METHODS
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patients
The diagnosis of all 12 patients (eight male and four female; median age, 61 years; age range, 42–74 years) with AMM was confirmed by bone marrow examination that showed collagen fibrosis as well as dysplastic megakaryocytic hyperplasia in all instances. In addition, all patients had a leukoerythroblastic peripheral blood smear that also displayed dacryocytosis. Ten of the 12 patients had cytogenetic studies performed, and four had detectable clonal abnormalities, including 20q-, 6p-, trisomy 21, and t(1;6). The Dupriez prognostic score [7] was 0 (low risk) in five patients, 1 (intermediate risk) in six patients, and 2 (high risk) in one patient. Eleven of the 12 patients had palpable splenomegaly that was marked in nine patients. None of the patients were receiving either cytoreductive or other AMM-directed therapy at the time of the current study, and only five had a prior history of cytoreductive treatment with interferon alfa, hydroxyurea, or anagrelide. Peripheral blood CD34 count was quantified by flow cytometry in all 12 patients and was found to be elevated in all 12, with a median of 94.5 per µl (range, 12.8 to 1,211).

Clinical Samples and DNA Extraction
After approval by the institutional review board, 30 ml of peripheral blood was collected from patients with AMM diagnosed according to conventional criteria. Hypaque density gradient centrifugation was used to separate the granulocyte and mono-nuclear cell layers (Sigma Diagnostics, St. Louis) from the samples. Each mononuclear cell layer was further cell fractionated by magnetic-activated cell sorting (Miltenyi Biotech, Auburn, CA) using antibodies that are specific to stem cells (CD34+). This particular procedure was performed according to the manufacturer’s recommendation. Sample purity in regard to CD34 cells was confirmed by flow cytometry. The patient samples described above were compared with CD34+ cells from healthy individuals. For the latter, bone marrow mononuclear cells were separated by density gradient centrifugation through Ficoll-Hypaque (Biochrom, Berlin). CD34+ cells were purified as described previously [8], and purity was evaluated by flow cytometry. Total RNA was extracted using TRIzol (Invitrogen Corporation, Carlsbad, CA). In addition, RNA was further purified over RNeasy spin columns (Qiagen Inc., Valencia, CA) according to the manufacturer’s instructions. To ensure that the RNA extracted from purified CD34+ cells was not degraded, we subjected the RNA to denaturating gel electrophoresis. We further evaluated the integrity of the RNA using an Agilent 2100 Bioanalyzer to calculate the 28S/18S ribosomal RNA ratio (acceptable range, ~1.8 to 2.3).

Oligonucleotide Microarray
The detailed protocol for the sample preparation and microarray processing is available from Affymetrix Inc. (Santa Clara, CA). Because of the limited number of CD34+ cells and the low content of RNA in these hematopoietic stem cells, a double in vitro transcription technique (nanogram scale assay) was used. To assay 50 ng of total RNA, the standard Affymetrix target amplification protocol was modified using first-round cRNA product to generate a double-stranded cDNA that was then used for a second round of in vitro transcription for synthesis of the biotinylated cRNA. To demonstrate that comparable results are obtained using the nanogram scale assay compared with the standard protocol, we performed a pilot experiment with U937 cell RNA in which the assays were carried out side by side. We hybridized the RNAs from each protocol to HG-U95Av2 microarrays (Affymetrix Inc.) and compared the number and overlap of genes assigned "present calls." In the standard assay (7.5 µg of starting RNA), 4,794 genes were given present calls, and 3,505 genes were present using the nanogram scale assay. In a Venn diagram comparing the overlap of genes with present calls between the two assays, we found that 3,305 genes were common (~70% from the standard protocol and 94% of the nanogram scale), thus demonstrating the competency of this approach.

