标题: Detection of Aberrant Gene Expression in CD34 Hematopoietic Stem Cells from Pat [打印本页] 作者: 江边孤钓 时间: 2009-3-5 10:50 标题: Detection of Aberrant Gene Expression in CD34 Hematopoietic Stem Cells from Pat
a Division of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA; 2 w1 W* F7 ?' b' U8 D$ z& Z% G2 J $ i2 F( I) Z3 k& f& ob Division of Hematology and Internal Medicine, Mayo Clinic and Mayo Foundation, Rochester, Minnesota, USA; 2 I; [3 E7 x A- S& d ' T% h/ Q" }$ Z2 P9 Y) [4 }c Division of Hematology/Oncology, Department of Medicine, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California, USA;$ Z `, p& W3 ?& m& v2 z' @
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d Department of Hematology, University Hospital, Frankfurt, Germany 1 l7 p2 J t9 w1 `3 s$ b* q3 G) H; O0 U
Key Words. Hematopoietic stem cells ? Agnogenic myeloid metaplasia ? Aberrant gene expression ' Q0 P) a& P( |) r9 s2 N 8 E; e( o2 M, n- KCorrespondence: 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@itsa.ucsf.edu * d. j6 y1 D- C) y5 m6 J) G# h( x 3 |/ N0 X/ k6 X) o! AABSTRACT3 x7 G9 t- ~# W$ [
# w5 {$ d4 ~8 ? k2 A% v3 Z3 s$ kAgnogenic 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 . Although aberrant expression of fibrogenic and angiogenic cytokines has been demonstrated in patients with AMM , the molecular events that lead to the disease process have not been characterized. - K) d% ~1 B; f: y2 }. n" x, K# l2 O1 A# @
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.* d, t' i: w$ [
4 d# V) t/ Q9 V% xTo 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 . 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. - }* K; f1 h- s" x$ L$ n9 {5 d # P9 o1 o9 f8 k/ S" PMATERIALS AND METHODS% x" S; k0 G h7 A% ] z3 P( k
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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 0 J: f5 R9 j' {6 n; i6 B4 Q: X8 I. l$ j0 z3 C% d
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. 9 w% x7 N' x, M4 L9 a" N6 V& l. @! m a
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 1. . E# g& u3 n/ {9 q; I3 B+ Q0 I, I( X8 V! |" R1 Q; t! ]' f* ?
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. 1 H1 {! l: d; J X; R$ S4 ]' I( @7 bClass Membership Prediction and Hierarchical Clustering5 {+ P! x' s* E6 p$ G( }% {- D9 Q1 |
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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. 2A). We generated two clusters, one containing the control samples and the other containing all of the AMM samples. 2 [% B8 u \1 } \9 l 1 Z. e$ H" f3 j0 y T. k9 `4 wFigure 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.- f$ s8 C& H9 }
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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 2B, 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. 2 h0 Y! V. x+ w0 T2 c9 z" b% o ! S7 W6 N% O! yDISCUSSION; \5 i2 N( [" L0 a
* Z2 h: Y/ P8 z- H; NThis 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.+ g+ r3 [" M" s$ ^
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