The mean percentage (± standard deviation) of genes present across all microarrays in our nanogram scale assay was 34.3% ± 7.17%. These probe sets had a mean signal intensity of 11295.7 ± 2708.7. The mean 3'/5' ratio (which divides the hybridization signal intensity of probe sets specific for the 3' and 5' ends of transcripts) for the housekeeping gene 18S was 0.92 ± 0.5, thus reflecting efficient cDNA synthesis in our samples.

Fifteen micrograms of fragmented, biotinylated cRNA was hybridized to a HG-U95Av2 microarray for 16 hours at 45°C with constant rotation at 60 rpm according to the Affymetrix protocol. This high-density oligonucleotide array targets consisted of 9,670 human genes as selected from the National Center for Biotechnology Information GenBank database with a total of 12,000 oligonucleotide sets. After hybridization, the microarray was washed and stained on an Affymetrix fluidics station and scanned with an argon-ion confocal laser, with a 488-nm emission and detection at 570 nm. The fluorescence intensity was measured for each microarray and normalized by global scaling to 2,500 and to the average fluorescence intensity for the entire microarray [9]. The data were imported into a Microsoft Excel 2000 (Microsoft, Redmond, WA) database.

Data Analysis
GeneChip image analysis was performed using the Microarray Analysis Suite 4.0.6 (Affymetrix, Inc.). Data analysis was performed with GeneSpring software version 4.0 (Silicon Genetics Inc., San Carlos, CA). To eliminate changes within the range of background noise and to select the most differentially expressed genes, the restrictions used to classify genes as either upregulated or downregulated included the following: raw data values >1,500; change in expression greater than fivefold; and the genes were called present by the Affymetrix data analysis. Statistical significance of changes was calculated by the nonparametric t-test with a p value < .05. We used the class membership prediction method [10–12] to determine whether the pattern of gene expression could be used to distinguish the AMM samples from the healthy control samples. The maximal number of genes to predict the class membership was set to 50. Hierarchical clustering analysis with Spearman’s confidence correlation was used to identify gene clusters. The separation ratio was set at 0.5.

Real-Time Polymerase Chain Reaction
Quantification of RNA of target genes in CD34+ cells by real-time polymerase chain reaction (PCR) was performed as described previously [13]. Amplification reactions contained 100 ng of cDNA and 12.5 µl of the Universal Taqman 2X mastermix (Applied Biosystems). The concentrations of primers (Invitrogen Corporation) and TaqMan probes (Applied Biosystems) were 300 and 100 nM, respectively, in a final volume of 25 µl. All reactions were performed in triplicate using the iCycler iQ system (BioRad, Hercules, CA) under the following conditions: 2 minutes at 50°C, 10 minutes at 95°C, followed by 45 cycles of 95°C for 15 seconds and 60°C for 1 minute. Expression of 18S was used as an internal control.


   RESULTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
To expand our knowledge of genetic defects in hematopoietic stem cells in AMM, we used oligonucleotide microarrays (Hu95aV2, Affymetrix) to analyze gene expression in purified CD34+ hematopoietic stem cells from eight patients with AMM. As a control, we compared expression with that of CD34+ cells from six healthy individuals. The expression data were analyzed using Gene Spring software 4.0 (Silicon Genetics), and genes that were differentially regulated in AMM compared with control were identified. Genes that were classified as either upregulated or downregulated had raw data values of at least 1,500 in the AMM and control samples, respectively, and demonstrated greater than fivefold changes in expression. The statistical significance of the changes was calculated by the nonparametric t-test with p < .05. Using these parameters, we identified 95 highly differentially expressed genes (48 upregulated and 47 downregulated) that could be involved in regulation of hematopoietic proliferation and differentiation. These genes are listed in supplementary online Tables 1 and 2.

Our data revealed enhanced expression of several transcription factors: the AP-1 protein JUNB, the hematopoietic transcription factor GATA2, proto-oncogene N-myc, and ATF-3, which stimulates the transcription of genes important for fibroblast growth in response to stress signals. Furthermore, we found that the G0/G1 switch regulatory genes G0S8 and G0S24, adhesion receptor GMP140, the angiogenic platelet-derived endothelial cell growth factor, and platelet factor 4 were all overexpressed in AMM.

Among the downregulated genes in AMM were those encoding two proteins whose activities are important for cell cycle mechanisms: BUB3, a mitotic checkpoint kinase, and Mad2, a monitor for spindle kinetochore attachment. We also found a downregulation of the DNA repair enzyme FEN1, the apoptosis susceptibility protein CSE-1, and calcineurin, an intracellular phosphatase. Differential expression of these genes in CD34+ cells was confirmed using real-time PCR. The results from selected genes are shown in Figure 1Go.



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  		Figure 1. Quantitative reverse transcription–polymerase chain reaction (PCR) analysis of gene expression in hematopoietic stem cells from patients with agnogenic myeloid metaplasia (AMM) compared with healthy individuals. Gene expression was measured by real-time PCR using RNA from CD34+ hematopoietic stem cells purified from patients with AMM (triangles) or healthy individuals (squares). Each sample was analyzed in triplicate, and 18S mRNA was used as an internal control.

 
Class Membership Prediction and Hierarchical Clustering
To determine whether the pattern of gene expression can be used to differentiate AMM samples from the normal controls, we applied class membership prediction to the data set described above (six control, eight AMM samples). Using this method, we identified 75 genes whose expression can accurately differentiate all AMM samples from the controls. To validate the genes selected by class membership prediction, we used the 75 genes for hierarchical clustering using the Spearman’s confidence correlation (Fig. 2AGo). We generated two clusters, one containing the control samples and the other containing all of the AMM samples.



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  		Figure 2. Identification of genes expressed in CD34+ marrow cells that can distinguish between patients with agnogenic myeloid metaplasia (AMM) and healthy controls. (A): Results represent analysis by hierarchical clustering with Spearman’s confidence correlation of 14 samples of CD34+ hematopoietic stem cells (training set). Seventy-five genes were selected to predict the class membership of each of the samples. Individual samples are shown vertically, and the genes are represented horizontally. (B): Validation of the 75 predictive genes used to distinguish between AMM samples and healthy controls by gene expression profiling. The 75 predictive genes were used for clustering analysis in a second data set obtained from CD34+ hematopoietic stem cells from eight additional samples (four AMM, four controls; test set). Two clusters corresponding to AMM and control were found. Blue indicates low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.

 
To evaluate the predictive power of these 75 genes in an independent data set, we used them to perform cluster analysis on eight additional samples obtained from CD34+ cells (four normal, four AMM). As shown in Figure 2BGo, we generated two clusters: one containing the controls and the other containing the AMM samples. None of the samples were misclassified in this test set. Our results indicate that a subset of genes may be used to differentiate patients with AMM from healthy individuals.


   DISCUSSION
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
AMM, also referred to as either primary or idiopathic myelofibrosis, is a stem cell disorder associated with proliferation of the granulocytic, erythroid, and megakaryocytic lineages. In addition to the clonal myeloproliferation, this disease has an increase in the number of stromal cells [14] and in the levels of extracellular matrix proteins [15] in the bone marrow. This stromal proliferation is thought to be a reactive process mediated by cytokines produced by the hematopoietic components. Because AMM is a complex disease that involves multiple cell types and the interplay between these cells and the microenvironment of the bone marrow, cell lines are not suitable experimental systems in which to study this disease. Furthermore, no suitable lines are available that faithfully represent the aberrant cells. Murine models for myelofibrosis have been generated by overexpression of thrombopoietin [16] and the GATA-1(low) mutation [17], and they share several features with human AMM. Although these systems shed some light on the pathology of AMM, they likely do not reflect the genetic changes in the stem cell that cause the disease process.

In the present study, we focus on alterations of gene expression in the hematopoietic stem cells of patients with AMM using oligonucleotide microarray analysis. One of the difficulties of performing microarray analysis on hematopoietic stem cells has been the need for microgram amounts of starting RNA. The rarity of AMM (0.5 to 1.5 cases per 100,000 people [18]) coupled with the difficulty of obtaining sufficient quantities of purified CD34+ cells from these patients makes it challenging to perform these studies. Furthermore, the RNA yield from this cell population is lower than that obtained from other hematopoietic cell types. We have overcome these obstacles by using the nanogram scale assay first reported by Hofmann et al. [8]. This double in vitro transcription technique starting with 50 ng of total RNA yielded expression data that could be confirmed by an independent method (quantitative PCR) in an independent group of samples with a confirmation rate of >70%.

Another technical challenge for us in these studies was to find the appropriate source of CD34+ cells with which to compare AMM patient stem cells. Given that patients with AMM usually have a "dry tap" due to fibrosis of the marrow compartment and, consequently, a significant increase in the number of CD34+ cells in the periphery, it is impossible to obtain a sufficient number of bone marrow–derived CD34+ cells from them. Several studies [19–21] have been published that focus on gene expression profiles of human CD34+ cells from different sources, including bone marrow, umbilical cord blood, and peripheral blood. Out of necessity, all of these studies use G-CSF–mobilized peripheral blood to increase the number of circulating stem cells. Collectively, these studies observe differences in the expression of genes related to cell cycle progression, DNA synthesis, and apoptosis. We feel strongly that stimulation with G-CSF is likely to induce subtle differences in the state of maturation of stem cells by altering the expression of responsive genes. Therefore, we opted to use nonstimulated cells, which can only be obtained from normal individuals in sufficient quantities from the bone marrow.

Class membership prediction analysis and hierarchical clustering using 75 selected genes enabled us to discriminate between patients with AMM and healthy individuals based on their gene expression profiles. All of the samples in the test group were accurately classified according to disease or control samples with only one subcluster (Fig. 2BGo), thus substantiating the importance of the selected genes. The ultimate goal of such studies is to design a custom array that can be used to diagnose AMM. We have taken the first step toward this end by demonstrating that AMM patient samples can be distinguished from the healthy controls based on gene expression patterns. A more critical task, however, is to distinguish AMM from other myeloproliferative diseases. The diagnosis of AMM is based on the exclusion of other clonal or nonclonal disease processes that may be associated with bone marrow fibrosis. Furthermore, collagen fibrosis in the bone marrow may not be detected in the early, cellular phase of AMM [22], thus making it difficult to distinguish it from either an atypical chronic myeloid disorder or essential thrombocythemia. Given the difficulty of differentiating these syndromes at their onset, the prospect of diagnosing myeloproliferative diseases based on gene profiling is exciting. Pellagatti et al. [23] have used gene expression profiling to study molecular abnormalities associated with polycythemia vera. It is difficult to compare directly our findings with theirs because they used neutrophils as the genetic source and we use CD34+ stem cells. With this in mind, it is not surprising that we see virtually no overlap between our lists of differentially regulated genes. Because myeloproliferative diseases are stem cell disorders, we believe that microarray studies using stem cell RNA are most likely to reveal the genetic insult that initiates these diseases. We are currently studying whether microarray technology may be used to differentially diagnose AMM, polycythemia vera, and essential thrombocythemia.

In the present study, we have identified 95 genes that are aberrantly regulated in CD34+ hematopoietic stem cells from patients with AMM compared with expression in healthy, unstimulated CD34+ cells. Of these differentially expressed genes, 48 are upregulated in the AMM samples, including genes encoding several transcription factors (FRAT2, JUNB, ATF3, GATA2, and N-MYC). Using quantitative PCR, we confirmed the overexpression of the protooncogene N-myc. Although noted for its overexpression in several pediatric tumors, N-MYC is also believed to promote cell cycle progression in cerebellar neuronal precursor cells during development [24]. N-MYC may promote cell cycle progression in affected hematopoietic stem cells in patients with AMM.

In addition, we identified several cytokines and growth factors that were overexpressed in AMM stem cells, including platelet-derived endothelial cell growth factor, G-CSF, interleukin-8, interleukin-1ß, and platelet factor 4. Previous studies have shown quantitative abnormalities in the levels of such fibrogenic cytokines as transforming growth factor ß, fibroblast growth factor, and platelet-derived growth factor (PDGF) [25]. Although two out of eight AMM samples showed very high levels of PDGF in the microarray analysis, the remaining six were consistent with our quantitative PCR results, which showed no significant change in expression in CD34+ cells. The lack of overexpression of these cytokines in CD34+ hematopoietic stem cells suggests that their role in the disease process occurs subsequent to the causative genetic insult.

Expression of the growth factor DLK1 was also upregulated in AMM stem cells. We confirmed DLK1 overexpression in CD34+ AMM samples using quantitative PCR (data not shown). Previous studies by us have shown that expression of DLK1 is elevated in early erythroid and megakaryocytic cell lines and, when overexpressed, stimulates the growth of these cells. Huang et al. [26] have implicated DLK1 overexpression in the conversion of hepatic stellate cells to myofibroblasts, leading to the liver fibrosis associated with biliary artesia. Furthermore, DLK1 has been shown to regulate human mesenchymal stem cell differentiation into osteoblasts and adipocytes at the level of the bipotential progenitor cell pool [27]. Therefore, overexpression of this growth factor may stimulate the aberrant growth of undifferentiated cells.

Our analysis reveals a significant downregulation of 47 genes in CD34+ cells from patients with AMM compared with normal stem cells (supplementary online Table 2). These include cell cycle–related proteins, DNA repair enzymes, a repressor of bHLH transcription factors (Id1), and the cellular apoptosis susceptibility protein CSE-1. Interestingly, the genes encoding Id1 and CSE-1 are located on the long arm of chromosome 20, one of the most common sites of deletion in patients with AMM [28].

We also observe a downregulation of the intracellular phosphatase calcineurin and the FK-506 binding protein 51 (FKBP5). Giraudier et al. [29] recently demonstrated an overexpression of FKBP5 in spontaneously growing megakaryocytes derived from patients with AMM and showed that these cells are resistant to apoptosis after cytokine deprivation. They propose that FKBP5 regulates the apoptotic program in these megakaryocytes by inhibiting calcineurin-mediated cell death. We concur with the notion that a loss of calcineurin activity (either by inhibition or decreased expression) could lead to expansion of the malignant clone in AMM. However, our results suggest that decreased expression of FKBP5 may play a role in the disease process in the hematopoietic stem cell. In a recent study by Bock et al. [30], FKBP5 gene expression was shown to be similar to that of control cases in total bone marrow as well as megakaryocytes isolated from patients with AMM. This discrepancy reinforces the inherent difficulty of studies that focus on myeloproliferative diseases. Depending on the stage of the disease as well as the stage of differentiation of the cellular components involved, cellular pathways may be different, making it difficult to distinguish causative events from reactive processes.

To focus on genetic events that occur at the earliest stage of AMM, we have performed microarray analysis on hematopoietic stem cells from patients with this disease. We have identified key genes whose aberrant regulation may lead to this disease. Furthermore, we have shown that gene expression profiles may be used to distinguish AMM. The results of this study greatly expand our knowledge of alterations of expression of genes in hematopoietic stem cells that exist in the early stages of AMM and provide a framework for future studies in this area.


   ACKNOWLEDGMENTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
This work was supported by grants from the National Institutes of Health to H.P.K. and L.C.J. (T32 CA-75956) and the generous support of the Brian and Phylis Harvey Fund. H.P.K. is a member of the Jonsson Comprehensive Cancer Center and the Molecular Biology Institute of UCLA and holds the endowed Mark Goodson Chair of Oncology Research at Cedars-Sinai Medical Center/ UCLA School of Medicine.


   REFERENCES
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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Received June 8, 2004; accepted for publication January 26, 2005.



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